Date: 2019-12-26 18:30:06 CET, cola version: 1.3.2
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First the variable is renamed to res_list.
res_list = rl
All available functions which can be applied to this res_list object:
res_list
#> A 'ConsensusPartitionList' object with 24 methods.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows are extracted by 'SD, CV, MAD, ATC' methods.
#> Subgroups are detected by 'hclust, kmeans, skmeans, pam, mclust, NMF' method.
#> Number of partitions are tried for k = 2, 3, 4, 5, 6.
#> Performed in total 30000 partitions by row resampling.
#>
#> Following methods can be applied to this 'ConsensusPartitionList' object:
#> [1] "cola_report" "collect_classes" "collect_plots" "collect_stats"
#> [5] "colnames" "functional_enrichment" "get_anno_col" "get_anno"
#> [9] "get_classes" "get_matrix" "get_membership" "get_stats"
#> [13] "is_best_k" "is_stable_k" "ncol" "nrow"
#> [17] "rownames" "show" "suggest_best_k" "test_to_known_factors"
#> [21] "top_rows_heatmap" "top_rows_overlap"
#>
#> You can get result for a single method by, e.g. object["SD", "hclust"] or object["SD:hclust"]
#> or a subset of methods by object[c("SD", "CV")], c("hclust", "kmeans")]
The call of run_all_consensus_partition_methods() was:
#> run_all_consensus_partition_methods(data = m, mc.cores = 4, anno = data.frame(cell_type = cell_type),
#> anno_col = list(cell_type = cell_col))
Dimension of the input matrix:
mat = get_matrix(res_list)
dim(mat)
#> [1] 5576 160
The density distribution for each sample is visualized as in one column in the following heatmap. The clustering is based on the distance which is the Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, top_annotation = HeatmapAnnotation(df = get_anno(res_list),
col = get_anno_col(res_list)), ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 4)
Folowing table shows the best k (number of partitions) for each combination
of top-value methods and partition methods. Clicking on the method name in
the table goes to the section for a single combination of methods.
The cola vignette explains the definition of the metrics used for determining the best number of partitions.
suggest_best_k(res_list)
| The best k | 1-PAC | Mean silhouette | Concordance | Optional k | ||
|---|---|---|---|---|---|---|
| SD:mclust | 2 | 1.000 | 0.988 | 0.995 | ** | |
| SD:NMF | 2 | 1.000 | 0.975 | 0.989 | ** | |
| CV:mclust | 2 | 1.000 | 0.988 | 0.995 | ** | |
| MAD:mclust | 2 | 1.000 | 0.988 | 0.995 | ** | |
| MAD:NMF | 2 | 1.000 | 0.983 | 0.993 | ** | |
| ATC:kmeans | 2 | 1.000 | 0.987 | 0.982 | ** | |
| ATC:NMF | 2 | 1.000 | 0.976 | 0.990 | ** | |
| CV:NMF | 2 | 0.961 | 0.950 | 0.979 | ** | |
| ATC:mclust | 3 | 0.931 | 0.926 | 0.968 | * | 2 |
| ATC:skmeans | 4 | 0.927 | 0.895 | 0.958 | * | 2,3 |
| SD:skmeans | 2 | 0.886 | 0.924 | 0.967 | ||
| MAD:skmeans | 2 | 0.877 | 0.926 | 0.970 | ||
| CV:skmeans | 2 | 0.875 | 0.936 | 0.970 | ||
| ATC:pam | 4 | 0.869 | 0.869 | 0.949 | ||
| SD:kmeans | 2 | 0.861 | 0.900 | 0.958 | ||
| CV:kmeans | 2 | 0.856 | 0.927 | 0.968 | ||
| MAD:kmeans | 2 | 0.829 | 0.932 | 0.970 | ||
| ATC:hclust | 5 | 0.768 | 0.775 | 0.880 | ||
| SD:hclust | 3 | 0.379 | 0.743 | 0.852 | ||
| MAD:pam | 3 | 0.367 | 0.607 | 0.844 | ||
| SD:pam | 3 | 0.324 | 0.781 | 0.845 | ||
| MAD:hclust | 3 | 0.300 | 0.693 | 0.825 | ||
| CV:hclust | 2 | 0.222 | 0.648 | 0.824 | ||
| CV:pam | 3 | 0.192 | 0.462 | 0.705 |
**: 1-PAC > 0.95, *: 1-PAC > 0.9
Cumulative distribution function curves of consensus matrix for all methods.
collect_plots(res_list, fun = plot_ecdf)
Consensus heatmaps for all methods. (What is a consensus heatmap?)
collect_plots(res_list, k = 2, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = consensus_heatmap, mc.cores = 4)
Membership heatmaps for all methods. (What is a membership heatmap?)
collect_plots(res_list, k = 2, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = membership_heatmap, mc.cores = 4)
Signature heatmaps for all methods. (What is a signature heatmap?)
Note in following heatmaps, rows are scaled.
collect_plots(res_list, k = 2, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 3, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 4, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 5, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 6, fun = get_signatures, mc.cores = 4)
The statistics used for measuring the stability of consensus partitioning. (How are they defined?)
get_stats(res_list, k = 2)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 2 1.000 0.975 0.989 0.501 0.500 0.500
#> CV:NMF 2 0.961 0.950 0.979 0.501 0.498 0.498
#> MAD:NMF 2 1.000 0.983 0.993 0.503 0.498 0.498
#> ATC:NMF 2 1.000 0.976 0.990 0.501 0.499 0.499
#> SD:skmeans 2 0.886 0.924 0.967 0.503 0.498 0.498
#> CV:skmeans 2 0.875 0.936 0.971 0.503 0.497 0.497
#> MAD:skmeans 2 0.877 0.926 0.970 0.503 0.498 0.498
#> ATC:skmeans 2 1.000 0.983 0.992 0.503 0.498 0.498
#> SD:mclust 2 1.000 0.988 0.995 0.504 0.497 0.497
#> CV:mclust 2 1.000 0.988 0.995 0.503 0.497 0.497
#> MAD:mclust 2 1.000 0.988 0.995 0.503 0.497 0.497
#> ATC:mclust 2 0.999 0.981 0.992 0.503 0.497 0.497
#> SD:kmeans 2 0.861 0.900 0.958 0.500 0.498 0.498
#> CV:kmeans 2 0.856 0.927 0.968 0.502 0.498 0.498
#> MAD:kmeans 2 0.829 0.932 0.970 0.501 0.500 0.500
#> ATC:kmeans 2 1.000 0.987 0.982 0.491 0.498 0.498
#> SD:pam 2 0.401 0.798 0.863 0.231 0.904 0.904
#> CV:pam 2 0.314 0.721 0.863 0.291 0.771 0.771
#> MAD:pam 2 0.703 0.911 0.945 0.148 0.904 0.904
#> ATC:pam 2 0.556 0.869 0.927 0.479 0.502 0.502
#> SD:hclust 2 0.279 0.656 0.807 0.348 0.554 0.554
#> CV:hclust 2 0.222 0.648 0.824 0.462 0.497 0.497
#> MAD:hclust 2 0.204 0.459 0.770 0.298 0.718 0.718
#> ATC:hclust 2 0.424 0.654 0.828 0.277 0.916 0.916
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.742 0.810 0.902 0.275 0.813 0.643
#> CV:NMF 3 0.835 0.871 0.940 0.291 0.794 0.609
#> MAD:NMF 3 0.650 0.717 0.854 0.262 0.800 0.617
#> ATC:NMF 3 0.806 0.828 0.921 0.296 0.809 0.633
#> SD:skmeans 3 0.825 0.860 0.941 0.317 0.758 0.551
#> CV:skmeans 3 0.801 0.841 0.934 0.315 0.761 0.555
#> MAD:skmeans 3 0.871 0.883 0.949 0.312 0.770 0.569
#> ATC:skmeans 3 0.957 0.943 0.967 0.250 0.848 0.702
#> SD:mclust 3 0.801 0.865 0.913 0.152 0.942 0.884
#> CV:mclust 3 0.810 0.878 0.922 0.231 0.894 0.786
#> MAD:mclust 3 0.766 0.800 0.891 0.171 0.941 0.881
#> ATC:mclust 3 0.931 0.926 0.968 0.136 0.939 0.878
#> SD:kmeans 3 0.588 0.677 0.845 0.251 0.857 0.719
#> CV:kmeans 3 0.615 0.726 0.837 0.301 0.769 0.568
#> MAD:kmeans 3 0.608 0.690 0.838 0.255 0.804 0.635
#> ATC:kmeans 3 0.834 0.840 0.905 0.176 0.960 0.919
#> SD:pam 3 0.324 0.781 0.845 1.171 0.575 0.532
#> CV:pam 3 0.192 0.462 0.705 0.823 0.585 0.487
#> MAD:pam 3 0.367 0.607 0.844 1.150 0.829 0.811
#> ATC:pam 3 0.734 0.765 0.829 0.245 0.831 0.668
#> SD:hclust 3 0.379 0.743 0.852 0.378 0.894 0.818
#> CV:hclust 3 0.342 0.639 0.782 0.349 0.794 0.606
#> MAD:hclust 3 0.300 0.693 0.825 0.686 0.596 0.488
#> ATC:hclust 3 0.607 0.759 0.902 0.750 0.583 0.545
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.652 0.769 0.870 0.0619 0.961 0.894
#> CV:NMF 4 0.613 0.713 0.842 0.0794 0.959 0.887
#> MAD:NMF 4 0.640 0.775 0.866 0.0621 0.904 0.759
#> ATC:NMF 4 0.765 0.791 0.889 0.0870 0.872 0.667
#> SD:skmeans 4 0.837 0.846 0.929 0.1181 0.875 0.654
#> CV:skmeans 4 0.786 0.819 0.909 0.1164 0.856 0.615
#> MAD:skmeans 4 0.828 0.830 0.923 0.1201 0.845 0.592
#> ATC:skmeans 4 0.927 0.895 0.958 0.1225 0.883 0.704
#> SD:mclust 4 0.835 0.843 0.919 0.1248 0.881 0.740
#> CV:mclust 4 0.725 0.789 0.886 0.1598 0.870 0.670
#> MAD:mclust 4 0.845 0.908 0.937 0.1116 0.886 0.747
#> ATC:mclust 4 0.818 0.823 0.918 0.1237 0.880 0.738
#> SD:kmeans 4 0.649 0.667 0.809 0.1233 0.773 0.513
#> CV:kmeans 4 0.724 0.804 0.881 0.1184 0.864 0.629
#> MAD:kmeans 4 0.655 0.619 0.787 0.1249 0.855 0.634
#> ATC:kmeans 4 0.718 0.847 0.882 0.1591 0.831 0.647
#> SD:pam 4 0.306 0.687 0.805 0.0967 0.974 0.948
#> CV:pam 4 0.219 0.218 0.626 0.1104 0.653 0.442
#> MAD:pam 4 0.308 0.659 0.815 0.2006 0.950 0.934
#> ATC:pam 4 0.869 0.869 0.949 0.1211 0.891 0.723
#> SD:hclust 4 0.482 0.700 0.761 0.2786 0.809 0.667
#> CV:hclust 4 0.523 0.632 0.760 0.1291 0.933 0.806
#> MAD:hclust 4 0.446 0.621 0.761 0.2579 0.809 0.614
#> ATC:hclust 4 0.578 0.738 0.894 0.0159 1.000 0.999
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.600 0.635 0.800 0.0715 0.957 0.881
#> CV:NMF 5 0.577 0.461 0.736 0.0647 0.929 0.805
#> MAD:NMF 5 0.575 0.634 0.808 0.0700 0.987 0.964
#> ATC:NMF 5 0.699 0.707 0.832 0.0364 0.977 0.922
#> SD:skmeans 5 0.744 0.684 0.822 0.0533 0.962 0.857
#> CV:skmeans 5 0.709 0.658 0.812 0.0545 0.964 0.867
#> MAD:skmeans 5 0.725 0.665 0.817 0.0533 0.969 0.885
#> ATC:skmeans 5 0.804 0.790 0.893 0.0717 0.921 0.750
#> SD:mclust 5 0.771 0.751 0.881 0.1371 0.878 0.659
#> CV:mclust 5 0.734 0.605 0.837 0.0480 0.940 0.797
#> MAD:mclust 5 0.794 0.818 0.907 0.1256 0.909 0.738
#> ATC:mclust 5 0.820 0.827 0.919 0.1691 0.846 0.590
#> SD:kmeans 5 0.686 0.733 0.815 0.0801 0.861 0.599
#> CV:kmeans 5 0.712 0.722 0.832 0.0568 0.957 0.841
#> MAD:kmeans 5 0.660 0.742 0.828 0.0748 0.892 0.643
#> ATC:kmeans 5 0.752 0.750 0.816 0.1108 0.892 0.675
#> SD:pam 5 0.330 0.361 0.777 0.0649 0.996 0.991
#> CV:pam 5 0.268 0.427 0.640 0.0431 0.737 0.476
#> MAD:pam 5 0.295 0.606 0.794 0.0880 0.965 0.953
#> ATC:pam 5 0.800 0.784 0.914 0.0669 0.950 0.850
#> SD:hclust 5 0.471 0.531 0.679 0.0999 0.831 0.650
#> CV:hclust 5 0.597 0.558 0.718 0.0732 0.927 0.754
#> MAD:hclust 5 0.506 0.486 0.761 0.0832 0.982 0.949
#> ATC:hclust 5 0.768 0.775 0.880 0.3077 0.777 0.577
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.609 0.553 0.765 0.0555 0.915 0.748
#> CV:NMF 6 0.594 0.574 0.729 0.0474 0.910 0.730
#> MAD:NMF 6 0.578 0.431 0.707 0.0604 0.962 0.891
#> ATC:NMF 6 0.652 0.548 0.766 0.0478 0.959 0.861
#> SD:skmeans 6 0.681 0.634 0.763 0.0377 0.970 0.876
#> CV:skmeans 6 0.649 0.514 0.734 0.0413 0.951 0.810
#> MAD:skmeans 6 0.669 0.617 0.753 0.0361 0.975 0.896
#> ATC:skmeans 6 0.765 0.675 0.834 0.0378 0.985 0.941
#> SD:mclust 6 0.730 0.674 0.833 0.0523 0.956 0.835
#> CV:mclust 6 0.665 0.624 0.774 0.0366 0.937 0.779
#> MAD:mclust 6 0.748 0.631 0.834 0.0623 0.950 0.817
#> ATC:mclust 6 0.718 0.708 0.797 0.0425 0.954 0.825
#> SD:kmeans 6 0.723 0.628 0.758 0.0481 0.927 0.712
#> CV:kmeans 6 0.715 0.592 0.755 0.0438 0.953 0.808
#> MAD:kmeans 6 0.706 0.717 0.797 0.0433 0.917 0.673
#> ATC:kmeans 6 0.702 0.619 0.775 0.0537 0.915 0.678
#> SD:pam 6 0.513 0.524 0.807 0.0771 0.951 0.895
#> CV:pam 6 0.382 0.303 0.695 0.0676 0.731 0.414
#> MAD:pam 6 0.415 0.646 0.831 0.3659 0.691 0.586
#> ATC:pam 6 0.769 0.729 0.889 0.0381 0.972 0.902
#> SD:hclust 6 0.508 0.443 0.686 0.0591 0.887 0.723
#> CV:hclust 6 0.602 0.527 0.721 0.0330 0.951 0.813
#> MAD:hclust 6 0.528 0.418 0.707 0.0557 0.855 0.636
#> ATC:hclust 6 0.625 0.708 0.841 0.0680 0.972 0.916
Following heatmap plots the partition for each combination of methods and the lightness correspond to the silhouette scores for samples in each method. On top the consensus subgroup is inferred from all methods by taking the mean silhouette scores as weight.
collect_stats(res_list, k = 2)
collect_stats(res_list, k = 3)
collect_stats(res_list, k = 4)
collect_stats(res_list, k = 5)
collect_stats(res_list, k = 6)
Collect partitions from all methods:
collect_classes(res_list, k = 2)
collect_classes(res_list, k = 3)
collect_classes(res_list, k = 4)
collect_classes(res_list, k = 5)
collect_classes(res_list, k = 6)
Overlap of top rows from different top-row methods:
top_rows_overlap(res_list, top_n = 558, method = "euler")
top_rows_overlap(res_list, top_n = 1116, method = "euler")
top_rows_overlap(res_list, top_n = 1673, method = "euler")
top_rows_overlap(res_list, top_n = 2230, method = "euler")
top_rows_overlap(res_list, top_n = 2788, method = "euler")
Also visualize the correspondance of rankings between different top-row methods:
top_rows_overlap(res_list, top_n = 558, method = "correspondance")
top_rows_overlap(res_list, top_n = 1116, method = "correspondance")
top_rows_overlap(res_list, top_n = 1673, method = "correspondance")
top_rows_overlap(res_list, top_n = 2230, method = "correspondance")
top_rows_overlap(res_list, top_n = 2788, method = "correspondance")
Heatmaps of the top rows:
top_rows_heatmap(res_list, top_n = 558)
top_rows_heatmap(res_list, top_n = 1116)
top_rows_heatmap(res_list, top_n = 1673)
top_rows_heatmap(res_list, top_n = 2230)
top_rows_heatmap(res_list, top_n = 2788)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res_list, k = 2)
#> n cell_type(p) k
#> SD:NMF 159 1.02e-26 2
#> CV:NMF 156 2.05e-28 2
#> MAD:NMF 159 2.51e-28 2
#> ATC:NMF 159 4.69e-29 2
#> SD:skmeans 158 4.65e-19 2
#> CV:skmeans 156 2.86e-21 2
#> MAD:skmeans 155 3.94e-19 2
#> ATC:skmeans 160 4.49e-20 2
#> SD:mclust 160 7.97e-29 2
#> CV:mclust 160 5.69e-31 2
#> MAD:mclust 160 3.08e-30 2
#> ATC:mclust 159 5.02e-30 2
#> SD:kmeans 155 1.04e-19 2
#> CV:kmeans 155 4.72e-21 2
#> MAD:kmeans 157 1.66e-17 2
#> ATC:kmeans 160 4.49e-20 2
#> SD:pam 160 2.38e-02 2
#> CV:pam 143 9.46e-02 2
#> MAD:pam 158 2.40e-02 2
#> ATC:pam 152 6.61e-24 2
#> SD:hclust 138 2.76e-14 2
#> CV:hclust 125 5.51e-11 2
#> MAD:hclust 99 7.49e-07 2
#> ATC:hclust 120 1.74e-01 2
test_to_known_factors(res_list, k = 3)
#> n cell_type(p) k
#> SD:NMF 146 1.88e-22 3
#> CV:NMF 151 4.96e-22 3
#> MAD:NMF 135 6.36e-20 3
#> ATC:NMF 145 1.67e-21 3
#> SD:skmeans 150 7.32e-22 3
#> CV:skmeans 148 1.81e-22 3
#> MAD:skmeans 148 5.16e-22 3
#> ATC:skmeans 157 1.19e-24 3
#> SD:mclust 154 2.46e-29 3
#> CV:mclust 151 1.88e-29 3
#> MAD:mclust 149 2.89e-28 3
#> ATC:mclust 156 9.21e-30 3
#> SD:kmeans 141 3.28e-20 3
#> CV:kmeans 144 1.58e-22 3
#> MAD:kmeans 141 2.34e-21 3
#> ATC:kmeans 155 1.79e-25 3
#> SD:pam 153 8.80e-19 3
#> CV:pam 102 4.63e-15 3
#> MAD:pam 115 1.12e-07 3
#> ATC:pam 140 1.46e-23 3
#> SD:hclust 143 2.08e-20 3
#> CV:hclust 122 5.79e-19 3
#> MAD:hclust 135 6.69e-26 3
#> ATC:hclust 138 7.06e-20 3
test_to_known_factors(res_list, k = 4)
#> n cell_type(p) k
#> SD:NMF 147 3.76e-21 4
#> CV:NMF 138 2.91e-19 4
#> MAD:NMF 148 1.48e-21 4
#> ATC:NMF 146 4.69e-19 4
#> SD:skmeans 148 4.34e-27 4
#> CV:skmeans 149 2.65e-27 4
#> MAD:skmeans 147 7.14e-27 4
#> ATC:skmeans 151 1.71e-28 4
#> SD:mclust 151 2.03e-26 4
#> CV:mclust 146 1.99e-27 4
#> MAD:mclust 155 7.53e-28 4
#> ATC:mclust 148 2.21e-26 4
#> SD:kmeans 137 4.66e-24 4
#> CV:kmeans 151 9.92e-28 4
#> MAD:kmeans 126 1.04e-21 4
#> ATC:kmeans 156 4.70e-28 4
#> SD:pam 149 1.84e-18 4
#> CV:pam 41 2.04e-07 4
#> MAD:pam 134 4.97e-02 4
#> ATC:pam 150 4.48e-24 4
#> SD:hclust 131 7.71e-25 4
#> CV:hclust 127 2.32e-23 4
#> MAD:hclust 109 8.28e-19 4
#> ATC:hclust 136 1.67e-19 4
test_to_known_factors(res_list, k = 5)
#> n cell_type(p) k
#> SD:NMF 129 2.91e-19 5
#> CV:NMF 93 1.66e-10 5
#> MAD:NMF 133 2.15e-18 5
#> ATC:NMF 131 1.51e-16 5
#> SD:skmeans 130 1.99e-22 5
#> CV:skmeans 131 1.19e-22 5
#> MAD:skmeans 127 1.30e-22 5
#> ATC:skmeans 142 1.01e-25 5
#> SD:mclust 137 6.49e-24 5
#> CV:mclust 116 4.06e-20 5
#> MAD:mclust 149 9.56e-26 5
#> ATC:mclust 146 3.94e-25 5
#> SD:kmeans 139 1.29e-23 5
#> CV:kmeans 141 9.08e-25 5
#> MAD:kmeans 146 4.45e-25 5
#> ATC:kmeans 146 2.06e-24 5
#> SD:pam 85 1.98e-14 5
#> CV:pam 90 2.76e-10 5
#> MAD:pam 129 1.08e-02 5
#> ATC:pam 137 1.21e-22 5
#> SD:hclust 97 4.22e-19 5
#> CV:hclust 100 1.29e-16 5
#> MAD:hclust 97 2.41e-17 5
#> ATC:hclust 145 1.22e-25 5
test_to_known_factors(res_list, k = 6)
#> n cell_type(p) k
#> SD:NMF 114 2.48e-14 6
#> CV:NMF 121 5.43e-15 6
#> MAD:NMF 82 5.63e-11 6
#> ATC:NMF 107 9.83e-12 6
#> SD:skmeans 116 2.14e-19 6
#> CV:skmeans 106 3.76e-18 6
#> MAD:skmeans 113 8.42e-19 6
#> ATC:skmeans 125 4.64e-22 6
#> SD:mclust 132 8.15e-23 6
#> CV:mclust 119 8.64e-21 6
#> MAD:mclust 134 1.62e-22 6
#> ATC:mclust 139 2.43e-24 6
#> SD:kmeans 118 1.38e-20 6
#> CV:kmeans 123 1.07e-21 6
#> MAD:kmeans 139 1.28e-23 6
#> ATC:kmeans 121 1.92e-20 6
#> SD:pam 101 3.35e-15 6
#> CV:pam 60 9.11e-04 6
#> MAD:pam 126 7.21e-14 6
#> ATC:pam 132 5.06e-20 6
#> SD:hclust 107 1.55e-20 6
#> CV:hclust 79 8.25e-12 6
#> MAD:hclust 97 1.78e-18 6
#> ATC:hclust 145 7.78e-25 6
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "hclust"]
# you can also extract it by
# res = res_list["SD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'SD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.279 0.656 0.807 0.3475 0.554 0.554
#> 3 3 0.379 0.743 0.852 0.3781 0.894 0.818
#> 4 4 0.482 0.700 0.761 0.2786 0.809 0.667
#> 5 5 0.471 0.531 0.679 0.0999 0.831 0.650
#> 6 6 0.508 0.443 0.686 0.0591 0.887 0.723
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.3584 0.8047 0.068 0.932
#> aberrant_ERR2585338 2 0.0672 0.8019 0.008 0.992
#> aberrant_ERR2585325 2 0.3584 0.8047 0.068 0.932
#> aberrant_ERR2585283 1 0.2236 0.5120 0.964 0.036
#> aberrant_ERR2585343 2 0.6438 0.7341 0.164 0.836
#> aberrant_ERR2585329 2 0.1633 0.8135 0.024 0.976
#> aberrant_ERR2585317 2 0.0938 0.8047 0.012 0.988
#> aberrant_ERR2585339 2 0.0000 0.8066 0.000 1.000
#> aberrant_ERR2585335 2 0.2236 0.8138 0.036 0.964
#> aberrant_ERR2585287 2 0.9661 0.2956 0.392 0.608
#> aberrant_ERR2585321 2 0.5629 0.7657 0.132 0.868
#> aberrant_ERR2585297 1 0.9635 0.7361 0.612 0.388
#> aberrant_ERR2585337 2 0.0000 0.8066 0.000 1.000
#> aberrant_ERR2585319 2 0.2423 0.8146 0.040 0.960
#> aberrant_ERR2585315 2 0.1414 0.8135 0.020 0.980
#> aberrant_ERR2585336 2 0.0000 0.8066 0.000 1.000
#> aberrant_ERR2585307 2 0.1633 0.8113 0.024 0.976
#> aberrant_ERR2585301 2 0.2236 0.8144 0.036 0.964
#> aberrant_ERR2585326 2 0.0672 0.8019 0.008 0.992
#> aberrant_ERR2585331 2 0.0672 0.8019 0.008 0.992
#> aberrant_ERR2585346 1 0.2236 0.5120 0.964 0.036
#> aberrant_ERR2585314 2 0.2236 0.8152 0.036 0.964
#> aberrant_ERR2585298 2 0.8207 0.4624 0.256 0.744
#> aberrant_ERR2585345 2 0.0938 0.8047 0.012 0.988
#> aberrant_ERR2585299 1 0.9754 0.7286 0.592 0.408
#> aberrant_ERR2585309 1 0.8499 0.7271 0.724 0.276
#> aberrant_ERR2585303 2 0.0938 0.8049 0.012 0.988
#> aberrant_ERR2585313 2 0.1414 0.8130 0.020 0.980
#> aberrant_ERR2585318 2 0.3274 0.8084 0.060 0.940
#> aberrant_ERR2585328 2 0.2778 0.8155 0.048 0.952
#> aberrant_ERR2585330 2 0.3431 0.8078 0.064 0.936
#> aberrant_ERR2585293 1 0.2236 0.5120 0.964 0.036
#> aberrant_ERR2585342 2 0.4298 0.7997 0.088 0.912
#> aberrant_ERR2585348 2 0.3584 0.8086 0.068 0.932
#> aberrant_ERR2585352 2 0.2603 0.8140 0.044 0.956
#> aberrant_ERR2585308 1 0.9044 0.7410 0.680 0.320
#> aberrant_ERR2585349 2 0.2043 0.8023 0.032 0.968
#> aberrant_ERR2585316 2 0.6247 0.7456 0.156 0.844
#> aberrant_ERR2585306 2 0.6887 0.7040 0.184 0.816
#> aberrant_ERR2585324 2 0.2423 0.8146 0.040 0.960
#> aberrant_ERR2585310 2 0.1633 0.8123 0.024 0.976
#> aberrant_ERR2585296 2 0.9933 -0.4104 0.452 0.548
#> aberrant_ERR2585275 1 0.2236 0.5120 0.964 0.036
#> aberrant_ERR2585311 2 0.4161 0.8011 0.084 0.916
#> aberrant_ERR2585292 1 0.2236 0.5120 0.964 0.036
#> aberrant_ERR2585282 2 0.3584 0.8064 0.068 0.932
#> aberrant_ERR2585305 2 0.3274 0.8081 0.060 0.940
#> aberrant_ERR2585278 2 0.0938 0.8110 0.012 0.988
#> aberrant_ERR2585347 2 0.5946 0.7569 0.144 0.856
#> aberrant_ERR2585332 2 0.4815 0.7847 0.104 0.896
#> aberrant_ERR2585280 2 0.4298 0.7986 0.088 0.912
#> aberrant_ERR2585304 2 0.3114 0.7939 0.056 0.944
#> aberrant_ERR2585322 2 0.0376 0.8084 0.004 0.996
#> aberrant_ERR2585279 2 0.0672 0.8019 0.008 0.992
#> aberrant_ERR2585277 2 0.0376 0.8045 0.004 0.996
#> aberrant_ERR2585295 2 0.2778 0.8157 0.048 0.952
#> aberrant_ERR2585333 2 0.4939 0.7881 0.108 0.892
#> aberrant_ERR2585285 2 0.3274 0.8094 0.060 0.940
#> aberrant_ERR2585286 2 0.0672 0.8019 0.008 0.992
#> aberrant_ERR2585294 2 0.2778 0.8143 0.048 0.952
#> aberrant_ERR2585300 2 0.6887 0.7040 0.184 0.816
#> aberrant_ERR2585334 2 0.0672 0.8019 0.008 0.992
#> aberrant_ERR2585361 2 0.3114 0.8127 0.056 0.944
#> aberrant_ERR2585372 2 0.3584 0.8052 0.068 0.932
#> round_ERR2585217 2 0.6438 0.6793 0.164 0.836
#> round_ERR2585205 1 0.9881 0.7050 0.564 0.436
#> round_ERR2585214 2 0.6973 0.6292 0.188 0.812
#> round_ERR2585202 2 0.3733 0.7823 0.072 0.928
#> aberrant_ERR2585367 2 0.3114 0.8127 0.056 0.944
#> round_ERR2585220 1 0.9977 0.6523 0.528 0.472
#> round_ERR2585238 1 0.9491 0.7420 0.632 0.368
#> aberrant_ERR2585276 2 0.3879 0.8046 0.076 0.924
#> round_ERR2585218 1 0.9815 0.7189 0.580 0.420
#> aberrant_ERR2585363 2 0.3114 0.8125 0.056 0.944
#> round_ERR2585201 2 0.8016 0.4950 0.244 0.756
#> round_ERR2585210 1 0.9815 0.7177 0.580 0.420
#> aberrant_ERR2585362 2 0.3114 0.8132 0.056 0.944
#> aberrant_ERR2585360 2 0.4161 0.8004 0.084 0.916
#> round_ERR2585209 2 0.8081 0.4815 0.248 0.752
#> round_ERR2585242 2 0.8144 0.4608 0.252 0.748
#> round_ERR2585216 1 1.0000 0.5920 0.504 0.496
#> round_ERR2585219 1 0.9977 0.6541 0.528 0.472
#> round_ERR2585237 2 0.6973 0.6304 0.188 0.812
#> round_ERR2585198 2 0.3584 0.7857 0.068 0.932
#> round_ERR2585211 1 0.9833 0.7169 0.576 0.424
#> round_ERR2585206 1 0.9850 0.7134 0.572 0.428
#> aberrant_ERR2585281 2 0.1633 0.8104 0.024 0.976
#> round_ERR2585212 1 0.9993 0.6286 0.516 0.484
#> round_ERR2585221 1 0.8955 0.7401 0.688 0.312
#> round_ERR2585243 1 0.9833 0.7178 0.576 0.424
#> round_ERR2585204 2 0.6343 0.6746 0.160 0.840
#> round_ERR2585213 2 0.4431 0.7583 0.092 0.908
#> aberrant_ERR2585373 2 0.4298 0.7981 0.088 0.912
#> aberrant_ERR2585358 2 0.5629 0.7646 0.132 0.868
#> aberrant_ERR2585365 2 0.0672 0.8102 0.008 0.992
#> aberrant_ERR2585359 2 0.6247 0.7399 0.156 0.844
#> aberrant_ERR2585370 2 0.0376 0.8045 0.004 0.996
#> round_ERR2585215 1 0.9087 0.7401 0.676 0.324
#> round_ERR2585262 2 0.6148 0.6855 0.152 0.848
#> round_ERR2585199 2 0.4022 0.7753 0.080 0.920
#> aberrant_ERR2585369 2 0.3114 0.8104 0.056 0.944
#> round_ERR2585208 1 0.9710 0.7320 0.600 0.400
#> round_ERR2585252 1 0.8081 0.7132 0.752 0.248
#> round_ERR2585236 2 0.9922 -0.4086 0.448 0.552
#> aberrant_ERR2585284 1 0.2236 0.5120 0.964 0.036
#> round_ERR2585224 1 0.7815 0.7027 0.768 0.232
#> round_ERR2585260 1 0.9933 0.6838 0.548 0.452
#> round_ERR2585229 1 0.9552 0.7400 0.624 0.376
#> aberrant_ERR2585364 1 0.6623 0.5510 0.828 0.172
#> round_ERR2585253 1 0.7815 0.7027 0.768 0.232
#> aberrant_ERR2585368 2 0.0672 0.8019 0.008 0.992
#> aberrant_ERR2585371 2 0.0672 0.8019 0.008 0.992
#> round_ERR2585239 1 0.9944 0.6793 0.544 0.456
#> round_ERR2585273 1 0.9754 0.7216 0.592 0.408
#> round_ERR2585256 2 0.8443 0.3987 0.272 0.728
#> round_ERR2585272 2 1.0000 -0.5787 0.496 0.504
#> round_ERR2585246 1 0.9129 0.7421 0.672 0.328
#> round_ERR2585261 2 0.8443 0.4107 0.272 0.728
#> round_ERR2585254 2 0.6887 0.6384 0.184 0.816
#> round_ERR2585225 2 0.7950 0.4943 0.240 0.760
#> round_ERR2585235 2 0.9393 0.0403 0.356 0.644
#> round_ERR2585271 1 0.9922 0.6910 0.552 0.448
#> round_ERR2585251 1 0.9977 0.6511 0.528 0.472
#> round_ERR2585255 2 0.7950 0.4930 0.240 0.760
#> round_ERR2585257 2 0.8081 0.4772 0.248 0.752
#> round_ERR2585226 1 0.9983 0.6445 0.524 0.476
#> round_ERR2585265 1 0.9970 0.6591 0.532 0.468
#> round_ERR2585259 2 0.9209 0.1803 0.336 0.664
#> round_ERR2585247 1 0.9286 0.7425 0.656 0.344
#> round_ERR2585241 1 0.9922 0.6905 0.552 0.448
#> round_ERR2585263 2 0.9996 -0.5551 0.488 0.512
#> round_ERR2585264 1 0.7815 0.7027 0.768 0.232
#> round_ERR2585233 2 0.8267 0.4422 0.260 0.740
#> round_ERR2585223 1 0.9954 0.6692 0.540 0.460
#> round_ERR2585234 2 0.6973 0.6268 0.188 0.812
#> round_ERR2585222 1 0.9996 0.6130 0.512 0.488
#> round_ERR2585228 1 0.9944 0.6771 0.544 0.456
#> round_ERR2585248 1 0.7815 0.7027 0.768 0.232
#> round_ERR2585240 2 0.9833 -0.3806 0.424 0.576
#> round_ERR2585270 1 0.9954 0.6706 0.540 0.460
#> round_ERR2585232 2 0.8813 0.2886 0.300 0.700
#> aberrant_ERR2585341 2 0.1414 0.8092 0.020 0.980
#> aberrant_ERR2585355 2 0.0672 0.8019 0.008 0.992
#> round_ERR2585227 1 0.9977 0.6529 0.528 0.472
#> aberrant_ERR2585351 2 0.3274 0.8116 0.060 0.940
#> round_ERR2585269 1 0.8909 0.7390 0.692 0.308
#> aberrant_ERR2585357 2 0.0376 0.8045 0.004 0.996
#> aberrant_ERR2585350 2 0.0000 0.8066 0.000 1.000
#> round_ERR2585250 2 0.9977 -0.4999 0.472 0.528
#> round_ERR2585245 1 0.7815 0.7027 0.768 0.232
#> aberrant_ERR2585353 2 0.3733 0.8082 0.072 0.928
#> round_ERR2585258 1 0.9970 0.6591 0.532 0.468
#> aberrant_ERR2585354 2 0.2778 0.8145 0.048 0.952
#> round_ERR2585249 1 0.8608 0.7313 0.716 0.284
#> round_ERR2585268 2 0.9933 -0.4224 0.452 0.548
#> aberrant_ERR2585356 2 0.6801 0.7131 0.180 0.820
#> round_ERR2585266 2 0.8207 0.4480 0.256 0.744
#> round_ERR2585231 1 0.8207 0.7181 0.744 0.256
#> round_ERR2585230 1 0.9983 0.6343 0.524 0.476
#> round_ERR2585267 1 0.8327 0.7222 0.736 0.264
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.2584 0.8410 0.008 0.928 0.064
#> aberrant_ERR2585338 2 0.0424 0.8412 0.000 0.992 0.008
#> aberrant_ERR2585325 2 0.2584 0.8410 0.008 0.928 0.064
#> aberrant_ERR2585283 3 0.1170 0.9663 0.016 0.008 0.976
#> aberrant_ERR2585343 2 0.4663 0.7868 0.016 0.828 0.156
#> aberrant_ERR2585329 2 0.1031 0.8460 0.000 0.976 0.024
#> aberrant_ERR2585317 2 0.0592 0.8427 0.000 0.988 0.012
#> aberrant_ERR2585339 2 0.0000 0.8431 0.000 1.000 0.000
#> aberrant_ERR2585335 2 0.1529 0.8455 0.000 0.960 0.040
#> aberrant_ERR2585287 2 0.6753 0.4011 0.016 0.596 0.388
#> aberrant_ERR2585321 2 0.4139 0.8082 0.016 0.860 0.124
#> aberrant_ERR2585297 1 0.4062 0.8305 0.836 0.164 0.000
#> aberrant_ERR2585337 2 0.0000 0.8431 0.000 1.000 0.000
#> aberrant_ERR2585319 2 0.1643 0.8460 0.000 0.956 0.044
#> aberrant_ERR2585315 2 0.0892 0.8463 0.000 0.980 0.020
#> aberrant_ERR2585336 2 0.0000 0.8431 0.000 1.000 0.000
#> aberrant_ERR2585307 2 0.1905 0.8366 0.028 0.956 0.016
#> aberrant_ERR2585301 2 0.1643 0.8460 0.000 0.956 0.044
#> aberrant_ERR2585326 2 0.0424 0.8412 0.000 0.992 0.008
#> aberrant_ERR2585331 2 0.0424 0.8412 0.000 0.992 0.008
#> aberrant_ERR2585346 3 0.1170 0.9663 0.016 0.008 0.976
#> aberrant_ERR2585314 2 0.1905 0.8454 0.016 0.956 0.028
#> aberrant_ERR2585298 2 0.6359 0.2604 0.364 0.628 0.008
#> aberrant_ERR2585345 2 0.0592 0.8427 0.000 0.988 0.012
#> aberrant_ERR2585299 1 0.4912 0.8441 0.796 0.196 0.008
#> aberrant_ERR2585309 1 0.1832 0.7200 0.956 0.036 0.008
#> aberrant_ERR2585303 2 0.0747 0.8439 0.000 0.984 0.016
#> aberrant_ERR2585313 2 0.0892 0.8458 0.000 0.980 0.020
#> aberrant_ERR2585318 2 0.2301 0.8421 0.004 0.936 0.060
#> aberrant_ERR2585328 2 0.2063 0.8473 0.008 0.948 0.044
#> aberrant_ERR2585330 2 0.2496 0.8410 0.004 0.928 0.068
#> aberrant_ERR2585293 3 0.1170 0.9663 0.016 0.008 0.976
#> aberrant_ERR2585342 2 0.3112 0.8319 0.004 0.900 0.096
#> aberrant_ERR2585348 2 0.2496 0.8412 0.004 0.928 0.068
#> aberrant_ERR2585352 2 0.1878 0.8456 0.004 0.952 0.044
#> aberrant_ERR2585308 1 0.3129 0.7753 0.904 0.088 0.008
#> aberrant_ERR2585349 2 0.1525 0.8298 0.032 0.964 0.004
#> aberrant_ERR2585316 2 0.4413 0.7915 0.008 0.832 0.160
#> aberrant_ERR2585306 2 0.5223 0.7600 0.024 0.800 0.176
#> aberrant_ERR2585324 2 0.1643 0.8460 0.000 0.956 0.044
#> aberrant_ERR2585310 2 0.2383 0.8294 0.044 0.940 0.016
#> aberrant_ERR2585296 1 0.6247 0.6513 0.620 0.376 0.004
#> aberrant_ERR2585275 3 0.1170 0.9663 0.016 0.008 0.976
#> aberrant_ERR2585311 2 0.2945 0.8341 0.004 0.908 0.088
#> aberrant_ERR2585292 3 0.1170 0.9663 0.016 0.008 0.976
#> aberrant_ERR2585282 2 0.2590 0.8391 0.004 0.924 0.072
#> aberrant_ERR2585305 2 0.2400 0.8413 0.004 0.932 0.064
#> aberrant_ERR2585278 2 0.0747 0.8457 0.000 0.984 0.016
#> aberrant_ERR2585347 2 0.4164 0.8017 0.008 0.848 0.144
#> aberrant_ERR2585332 2 0.3454 0.8234 0.008 0.888 0.104
#> aberrant_ERR2585280 2 0.3129 0.8354 0.008 0.904 0.088
#> aberrant_ERR2585304 2 0.3682 0.7578 0.116 0.876 0.008
#> aberrant_ERR2585322 2 0.0237 0.8439 0.000 0.996 0.004
#> aberrant_ERR2585279 2 0.0424 0.8412 0.000 0.992 0.008
#> aberrant_ERR2585277 2 0.0237 0.8423 0.000 0.996 0.004
#> aberrant_ERR2585295 2 0.2229 0.8468 0.012 0.944 0.044
#> aberrant_ERR2585333 2 0.3532 0.8234 0.008 0.884 0.108
#> aberrant_ERR2585285 2 0.2400 0.8427 0.004 0.932 0.064
#> aberrant_ERR2585286 2 0.0424 0.8412 0.000 0.992 0.008
#> aberrant_ERR2585294 2 0.1964 0.8454 0.000 0.944 0.056
#> aberrant_ERR2585300 2 0.5147 0.7594 0.020 0.800 0.180
#> aberrant_ERR2585334 2 0.0424 0.8412 0.000 0.992 0.008
#> aberrant_ERR2585361 2 0.2200 0.8444 0.004 0.940 0.056
#> aberrant_ERR2585372 2 0.2496 0.8399 0.004 0.928 0.068
#> round_ERR2585217 2 0.5247 0.6146 0.224 0.768 0.008
#> round_ERR2585205 1 0.5158 0.8454 0.764 0.232 0.004
#> round_ERR2585214 2 0.5728 0.5155 0.272 0.720 0.008
#> round_ERR2585202 2 0.4099 0.7333 0.140 0.852 0.008
#> aberrant_ERR2585367 2 0.2200 0.8444 0.004 0.940 0.056
#> round_ERR2585220 1 0.5291 0.8275 0.732 0.268 0.000
#> round_ERR2585238 1 0.3784 0.8131 0.864 0.132 0.004
#> aberrant_ERR2585276 2 0.2955 0.8368 0.008 0.912 0.080
#> round_ERR2585218 1 0.4654 0.8489 0.792 0.208 0.000
#> aberrant_ERR2585363 2 0.2200 0.8444 0.004 0.940 0.056
#> round_ERR2585201 2 0.6297 0.2987 0.352 0.640 0.008
#> round_ERR2585210 1 0.4796 0.8459 0.780 0.220 0.000
#> aberrant_ERR2585362 2 0.2200 0.8446 0.004 0.940 0.056
#> aberrant_ERR2585360 2 0.2860 0.8341 0.004 0.912 0.084
#> round_ERR2585209 2 0.6228 0.2411 0.372 0.624 0.004
#> round_ERR2585242 2 0.6209 0.2523 0.368 0.628 0.004
#> round_ERR2585216 1 0.5465 0.8071 0.712 0.288 0.000
#> round_ERR2585219 1 0.5254 0.8312 0.736 0.264 0.000
#> round_ERR2585237 2 0.5831 0.4886 0.284 0.708 0.008
#> round_ERR2585198 2 0.3896 0.7443 0.128 0.864 0.008
#> round_ERR2585211 1 0.5024 0.8471 0.776 0.220 0.004
#> round_ERR2585206 1 0.5070 0.8469 0.772 0.224 0.004
#> aberrant_ERR2585281 2 0.1267 0.8459 0.004 0.972 0.024
#> round_ERR2585212 1 0.5363 0.8207 0.724 0.276 0.000
#> round_ERR2585221 1 0.2261 0.7603 0.932 0.068 0.000
#> round_ERR2585243 1 0.5115 0.8470 0.768 0.228 0.004
#> round_ERR2585204 2 0.5502 0.5637 0.248 0.744 0.008
#> round_ERR2585213 2 0.4291 0.7129 0.152 0.840 0.008
#> aberrant_ERR2585373 2 0.3129 0.8323 0.008 0.904 0.088
#> aberrant_ERR2585358 2 0.4128 0.8062 0.012 0.856 0.132
#> aberrant_ERR2585365 2 0.0592 0.8454 0.000 0.988 0.012
#> aberrant_ERR2585359 2 0.4575 0.7854 0.012 0.828 0.160
#> aberrant_ERR2585370 2 0.0237 0.8424 0.000 0.996 0.004
#> round_ERR2585215 1 0.3295 0.7805 0.896 0.096 0.008
#> round_ERR2585262 2 0.5061 0.6274 0.208 0.784 0.008
#> round_ERR2585199 2 0.4099 0.7298 0.140 0.852 0.008
#> aberrant_ERR2585369 2 0.2165 0.8420 0.000 0.936 0.064
#> round_ERR2585208 1 0.4399 0.8413 0.812 0.188 0.000
#> round_ERR2585252 1 0.1182 0.6853 0.976 0.012 0.012
#> round_ERR2585236 1 0.6026 0.6492 0.624 0.376 0.000
#> aberrant_ERR2585284 3 0.0661 0.9564 0.004 0.008 0.988
#> round_ERR2585224 1 0.1031 0.6551 0.976 0.000 0.024
#> round_ERR2585260 1 0.5138 0.8378 0.748 0.252 0.000
#> round_ERR2585229 1 0.4291 0.8238 0.840 0.152 0.008
#> aberrant_ERR2585364 3 0.4475 0.7946 0.016 0.144 0.840
#> round_ERR2585253 1 0.0747 0.6635 0.984 0.000 0.016
#> aberrant_ERR2585368 2 0.0424 0.8412 0.000 0.992 0.008
#> aberrant_ERR2585371 2 0.0424 0.8412 0.000 0.992 0.008
#> round_ERR2585239 1 0.5138 0.8391 0.748 0.252 0.000
#> round_ERR2585273 1 0.4235 0.8277 0.824 0.176 0.000
#> round_ERR2585256 2 0.6386 0.0810 0.412 0.584 0.004
#> round_ERR2585272 1 0.5529 0.7969 0.704 0.296 0.000
#> round_ERR2585246 1 0.3295 0.7828 0.896 0.096 0.008
#> round_ERR2585261 2 0.6527 0.0961 0.404 0.588 0.008
#> round_ERR2585254 2 0.5541 0.5578 0.252 0.740 0.008
#> round_ERR2585225 2 0.6148 0.2900 0.356 0.640 0.004
#> round_ERR2585235 2 0.6308 -0.2517 0.492 0.508 0.000
#> round_ERR2585271 1 0.5098 0.8408 0.752 0.248 0.000
#> round_ERR2585251 1 0.5327 0.8231 0.728 0.272 0.000
#> round_ERR2585255 2 0.6104 0.3146 0.348 0.648 0.004
#> round_ERR2585257 2 0.6169 0.2791 0.360 0.636 0.004
#> round_ERR2585226 1 0.5553 0.8247 0.724 0.272 0.004
#> round_ERR2585265 1 0.5254 0.8305 0.736 0.264 0.000
#> round_ERR2585259 2 0.6299 -0.1831 0.476 0.524 0.000
#> round_ERR2585247 1 0.2959 0.7864 0.900 0.100 0.000
#> round_ERR2585241 1 0.5244 0.8433 0.756 0.240 0.004
#> round_ERR2585263 1 0.5706 0.7635 0.680 0.320 0.000
#> round_ERR2585264 1 0.0747 0.6635 0.984 0.000 0.016
#> round_ERR2585233 2 0.6228 0.2382 0.372 0.624 0.004
#> round_ERR2585223 1 0.5365 0.8377 0.744 0.252 0.004
#> round_ERR2585234 2 0.5831 0.4872 0.284 0.708 0.008
#> round_ERR2585222 1 0.5431 0.8114 0.716 0.284 0.000
#> round_ERR2585228 1 0.5138 0.8373 0.748 0.252 0.000
#> round_ERR2585248 1 0.0747 0.6635 0.984 0.000 0.016
#> round_ERR2585240 1 0.6079 0.6401 0.612 0.388 0.000
#> round_ERR2585270 1 0.5216 0.8334 0.740 0.260 0.000
#> round_ERR2585232 2 0.6476 -0.0909 0.448 0.548 0.004
#> aberrant_ERR2585341 2 0.0983 0.8448 0.004 0.980 0.016
#> aberrant_ERR2585355 2 0.0424 0.8412 0.000 0.992 0.008
#> round_ERR2585227 1 0.5178 0.8297 0.744 0.256 0.000
#> aberrant_ERR2585351 2 0.2400 0.8429 0.004 0.932 0.064
#> round_ERR2585269 1 0.2400 0.7552 0.932 0.064 0.004
#> aberrant_ERR2585357 2 0.0237 0.8424 0.000 0.996 0.004
#> aberrant_ERR2585350 2 0.0000 0.8431 0.000 1.000 0.000
#> round_ERR2585250 1 0.5835 0.7270 0.660 0.340 0.000
#> round_ERR2585245 1 0.0747 0.6635 0.984 0.000 0.016
#> aberrant_ERR2585353 2 0.2772 0.8385 0.004 0.916 0.080
#> round_ERR2585258 1 0.5254 0.8305 0.736 0.264 0.000
#> aberrant_ERR2585354 2 0.1964 0.8453 0.000 0.944 0.056
#> round_ERR2585249 1 0.1765 0.7289 0.956 0.040 0.004
#> round_ERR2585268 1 0.5968 0.6772 0.636 0.364 0.000
#> aberrant_ERR2585356 2 0.5062 0.7585 0.016 0.800 0.184
#> round_ERR2585266 2 0.6228 0.2374 0.372 0.624 0.004
#> round_ERR2585231 1 0.1170 0.6945 0.976 0.016 0.008
#> round_ERR2585230 1 0.5254 0.8302 0.736 0.264 0.000
#> round_ERR2585267 1 0.2269 0.7188 0.944 0.040 0.016
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 2 0.1863 0.8505 0.004 0.944 NA 0.012
#> aberrant_ERR2585338 2 0.3552 0.8180 0.024 0.848 NA 0.000
#> aberrant_ERR2585325 2 0.1863 0.8505 0.004 0.944 NA 0.012
#> aberrant_ERR2585283 4 0.0188 0.9576 0.000 0.004 NA 0.996
#> aberrant_ERR2585343 2 0.4713 0.7668 0.008 0.804 NA 0.116
#> aberrant_ERR2585329 2 0.2255 0.8497 0.012 0.920 NA 0.000
#> aberrant_ERR2585317 2 0.2473 0.8437 0.012 0.908 NA 0.000
#> aberrant_ERR2585339 2 0.3037 0.8340 0.020 0.880 NA 0.000
#> aberrant_ERR2585335 2 0.1004 0.8539 0.004 0.972 NA 0.000
#> aberrant_ERR2585287 2 0.6014 0.4181 0.000 0.588 NA 0.360
#> aberrant_ERR2585321 2 0.4346 0.7892 0.004 0.824 NA 0.096
#> aberrant_ERR2585297 1 0.4194 0.6887 0.800 0.028 NA 0.000
#> aberrant_ERR2585337 2 0.2542 0.8412 0.012 0.904 NA 0.000
#> aberrant_ERR2585319 2 0.1305 0.8553 0.000 0.960 NA 0.004
#> aberrant_ERR2585315 2 0.1909 0.8532 0.008 0.940 NA 0.004
#> aberrant_ERR2585336 2 0.2706 0.8408 0.020 0.900 NA 0.000
#> aberrant_ERR2585307 2 0.3623 0.8315 0.048 0.864 NA 0.004
#> aberrant_ERR2585301 2 0.1732 0.8529 0.008 0.948 NA 0.004
#> aberrant_ERR2585326 2 0.2924 0.8351 0.016 0.884 NA 0.000
#> aberrant_ERR2585331 2 0.3803 0.8105 0.032 0.836 NA 0.000
#> aberrant_ERR2585346 4 0.0336 0.9558 0.000 0.008 NA 0.992
#> aberrant_ERR2585314 2 0.2742 0.8492 0.024 0.900 NA 0.000
#> aberrant_ERR2585298 1 0.7456 0.4805 0.492 0.200 NA 0.000
#> aberrant_ERR2585345 2 0.2255 0.8473 0.012 0.920 NA 0.000
#> aberrant_ERR2585299 1 0.4127 0.7117 0.824 0.052 NA 0.000
#> aberrant_ERR2585309 1 0.5428 0.5169 0.620 0.016 NA 0.004
#> aberrant_ERR2585303 2 0.2255 0.8547 0.012 0.920 NA 0.000
#> aberrant_ERR2585313 2 0.2271 0.8489 0.008 0.916 NA 0.000
#> aberrant_ERR2585318 2 0.2207 0.8441 0.004 0.928 NA 0.012
#> aberrant_ERR2585328 2 0.3495 0.8508 0.020 0.868 NA 0.012
#> aberrant_ERR2585330 2 0.2604 0.8482 0.012 0.916 NA 0.016
#> aberrant_ERR2585293 4 0.0188 0.9576 0.000 0.004 NA 0.996
#> aberrant_ERR2585342 2 0.2944 0.8376 0.004 0.900 NA 0.044
#> aberrant_ERR2585348 2 0.2877 0.8438 0.008 0.904 NA 0.028
#> aberrant_ERR2585352 2 0.2010 0.8561 0.012 0.940 NA 0.008
#> aberrant_ERR2585308 1 0.5200 0.6170 0.704 0.028 NA 0.004
#> aberrant_ERR2585349 2 0.5672 0.6845 0.100 0.712 NA 0.000
#> aberrant_ERR2585316 2 0.4333 0.7839 0.004 0.820 NA 0.120
#> aberrant_ERR2585306 2 0.5205 0.7341 0.012 0.768 NA 0.156
#> aberrant_ERR2585324 2 0.1305 0.8553 0.000 0.960 NA 0.004
#> aberrant_ERR2585310 2 0.4287 0.8038 0.080 0.828 NA 0.004
#> aberrant_ERR2585296 1 0.4786 0.6953 0.788 0.104 NA 0.000
#> aberrant_ERR2585275 4 0.0188 0.9576 0.000 0.004 NA 0.996
#> aberrant_ERR2585311 2 0.2845 0.8315 0.004 0.904 NA 0.036
#> aberrant_ERR2585292 4 0.0188 0.9576 0.000 0.004 NA 0.996
#> aberrant_ERR2585282 2 0.2706 0.8413 0.004 0.908 NA 0.024
#> aberrant_ERR2585305 2 0.2433 0.8421 0.008 0.920 NA 0.012
#> aberrant_ERR2585278 2 0.2271 0.8546 0.012 0.928 NA 0.008
#> aberrant_ERR2585347 2 0.3962 0.8056 0.004 0.844 NA 0.100
#> aberrant_ERR2585332 2 0.3464 0.8100 0.000 0.868 NA 0.056
#> aberrant_ERR2585280 2 0.2505 0.8458 0.004 0.920 NA 0.040
#> aberrant_ERR2585304 2 0.6724 0.5142 0.192 0.616 NA 0.000
#> aberrant_ERR2585322 2 0.2473 0.8450 0.012 0.908 NA 0.000
#> aberrant_ERR2585279 2 0.4562 0.7783 0.056 0.792 NA 0.000
#> aberrant_ERR2585277 2 0.3787 0.8125 0.036 0.840 NA 0.000
#> aberrant_ERR2585295 2 0.2222 0.8572 0.008 0.928 NA 0.008
#> aberrant_ERR2585333 2 0.3411 0.8240 0.008 0.880 NA 0.064
#> aberrant_ERR2585285 2 0.1796 0.8539 0.004 0.948 NA 0.016
#> aberrant_ERR2585286 2 0.3787 0.8122 0.036 0.840 NA 0.000
#> aberrant_ERR2585294 2 0.2039 0.8511 0.008 0.940 NA 0.016
#> aberrant_ERR2585300 2 0.5150 0.7347 0.008 0.768 NA 0.156
#> aberrant_ERR2585334 2 0.3895 0.8076 0.036 0.832 NA 0.000
#> aberrant_ERR2585361 2 0.2421 0.8538 0.008 0.924 NA 0.020
#> aberrant_ERR2585372 2 0.2485 0.8346 0.004 0.916 NA 0.016
#> round_ERR2585217 2 0.7650 0.0604 0.328 0.448 NA 0.000
#> round_ERR2585205 1 0.3547 0.7277 0.864 0.064 NA 0.000
#> round_ERR2585214 1 0.7851 0.2817 0.400 0.312 NA 0.000
#> round_ERR2585202 2 0.7497 0.2768 0.260 0.500 NA 0.000
#> aberrant_ERR2585367 2 0.2505 0.8529 0.008 0.920 NA 0.020
#> round_ERR2585220 1 0.2739 0.7279 0.904 0.060 NA 0.000
#> round_ERR2585238 1 0.4244 0.6790 0.800 0.032 NA 0.000
#> aberrant_ERR2585276 2 0.2731 0.8420 0.008 0.912 NA 0.032
#> round_ERR2585218 1 0.3634 0.7227 0.856 0.048 NA 0.000
#> aberrant_ERR2585363 2 0.2317 0.8506 0.012 0.928 NA 0.012
#> round_ERR2585201 1 0.7527 0.4711 0.484 0.216 NA 0.000
#> round_ERR2585210 1 0.4072 0.7186 0.828 0.052 NA 0.000
#> aberrant_ERR2585362 2 0.2725 0.8518 0.016 0.912 NA 0.016
#> aberrant_ERR2585360 2 0.2814 0.8421 0.008 0.908 NA 0.032
#> round_ERR2585209 1 0.7459 0.4818 0.508 0.244 NA 0.000
#> round_ERR2585242 1 0.7442 0.4842 0.496 0.200 NA 0.000
#> round_ERR2585216 1 0.3144 0.7263 0.884 0.072 NA 0.000
#> round_ERR2585219 1 0.2485 0.7290 0.916 0.064 NA 0.004
#> round_ERR2585237 1 0.7811 0.2906 0.412 0.320 NA 0.000
#> round_ERR2585198 2 0.6917 0.4688 0.208 0.592 NA 0.000
#> round_ERR2585211 1 0.3687 0.7244 0.856 0.064 NA 0.000
#> round_ERR2585206 1 0.3547 0.7249 0.864 0.064 NA 0.000
#> aberrant_ERR2585281 2 0.3328 0.8351 0.024 0.872 NA 0.004
#> round_ERR2585212 1 0.2413 0.7264 0.916 0.064 NA 0.000
#> round_ERR2585221 1 0.5088 0.5931 0.700 0.020 NA 0.004
#> round_ERR2585243 1 0.3601 0.7304 0.860 0.056 NA 0.000
#> round_ERR2585204 1 0.7896 0.1693 0.360 0.348 NA 0.000
#> round_ERR2585213 2 0.7519 0.2666 0.256 0.496 NA 0.000
#> aberrant_ERR2585373 2 0.3312 0.8228 0.008 0.884 NA 0.040
#> aberrant_ERR2585358 2 0.4362 0.7842 0.008 0.828 NA 0.088
#> aberrant_ERR2585365 2 0.2164 0.8571 0.004 0.924 NA 0.004
#> aberrant_ERR2585359 2 0.4626 0.7661 0.004 0.804 NA 0.120
#> aberrant_ERR2585370 2 0.2796 0.8380 0.016 0.892 NA 0.000
#> round_ERR2585215 1 0.4695 0.6039 0.732 0.012 NA 0.004
#> round_ERR2585262 2 0.7900 -0.1207 0.320 0.372 NA 0.000
#> round_ERR2585199 2 0.7064 0.4251 0.220 0.572 NA 0.000
#> aberrant_ERR2585369 2 0.2161 0.8438 0.004 0.932 NA 0.016
#> round_ERR2585208 1 0.4150 0.7107 0.824 0.056 NA 0.000
#> round_ERR2585252 1 0.5299 0.4796 0.600 0.008 NA 0.004
#> round_ERR2585236 1 0.5510 0.6882 0.744 0.120 NA 0.004
#> aberrant_ERR2585284 4 0.3311 0.8974 0.000 0.000 NA 0.828
#> round_ERR2585224 1 0.5353 0.4057 0.556 0.000 NA 0.012
#> round_ERR2585260 1 0.3004 0.7291 0.892 0.060 NA 0.000
#> round_ERR2585229 1 0.4467 0.6830 0.788 0.040 NA 0.000
#> aberrant_ERR2585364 4 0.3908 0.8204 0.008 0.116 NA 0.844
#> round_ERR2585253 1 0.5097 0.4226 0.568 0.000 NA 0.004
#> aberrant_ERR2585368 2 0.3048 0.8326 0.016 0.876 NA 0.000
#> aberrant_ERR2585371 2 0.3048 0.8326 0.016 0.876 NA 0.000
#> round_ERR2585239 1 0.3168 0.7312 0.884 0.056 NA 0.000
#> round_ERR2585273 1 0.4994 0.6751 0.744 0.048 NA 0.000
#> round_ERR2585256 1 0.7122 0.5249 0.560 0.248 NA 0.000
#> round_ERR2585272 1 0.3900 0.7292 0.844 0.072 NA 0.000
#> round_ERR2585246 1 0.5263 0.6240 0.704 0.032 NA 0.004
#> round_ERR2585261 1 0.7322 0.5041 0.532 0.244 NA 0.000
#> round_ERR2585254 2 0.7785 -0.0890 0.348 0.404 NA 0.000
#> round_ERR2585225 1 0.7530 0.4700 0.480 0.212 NA 0.000
#> round_ERR2585235 1 0.6835 0.5847 0.592 0.156 NA 0.000
#> round_ERR2585271 1 0.3584 0.7302 0.868 0.064 NA 0.004
#> round_ERR2585251 1 0.3004 0.7269 0.892 0.060 NA 0.000
#> round_ERR2585255 1 0.7565 0.4603 0.472 0.216 NA 0.000
#> round_ERR2585257 1 0.7503 0.4779 0.488 0.212 NA 0.000
#> round_ERR2585226 1 0.3320 0.7277 0.876 0.056 NA 0.000
#> round_ERR2585265 1 0.3168 0.7277 0.884 0.060 NA 0.000
#> round_ERR2585259 1 0.6476 0.6005 0.644 0.176 NA 0.000
#> round_ERR2585247 1 0.5051 0.6369 0.724 0.028 NA 0.004
#> round_ERR2585241 1 0.3088 0.7267 0.888 0.060 NA 0.000
#> round_ERR2585263 1 0.3399 0.7202 0.868 0.092 NA 0.000
#> round_ERR2585264 1 0.5105 0.4171 0.564 0.000 NA 0.004
#> round_ERR2585233 1 0.7429 0.4828 0.496 0.196 NA 0.000
#> round_ERR2585223 1 0.3245 0.7290 0.880 0.056 NA 0.000
#> round_ERR2585234 1 0.7838 0.2804 0.404 0.316 NA 0.000
#> round_ERR2585222 1 0.3229 0.7293 0.880 0.072 NA 0.000
#> round_ERR2585228 1 0.2739 0.7291 0.904 0.060 NA 0.000
#> round_ERR2585248 1 0.5105 0.4171 0.564 0.000 NA 0.004
#> round_ERR2585240 1 0.5515 0.6709 0.732 0.116 NA 0.000
#> round_ERR2585270 1 0.2892 0.7318 0.896 0.068 NA 0.000
#> round_ERR2585232 1 0.6970 0.5562 0.576 0.168 NA 0.000
#> aberrant_ERR2585341 2 0.2238 0.8539 0.004 0.920 NA 0.004
#> aberrant_ERR2585355 2 0.3367 0.8268 0.028 0.864 NA 0.000
#> round_ERR2585227 1 0.4731 0.7158 0.780 0.060 NA 0.000
#> aberrant_ERR2585351 2 0.2275 0.8457 0.004 0.928 NA 0.020
#> round_ERR2585269 1 0.5213 0.5622 0.652 0.020 NA 0.000
#> aberrant_ERR2585357 2 0.2676 0.8394 0.012 0.896 NA 0.000
#> aberrant_ERR2585350 2 0.2730 0.8388 0.016 0.896 NA 0.000
#> round_ERR2585250 1 0.4104 0.7100 0.832 0.080 NA 0.000
#> round_ERR2585245 1 0.5105 0.4171 0.564 0.000 NA 0.004
#> aberrant_ERR2585353 2 0.2982 0.8367 0.004 0.896 NA 0.032
#> round_ERR2585258 1 0.3168 0.7277 0.884 0.060 NA 0.000
#> aberrant_ERR2585354 2 0.2365 0.8476 0.004 0.920 NA 0.012
#> round_ERR2585249 1 0.5054 0.5370 0.660 0.008 NA 0.004
#> round_ERR2585268 1 0.4605 0.6984 0.800 0.092 NA 0.000
#> aberrant_ERR2585356 2 0.5193 0.7310 0.008 0.768 NA 0.148
#> round_ERR2585266 1 0.7412 0.4916 0.504 0.200 NA 0.000
#> round_ERR2585231 1 0.5004 0.4643 0.604 0.000 NA 0.004
#> round_ERR2585230 1 0.2816 0.7304 0.900 0.064 NA 0.000
#> round_ERR2585267 1 0.5165 0.4768 0.604 0.004 NA 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 2 0.2666 0.830829 0.000 0.892 0.020 0.012 0.076
#> aberrant_ERR2585338 2 0.4789 0.745535 0.000 0.728 0.156 0.000 0.116
#> aberrant_ERR2585325 2 0.2666 0.830829 0.000 0.892 0.020 0.012 0.076
#> aberrant_ERR2585283 4 0.0000 0.868242 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585343 2 0.5222 0.683181 0.000 0.696 0.008 0.100 0.196
#> aberrant_ERR2585329 2 0.3401 0.816937 0.000 0.840 0.096 0.000 0.064
#> aberrant_ERR2585317 2 0.3691 0.803814 0.000 0.820 0.104 0.000 0.076
#> aberrant_ERR2585339 2 0.4266 0.784080 0.000 0.776 0.120 0.000 0.104
#> aberrant_ERR2585335 2 0.2149 0.833474 0.000 0.916 0.036 0.000 0.048
#> aberrant_ERR2585287 2 0.6115 0.325141 0.000 0.520 0.004 0.356 0.120
#> aberrant_ERR2585321 2 0.4922 0.712079 0.000 0.720 0.004 0.096 0.180
#> aberrant_ERR2585297 1 0.4599 0.494106 0.600 0.000 0.384 0.000 0.016
#> aberrant_ERR2585337 2 0.3967 0.794165 0.000 0.800 0.108 0.000 0.092
#> aberrant_ERR2585319 2 0.2536 0.834163 0.000 0.900 0.052 0.004 0.044
#> aberrant_ERR2585315 2 0.3047 0.824144 0.000 0.868 0.084 0.004 0.044
#> aberrant_ERR2585336 2 0.3906 0.796243 0.000 0.804 0.112 0.000 0.084
#> aberrant_ERR2585307 2 0.4315 0.790043 0.000 0.772 0.156 0.004 0.068
#> aberrant_ERR2585301 2 0.2206 0.826955 0.000 0.912 0.016 0.004 0.068
#> aberrant_ERR2585326 2 0.4121 0.788972 0.000 0.788 0.112 0.000 0.100
#> aberrant_ERR2585331 2 0.4901 0.733522 0.000 0.716 0.168 0.000 0.116
#> aberrant_ERR2585346 4 0.0324 0.861272 0.000 0.004 0.000 0.992 0.004
#> aberrant_ERR2585314 2 0.3410 0.824489 0.000 0.840 0.092 0.000 0.068
#> aberrant_ERR2585298 3 0.2581 0.467893 0.020 0.048 0.904 0.000 0.028
#> aberrant_ERR2585345 2 0.3464 0.810860 0.000 0.836 0.096 0.000 0.068
#> aberrant_ERR2585299 1 0.4830 0.427522 0.560 0.004 0.420 0.000 0.016
#> aberrant_ERR2585309 1 0.3375 0.608595 0.840 0.000 0.104 0.000 0.056
#> aberrant_ERR2585303 2 0.3307 0.829335 0.000 0.844 0.052 0.000 0.104
#> aberrant_ERR2585313 2 0.3464 0.813249 0.000 0.836 0.096 0.000 0.068
#> aberrant_ERR2585318 2 0.2464 0.814842 0.000 0.892 0.004 0.012 0.092
#> aberrant_ERR2585328 2 0.4306 0.816603 0.000 0.792 0.100 0.012 0.096
#> aberrant_ERR2585330 2 0.3009 0.827938 0.000 0.876 0.028 0.016 0.080
#> aberrant_ERR2585293 4 0.0000 0.868242 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585342 2 0.3776 0.800733 0.000 0.820 0.012 0.040 0.128
#> aberrant_ERR2585348 2 0.3685 0.810064 0.000 0.824 0.016 0.028 0.132
#> aberrant_ERR2585352 2 0.3078 0.834636 0.000 0.872 0.064 0.008 0.056
#> aberrant_ERR2585308 1 0.3970 0.616699 0.752 0.000 0.224 0.000 0.024
#> aberrant_ERR2585349 2 0.5629 0.532111 0.000 0.588 0.312 0.000 0.100
#> aberrant_ERR2585316 2 0.5199 0.699135 0.000 0.704 0.008 0.112 0.176
#> aberrant_ERR2585306 2 0.5873 0.642172 0.008 0.652 0.008 0.132 0.200
#> aberrant_ERR2585324 2 0.2536 0.834163 0.000 0.900 0.052 0.004 0.044
#> aberrant_ERR2585310 2 0.4645 0.772147 0.004 0.756 0.156 0.004 0.080
#> aberrant_ERR2585296 3 0.5026 0.190471 0.328 0.028 0.632 0.000 0.012
#> aberrant_ERR2585275 4 0.0162 0.866034 0.000 0.000 0.000 0.996 0.004
#> aberrant_ERR2585311 2 0.3609 0.779197 0.000 0.816 0.004 0.032 0.148
#> aberrant_ERR2585292 4 0.0000 0.868242 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585282 2 0.3170 0.800794 0.000 0.848 0.004 0.024 0.124
#> aberrant_ERR2585305 2 0.2811 0.811499 0.000 0.876 0.012 0.012 0.100
#> aberrant_ERR2585278 2 0.3337 0.829307 0.000 0.856 0.072 0.008 0.064
#> aberrant_ERR2585347 2 0.4675 0.757959 0.000 0.760 0.012 0.092 0.136
#> aberrant_ERR2585332 2 0.4031 0.739190 0.000 0.772 0.000 0.044 0.184
#> aberrant_ERR2585280 2 0.2965 0.821529 0.000 0.880 0.012 0.040 0.068
#> aberrant_ERR2585304 3 0.5945 -0.174465 0.008 0.456 0.456 0.000 0.080
#> aberrant_ERR2585322 2 0.3639 0.806779 0.000 0.824 0.100 0.000 0.076
#> aberrant_ERR2585279 2 0.5295 0.678536 0.000 0.664 0.224 0.000 0.112
#> aberrant_ERR2585277 2 0.4772 0.741392 0.000 0.728 0.164 0.000 0.108
#> aberrant_ERR2585295 2 0.3210 0.835667 0.000 0.860 0.040 0.008 0.092
#> aberrant_ERR2585333 2 0.4014 0.776963 0.000 0.804 0.008 0.060 0.128
#> aberrant_ERR2585285 2 0.2721 0.833797 0.000 0.896 0.036 0.016 0.052
#> aberrant_ERR2585286 2 0.4855 0.736036 0.000 0.720 0.168 0.000 0.112
#> aberrant_ERR2585294 2 0.2312 0.824742 0.000 0.912 0.012 0.016 0.060
#> aberrant_ERR2585300 2 0.5673 0.643394 0.000 0.652 0.008 0.136 0.204
#> aberrant_ERR2585334 2 0.4936 0.728722 0.000 0.712 0.172 0.000 0.116
#> aberrant_ERR2585361 2 0.3496 0.832813 0.000 0.848 0.036 0.020 0.096
#> aberrant_ERR2585372 2 0.2881 0.805419 0.000 0.860 0.004 0.012 0.124
#> round_ERR2585217 3 0.5961 0.322348 0.040 0.320 0.588 0.000 0.052
#> round_ERR2585205 1 0.4911 0.257597 0.504 0.008 0.476 0.000 0.012
#> round_ERR2585214 3 0.3875 0.452951 0.008 0.140 0.808 0.000 0.044
#> round_ERR2585202 3 0.5921 0.197591 0.012 0.344 0.560 0.000 0.084
#> aberrant_ERR2585367 2 0.3387 0.830033 0.000 0.852 0.028 0.020 0.100
#> round_ERR2585220 3 0.4397 -0.066445 0.432 0.004 0.564 0.000 0.000
#> round_ERR2585238 1 0.4624 0.541810 0.636 0.000 0.340 0.000 0.024
#> aberrant_ERR2585276 2 0.2990 0.814928 0.000 0.876 0.012 0.032 0.080
#> round_ERR2585218 1 0.4899 0.299012 0.524 0.008 0.456 0.000 0.012
#> aberrant_ERR2585363 2 0.2696 0.829230 0.000 0.892 0.024 0.012 0.072
#> round_ERR2585201 3 0.2445 0.471038 0.016 0.056 0.908 0.000 0.020
#> round_ERR2585210 1 0.5227 0.308447 0.504 0.008 0.460 0.000 0.028
#> aberrant_ERR2585362 2 0.3272 0.820588 0.000 0.848 0.016 0.016 0.120
#> aberrant_ERR2585360 2 0.3344 0.816220 0.000 0.852 0.016 0.028 0.104
#> round_ERR2585209 3 0.3882 0.468342 0.060 0.100 0.824 0.000 0.016
#> round_ERR2585242 3 0.2492 0.467491 0.020 0.048 0.908 0.000 0.024
#> round_ERR2585216 3 0.4621 -0.010323 0.412 0.008 0.576 0.000 0.004
#> round_ERR2585219 3 0.4576 -0.129747 0.456 0.004 0.536 0.000 0.004
#> round_ERR2585237 3 0.4472 0.447595 0.024 0.184 0.760 0.000 0.032
#> round_ERR2585198 3 0.5931 -0.068663 0.008 0.424 0.488 0.000 0.080
#> round_ERR2585211 1 0.4997 0.285100 0.508 0.008 0.468 0.000 0.016
#> round_ERR2585206 1 0.4909 0.273974 0.508 0.008 0.472 0.000 0.012
#> aberrant_ERR2585281 2 0.4220 0.792349 0.000 0.788 0.116 0.004 0.092
#> round_ERR2585212 3 0.4524 -0.043416 0.420 0.004 0.572 0.000 0.004
#> round_ERR2585221 1 0.4347 0.619793 0.744 0.004 0.212 0.000 0.040
#> round_ERR2585243 3 0.4913 -0.261812 0.484 0.008 0.496 0.000 0.012
#> round_ERR2585204 3 0.4750 0.427050 0.012 0.208 0.728 0.000 0.052
#> round_ERR2585213 3 0.5719 0.187004 0.004 0.348 0.564 0.000 0.084
#> aberrant_ERR2585373 2 0.3811 0.773778 0.000 0.808 0.008 0.036 0.148
#> aberrant_ERR2585358 2 0.4943 0.698625 0.000 0.716 0.008 0.076 0.200
#> aberrant_ERR2585365 2 0.3105 0.832136 0.000 0.864 0.044 0.004 0.088
#> aberrant_ERR2585359 2 0.5213 0.672977 0.000 0.688 0.004 0.104 0.204
#> aberrant_ERR2585370 2 0.4069 0.790583 0.000 0.792 0.112 0.000 0.096
#> round_ERR2585215 1 0.4477 0.590642 0.708 0.000 0.252 0.000 0.040
#> round_ERR2585262 3 0.4593 0.410937 0.000 0.184 0.736 0.000 0.080
#> round_ERR2585199 3 0.5908 0.011286 0.008 0.404 0.508 0.000 0.080
#> aberrant_ERR2585369 2 0.2507 0.821467 0.000 0.900 0.012 0.016 0.072
#> round_ERR2585208 1 0.4928 0.402051 0.564 0.008 0.412 0.000 0.016
#> round_ERR2585252 1 0.2927 0.597852 0.872 0.000 0.068 0.000 0.060
#> round_ERR2585236 3 0.5749 0.129370 0.312 0.036 0.612 0.004 0.036
#> aberrant_ERR2585284 5 0.4961 0.000000 0.004 0.000 0.020 0.456 0.520
#> round_ERR2585224 1 0.2352 0.511562 0.896 0.000 0.008 0.004 0.092
#> round_ERR2585260 3 0.4702 -0.181815 0.476 0.008 0.512 0.000 0.004
#> round_ERR2585229 1 0.4283 0.526676 0.644 0.000 0.348 0.000 0.008
#> aberrant_ERR2585364 4 0.3635 0.447884 0.000 0.088 0.008 0.836 0.068
#> round_ERR2585253 1 0.2011 0.529003 0.908 0.000 0.004 0.000 0.088
#> aberrant_ERR2585368 2 0.4361 0.774700 0.000 0.768 0.124 0.000 0.108
#> aberrant_ERR2585371 2 0.4361 0.774700 0.000 0.768 0.124 0.000 0.108
#> round_ERR2585239 3 0.4809 -0.178206 0.468 0.008 0.516 0.000 0.008
#> round_ERR2585273 1 0.4714 0.500228 0.608 0.004 0.372 0.000 0.016
#> round_ERR2585256 3 0.5229 0.436613 0.136 0.140 0.712 0.000 0.012
#> round_ERR2585272 3 0.4664 -0.072491 0.436 0.008 0.552 0.000 0.004
#> round_ERR2585246 1 0.3999 0.612244 0.740 0.000 0.240 0.000 0.020
#> round_ERR2585261 3 0.4735 0.454638 0.100 0.132 0.756 0.000 0.012
#> round_ERR2585254 3 0.4948 0.400651 0.016 0.276 0.676 0.000 0.032
#> round_ERR2585225 3 0.2321 0.468471 0.008 0.056 0.912 0.000 0.024
#> round_ERR2585235 3 0.3759 0.388672 0.148 0.028 0.812 0.000 0.012
#> round_ERR2585271 1 0.4816 0.197696 0.496 0.008 0.488 0.000 0.008
#> round_ERR2585251 3 0.4497 -0.038138 0.424 0.008 0.568 0.000 0.000
#> round_ERR2585255 3 0.2284 0.466713 0.004 0.056 0.912 0.000 0.028
#> round_ERR2585257 3 0.2363 0.467070 0.012 0.052 0.912 0.000 0.024
#> round_ERR2585226 3 0.4517 -0.071584 0.436 0.008 0.556 0.000 0.000
#> round_ERR2585265 3 0.4510 -0.064074 0.432 0.008 0.560 0.000 0.000
#> round_ERR2585259 3 0.4237 0.378048 0.160 0.036 0.784 0.000 0.020
#> round_ERR2585247 1 0.4725 0.592857 0.680 0.004 0.280 0.000 0.036
#> round_ERR2585241 1 0.4816 0.213929 0.496 0.008 0.488 0.000 0.008
#> round_ERR2585263 3 0.5051 0.050478 0.392 0.024 0.576 0.000 0.008
#> round_ERR2585264 1 0.1952 0.532474 0.912 0.000 0.004 0.000 0.084
#> round_ERR2585233 3 0.1934 0.461239 0.008 0.040 0.932 0.000 0.020
#> round_ERR2585223 3 0.4561 -0.206706 0.488 0.008 0.504 0.000 0.000
#> round_ERR2585234 3 0.4137 0.448331 0.016 0.176 0.780 0.000 0.028
#> round_ERR2585222 3 0.4604 -0.000138 0.404 0.008 0.584 0.000 0.004
#> round_ERR2585228 3 0.4698 -0.170186 0.468 0.004 0.520 0.000 0.008
#> round_ERR2585248 1 0.2233 0.508899 0.892 0.000 0.004 0.000 0.104
#> round_ERR2585240 3 0.4478 0.253380 0.272 0.020 0.700 0.000 0.008
#> round_ERR2585270 3 0.4809 -0.176406 0.468 0.008 0.516 0.000 0.008
#> round_ERR2585232 3 0.3794 0.429709 0.112 0.036 0.828 0.000 0.024
#> aberrant_ERR2585341 2 0.3481 0.821476 0.000 0.840 0.056 0.004 0.100
#> aberrant_ERR2585355 2 0.4528 0.767058 0.000 0.752 0.144 0.000 0.104
#> round_ERR2585227 1 0.4913 0.265621 0.492 0.008 0.488 0.000 0.012
#> aberrant_ERR2585351 2 0.2900 0.824615 0.000 0.876 0.012 0.020 0.092
#> round_ERR2585269 1 0.3536 0.622547 0.812 0.000 0.156 0.000 0.032
#> aberrant_ERR2585357 2 0.3962 0.795045 0.000 0.800 0.112 0.000 0.088
#> aberrant_ERR2585350 2 0.4020 0.791612 0.000 0.796 0.108 0.000 0.096
#> round_ERR2585250 3 0.4647 0.135122 0.352 0.016 0.628 0.000 0.004
#> round_ERR2585245 1 0.1892 0.531191 0.916 0.000 0.004 0.000 0.080
#> aberrant_ERR2585353 2 0.3563 0.797210 0.000 0.824 0.008 0.028 0.140
#> round_ERR2585258 3 0.4510 -0.064074 0.432 0.008 0.560 0.000 0.000
#> aberrant_ERR2585354 2 0.2756 0.823813 0.000 0.880 0.012 0.012 0.096
#> round_ERR2585249 1 0.3002 0.620831 0.856 0.000 0.116 0.000 0.028
#> round_ERR2585268 3 0.4500 0.191693 0.316 0.016 0.664 0.000 0.004
#> aberrant_ERR2585356 2 0.5718 0.620832 0.000 0.644 0.008 0.132 0.216
#> round_ERR2585266 3 0.2673 0.466570 0.028 0.048 0.900 0.000 0.024
#> round_ERR2585231 1 0.2659 0.592652 0.888 0.000 0.052 0.000 0.060
#> round_ERR2585230 3 0.4792 -0.106892 0.448 0.008 0.536 0.000 0.008
#> round_ERR2585267 1 0.3239 0.584054 0.852 0.000 0.068 0.000 0.080
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 5 0.2378 0.6865 0.000 0.000 0.000 0.000 0.848 0.152
#> aberrant_ERR2585338 5 0.3971 0.3860 0.004 0.000 0.000 0.000 0.548 0.448
#> aberrant_ERR2585325 5 0.2378 0.6865 0.000 0.000 0.000 0.000 0.848 0.152
#> aberrant_ERR2585283 4 0.0000 0.9558 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585343 5 0.4484 0.4944 0.000 0.012 0.000 0.048 0.688 0.252
#> aberrant_ERR2585329 5 0.3198 0.6163 0.000 0.000 0.000 0.000 0.740 0.260
#> aberrant_ERR2585317 5 0.3499 0.5730 0.000 0.000 0.000 0.000 0.680 0.320
#> aberrant_ERR2585339 5 0.3717 0.5111 0.000 0.000 0.000 0.000 0.616 0.384
#> aberrant_ERR2585335 5 0.2135 0.6793 0.000 0.000 0.000 0.000 0.872 0.128
#> aberrant_ERR2585287 5 0.5881 0.2363 0.000 0.004 0.004 0.320 0.504 0.168
#> aberrant_ERR2585321 5 0.4256 0.5519 0.000 0.016 0.004 0.044 0.744 0.192
#> aberrant_ERR2585297 1 0.4158 0.3013 0.716 0.240 0.032 0.000 0.000 0.012
#> aberrant_ERR2585337 5 0.3620 0.5415 0.000 0.000 0.000 0.000 0.648 0.352
#> aberrant_ERR2585319 5 0.2558 0.6747 0.000 0.000 0.004 0.000 0.840 0.156
#> aberrant_ERR2585315 5 0.3151 0.6323 0.000 0.000 0.000 0.000 0.748 0.252
#> aberrant_ERR2585336 5 0.3578 0.5585 0.000 0.000 0.000 0.000 0.660 0.340
#> aberrant_ERR2585307 5 0.4607 0.5323 0.040 0.000 0.024 0.000 0.684 0.252
#> aberrant_ERR2585301 5 0.1858 0.6827 0.000 0.000 0.004 0.000 0.904 0.092
#> aberrant_ERR2585326 5 0.3695 0.5164 0.000 0.000 0.000 0.000 0.624 0.376
#> aberrant_ERR2585331 5 0.4211 0.3445 0.008 0.000 0.004 0.000 0.532 0.456
#> aberrant_ERR2585346 4 0.0291 0.9533 0.000 0.004 0.000 0.992 0.004 0.000
#> aberrant_ERR2585314 5 0.3636 0.6299 0.016 0.000 0.016 0.000 0.772 0.196
#> aberrant_ERR2585298 1 0.6019 -0.3095 0.452 0.000 0.396 0.000 0.024 0.128
#> aberrant_ERR2585345 5 0.3390 0.5990 0.000 0.000 0.000 0.000 0.704 0.296
#> aberrant_ERR2585299 1 0.3419 0.4185 0.792 0.180 0.016 0.000 0.000 0.012
#> aberrant_ERR2585309 2 0.4394 0.7212 0.392 0.584 0.012 0.000 0.000 0.012
#> aberrant_ERR2585303 5 0.3151 0.6441 0.000 0.000 0.000 0.000 0.748 0.252
#> aberrant_ERR2585313 5 0.3266 0.6074 0.000 0.000 0.000 0.000 0.728 0.272
#> aberrant_ERR2585318 5 0.1493 0.6720 0.000 0.004 0.004 0.000 0.936 0.056
#> aberrant_ERR2585328 5 0.3925 0.5978 0.012 0.004 0.004 0.000 0.700 0.280
#> aberrant_ERR2585330 5 0.1531 0.6850 0.000 0.004 0.000 0.000 0.928 0.068
#> aberrant_ERR2585293 4 0.0000 0.9558 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585342 5 0.2653 0.6536 0.000 0.000 0.000 0.012 0.844 0.144
#> aberrant_ERR2585348 5 0.2778 0.6613 0.000 0.008 0.000 0.000 0.824 0.168
#> aberrant_ERR2585352 5 0.2527 0.6736 0.000 0.000 0.000 0.000 0.832 0.168
#> aberrant_ERR2585308 1 0.4541 -0.3543 0.544 0.428 0.016 0.000 0.000 0.012
#> aberrant_ERR2585349 5 0.6450 -0.2384 0.064 0.000 0.116 0.000 0.432 0.388
#> aberrant_ERR2585316 5 0.4605 0.5141 0.000 0.004 0.004 0.068 0.688 0.236
#> aberrant_ERR2585306 5 0.5523 0.4608 0.004 0.024 0.008 0.080 0.632 0.252
#> aberrant_ERR2585324 5 0.2558 0.6747 0.000 0.000 0.004 0.000 0.840 0.156
#> aberrant_ERR2585310 5 0.4867 0.5036 0.080 0.000 0.036 0.000 0.708 0.176
#> aberrant_ERR2585296 1 0.3856 0.5380 0.804 0.012 0.124 0.000 0.016 0.044
#> aberrant_ERR2585275 4 0.0146 0.9551 0.000 0.004 0.000 0.996 0.000 0.000
#> aberrant_ERR2585311 5 0.2462 0.6257 0.000 0.004 0.000 0.004 0.860 0.132
#> aberrant_ERR2585292 4 0.0000 0.9558 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585282 5 0.2006 0.6515 0.000 0.004 0.000 0.000 0.892 0.104
#> aberrant_ERR2585305 5 0.1555 0.6652 0.000 0.004 0.004 0.000 0.932 0.060
#> aberrant_ERR2585278 5 0.2994 0.6539 0.000 0.004 0.000 0.000 0.788 0.208
#> aberrant_ERR2585347 5 0.4393 0.5908 0.000 0.004 0.004 0.056 0.708 0.228
#> aberrant_ERR2585332 5 0.3651 0.5617 0.000 0.016 0.000 0.008 0.752 0.224
#> aberrant_ERR2585280 5 0.2446 0.6775 0.000 0.000 0.000 0.012 0.864 0.124
#> aberrant_ERR2585304 5 0.7451 -0.7954 0.188 0.000 0.156 0.000 0.336 0.320
#> aberrant_ERR2585322 5 0.3409 0.5933 0.000 0.000 0.000 0.000 0.700 0.300
#> aberrant_ERR2585279 5 0.5293 0.1975 0.028 0.000 0.044 0.000 0.492 0.436
#> aberrant_ERR2585277 5 0.4195 0.3781 0.008 0.000 0.004 0.000 0.548 0.440
#> aberrant_ERR2585295 5 0.2948 0.6813 0.000 0.000 0.008 0.000 0.804 0.188
#> aberrant_ERR2585333 5 0.3324 0.6188 0.000 0.012 0.004 0.024 0.824 0.136
#> aberrant_ERR2585285 5 0.2053 0.6859 0.000 0.004 0.000 0.000 0.888 0.108
#> aberrant_ERR2585286 5 0.4080 0.3567 0.008 0.000 0.000 0.000 0.536 0.456
#> aberrant_ERR2585294 5 0.2288 0.6805 0.000 0.004 0.004 0.000 0.876 0.116
#> aberrant_ERR2585300 5 0.5358 0.4639 0.000 0.020 0.008 0.084 0.636 0.252
#> aberrant_ERR2585334 5 0.4214 0.3361 0.008 0.000 0.004 0.000 0.528 0.460
#> aberrant_ERR2585361 5 0.2482 0.6863 0.000 0.004 0.000 0.000 0.848 0.148
#> aberrant_ERR2585372 5 0.1863 0.6667 0.000 0.000 0.000 0.000 0.896 0.104
#> round_ERR2585217 1 0.7691 -0.7446 0.304 0.000 0.224 0.000 0.248 0.224
#> round_ERR2585205 1 0.2682 0.5362 0.876 0.084 0.020 0.000 0.000 0.020
#> round_ERR2585214 3 0.7053 0.2230 0.356 0.000 0.372 0.000 0.092 0.180
#> round_ERR2585202 6 0.7713 0.6800 0.252 0.000 0.212 0.000 0.268 0.268
#> aberrant_ERR2585367 5 0.2362 0.6868 0.000 0.004 0.000 0.000 0.860 0.136
#> round_ERR2585220 1 0.1498 0.5887 0.940 0.028 0.032 0.000 0.000 0.000
#> round_ERR2585238 1 0.3853 0.2070 0.708 0.272 0.012 0.000 0.000 0.008
#> aberrant_ERR2585276 5 0.2162 0.6675 0.000 0.000 0.004 0.012 0.896 0.088
#> round_ERR2585218 1 0.3111 0.5286 0.840 0.120 0.020 0.000 0.000 0.020
#> aberrant_ERR2585363 5 0.1714 0.6874 0.000 0.000 0.000 0.000 0.908 0.092
#> round_ERR2585201 1 0.6174 -0.3453 0.440 0.000 0.400 0.000 0.036 0.124
#> round_ERR2585210 1 0.3935 0.4917 0.800 0.104 0.052 0.000 0.000 0.044
#> aberrant_ERR2585362 5 0.2362 0.6712 0.000 0.000 0.004 0.000 0.860 0.136
#> aberrant_ERR2585360 5 0.2389 0.6690 0.000 0.000 0.000 0.008 0.864 0.128
#> round_ERR2585209 1 0.6497 -0.2734 0.484 0.000 0.320 0.000 0.072 0.124
#> round_ERR2585242 1 0.5990 -0.3038 0.456 0.000 0.396 0.000 0.024 0.124
#> round_ERR2585216 1 0.2051 0.5966 0.920 0.020 0.044 0.000 0.004 0.012
#> round_ERR2585219 1 0.1391 0.5804 0.944 0.040 0.016 0.000 0.000 0.000
#> round_ERR2585237 1 0.7284 -0.4923 0.372 0.000 0.312 0.000 0.120 0.196
#> round_ERR2585198 6 0.7551 0.7811 0.208 0.000 0.168 0.000 0.308 0.316
#> round_ERR2585211 1 0.2964 0.5179 0.856 0.100 0.020 0.000 0.000 0.024
#> round_ERR2585206 1 0.2834 0.5238 0.864 0.096 0.020 0.000 0.000 0.020
#> aberrant_ERR2585281 5 0.3986 0.5042 0.004 0.000 0.004 0.000 0.608 0.384
#> round_ERR2585212 1 0.0972 0.5909 0.964 0.008 0.028 0.000 0.000 0.000
#> round_ERR2585221 1 0.4569 -0.3345 0.560 0.408 0.008 0.000 0.000 0.024
#> round_ERR2585243 1 0.3293 0.5438 0.844 0.084 0.040 0.000 0.000 0.032
#> round_ERR2585204 3 0.7472 -0.1213 0.312 0.000 0.332 0.000 0.148 0.208
#> round_ERR2585213 6 0.7685 0.7043 0.216 0.000 0.240 0.000 0.236 0.308
#> aberrant_ERR2585373 5 0.2504 0.6214 0.000 0.004 0.004 0.000 0.856 0.136
#> aberrant_ERR2585358 5 0.3909 0.5262 0.000 0.012 0.000 0.020 0.732 0.236
#> aberrant_ERR2585365 5 0.2996 0.6597 0.000 0.000 0.000 0.000 0.772 0.228
#> aberrant_ERR2585359 5 0.4821 0.4839 0.000 0.024 0.004 0.048 0.680 0.244
#> aberrant_ERR2585370 5 0.3659 0.5310 0.000 0.000 0.000 0.000 0.636 0.364
#> round_ERR2585215 1 0.5695 -0.1678 0.584 0.288 0.048 0.000 0.000 0.080
#> round_ERR2585262 3 0.7070 0.0594 0.252 0.000 0.392 0.000 0.076 0.280
#> round_ERR2585199 6 0.7615 0.7999 0.216 0.000 0.184 0.000 0.288 0.312
#> aberrant_ERR2585369 5 0.1082 0.6807 0.000 0.004 0.000 0.000 0.956 0.040
#> round_ERR2585208 1 0.3800 0.4349 0.776 0.176 0.028 0.000 0.000 0.020
#> round_ERR2585252 2 0.3819 0.7798 0.340 0.652 0.000 0.000 0.000 0.008
#> round_ERR2585236 1 0.4936 0.5322 0.752 0.044 0.112 0.004 0.024 0.064
#> aberrant_ERR2585284 3 0.6880 -0.5766 0.000 0.128 0.488 0.244 0.000 0.140
#> round_ERR2585224 2 0.3704 0.7991 0.204 0.764 0.004 0.004 0.000 0.024
#> round_ERR2585260 1 0.1728 0.5718 0.924 0.064 0.008 0.000 0.000 0.004
#> round_ERR2585229 1 0.3809 0.2388 0.716 0.264 0.012 0.000 0.000 0.008
#> aberrant_ERR2585364 4 0.3858 0.7878 0.000 0.016 0.004 0.804 0.092 0.084
#> round_ERR2585253 2 0.4540 0.7999 0.208 0.716 0.032 0.000 0.000 0.044
#> aberrant_ERR2585368 5 0.3774 0.4725 0.000 0.000 0.000 0.000 0.592 0.408
#> aberrant_ERR2585371 5 0.3774 0.4725 0.000 0.000 0.000 0.000 0.592 0.408
#> round_ERR2585239 1 0.2285 0.5753 0.900 0.064 0.028 0.000 0.000 0.008
#> round_ERR2585273 1 0.4885 0.2078 0.656 0.268 0.048 0.000 0.000 0.028
#> round_ERR2585256 1 0.6543 -0.0934 0.552 0.004 0.220 0.000 0.100 0.124
#> round_ERR2585272 1 0.3264 0.5795 0.844 0.080 0.056 0.000 0.000 0.020
#> round_ERR2585246 1 0.4502 -0.2847 0.568 0.404 0.016 0.000 0.000 0.012
#> round_ERR2585261 1 0.6542 -0.1656 0.528 0.000 0.244 0.000 0.092 0.136
#> round_ERR2585254 1 0.7639 -0.6520 0.308 0.000 0.284 0.000 0.208 0.200
#> round_ERR2585225 1 0.6104 -0.3642 0.428 0.000 0.408 0.000 0.024 0.140
#> round_ERR2585235 1 0.5623 0.1138 0.556 0.020 0.344 0.000 0.012 0.068
#> round_ERR2585271 1 0.2265 0.5620 0.896 0.076 0.024 0.000 0.000 0.004
#> round_ERR2585251 1 0.1793 0.5909 0.928 0.032 0.036 0.000 0.000 0.004
#> round_ERR2585255 3 0.6153 0.2414 0.412 0.000 0.416 0.000 0.024 0.148
#> round_ERR2585257 1 0.6055 -0.3611 0.424 0.000 0.420 0.000 0.024 0.132
#> round_ERR2585226 1 0.2321 0.5858 0.900 0.052 0.040 0.000 0.000 0.008
#> round_ERR2585265 1 0.1793 0.5877 0.928 0.036 0.032 0.000 0.000 0.004
#> round_ERR2585259 1 0.5498 0.2420 0.624 0.008 0.256 0.000 0.024 0.088
#> round_ERR2585247 1 0.4622 -0.0359 0.624 0.332 0.024 0.000 0.000 0.020
#> round_ERR2585241 1 0.2401 0.5458 0.892 0.076 0.016 0.000 0.000 0.016
#> round_ERR2585263 1 0.2095 0.5987 0.916 0.004 0.052 0.000 0.012 0.016
#> round_ERR2585264 2 0.4366 0.8025 0.204 0.728 0.024 0.000 0.000 0.044
#> round_ERR2585233 1 0.5945 -0.3328 0.432 0.000 0.428 0.000 0.024 0.116
#> round_ERR2585223 1 0.2262 0.5612 0.896 0.080 0.016 0.000 0.000 0.008
#> round_ERR2585234 1 0.7267 -0.5121 0.364 0.000 0.328 0.000 0.120 0.188
#> round_ERR2585222 1 0.1750 0.5974 0.932 0.016 0.040 0.000 0.000 0.012
#> round_ERR2585228 1 0.1500 0.5730 0.936 0.052 0.012 0.000 0.000 0.000
#> round_ERR2585248 2 0.4600 0.7684 0.184 0.724 0.032 0.000 0.000 0.060
#> round_ERR2585240 1 0.4379 0.4863 0.752 0.028 0.172 0.000 0.008 0.040
#> round_ERR2585270 1 0.1863 0.5727 0.920 0.060 0.016 0.000 0.004 0.000
#> round_ERR2585232 1 0.5558 0.0412 0.564 0.008 0.332 0.000 0.016 0.080
#> aberrant_ERR2585341 5 0.3482 0.6238 0.000 0.000 0.000 0.000 0.684 0.316
#> aberrant_ERR2585355 5 0.3899 0.4621 0.004 0.000 0.000 0.000 0.592 0.404
#> round_ERR2585227 1 0.4240 0.4806 0.752 0.164 0.068 0.000 0.000 0.016
#> aberrant_ERR2585351 5 0.1910 0.6824 0.000 0.000 0.000 0.000 0.892 0.108
#> round_ERR2585269 2 0.4406 0.5507 0.464 0.516 0.008 0.000 0.000 0.012
#> aberrant_ERR2585357 5 0.3634 0.5416 0.000 0.000 0.000 0.000 0.644 0.356
#> aberrant_ERR2585350 5 0.3647 0.5334 0.000 0.000 0.000 0.000 0.640 0.360
#> round_ERR2585250 1 0.3124 0.5812 0.852 0.016 0.096 0.000 0.004 0.032
#> round_ERR2585245 2 0.3483 0.8156 0.212 0.764 0.000 0.000 0.000 0.024
#> aberrant_ERR2585353 5 0.2488 0.6509 0.000 0.008 0.000 0.004 0.864 0.124
#> round_ERR2585258 1 0.1793 0.5877 0.928 0.036 0.032 0.000 0.000 0.004
#> aberrant_ERR2585354 5 0.1757 0.6858 0.000 0.008 0.000 0.000 0.916 0.076
#> round_ERR2585249 2 0.4357 0.6537 0.420 0.560 0.008 0.000 0.000 0.012
#> round_ERR2585268 1 0.3392 0.5467 0.820 0.012 0.128 0.000 0.000 0.040
#> aberrant_ERR2585356 5 0.5229 0.4418 0.000 0.020 0.008 0.068 0.640 0.264
#> round_ERR2585266 1 0.5930 -0.2795 0.464 0.000 0.396 0.000 0.024 0.116
#> round_ERR2585231 2 0.3935 0.8112 0.292 0.688 0.004 0.000 0.000 0.016
#> round_ERR2585230 1 0.1708 0.5848 0.932 0.040 0.024 0.000 0.000 0.004
#> round_ERR2585267 2 0.4618 0.7874 0.320 0.632 0.012 0.000 0.000 0.036
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> SD:hclust 138 2.76e-14 2
#> SD:hclust 143 2.08e-20 3
#> SD:hclust 131 7.71e-25 4
#> SD:hclust 97 4.22e-19 5
#> SD:hclust 107 1.55e-20 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "kmeans"]
# you can also extract it by
# res = res_list["SD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.861 0.900 0.958 0.4999 0.498 0.498
#> 3 3 0.588 0.677 0.845 0.2507 0.857 0.719
#> 4 4 0.649 0.667 0.809 0.1233 0.773 0.513
#> 5 5 0.686 0.733 0.815 0.0801 0.861 0.599
#> 6 6 0.723 0.628 0.758 0.0481 0.927 0.712
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585338 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585325 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585283 1 0.9129 0.550 0.672 0.328
#> aberrant_ERR2585343 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585329 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585317 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585339 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585335 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585287 2 0.2236 0.938 0.036 0.964
#> aberrant_ERR2585321 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585297 1 0.0000 0.936 1.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585319 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585315 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585336 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585307 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585301 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585326 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585331 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585346 1 0.9129 0.550 0.672 0.328
#> aberrant_ERR2585314 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585298 1 0.3114 0.898 0.944 0.056
#> aberrant_ERR2585345 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585299 1 0.0000 0.936 1.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.936 1.000 0.000
#> aberrant_ERR2585303 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585313 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585318 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585328 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585330 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585293 1 0.8861 0.592 0.696 0.304
#> aberrant_ERR2585342 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585348 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585352 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585308 1 0.0000 0.936 1.000 0.000
#> aberrant_ERR2585349 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585316 2 0.1414 0.953 0.020 0.980
#> aberrant_ERR2585306 1 0.9358 0.504 0.648 0.352
#> aberrant_ERR2585324 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585310 2 0.1184 0.958 0.016 0.984
#> aberrant_ERR2585296 1 0.9977 0.108 0.528 0.472
#> aberrant_ERR2585275 1 0.9323 0.512 0.652 0.348
#> aberrant_ERR2585311 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585292 1 0.8861 0.592 0.696 0.304
#> aberrant_ERR2585282 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585305 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585278 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585347 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585332 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585280 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585304 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585322 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585279 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585277 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585295 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585333 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585285 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585286 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585294 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585300 2 0.0376 0.968 0.004 0.996
#> aberrant_ERR2585334 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585361 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585372 2 0.0000 0.971 0.000 1.000
#> round_ERR2585217 2 0.9460 0.418 0.364 0.636
#> round_ERR2585205 1 0.0000 0.936 1.000 0.000
#> round_ERR2585214 2 0.8909 0.542 0.308 0.692
#> round_ERR2585202 2 0.1414 0.954 0.020 0.980
#> aberrant_ERR2585367 2 0.0000 0.971 0.000 1.000
#> round_ERR2585220 1 0.0000 0.936 1.000 0.000
#> round_ERR2585238 1 0.0000 0.936 1.000 0.000
#> aberrant_ERR2585276 2 0.0000 0.971 0.000 1.000
#> round_ERR2585218 1 0.0000 0.936 1.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.971 0.000 1.000
#> round_ERR2585201 1 0.8555 0.611 0.720 0.280
#> round_ERR2585210 1 0.0000 0.936 1.000 0.000
#> aberrant_ERR2585362 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585360 2 0.0000 0.971 0.000 1.000
#> round_ERR2585209 1 0.1414 0.923 0.980 0.020
#> round_ERR2585242 1 0.3114 0.898 0.944 0.056
#> round_ERR2585216 1 0.0000 0.936 1.000 0.000
#> round_ERR2585219 1 0.0000 0.936 1.000 0.000
#> round_ERR2585237 2 0.9170 0.491 0.332 0.668
#> round_ERR2585198 2 0.9000 0.525 0.316 0.684
#> round_ERR2585211 1 0.0000 0.936 1.000 0.000
#> round_ERR2585206 1 0.0000 0.936 1.000 0.000
#> aberrant_ERR2585281 2 0.0000 0.971 0.000 1.000
#> round_ERR2585212 1 0.0000 0.936 1.000 0.000
#> round_ERR2585221 1 0.0000 0.936 1.000 0.000
#> round_ERR2585243 1 0.0000 0.936 1.000 0.000
#> round_ERR2585204 2 0.5842 0.818 0.140 0.860
#> round_ERR2585213 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585373 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585358 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585365 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585359 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585370 2 0.0000 0.971 0.000 1.000
#> round_ERR2585215 1 0.0000 0.936 1.000 0.000
#> round_ERR2585262 2 0.3431 0.911 0.064 0.936
#> round_ERR2585199 2 0.5946 0.813 0.144 0.856
#> aberrant_ERR2585369 2 0.0000 0.971 0.000 1.000
#> round_ERR2585208 1 0.0000 0.936 1.000 0.000
#> round_ERR2585252 1 0.0000 0.936 1.000 0.000
#> round_ERR2585236 1 0.0000 0.936 1.000 0.000
#> aberrant_ERR2585284 1 0.9209 0.537 0.664 0.336
#> round_ERR2585224 1 0.0000 0.936 1.000 0.000
#> round_ERR2585260 1 0.0000 0.936 1.000 0.000
#> round_ERR2585229 1 0.0000 0.936 1.000 0.000
#> aberrant_ERR2585364 1 0.9248 0.530 0.660 0.340
#> round_ERR2585253 1 0.0000 0.936 1.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585371 2 0.0000 0.971 0.000 1.000
#> round_ERR2585239 1 0.0000 0.936 1.000 0.000
#> round_ERR2585273 1 0.0000 0.936 1.000 0.000
#> round_ERR2585256 1 0.3114 0.898 0.944 0.056
#> round_ERR2585272 1 0.0000 0.936 1.000 0.000
#> round_ERR2585246 1 0.0000 0.936 1.000 0.000
#> round_ERR2585261 1 0.8081 0.666 0.752 0.248
#> round_ERR2585254 1 0.9983 0.096 0.524 0.476
#> round_ERR2585225 1 0.1843 0.918 0.972 0.028
#> round_ERR2585235 1 0.0000 0.936 1.000 0.000
#> round_ERR2585271 1 0.0000 0.936 1.000 0.000
#> round_ERR2585251 1 0.0000 0.936 1.000 0.000
#> round_ERR2585255 1 0.3114 0.898 0.944 0.056
#> round_ERR2585257 1 0.3114 0.898 0.944 0.056
#> round_ERR2585226 1 0.0000 0.936 1.000 0.000
#> round_ERR2585265 1 0.0000 0.936 1.000 0.000
#> round_ERR2585259 1 0.0000 0.936 1.000 0.000
#> round_ERR2585247 1 0.0000 0.936 1.000 0.000
#> round_ERR2585241 1 0.0000 0.936 1.000 0.000
#> round_ERR2585263 1 0.0000 0.936 1.000 0.000
#> round_ERR2585264 1 0.0000 0.936 1.000 0.000
#> round_ERR2585233 1 0.0000 0.936 1.000 0.000
#> round_ERR2585223 1 0.0000 0.936 1.000 0.000
#> round_ERR2585234 2 0.9608 0.366 0.384 0.616
#> round_ERR2585222 1 0.0000 0.936 1.000 0.000
#> round_ERR2585228 1 0.0000 0.936 1.000 0.000
#> round_ERR2585248 1 0.0000 0.936 1.000 0.000
#> round_ERR2585240 1 0.1633 0.921 0.976 0.024
#> round_ERR2585270 1 0.0000 0.936 1.000 0.000
#> round_ERR2585232 1 0.0000 0.936 1.000 0.000
#> aberrant_ERR2585341 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585355 2 0.0000 0.971 0.000 1.000
#> round_ERR2585227 1 0.0000 0.936 1.000 0.000
#> aberrant_ERR2585351 2 0.0000 0.971 0.000 1.000
#> round_ERR2585269 1 0.0000 0.936 1.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.971 0.000 1.000
#> aberrant_ERR2585350 2 0.0000 0.971 0.000 1.000
#> round_ERR2585250 1 0.0000 0.936 1.000 0.000
#> round_ERR2585245 1 0.0000 0.936 1.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.971 0.000 1.000
#> round_ERR2585258 1 0.0000 0.936 1.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.971 0.000 1.000
#> round_ERR2585249 1 0.0000 0.936 1.000 0.000
#> round_ERR2585268 1 0.0000 0.936 1.000 0.000
#> aberrant_ERR2585356 2 0.0000 0.971 0.000 1.000
#> round_ERR2585266 1 0.2948 0.901 0.948 0.052
#> round_ERR2585231 1 0.0000 0.936 1.000 0.000
#> round_ERR2585230 1 0.0000 0.936 1.000 0.000
#> round_ERR2585267 1 0.0000 0.936 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.5810 0.3028 0.000 0.664 0.336
#> aberrant_ERR2585338 2 0.0000 0.7280 0.000 1.000 0.000
#> aberrant_ERR2585325 2 0.5810 0.3028 0.000 0.664 0.336
#> aberrant_ERR2585283 3 0.0475 0.6060 0.004 0.004 0.992
#> aberrant_ERR2585343 3 0.5988 0.6482 0.000 0.368 0.632
#> aberrant_ERR2585329 2 0.0000 0.7280 0.000 1.000 0.000
#> aberrant_ERR2585317 2 0.0000 0.7280 0.000 1.000 0.000
#> aberrant_ERR2585339 2 0.0000 0.7280 0.000 1.000 0.000
#> aberrant_ERR2585335 2 0.4504 0.5789 0.000 0.804 0.196
#> aberrant_ERR2585287 3 0.2625 0.6191 0.000 0.084 0.916
#> aberrant_ERR2585321 3 0.6140 0.6153 0.000 0.404 0.596
#> aberrant_ERR2585297 1 0.3412 0.8939 0.876 0.000 0.124
#> aberrant_ERR2585337 2 0.0000 0.7280 0.000 1.000 0.000
#> aberrant_ERR2585319 2 0.4654 0.5647 0.000 0.792 0.208
#> aberrant_ERR2585315 2 0.4002 0.6177 0.000 0.840 0.160
#> aberrant_ERR2585336 2 0.0000 0.7280 0.000 1.000 0.000
#> aberrant_ERR2585307 2 0.0237 0.7264 0.000 0.996 0.004
#> aberrant_ERR2585301 2 0.4654 0.5647 0.000 0.792 0.208
#> aberrant_ERR2585326 2 0.0000 0.7280 0.000 1.000 0.000
#> aberrant_ERR2585331 2 0.0424 0.7244 0.000 0.992 0.008
#> aberrant_ERR2585346 3 0.0475 0.6060 0.004 0.004 0.992
#> aberrant_ERR2585314 2 0.0237 0.7264 0.000 0.996 0.004
#> aberrant_ERR2585298 1 0.5109 0.7364 0.780 0.212 0.008
#> aberrant_ERR2585345 2 0.0000 0.7280 0.000 1.000 0.000
#> aberrant_ERR2585299 1 0.3192 0.8975 0.888 0.000 0.112
#> aberrant_ERR2585309 1 0.3686 0.8880 0.860 0.000 0.140
#> aberrant_ERR2585303 2 0.0892 0.7223 0.000 0.980 0.020
#> aberrant_ERR2585313 2 0.0747 0.7237 0.000 0.984 0.016
#> aberrant_ERR2585318 2 0.6079 0.1286 0.000 0.612 0.388
#> aberrant_ERR2585328 2 0.6260 -0.1377 0.000 0.552 0.448
#> aberrant_ERR2585330 2 0.5859 0.2798 0.000 0.656 0.344
#> aberrant_ERR2585293 3 0.0424 0.6028 0.008 0.000 0.992
#> aberrant_ERR2585342 2 0.6274 -0.1742 0.000 0.544 0.456
#> aberrant_ERR2585348 3 0.6180 0.5941 0.000 0.416 0.584
#> aberrant_ERR2585352 2 0.4605 0.5695 0.000 0.796 0.204
#> aberrant_ERR2585308 1 0.3686 0.8880 0.860 0.000 0.140
#> aberrant_ERR2585349 2 0.1453 0.7102 0.024 0.968 0.008
#> aberrant_ERR2585316 3 0.5591 0.6489 0.000 0.304 0.696
#> aberrant_ERR2585306 3 0.6007 0.5058 0.184 0.048 0.768
#> aberrant_ERR2585324 2 0.4654 0.5647 0.000 0.792 0.208
#> aberrant_ERR2585310 2 0.3965 0.6210 0.132 0.860 0.008
#> aberrant_ERR2585296 1 0.4963 0.7341 0.792 0.200 0.008
#> aberrant_ERR2585275 3 0.0424 0.6081 0.000 0.008 0.992
#> aberrant_ERR2585311 3 0.6308 0.3547 0.000 0.492 0.508
#> aberrant_ERR2585292 3 0.0424 0.6028 0.008 0.000 0.992
#> aberrant_ERR2585282 3 0.6180 0.5940 0.000 0.416 0.584
#> aberrant_ERR2585305 2 0.5465 0.4241 0.000 0.712 0.288
#> aberrant_ERR2585278 2 0.4504 0.5789 0.000 0.804 0.196
#> aberrant_ERR2585347 3 0.5882 0.6506 0.000 0.348 0.652
#> aberrant_ERR2585332 3 0.6126 0.6212 0.000 0.400 0.600
#> aberrant_ERR2585280 2 0.6252 -0.1204 0.000 0.556 0.444
#> aberrant_ERR2585304 2 0.2173 0.6930 0.048 0.944 0.008
#> aberrant_ERR2585322 2 0.0237 0.7271 0.000 0.996 0.004
#> aberrant_ERR2585279 2 0.1832 0.7019 0.036 0.956 0.008
#> aberrant_ERR2585277 2 0.0000 0.7280 0.000 1.000 0.000
#> aberrant_ERR2585295 2 0.5327 0.4280 0.000 0.728 0.272
#> aberrant_ERR2585333 3 0.6111 0.6259 0.000 0.396 0.604
#> aberrant_ERR2585285 2 0.4974 0.5235 0.000 0.764 0.236
#> aberrant_ERR2585286 2 0.0000 0.7280 0.000 1.000 0.000
#> aberrant_ERR2585294 2 0.4750 0.5542 0.000 0.784 0.216
#> aberrant_ERR2585300 3 0.6008 0.6469 0.000 0.372 0.628
#> aberrant_ERR2585334 2 0.0424 0.7244 0.000 0.992 0.008
#> aberrant_ERR2585361 2 0.6244 -0.1012 0.000 0.560 0.440
#> aberrant_ERR2585372 3 0.6235 0.5431 0.000 0.436 0.564
#> round_ERR2585217 2 0.4963 0.5379 0.200 0.792 0.008
#> round_ERR2585205 1 0.3038 0.8992 0.896 0.000 0.104
#> round_ERR2585214 2 0.4164 0.6080 0.144 0.848 0.008
#> round_ERR2585202 2 0.4033 0.6169 0.136 0.856 0.008
#> aberrant_ERR2585367 2 0.6168 0.0276 0.000 0.588 0.412
#> round_ERR2585220 1 0.0000 0.8970 1.000 0.000 0.000
#> round_ERR2585238 1 0.3192 0.8975 0.888 0.000 0.112
#> aberrant_ERR2585276 2 0.6062 0.1443 0.000 0.616 0.384
#> round_ERR2585218 1 0.3267 0.8964 0.884 0.000 0.116
#> aberrant_ERR2585363 2 0.4702 0.5595 0.000 0.788 0.212
#> round_ERR2585201 1 0.5502 0.6927 0.744 0.248 0.008
#> round_ERR2585210 1 0.2711 0.9020 0.912 0.000 0.088
#> aberrant_ERR2585362 2 0.6168 0.0281 0.000 0.588 0.412
#> aberrant_ERR2585360 2 0.6291 -0.2263 0.000 0.532 0.468
#> round_ERR2585209 1 0.3043 0.8463 0.908 0.084 0.008
#> round_ERR2585242 1 0.5156 0.7320 0.776 0.216 0.008
#> round_ERR2585216 1 0.0000 0.8970 1.000 0.000 0.000
#> round_ERR2585219 1 0.1163 0.9025 0.972 0.000 0.028
#> round_ERR2585237 2 0.4755 0.5571 0.184 0.808 0.008
#> round_ERR2585198 2 0.4099 0.6127 0.140 0.852 0.008
#> round_ERR2585211 1 0.3482 0.8924 0.872 0.000 0.128
#> round_ERR2585206 1 0.3116 0.8983 0.892 0.000 0.108
#> aberrant_ERR2585281 2 0.1643 0.7112 0.000 0.956 0.044
#> round_ERR2585212 1 0.0424 0.8944 0.992 0.000 0.008
#> round_ERR2585221 1 0.3686 0.8880 0.860 0.000 0.140
#> round_ERR2585243 1 0.2796 0.9012 0.908 0.000 0.092
#> round_ERR2585204 2 0.4099 0.6127 0.140 0.852 0.008
#> round_ERR2585213 2 0.3826 0.6283 0.124 0.868 0.008
#> aberrant_ERR2585373 3 0.6168 0.6018 0.000 0.412 0.588
#> aberrant_ERR2585358 3 0.6126 0.6210 0.000 0.400 0.600
#> aberrant_ERR2585365 2 0.1289 0.7167 0.000 0.968 0.032
#> aberrant_ERR2585359 3 0.6008 0.6469 0.000 0.372 0.628
#> aberrant_ERR2585370 2 0.0000 0.7280 0.000 1.000 0.000
#> round_ERR2585215 1 0.3686 0.8880 0.860 0.000 0.140
#> round_ERR2585262 2 0.4700 0.5677 0.180 0.812 0.008
#> round_ERR2585199 2 0.4033 0.6169 0.136 0.856 0.008
#> aberrant_ERR2585369 2 0.6168 0.0273 0.000 0.588 0.412
#> round_ERR2585208 1 0.3412 0.8940 0.876 0.000 0.124
#> round_ERR2585252 1 0.3686 0.8880 0.860 0.000 0.140
#> round_ERR2585236 1 0.1964 0.9042 0.944 0.000 0.056
#> aberrant_ERR2585284 3 0.0424 0.6081 0.000 0.008 0.992
#> round_ERR2585224 1 0.3686 0.8880 0.860 0.000 0.140
#> round_ERR2585260 1 0.1643 0.9039 0.956 0.000 0.044
#> round_ERR2585229 1 0.3686 0.8880 0.860 0.000 0.140
#> aberrant_ERR2585364 3 0.0424 0.6081 0.000 0.008 0.992
#> round_ERR2585253 1 0.3686 0.8880 0.860 0.000 0.140
#> aberrant_ERR2585368 2 0.0000 0.7280 0.000 1.000 0.000
#> aberrant_ERR2585371 2 0.0000 0.7280 0.000 1.000 0.000
#> round_ERR2585239 1 0.2066 0.9040 0.940 0.000 0.060
#> round_ERR2585273 1 0.0747 0.9005 0.984 0.000 0.016
#> round_ERR2585256 1 0.4473 0.7826 0.828 0.164 0.008
#> round_ERR2585272 1 0.0892 0.9012 0.980 0.000 0.020
#> round_ERR2585246 1 0.3192 0.8976 0.888 0.000 0.112
#> round_ERR2585261 1 0.5335 0.7131 0.760 0.232 0.008
#> round_ERR2585254 1 0.5928 0.6087 0.696 0.296 0.008
#> round_ERR2585225 1 0.4808 0.7605 0.804 0.188 0.008
#> round_ERR2585235 1 0.1529 0.9040 0.960 0.000 0.040
#> round_ERR2585271 1 0.2356 0.9034 0.928 0.000 0.072
#> round_ERR2585251 1 0.0237 0.8957 0.996 0.000 0.004
#> round_ERR2585255 1 0.5109 0.7364 0.780 0.212 0.008
#> round_ERR2585257 1 0.4861 0.7568 0.800 0.192 0.008
#> round_ERR2585226 1 0.0000 0.8970 1.000 0.000 0.000
#> round_ERR2585265 1 0.0000 0.8970 1.000 0.000 0.000
#> round_ERR2585259 1 0.0237 0.8957 0.996 0.000 0.004
#> round_ERR2585247 1 0.3267 0.8965 0.884 0.000 0.116
#> round_ERR2585241 1 0.2878 0.9007 0.904 0.000 0.096
#> round_ERR2585263 1 0.0424 0.8944 0.992 0.000 0.008
#> round_ERR2585264 1 0.3686 0.8880 0.860 0.000 0.140
#> round_ERR2585233 1 0.0848 0.8916 0.984 0.008 0.008
#> round_ERR2585223 1 0.1860 0.9039 0.948 0.000 0.052
#> round_ERR2585234 2 0.6275 0.3213 0.348 0.644 0.008
#> round_ERR2585222 1 0.1031 0.9018 0.976 0.000 0.024
#> round_ERR2585228 1 0.1643 0.9039 0.956 0.000 0.044
#> round_ERR2585248 1 0.3686 0.8880 0.860 0.000 0.140
#> round_ERR2585240 1 0.4808 0.7605 0.804 0.188 0.008
#> round_ERR2585270 1 0.0000 0.8970 1.000 0.000 0.000
#> round_ERR2585232 1 0.1170 0.8882 0.976 0.016 0.008
#> aberrant_ERR2585341 2 0.0892 0.7222 0.000 0.980 0.020
#> aberrant_ERR2585355 2 0.0000 0.7280 0.000 1.000 0.000
#> round_ERR2585227 1 0.0000 0.8970 1.000 0.000 0.000
#> aberrant_ERR2585351 2 0.5216 0.4806 0.000 0.740 0.260
#> round_ERR2585269 1 0.3686 0.8880 0.860 0.000 0.140
#> aberrant_ERR2585357 2 0.0000 0.7280 0.000 1.000 0.000
#> aberrant_ERR2585350 2 0.0000 0.7280 0.000 1.000 0.000
#> round_ERR2585250 1 0.0424 0.8944 0.992 0.000 0.008
#> round_ERR2585245 1 0.3686 0.8880 0.860 0.000 0.140
#> aberrant_ERR2585353 3 0.6204 0.5754 0.000 0.424 0.576
#> round_ERR2585258 1 0.0000 0.8970 1.000 0.000 0.000
#> aberrant_ERR2585354 2 0.6309 -0.3426 0.000 0.504 0.496
#> round_ERR2585249 1 0.3686 0.8880 0.860 0.000 0.140
#> round_ERR2585268 1 0.0237 0.8957 0.996 0.000 0.004
#> aberrant_ERR2585356 3 0.6008 0.6469 0.000 0.372 0.628
#> round_ERR2585266 1 0.5109 0.7364 0.780 0.212 0.008
#> round_ERR2585231 1 0.3686 0.8880 0.860 0.000 0.140
#> round_ERR2585230 1 0.1411 0.9034 0.964 0.000 0.036
#> round_ERR2585267 1 0.3686 0.8880 0.860 0.000 0.140
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 2 0.1938 0.6766 0.000 0.936 0.012 0.052
#> aberrant_ERR2585338 2 0.5823 0.5576 0.000 0.608 0.348 0.044
#> aberrant_ERR2585325 2 0.1938 0.6766 0.000 0.936 0.012 0.052
#> aberrant_ERR2585283 4 0.1389 0.9732 0.000 0.048 0.000 0.952
#> aberrant_ERR2585343 2 0.4781 0.3674 0.000 0.660 0.004 0.336
#> aberrant_ERR2585329 2 0.5823 0.5576 0.000 0.608 0.348 0.044
#> aberrant_ERR2585317 2 0.5823 0.5576 0.000 0.608 0.348 0.044
#> aberrant_ERR2585339 2 0.5773 0.5686 0.000 0.620 0.336 0.044
#> aberrant_ERR2585335 2 0.3351 0.6717 0.000 0.844 0.148 0.008
#> aberrant_ERR2585287 4 0.2281 0.9370 0.000 0.096 0.000 0.904
#> aberrant_ERR2585321 2 0.4401 0.4836 0.000 0.724 0.004 0.272
#> aberrant_ERR2585297 1 0.0188 0.9031 0.996 0.000 0.000 0.004
#> aberrant_ERR2585337 2 0.5823 0.5576 0.000 0.608 0.348 0.044
#> aberrant_ERR2585319 2 0.3647 0.6714 0.000 0.832 0.152 0.016
#> aberrant_ERR2585315 2 0.4224 0.6623 0.000 0.812 0.144 0.044
#> aberrant_ERR2585336 2 0.5823 0.5576 0.000 0.608 0.348 0.044
#> aberrant_ERR2585307 2 0.5884 0.5370 0.000 0.592 0.364 0.044
#> aberrant_ERR2585301 2 0.0895 0.6837 0.000 0.976 0.004 0.020
#> aberrant_ERR2585326 2 0.5823 0.5576 0.000 0.608 0.348 0.044
#> aberrant_ERR2585331 2 0.6064 0.3966 0.000 0.512 0.444 0.044
#> aberrant_ERR2585346 4 0.1389 0.9732 0.000 0.048 0.000 0.952
#> aberrant_ERR2585314 2 0.5713 0.5672 0.000 0.620 0.340 0.040
#> aberrant_ERR2585298 3 0.3444 0.7153 0.184 0.000 0.816 0.000
#> aberrant_ERR2585345 2 0.5823 0.5576 0.000 0.608 0.348 0.044
#> aberrant_ERR2585299 1 0.0000 0.9037 1.000 0.000 0.000 0.000
#> aberrant_ERR2585309 1 0.0336 0.9018 0.992 0.000 0.000 0.008
#> aberrant_ERR2585303 2 0.2675 0.6811 0.000 0.908 0.048 0.044
#> aberrant_ERR2585313 2 0.5446 0.6134 0.000 0.680 0.276 0.044
#> aberrant_ERR2585318 2 0.2589 0.6520 0.000 0.884 0.000 0.116
#> aberrant_ERR2585328 2 0.2266 0.6673 0.000 0.912 0.004 0.084
#> aberrant_ERR2585330 2 0.1389 0.6797 0.000 0.952 0.000 0.048
#> aberrant_ERR2585293 4 0.1302 0.9708 0.000 0.044 0.000 0.956
#> aberrant_ERR2585342 2 0.2530 0.6528 0.000 0.888 0.000 0.112
#> aberrant_ERR2585348 2 0.3870 0.5650 0.000 0.788 0.004 0.208
#> aberrant_ERR2585352 2 0.1520 0.6861 0.000 0.956 0.020 0.024
#> aberrant_ERR2585308 1 0.0188 0.9031 0.996 0.000 0.000 0.004
#> aberrant_ERR2585349 3 0.5807 0.0667 0.000 0.344 0.612 0.044
#> aberrant_ERR2585316 2 0.4889 0.3116 0.000 0.636 0.004 0.360
#> aberrant_ERR2585306 2 0.7386 -0.0761 0.184 0.496 0.000 0.320
#> aberrant_ERR2585324 2 0.3647 0.6714 0.000 0.832 0.152 0.016
#> aberrant_ERR2585310 3 0.3896 0.6289 0.012 0.120 0.844 0.024
#> aberrant_ERR2585296 3 0.2647 0.7280 0.120 0.000 0.880 0.000
#> aberrant_ERR2585275 4 0.1389 0.9732 0.000 0.048 0.000 0.952
#> aberrant_ERR2585311 2 0.3157 0.6284 0.000 0.852 0.004 0.144
#> aberrant_ERR2585292 4 0.1302 0.9708 0.000 0.044 0.000 0.956
#> aberrant_ERR2585282 2 0.4018 0.5473 0.000 0.772 0.004 0.224
#> aberrant_ERR2585305 2 0.1743 0.6769 0.000 0.940 0.004 0.056
#> aberrant_ERR2585278 2 0.3958 0.6620 0.000 0.816 0.160 0.024
#> aberrant_ERR2585347 2 0.4837 0.3387 0.000 0.648 0.004 0.348
#> aberrant_ERR2585332 2 0.4401 0.4826 0.000 0.724 0.004 0.272
#> aberrant_ERR2585280 2 0.2216 0.6651 0.000 0.908 0.000 0.092
#> aberrant_ERR2585304 3 0.5365 0.3301 0.000 0.264 0.692 0.044
#> aberrant_ERR2585322 2 0.5678 0.5849 0.000 0.640 0.316 0.044
#> aberrant_ERR2585279 3 0.5497 0.2782 0.000 0.284 0.672 0.044
#> aberrant_ERR2585277 2 0.5839 0.5527 0.000 0.604 0.352 0.044
#> aberrant_ERR2585295 2 0.2706 0.6854 0.000 0.900 0.020 0.080
#> aberrant_ERR2585333 2 0.4134 0.5043 0.000 0.740 0.000 0.260
#> aberrant_ERR2585285 2 0.1109 0.6834 0.000 0.968 0.004 0.028
#> aberrant_ERR2585286 2 0.5839 0.5528 0.000 0.604 0.352 0.044
#> aberrant_ERR2585294 2 0.0779 0.6844 0.000 0.980 0.004 0.016
#> aberrant_ERR2585300 2 0.4741 0.3839 0.000 0.668 0.004 0.328
#> aberrant_ERR2585334 2 0.6055 0.4142 0.000 0.520 0.436 0.044
#> aberrant_ERR2585361 2 0.2011 0.6698 0.000 0.920 0.000 0.080
#> aberrant_ERR2585372 2 0.3306 0.6178 0.000 0.840 0.004 0.156
#> round_ERR2585217 3 0.0672 0.7185 0.008 0.008 0.984 0.000
#> round_ERR2585205 1 0.0000 0.9037 1.000 0.000 0.000 0.000
#> round_ERR2585214 3 0.0524 0.7167 0.004 0.008 0.988 0.000
#> round_ERR2585202 3 0.0672 0.7114 0.000 0.008 0.984 0.008
#> aberrant_ERR2585367 2 0.1474 0.6827 0.000 0.948 0.000 0.052
#> round_ERR2585220 1 0.4222 0.5884 0.728 0.000 0.272 0.000
#> round_ERR2585238 1 0.0000 0.9037 1.000 0.000 0.000 0.000
#> aberrant_ERR2585276 2 0.2401 0.6645 0.000 0.904 0.004 0.092
#> round_ERR2585218 1 0.0000 0.9037 1.000 0.000 0.000 0.000
#> aberrant_ERR2585363 2 0.1510 0.6876 0.000 0.956 0.028 0.016
#> round_ERR2585201 3 0.1557 0.7323 0.056 0.000 0.944 0.000
#> round_ERR2585210 1 0.0000 0.9037 1.000 0.000 0.000 0.000
#> aberrant_ERR2585362 2 0.2334 0.6650 0.000 0.908 0.004 0.088
#> aberrant_ERR2585360 2 0.2831 0.6460 0.000 0.876 0.004 0.120
#> round_ERR2585209 3 0.4477 0.5426 0.312 0.000 0.688 0.000
#> round_ERR2585242 3 0.3528 0.7097 0.192 0.000 0.808 0.000
#> round_ERR2585216 1 0.3649 0.7001 0.796 0.000 0.204 0.000
#> round_ERR2585219 1 0.0469 0.8976 0.988 0.000 0.012 0.000
#> round_ERR2585237 3 0.0524 0.7167 0.004 0.008 0.988 0.000
#> round_ERR2585198 3 0.0336 0.7144 0.000 0.008 0.992 0.000
#> round_ERR2585211 1 0.0000 0.9037 1.000 0.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.9037 1.000 0.000 0.000 0.000
#> aberrant_ERR2585281 2 0.3850 0.6676 0.000 0.840 0.116 0.044
#> round_ERR2585212 3 0.4998 0.0686 0.488 0.000 0.512 0.000
#> round_ERR2585221 1 0.0336 0.9018 0.992 0.000 0.000 0.008
#> round_ERR2585243 1 0.0000 0.9037 1.000 0.000 0.000 0.000
#> round_ERR2585204 3 0.0336 0.7144 0.000 0.008 0.992 0.000
#> round_ERR2585213 3 0.2908 0.6507 0.000 0.064 0.896 0.040
#> aberrant_ERR2585373 2 0.4155 0.5278 0.000 0.756 0.004 0.240
#> aberrant_ERR2585358 2 0.4103 0.5091 0.000 0.744 0.000 0.256
#> aberrant_ERR2585365 2 0.2214 0.6829 0.000 0.928 0.028 0.044
#> aberrant_ERR2585359 2 0.4720 0.3919 0.000 0.672 0.004 0.324
#> aberrant_ERR2585370 2 0.5823 0.5576 0.000 0.608 0.348 0.044
#> round_ERR2585215 1 0.0336 0.9018 0.992 0.000 0.000 0.008
#> round_ERR2585262 3 0.2915 0.6918 0.028 0.080 0.892 0.000
#> round_ERR2585199 3 0.1004 0.7043 0.000 0.024 0.972 0.004
#> aberrant_ERR2585369 2 0.2149 0.6660 0.000 0.912 0.000 0.088
#> round_ERR2585208 1 0.0000 0.9037 1.000 0.000 0.000 0.000
#> round_ERR2585252 1 0.0336 0.9018 0.992 0.000 0.000 0.008
#> round_ERR2585236 1 0.2011 0.8431 0.920 0.000 0.080 0.000
#> aberrant_ERR2585284 4 0.1389 0.9732 0.000 0.048 0.000 0.952
#> round_ERR2585224 1 0.0336 0.9018 0.992 0.000 0.000 0.008
#> round_ERR2585260 1 0.0000 0.9037 1.000 0.000 0.000 0.000
#> round_ERR2585229 1 0.0188 0.9031 0.996 0.000 0.000 0.004
#> aberrant_ERR2585364 4 0.3024 0.8778 0.000 0.148 0.000 0.852
#> round_ERR2585253 1 0.0336 0.9018 0.992 0.000 0.000 0.008
#> aberrant_ERR2585368 2 0.5897 0.5324 0.000 0.588 0.368 0.044
#> aberrant_ERR2585371 2 0.5897 0.5324 0.000 0.588 0.368 0.044
#> round_ERR2585239 1 0.0000 0.9037 1.000 0.000 0.000 0.000
#> round_ERR2585273 1 0.0188 0.9023 0.996 0.000 0.004 0.000
#> round_ERR2585256 3 0.3764 0.6892 0.216 0.000 0.784 0.000
#> round_ERR2585272 1 0.0188 0.9022 0.996 0.000 0.004 0.000
#> round_ERR2585246 1 0.0188 0.9031 0.996 0.000 0.000 0.004
#> round_ERR2585261 3 0.1867 0.7332 0.072 0.000 0.928 0.000
#> round_ERR2585254 3 0.1302 0.7299 0.044 0.000 0.956 0.000
#> round_ERR2585225 3 0.4277 0.6013 0.280 0.000 0.720 0.000
#> round_ERR2585235 1 0.2216 0.8318 0.908 0.000 0.092 0.000
#> round_ERR2585271 1 0.0000 0.9037 1.000 0.000 0.000 0.000
#> round_ERR2585251 1 0.4977 0.0822 0.540 0.000 0.460 0.000
#> round_ERR2585255 3 0.3688 0.6970 0.208 0.000 0.792 0.000
#> round_ERR2585257 3 0.3907 0.6700 0.232 0.000 0.768 0.000
#> round_ERR2585226 1 0.3219 0.7522 0.836 0.000 0.164 0.000
#> round_ERR2585265 1 0.3801 0.6756 0.780 0.000 0.220 0.000
#> round_ERR2585259 3 0.4985 0.1429 0.468 0.000 0.532 0.000
#> round_ERR2585247 1 0.0188 0.9031 0.996 0.000 0.000 0.004
#> round_ERR2585241 1 0.0000 0.9037 1.000 0.000 0.000 0.000
#> round_ERR2585263 1 0.4992 0.0164 0.524 0.000 0.476 0.000
#> round_ERR2585264 1 0.0336 0.9018 0.992 0.000 0.000 0.008
#> round_ERR2585233 3 0.4888 0.3186 0.412 0.000 0.588 0.000
#> round_ERR2585223 1 0.0000 0.9037 1.000 0.000 0.000 0.000
#> round_ERR2585234 3 0.0657 0.7202 0.012 0.004 0.984 0.000
#> round_ERR2585222 1 0.0336 0.9003 0.992 0.000 0.008 0.000
#> round_ERR2585228 1 0.0000 0.9037 1.000 0.000 0.000 0.000
#> round_ERR2585248 1 0.0336 0.9018 0.992 0.000 0.000 0.008
#> round_ERR2585240 3 0.4250 0.6079 0.276 0.000 0.724 0.000
#> round_ERR2585270 1 0.4406 0.5315 0.700 0.000 0.300 0.000
#> round_ERR2585232 3 0.4713 0.4405 0.360 0.000 0.640 0.000
#> aberrant_ERR2585341 2 0.3612 0.6706 0.000 0.856 0.100 0.044
#> aberrant_ERR2585355 2 0.5807 0.5615 0.000 0.612 0.344 0.044
#> round_ERR2585227 1 0.4746 0.3873 0.632 0.000 0.368 0.000
#> aberrant_ERR2585351 2 0.1022 0.6820 0.000 0.968 0.000 0.032
#> round_ERR2585269 1 0.0188 0.9031 0.996 0.000 0.000 0.004
#> aberrant_ERR2585357 2 0.5855 0.5480 0.000 0.600 0.356 0.044
#> aberrant_ERR2585350 2 0.5823 0.5576 0.000 0.608 0.348 0.044
#> round_ERR2585250 1 0.4989 0.0339 0.528 0.000 0.472 0.000
#> round_ERR2585245 1 0.0336 0.9018 0.992 0.000 0.000 0.008
#> aberrant_ERR2585353 2 0.3583 0.5951 0.000 0.816 0.004 0.180
#> round_ERR2585258 1 0.3266 0.7486 0.832 0.000 0.168 0.000
#> aberrant_ERR2585354 2 0.3052 0.6341 0.000 0.860 0.004 0.136
#> round_ERR2585249 1 0.0336 0.9018 0.992 0.000 0.000 0.008
#> round_ERR2585268 1 0.4998 -0.0247 0.512 0.000 0.488 0.000
#> aberrant_ERR2585356 2 0.4741 0.3839 0.000 0.668 0.004 0.328
#> round_ERR2585266 3 0.3726 0.6930 0.212 0.000 0.788 0.000
#> round_ERR2585231 1 0.0336 0.9018 0.992 0.000 0.000 0.008
#> round_ERR2585230 1 0.0592 0.8949 0.984 0.000 0.016 0.000
#> round_ERR2585267 1 0.0336 0.9018 0.992 0.000 0.000 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 5 0.4921 0.7651 0.000 0.340 0.040 0.000 0.620
#> aberrant_ERR2585338 2 0.0510 0.8071 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585325 5 0.4921 0.7651 0.000 0.340 0.040 0.000 0.620
#> aberrant_ERR2585283 4 0.2583 0.9295 0.004 0.000 0.000 0.864 0.132
#> aberrant_ERR2585343 5 0.3407 0.8502 0.000 0.132 0.020 0.012 0.836
#> aberrant_ERR2585329 2 0.0510 0.8071 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585317 2 0.0510 0.8071 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585339 2 0.0404 0.8052 0.000 0.988 0.012 0.000 0.000
#> aberrant_ERR2585335 2 0.3876 0.3272 0.000 0.684 0.000 0.000 0.316
#> aberrant_ERR2585287 4 0.4116 0.8291 0.000 0.004 0.016 0.732 0.248
#> aberrant_ERR2585321 5 0.3456 0.8975 0.000 0.184 0.016 0.000 0.800
#> aberrant_ERR2585297 1 0.3442 0.8163 0.836 0.000 0.000 0.060 0.104
#> aberrant_ERR2585337 2 0.0510 0.8071 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585319 2 0.4213 0.3309 0.000 0.680 0.012 0.000 0.308
#> aberrant_ERR2585315 2 0.2172 0.7274 0.000 0.908 0.016 0.000 0.076
#> aberrant_ERR2585336 2 0.0510 0.8071 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585307 2 0.0510 0.8071 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585301 5 0.4161 0.8731 0.000 0.280 0.016 0.000 0.704
#> aberrant_ERR2585326 2 0.0510 0.8071 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585331 2 0.3003 0.6188 0.000 0.812 0.188 0.000 0.000
#> aberrant_ERR2585346 4 0.2583 0.9295 0.004 0.000 0.000 0.864 0.132
#> aberrant_ERR2585314 2 0.2313 0.7635 0.000 0.912 0.040 0.004 0.044
#> aberrant_ERR2585298 3 0.2696 0.7964 0.052 0.040 0.896 0.012 0.000
#> aberrant_ERR2585345 2 0.0510 0.8071 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585299 1 0.1911 0.8371 0.932 0.000 0.004 0.036 0.028
#> aberrant_ERR2585309 1 0.4158 0.7963 0.784 0.000 0.000 0.092 0.124
#> aberrant_ERR2585303 2 0.4350 0.4137 0.000 0.704 0.028 0.000 0.268
#> aberrant_ERR2585313 2 0.0162 0.8011 0.000 0.996 0.004 0.000 0.000
#> aberrant_ERR2585318 5 0.3305 0.9090 0.000 0.224 0.000 0.000 0.776
#> aberrant_ERR2585328 5 0.3988 0.8987 0.000 0.252 0.016 0.000 0.732
#> aberrant_ERR2585330 5 0.4127 0.8375 0.000 0.312 0.008 0.000 0.680
#> aberrant_ERR2585293 4 0.2818 0.9267 0.004 0.000 0.008 0.860 0.128
#> aberrant_ERR2585342 5 0.3789 0.9085 0.000 0.224 0.016 0.000 0.760
#> aberrant_ERR2585348 5 0.3563 0.9066 0.000 0.208 0.012 0.000 0.780
#> aberrant_ERR2585352 2 0.4528 -0.2329 0.000 0.548 0.008 0.000 0.444
#> aberrant_ERR2585308 1 0.4104 0.7967 0.788 0.000 0.000 0.088 0.124
#> aberrant_ERR2585349 2 0.4126 0.2842 0.000 0.620 0.380 0.000 0.000
#> aberrant_ERR2585316 5 0.3722 0.8269 0.000 0.120 0.020 0.032 0.828
#> aberrant_ERR2585306 5 0.2514 0.6963 0.012 0.040 0.028 0.008 0.912
#> aberrant_ERR2585324 2 0.4213 0.3309 0.000 0.680 0.012 0.000 0.308
#> aberrant_ERR2585310 3 0.4834 0.7009 0.016 0.152 0.764 0.016 0.052
#> aberrant_ERR2585296 3 0.3076 0.7962 0.052 0.052 0.880 0.012 0.004
#> aberrant_ERR2585275 4 0.2583 0.9295 0.004 0.000 0.000 0.864 0.132
#> aberrant_ERR2585311 5 0.3727 0.9084 0.000 0.216 0.016 0.000 0.768
#> aberrant_ERR2585292 4 0.2818 0.9267 0.004 0.000 0.008 0.860 0.128
#> aberrant_ERR2585282 5 0.3388 0.9048 0.000 0.200 0.008 0.000 0.792
#> aberrant_ERR2585305 5 0.4106 0.8940 0.000 0.256 0.020 0.000 0.724
#> aberrant_ERR2585278 2 0.3766 0.4451 0.000 0.728 0.004 0.000 0.268
#> aberrant_ERR2585347 5 0.3431 0.8578 0.000 0.144 0.020 0.008 0.828
#> aberrant_ERR2585332 5 0.3242 0.8871 0.000 0.172 0.012 0.000 0.816
#> aberrant_ERR2585280 5 0.4250 0.8974 0.000 0.252 0.028 0.000 0.720
#> aberrant_ERR2585304 2 0.3366 0.5620 0.000 0.768 0.232 0.000 0.000
#> aberrant_ERR2585322 2 0.0566 0.8035 0.000 0.984 0.012 0.000 0.004
#> aberrant_ERR2585279 2 0.3305 0.5674 0.000 0.776 0.224 0.000 0.000
#> aberrant_ERR2585277 2 0.0510 0.8071 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585295 5 0.4917 0.6099 0.000 0.416 0.028 0.000 0.556
#> aberrant_ERR2585333 5 0.3863 0.9034 0.000 0.200 0.028 0.000 0.772
#> aberrant_ERR2585285 5 0.4086 0.8726 0.000 0.284 0.012 0.000 0.704
#> aberrant_ERR2585286 2 0.0510 0.8071 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585294 5 0.4181 0.8844 0.000 0.268 0.020 0.000 0.712
#> aberrant_ERR2585300 5 0.3498 0.8500 0.000 0.132 0.024 0.012 0.832
#> aberrant_ERR2585334 2 0.3003 0.6188 0.000 0.812 0.188 0.000 0.000
#> aberrant_ERR2585361 5 0.3700 0.9043 0.000 0.240 0.008 0.000 0.752
#> aberrant_ERR2585372 5 0.3491 0.9082 0.000 0.228 0.004 0.000 0.768
#> round_ERR2585217 3 0.2077 0.7812 0.000 0.084 0.908 0.008 0.000
#> round_ERR2585205 1 0.0932 0.8336 0.972 0.000 0.004 0.004 0.020
#> round_ERR2585214 3 0.2011 0.7779 0.000 0.088 0.908 0.004 0.000
#> round_ERR2585202 3 0.1965 0.7738 0.000 0.096 0.904 0.000 0.000
#> aberrant_ERR2585367 5 0.4290 0.8481 0.000 0.304 0.016 0.000 0.680
#> round_ERR2585220 1 0.4763 0.2540 0.616 0.000 0.360 0.020 0.004
#> round_ERR2585238 1 0.2157 0.8374 0.920 0.000 0.004 0.040 0.036
#> aberrant_ERR2585276 5 0.3942 0.9065 0.000 0.232 0.020 0.000 0.748
#> round_ERR2585218 1 0.1074 0.8346 0.968 0.000 0.004 0.016 0.012
#> aberrant_ERR2585363 2 0.4552 -0.3173 0.000 0.524 0.008 0.000 0.468
#> round_ERR2585201 3 0.2507 0.7878 0.016 0.072 0.900 0.012 0.000
#> round_ERR2585210 1 0.1173 0.8244 0.964 0.000 0.004 0.020 0.012
#> aberrant_ERR2585362 5 0.3700 0.9037 0.000 0.240 0.008 0.000 0.752
#> aberrant_ERR2585360 5 0.3759 0.9085 0.000 0.220 0.016 0.000 0.764
#> round_ERR2585209 3 0.2912 0.7773 0.104 0.008 0.872 0.012 0.004
#> round_ERR2585242 3 0.2625 0.7961 0.048 0.040 0.900 0.012 0.000
#> round_ERR2585216 1 0.4809 0.3830 0.648 0.000 0.320 0.024 0.008
#> round_ERR2585219 1 0.2733 0.7551 0.872 0.000 0.112 0.012 0.004
#> round_ERR2585237 3 0.2011 0.7790 0.000 0.088 0.908 0.004 0.000
#> round_ERR2585198 3 0.2020 0.7707 0.000 0.100 0.900 0.000 0.000
#> round_ERR2585211 1 0.1153 0.8350 0.964 0.000 0.004 0.008 0.024
#> round_ERR2585206 1 0.1179 0.8347 0.964 0.000 0.004 0.016 0.016
#> aberrant_ERR2585281 2 0.4666 0.3628 0.000 0.676 0.040 0.000 0.284
#> round_ERR2585212 3 0.4837 0.5451 0.348 0.000 0.624 0.020 0.008
#> round_ERR2585221 1 0.3875 0.8036 0.804 0.000 0.000 0.072 0.124
#> round_ERR2585243 1 0.0451 0.8294 0.988 0.000 0.000 0.008 0.004
#> round_ERR2585204 3 0.2127 0.7648 0.000 0.108 0.892 0.000 0.000
#> round_ERR2585213 3 0.4307 0.0858 0.000 0.496 0.504 0.000 0.000
#> aberrant_ERR2585373 5 0.3562 0.9026 0.000 0.196 0.016 0.000 0.788
#> aberrant_ERR2585358 5 0.3280 0.8914 0.000 0.176 0.012 0.000 0.812
#> aberrant_ERR2585365 2 0.4682 -0.1316 0.000 0.564 0.016 0.000 0.420
#> aberrant_ERR2585359 5 0.3297 0.8531 0.000 0.132 0.020 0.008 0.840
#> aberrant_ERR2585370 2 0.0510 0.8071 0.000 0.984 0.016 0.000 0.000
#> round_ERR2585215 1 0.3449 0.8225 0.844 0.000 0.004 0.064 0.088
#> round_ERR2585262 3 0.2822 0.7771 0.008 0.056 0.896 0.016 0.024
#> round_ERR2585199 3 0.3999 0.4466 0.000 0.344 0.656 0.000 0.000
#> aberrant_ERR2585369 5 0.3579 0.9046 0.000 0.240 0.004 0.000 0.756
#> round_ERR2585208 1 0.1764 0.8369 0.928 0.000 0.000 0.008 0.064
#> round_ERR2585252 1 0.4205 0.7990 0.788 0.000 0.004 0.084 0.124
#> round_ERR2585236 1 0.3774 0.6782 0.804 0.000 0.160 0.028 0.008
#> aberrant_ERR2585284 4 0.2818 0.9274 0.004 0.000 0.008 0.860 0.128
#> round_ERR2585224 1 0.4104 0.7967 0.788 0.000 0.000 0.088 0.124
#> round_ERR2585260 1 0.1300 0.8213 0.956 0.000 0.028 0.016 0.000
#> round_ERR2585229 1 0.2819 0.8319 0.884 0.000 0.004 0.052 0.060
#> aberrant_ERR2585364 4 0.4866 0.6048 0.004 0.000 0.020 0.580 0.396
#> round_ERR2585253 1 0.4359 0.7952 0.776 0.000 0.004 0.092 0.128
#> aberrant_ERR2585368 2 0.0671 0.8053 0.000 0.980 0.016 0.004 0.000
#> aberrant_ERR2585371 2 0.0671 0.8053 0.000 0.980 0.016 0.004 0.000
#> round_ERR2585239 1 0.1597 0.8164 0.948 0.000 0.024 0.020 0.008
#> round_ERR2585273 1 0.3289 0.7734 0.852 0.000 0.096 0.048 0.004
#> round_ERR2585256 3 0.2792 0.7944 0.068 0.024 0.892 0.012 0.004
#> round_ERR2585272 1 0.2074 0.7934 0.920 0.000 0.060 0.016 0.004
#> round_ERR2585246 1 0.2291 0.8350 0.908 0.000 0.000 0.056 0.036
#> round_ERR2585261 3 0.2206 0.7899 0.016 0.068 0.912 0.004 0.000
#> round_ERR2585254 3 0.2513 0.7874 0.008 0.076 0.900 0.012 0.004
#> round_ERR2585225 3 0.2568 0.7836 0.092 0.004 0.888 0.016 0.000
#> round_ERR2585235 1 0.4832 0.5914 0.712 0.000 0.228 0.048 0.012
#> round_ERR2585271 1 0.0290 0.8324 0.992 0.000 0.000 0.008 0.000
#> round_ERR2585251 3 0.4928 0.3965 0.428 0.000 0.548 0.020 0.004
#> round_ERR2585255 3 0.2791 0.7958 0.056 0.036 0.892 0.016 0.000
#> round_ERR2585257 3 0.2805 0.7920 0.072 0.020 0.888 0.020 0.000
#> round_ERR2585226 1 0.4347 0.5277 0.716 0.000 0.256 0.024 0.004
#> round_ERR2585265 1 0.4449 0.4567 0.688 0.000 0.288 0.020 0.004
#> round_ERR2585259 3 0.5071 0.5711 0.328 0.000 0.628 0.036 0.008
#> round_ERR2585247 1 0.3506 0.8174 0.832 0.000 0.000 0.064 0.104
#> round_ERR2585241 1 0.0486 0.8297 0.988 0.000 0.004 0.004 0.004
#> round_ERR2585263 3 0.4945 0.3519 0.440 0.000 0.536 0.020 0.004
#> round_ERR2585264 1 0.4359 0.7952 0.776 0.000 0.004 0.092 0.128
#> round_ERR2585233 3 0.4430 0.6561 0.264 0.000 0.708 0.020 0.008
#> round_ERR2585223 1 0.0510 0.8324 0.984 0.000 0.000 0.016 0.000
#> round_ERR2585234 3 0.1851 0.7782 0.000 0.088 0.912 0.000 0.000
#> round_ERR2585222 1 0.2238 0.7884 0.912 0.000 0.064 0.020 0.004
#> round_ERR2585228 1 0.1173 0.8220 0.964 0.000 0.012 0.020 0.004
#> round_ERR2585248 1 0.4403 0.7942 0.772 0.000 0.004 0.092 0.132
#> round_ERR2585240 3 0.2305 0.7829 0.092 0.000 0.896 0.012 0.000
#> round_ERR2585270 1 0.4789 0.2366 0.608 0.000 0.368 0.020 0.004
#> round_ERR2585232 3 0.3962 0.6916 0.240 0.000 0.744 0.012 0.004
#> aberrant_ERR2585341 2 0.3876 0.5691 0.000 0.776 0.032 0.000 0.192
#> aberrant_ERR2585355 2 0.0510 0.8071 0.000 0.984 0.016 0.000 0.000
#> round_ERR2585227 3 0.4949 0.3127 0.444 0.000 0.532 0.020 0.004
#> aberrant_ERR2585351 5 0.3980 0.8708 0.000 0.284 0.008 0.000 0.708
#> round_ERR2585269 1 0.4158 0.7963 0.784 0.000 0.000 0.092 0.124
#> aberrant_ERR2585357 2 0.0510 0.8071 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585350 2 0.0510 0.8071 0.000 0.984 0.016 0.000 0.000
#> round_ERR2585250 3 0.4981 0.4318 0.412 0.000 0.560 0.024 0.004
#> round_ERR2585245 1 0.4104 0.7967 0.788 0.000 0.000 0.088 0.124
#> aberrant_ERR2585353 5 0.3551 0.9084 0.000 0.220 0.008 0.000 0.772
#> round_ERR2585258 1 0.4372 0.5209 0.712 0.000 0.260 0.024 0.004
#> aberrant_ERR2585354 5 0.3305 0.9088 0.000 0.224 0.000 0.000 0.776
#> round_ERR2585249 1 0.4104 0.7967 0.788 0.000 0.000 0.088 0.124
#> round_ERR2585268 3 0.4884 0.4526 0.404 0.000 0.572 0.020 0.004
#> aberrant_ERR2585356 5 0.3498 0.8500 0.000 0.132 0.024 0.012 0.832
#> round_ERR2585266 3 0.2760 0.7946 0.064 0.028 0.892 0.016 0.000
#> round_ERR2585231 1 0.4049 0.7987 0.792 0.000 0.000 0.084 0.124
#> round_ERR2585230 1 0.2395 0.7882 0.904 0.000 0.072 0.016 0.008
#> round_ERR2585267 1 0.4149 0.7958 0.784 0.000 0.000 0.088 0.128
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 5 0.4828 0.68862 0.000 0.116 0.008 0.000 0.684 0.192
#> aberrant_ERR2585338 2 0.1477 0.85575 0.000 0.940 0.004 0.000 0.048 0.008
#> aberrant_ERR2585325 5 0.4828 0.68862 0.000 0.116 0.008 0.000 0.684 0.192
#> aberrant_ERR2585283 4 0.0865 0.89649 0.000 0.000 0.000 0.964 0.036 0.000
#> aberrant_ERR2585343 5 0.2752 0.80507 0.000 0.000 0.000 0.036 0.856 0.108
#> aberrant_ERR2585329 2 0.1477 0.85664 0.000 0.940 0.004 0.000 0.048 0.008
#> aberrant_ERR2585317 2 0.1364 0.85711 0.000 0.944 0.004 0.000 0.048 0.004
#> aberrant_ERR2585339 2 0.1219 0.85635 0.000 0.948 0.000 0.000 0.048 0.004
#> aberrant_ERR2585335 2 0.4685 0.36222 0.000 0.568 0.004 0.000 0.388 0.040
#> aberrant_ERR2585287 4 0.4402 0.72815 0.000 0.000 0.000 0.712 0.184 0.104
#> aberrant_ERR2585321 5 0.1644 0.83761 0.000 0.000 0.000 0.004 0.920 0.076
#> aberrant_ERR2585297 1 0.4180 -0.58988 0.632 0.012 0.000 0.008 0.000 0.348
#> aberrant_ERR2585337 2 0.1364 0.85711 0.000 0.944 0.004 0.000 0.048 0.004
#> aberrant_ERR2585319 2 0.5027 0.35862 0.000 0.552 0.004 0.000 0.376 0.068
#> aberrant_ERR2585315 2 0.3155 0.78291 0.000 0.828 0.004 0.000 0.132 0.036
#> aberrant_ERR2585336 2 0.1364 0.85711 0.000 0.944 0.004 0.000 0.048 0.004
#> aberrant_ERR2585307 2 0.1578 0.85550 0.000 0.936 0.012 0.000 0.048 0.004
#> aberrant_ERR2585301 5 0.2767 0.83331 0.000 0.056 0.004 0.000 0.868 0.072
#> aberrant_ERR2585326 2 0.1219 0.85694 0.000 0.948 0.004 0.000 0.048 0.000
#> aberrant_ERR2585331 2 0.1542 0.81276 0.000 0.936 0.052 0.000 0.004 0.008
#> aberrant_ERR2585346 4 0.1124 0.89677 0.000 0.000 0.000 0.956 0.036 0.008
#> aberrant_ERR2585314 2 0.3689 0.77842 0.000 0.808 0.032 0.000 0.124 0.036
#> aberrant_ERR2585298 3 0.1261 0.87253 0.028 0.004 0.956 0.008 0.000 0.004
#> aberrant_ERR2585345 2 0.1364 0.85711 0.000 0.944 0.004 0.000 0.048 0.004
#> aberrant_ERR2585299 1 0.2473 0.29543 0.856 0.008 0.000 0.000 0.000 0.136
#> aberrant_ERR2585309 6 0.3868 0.98562 0.492 0.000 0.000 0.000 0.000 0.508
#> aberrant_ERR2585303 2 0.4921 0.41817 0.000 0.592 0.004 0.000 0.336 0.068
#> aberrant_ERR2585313 2 0.1542 0.85314 0.000 0.936 0.004 0.000 0.052 0.008
#> aberrant_ERR2585318 5 0.1693 0.85313 0.000 0.020 0.004 0.000 0.932 0.044
#> aberrant_ERR2585328 5 0.2526 0.83572 0.000 0.024 0.004 0.000 0.876 0.096
#> aberrant_ERR2585330 5 0.3705 0.75963 0.000 0.144 0.008 0.000 0.792 0.056
#> aberrant_ERR2585293 4 0.2901 0.88183 0.000 0.004 0.012 0.868 0.036 0.080
#> aberrant_ERR2585342 5 0.1643 0.85438 0.000 0.008 0.000 0.000 0.924 0.068
#> aberrant_ERR2585348 5 0.2584 0.83047 0.000 0.004 0.004 0.000 0.848 0.144
#> aberrant_ERR2585352 5 0.5414 0.00156 0.000 0.440 0.008 0.000 0.464 0.088
#> aberrant_ERR2585308 1 0.3971 -0.85181 0.548 0.004 0.000 0.000 0.000 0.448
#> aberrant_ERR2585349 2 0.4179 0.44724 0.000 0.652 0.324 0.000 0.008 0.016
#> aberrant_ERR2585316 5 0.2776 0.79829 0.000 0.000 0.000 0.052 0.860 0.088
#> aberrant_ERR2585306 5 0.2877 0.79802 0.000 0.008 0.000 0.020 0.848 0.124
#> aberrant_ERR2585324 2 0.5027 0.35862 0.000 0.552 0.004 0.000 0.376 0.068
#> aberrant_ERR2585310 3 0.5976 0.67051 0.124 0.096 0.680 0.012 0.056 0.032
#> aberrant_ERR2585296 3 0.2821 0.84726 0.064 0.028 0.880 0.008 0.000 0.020
#> aberrant_ERR2585275 4 0.1124 0.89677 0.000 0.000 0.000 0.956 0.036 0.008
#> aberrant_ERR2585311 5 0.1267 0.84770 0.000 0.000 0.000 0.000 0.940 0.060
#> aberrant_ERR2585292 4 0.2901 0.88183 0.000 0.004 0.012 0.868 0.036 0.080
#> aberrant_ERR2585282 5 0.1588 0.85090 0.000 0.000 0.004 0.000 0.924 0.072
#> aberrant_ERR2585305 5 0.2487 0.84435 0.004 0.028 0.004 0.000 0.888 0.076
#> aberrant_ERR2585278 2 0.4568 0.47619 0.000 0.612 0.004 0.000 0.344 0.040
#> aberrant_ERR2585347 5 0.3014 0.80916 0.000 0.000 0.000 0.036 0.832 0.132
#> aberrant_ERR2585332 5 0.1444 0.84660 0.000 0.000 0.000 0.000 0.928 0.072
#> aberrant_ERR2585280 5 0.2932 0.83164 0.000 0.020 0.004 0.000 0.836 0.140
#> aberrant_ERR2585304 2 0.1970 0.78250 0.000 0.900 0.092 0.000 0.000 0.008
#> aberrant_ERR2585322 2 0.1219 0.85643 0.000 0.948 0.000 0.000 0.048 0.004
#> aberrant_ERR2585279 2 0.1701 0.79433 0.000 0.920 0.072 0.000 0.000 0.008
#> aberrant_ERR2585277 2 0.1364 0.85628 0.000 0.944 0.004 0.000 0.048 0.004
#> aberrant_ERR2585295 5 0.4788 0.66832 0.000 0.180 0.004 0.000 0.684 0.132
#> aberrant_ERR2585333 5 0.2121 0.82982 0.000 0.000 0.000 0.012 0.892 0.096
#> aberrant_ERR2585285 5 0.2705 0.82815 0.000 0.072 0.004 0.000 0.872 0.052
#> aberrant_ERR2585286 2 0.1364 0.85628 0.000 0.944 0.004 0.000 0.048 0.004
#> aberrant_ERR2585294 5 0.2685 0.83921 0.000 0.044 0.004 0.000 0.872 0.080
#> aberrant_ERR2585300 5 0.2462 0.81308 0.000 0.000 0.000 0.028 0.876 0.096
#> aberrant_ERR2585334 2 0.1542 0.81276 0.000 0.936 0.052 0.000 0.004 0.008
#> aberrant_ERR2585361 5 0.2812 0.83058 0.000 0.028 0.008 0.000 0.860 0.104
#> aberrant_ERR2585372 5 0.1901 0.85040 0.000 0.008 0.004 0.000 0.912 0.076
#> round_ERR2585217 3 0.1552 0.86744 0.000 0.036 0.940 0.004 0.000 0.020
#> round_ERR2585205 1 0.1838 0.43636 0.916 0.016 0.000 0.000 0.000 0.068
#> round_ERR2585214 3 0.1155 0.86627 0.000 0.036 0.956 0.004 0.000 0.004
#> round_ERR2585202 3 0.1686 0.85037 0.000 0.064 0.924 0.000 0.000 0.012
#> aberrant_ERR2585367 5 0.3546 0.80429 0.000 0.056 0.008 0.000 0.808 0.128
#> round_ERR2585220 1 0.3969 0.49327 0.708 0.004 0.268 0.008 0.000 0.012
#> round_ERR2585238 1 0.2558 0.24717 0.840 0.004 0.000 0.000 0.000 0.156
#> aberrant_ERR2585276 5 0.2182 0.84742 0.000 0.020 0.004 0.000 0.900 0.076
#> round_ERR2585218 1 0.2196 0.37271 0.884 0.004 0.000 0.004 0.000 0.108
#> aberrant_ERR2585363 5 0.5234 0.22530 0.000 0.384 0.008 0.000 0.532 0.076
#> round_ERR2585201 3 0.1375 0.87073 0.008 0.028 0.952 0.008 0.000 0.004
#> round_ERR2585210 1 0.1096 0.50296 0.964 0.008 0.004 0.004 0.000 0.020
#> aberrant_ERR2585362 5 0.2290 0.84638 0.000 0.020 0.004 0.000 0.892 0.084
#> aberrant_ERR2585360 5 0.1477 0.85363 0.000 0.008 0.004 0.000 0.940 0.048
#> round_ERR2585209 3 0.1749 0.86565 0.044 0.004 0.932 0.004 0.000 0.016
#> round_ERR2585242 3 0.1476 0.87148 0.028 0.004 0.948 0.008 0.000 0.012
#> round_ERR2585216 1 0.4097 0.50467 0.720 0.012 0.244 0.004 0.000 0.020
#> round_ERR2585219 1 0.2518 0.53554 0.880 0.012 0.092 0.000 0.000 0.016
#> round_ERR2585237 3 0.1464 0.86720 0.000 0.036 0.944 0.004 0.000 0.016
#> round_ERR2585198 3 0.1471 0.84738 0.000 0.064 0.932 0.000 0.000 0.004
#> round_ERR2585211 1 0.2203 0.40802 0.896 0.016 0.000 0.004 0.000 0.084
#> round_ERR2585206 1 0.2110 0.41526 0.900 0.012 0.000 0.004 0.000 0.084
#> aberrant_ERR2585281 2 0.5504 0.20501 0.000 0.500 0.004 0.000 0.380 0.116
#> round_ERR2585212 1 0.4763 0.19021 0.544 0.008 0.420 0.012 0.000 0.016
#> round_ERR2585221 1 0.4199 -0.86249 0.544 0.004 0.000 0.008 0.000 0.444
#> round_ERR2585243 1 0.1938 0.47016 0.920 0.020 0.000 0.008 0.000 0.052
#> round_ERR2585204 3 0.1644 0.83735 0.000 0.076 0.920 0.000 0.000 0.004
#> round_ERR2585213 3 0.3996 0.09203 0.000 0.484 0.512 0.000 0.000 0.004
#> aberrant_ERR2585373 5 0.1267 0.84222 0.000 0.000 0.000 0.000 0.940 0.060
#> aberrant_ERR2585358 5 0.1714 0.84354 0.000 0.000 0.000 0.000 0.908 0.092
#> aberrant_ERR2585365 5 0.5083 0.21105 0.000 0.404 0.008 0.000 0.528 0.060
#> aberrant_ERR2585359 5 0.2361 0.81436 0.000 0.000 0.000 0.028 0.884 0.088
#> aberrant_ERR2585370 2 0.1364 0.85628 0.000 0.944 0.004 0.000 0.048 0.004
#> round_ERR2585215 1 0.4496 -0.75057 0.564 0.020 0.000 0.008 0.000 0.408
#> round_ERR2585262 3 0.1929 0.85222 0.000 0.004 0.924 0.016 0.008 0.048
#> round_ERR2585199 3 0.3758 0.50356 0.000 0.324 0.668 0.000 0.000 0.008
#> aberrant_ERR2585369 5 0.1636 0.85033 0.000 0.024 0.004 0.000 0.936 0.036
#> round_ERR2585208 1 0.3122 0.21271 0.816 0.020 0.000 0.004 0.000 0.160
#> round_ERR2585252 1 0.4185 -0.97668 0.496 0.012 0.000 0.000 0.000 0.492
#> round_ERR2585236 1 0.4292 0.52003 0.780 0.016 0.136 0.020 0.004 0.044
#> aberrant_ERR2585284 4 0.1268 0.89598 0.000 0.004 0.008 0.952 0.036 0.000
#> round_ERR2585224 6 0.3997 0.98504 0.488 0.004 0.000 0.000 0.000 0.508
#> round_ERR2585260 1 0.1138 0.49769 0.960 0.004 0.012 0.000 0.000 0.024
#> round_ERR2585229 1 0.2994 0.06402 0.788 0.004 0.000 0.000 0.000 0.208
#> aberrant_ERR2585364 4 0.4660 0.50794 0.000 0.000 0.000 0.612 0.328 0.060
#> round_ERR2585253 6 0.4097 0.98458 0.488 0.008 0.000 0.000 0.000 0.504
#> aberrant_ERR2585368 2 0.1991 0.84756 0.000 0.920 0.012 0.000 0.044 0.024
#> aberrant_ERR2585371 2 0.1991 0.84756 0.000 0.920 0.012 0.000 0.044 0.024
#> round_ERR2585239 1 0.0909 0.51619 0.968 0.000 0.020 0.000 0.000 0.012
#> round_ERR2585273 1 0.3346 0.50980 0.844 0.008 0.076 0.012 0.000 0.060
#> round_ERR2585256 3 0.1862 0.86433 0.044 0.004 0.928 0.008 0.000 0.016
#> round_ERR2585272 1 0.1816 0.52828 0.928 0.016 0.048 0.004 0.000 0.004
#> round_ERR2585246 1 0.2882 0.19025 0.812 0.008 0.000 0.000 0.000 0.180
#> round_ERR2585261 3 0.1686 0.87207 0.016 0.024 0.940 0.004 0.000 0.016
#> round_ERR2585254 3 0.1785 0.87097 0.012 0.028 0.936 0.008 0.000 0.016
#> round_ERR2585225 3 0.1851 0.86536 0.036 0.000 0.928 0.012 0.000 0.024
#> round_ERR2585235 1 0.4644 0.45697 0.728 0.008 0.184 0.024 0.000 0.056
#> round_ERR2585271 1 0.1563 0.44926 0.932 0.012 0.000 0.000 0.000 0.056
#> round_ERR2585251 1 0.4355 0.29410 0.584 0.004 0.396 0.008 0.000 0.008
#> round_ERR2585255 3 0.1851 0.86536 0.036 0.000 0.928 0.012 0.000 0.024
#> round_ERR2585257 3 0.2259 0.86207 0.040 0.000 0.908 0.020 0.000 0.032
#> round_ERR2585226 1 0.3804 0.51521 0.756 0.004 0.212 0.012 0.000 0.016
#> round_ERR2585265 1 0.3684 0.50938 0.744 0.004 0.236 0.008 0.000 0.008
#> round_ERR2585259 3 0.5335 0.26157 0.376 0.012 0.552 0.020 0.000 0.040
#> round_ERR2585247 1 0.4130 -0.46150 0.664 0.016 0.000 0.008 0.000 0.312
#> round_ERR2585241 1 0.1320 0.47026 0.948 0.016 0.000 0.000 0.000 0.036
#> round_ERR2585263 1 0.4446 0.28352 0.580 0.008 0.396 0.004 0.000 0.012
#> round_ERR2585264 6 0.3996 0.98365 0.484 0.004 0.000 0.000 0.000 0.512
#> round_ERR2585233 3 0.3649 0.76616 0.140 0.008 0.808 0.020 0.000 0.024
#> round_ERR2585223 1 0.1462 0.45231 0.936 0.008 0.000 0.000 0.000 0.056
#> round_ERR2585234 3 0.1010 0.86619 0.000 0.036 0.960 0.000 0.000 0.004
#> round_ERR2585222 1 0.2179 0.53357 0.908 0.004 0.064 0.008 0.000 0.016
#> round_ERR2585228 1 0.0696 0.50577 0.980 0.004 0.004 0.004 0.000 0.008
#> round_ERR2585248 6 0.4313 0.97187 0.480 0.012 0.000 0.004 0.000 0.504
#> round_ERR2585240 3 0.1577 0.86765 0.036 0.000 0.940 0.008 0.000 0.016
#> round_ERR2585270 1 0.3973 0.47841 0.684 0.000 0.296 0.008 0.000 0.012
#> round_ERR2585232 3 0.3686 0.69084 0.196 0.004 0.772 0.012 0.000 0.016
#> aberrant_ERR2585341 2 0.5030 0.53089 0.000 0.628 0.004 0.000 0.264 0.104
#> aberrant_ERR2585355 2 0.1434 0.85446 0.000 0.940 0.000 0.000 0.048 0.012
#> round_ERR2585227 1 0.4952 0.14987 0.504 0.008 0.452 0.016 0.000 0.020
#> aberrant_ERR2585351 5 0.2939 0.82458 0.000 0.072 0.008 0.000 0.860 0.060
#> round_ERR2585269 6 0.3868 0.98562 0.492 0.000 0.000 0.000 0.000 0.508
#> aberrant_ERR2585357 2 0.1364 0.85711 0.000 0.944 0.004 0.000 0.048 0.004
#> aberrant_ERR2585350 2 0.1219 0.85553 0.000 0.948 0.004 0.000 0.048 0.000
#> round_ERR2585250 1 0.4781 0.25648 0.564 0.008 0.396 0.008 0.000 0.024
#> round_ERR2585245 6 0.3867 0.98532 0.488 0.000 0.000 0.000 0.000 0.512
#> aberrant_ERR2585353 5 0.1918 0.84794 0.000 0.008 0.000 0.000 0.904 0.088
#> round_ERR2585258 1 0.3521 0.51746 0.768 0.004 0.212 0.008 0.000 0.008
#> aberrant_ERR2585354 5 0.1410 0.85373 0.000 0.008 0.004 0.000 0.944 0.044
#> round_ERR2585249 1 0.3999 -0.96997 0.500 0.004 0.000 0.000 0.000 0.496
#> round_ERR2585268 1 0.4744 0.20836 0.548 0.008 0.416 0.008 0.000 0.020
#> aberrant_ERR2585356 5 0.2384 0.81166 0.000 0.000 0.000 0.032 0.884 0.084
#> round_ERR2585266 3 0.1851 0.86536 0.036 0.000 0.928 0.012 0.000 0.024
#> round_ERR2585231 6 0.3868 0.98562 0.492 0.000 0.000 0.000 0.000 0.508
#> round_ERR2585230 1 0.1429 0.53080 0.940 0.000 0.052 0.004 0.000 0.004
#> round_ERR2585267 6 0.3996 0.98419 0.484 0.004 0.000 0.000 0.000 0.512
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> SD:kmeans 155 1.04e-19 2
#> SD:kmeans 141 3.28e-20 3
#> SD:kmeans 137 4.66e-24 4
#> SD:kmeans 139 1.29e-23 5
#> SD:kmeans 118 1.38e-20 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "skmeans"]
# you can also extract it by
# res = res_list["SD:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'SD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.886 0.924 0.967 0.5026 0.498 0.498
#> 3 3 0.825 0.860 0.941 0.3171 0.758 0.551
#> 4 4 0.837 0.846 0.929 0.1181 0.875 0.654
#> 5 5 0.744 0.684 0.822 0.0533 0.962 0.857
#> 6 6 0.681 0.634 0.763 0.0377 0.970 0.876
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585338 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585325 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585283 1 0.8955 0.578 0.688 0.312
#> aberrant_ERR2585343 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585329 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585317 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585339 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585335 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585287 2 0.2236 0.938 0.036 0.964
#> aberrant_ERR2585321 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585297 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585319 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585315 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585336 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585307 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585301 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585326 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585331 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585346 1 0.8955 0.578 0.688 0.312
#> aberrant_ERR2585314 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585298 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585345 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585299 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585303 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585313 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585318 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585328 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585330 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585293 1 0.7815 0.706 0.768 0.232
#> aberrant_ERR2585342 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585348 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585352 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585308 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585349 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585316 2 0.2948 0.922 0.052 0.948
#> aberrant_ERR2585306 1 0.9044 0.563 0.680 0.320
#> aberrant_ERR2585324 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585310 2 0.5178 0.857 0.116 0.884
#> aberrant_ERR2585296 1 0.2948 0.914 0.948 0.052
#> aberrant_ERR2585275 1 0.9087 0.555 0.676 0.324
#> aberrant_ERR2585311 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585292 1 0.7815 0.706 0.768 0.232
#> aberrant_ERR2585282 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585305 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585278 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585347 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585332 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585280 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585304 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585322 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585279 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585277 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585295 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585333 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585285 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585286 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585294 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585300 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585334 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585361 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585372 2 0.0000 0.970 0.000 1.000
#> round_ERR2585217 2 0.9732 0.350 0.404 0.596
#> round_ERR2585205 1 0.0000 0.960 1.000 0.000
#> round_ERR2585214 2 0.8955 0.562 0.312 0.688
#> round_ERR2585202 2 0.4431 0.882 0.092 0.908
#> aberrant_ERR2585367 2 0.0000 0.970 0.000 1.000
#> round_ERR2585220 1 0.0000 0.960 1.000 0.000
#> round_ERR2585238 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585276 2 0.0000 0.970 0.000 1.000
#> round_ERR2585218 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.970 0.000 1.000
#> round_ERR2585201 1 0.0376 0.956 0.996 0.004
#> round_ERR2585210 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585362 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585360 2 0.0000 0.970 0.000 1.000
#> round_ERR2585209 1 0.0000 0.960 1.000 0.000
#> round_ERR2585242 1 0.0000 0.960 1.000 0.000
#> round_ERR2585216 1 0.0000 0.960 1.000 0.000
#> round_ERR2585219 1 0.0000 0.960 1.000 0.000
#> round_ERR2585237 2 0.9129 0.530 0.328 0.672
#> round_ERR2585198 2 0.8955 0.562 0.312 0.688
#> round_ERR2585211 1 0.0000 0.960 1.000 0.000
#> round_ERR2585206 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585281 2 0.0000 0.970 0.000 1.000
#> round_ERR2585212 1 0.0000 0.960 1.000 0.000
#> round_ERR2585221 1 0.0000 0.960 1.000 0.000
#> round_ERR2585243 1 0.0000 0.960 1.000 0.000
#> round_ERR2585204 2 0.7602 0.718 0.220 0.780
#> round_ERR2585213 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585373 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585358 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585365 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585359 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585370 2 0.0000 0.970 0.000 1.000
#> round_ERR2585215 1 0.0000 0.960 1.000 0.000
#> round_ERR2585262 2 0.8443 0.633 0.272 0.728
#> round_ERR2585199 2 0.7056 0.759 0.192 0.808
#> aberrant_ERR2585369 2 0.0000 0.970 0.000 1.000
#> round_ERR2585208 1 0.0000 0.960 1.000 0.000
#> round_ERR2585252 1 0.0000 0.960 1.000 0.000
#> round_ERR2585236 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585284 1 0.8955 0.578 0.688 0.312
#> round_ERR2585224 1 0.0000 0.960 1.000 0.000
#> round_ERR2585260 1 0.0000 0.960 1.000 0.000
#> round_ERR2585229 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585364 1 0.9000 0.571 0.684 0.316
#> round_ERR2585253 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585371 2 0.0000 0.970 0.000 1.000
#> round_ERR2585239 1 0.0000 0.960 1.000 0.000
#> round_ERR2585273 1 0.0000 0.960 1.000 0.000
#> round_ERR2585256 1 0.0000 0.960 1.000 0.000
#> round_ERR2585272 1 0.0000 0.960 1.000 0.000
#> round_ERR2585246 1 0.0000 0.960 1.000 0.000
#> round_ERR2585261 1 0.0672 0.953 0.992 0.008
#> round_ERR2585254 1 0.3584 0.898 0.932 0.068
#> round_ERR2585225 1 0.0000 0.960 1.000 0.000
#> round_ERR2585235 1 0.0000 0.960 1.000 0.000
#> round_ERR2585271 1 0.0000 0.960 1.000 0.000
#> round_ERR2585251 1 0.0000 0.960 1.000 0.000
#> round_ERR2585255 1 0.0000 0.960 1.000 0.000
#> round_ERR2585257 1 0.0000 0.960 1.000 0.000
#> round_ERR2585226 1 0.0000 0.960 1.000 0.000
#> round_ERR2585265 1 0.0000 0.960 1.000 0.000
#> round_ERR2585259 1 0.0000 0.960 1.000 0.000
#> round_ERR2585247 1 0.0000 0.960 1.000 0.000
#> round_ERR2585241 1 0.0000 0.960 1.000 0.000
#> round_ERR2585263 1 0.0000 0.960 1.000 0.000
#> round_ERR2585264 1 0.0000 0.960 1.000 0.000
#> round_ERR2585233 1 0.0000 0.960 1.000 0.000
#> round_ERR2585223 1 0.0000 0.960 1.000 0.000
#> round_ERR2585234 1 0.9754 0.282 0.592 0.408
#> round_ERR2585222 1 0.0000 0.960 1.000 0.000
#> round_ERR2585228 1 0.0000 0.960 1.000 0.000
#> round_ERR2585248 1 0.0000 0.960 1.000 0.000
#> round_ERR2585240 1 0.0000 0.960 1.000 0.000
#> round_ERR2585270 1 0.0000 0.960 1.000 0.000
#> round_ERR2585232 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585341 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585355 2 0.0000 0.970 0.000 1.000
#> round_ERR2585227 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585351 2 0.0000 0.970 0.000 1.000
#> round_ERR2585269 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.970 0.000 1.000
#> aberrant_ERR2585350 2 0.0000 0.970 0.000 1.000
#> round_ERR2585250 1 0.0000 0.960 1.000 0.000
#> round_ERR2585245 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.970 0.000 1.000
#> round_ERR2585258 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.970 0.000 1.000
#> round_ERR2585249 1 0.0000 0.960 1.000 0.000
#> round_ERR2585268 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585356 2 0.0000 0.970 0.000 1.000
#> round_ERR2585266 1 0.0000 0.960 1.000 0.000
#> round_ERR2585231 1 0.0000 0.960 1.000 0.000
#> round_ERR2585230 1 0.0000 0.960 1.000 0.000
#> round_ERR2585267 1 0.0000 0.960 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 3 0.0424 0.9384 0.000 0.008 0.992
#> aberrant_ERR2585338 2 0.0237 0.8861 0.000 0.996 0.004
#> aberrant_ERR2585325 3 0.0424 0.9384 0.000 0.008 0.992
#> aberrant_ERR2585283 3 0.0237 0.9404 0.004 0.000 0.996
#> aberrant_ERR2585343 3 0.0000 0.9417 0.000 0.000 1.000
#> aberrant_ERR2585329 2 0.0237 0.8861 0.000 0.996 0.004
#> aberrant_ERR2585317 2 0.0237 0.8861 0.000 0.996 0.004
#> aberrant_ERR2585339 2 0.0237 0.8861 0.000 0.996 0.004
#> aberrant_ERR2585335 2 0.4842 0.6929 0.000 0.776 0.224
#> aberrant_ERR2585287 3 0.0237 0.9404 0.004 0.000 0.996
#> aberrant_ERR2585321 3 0.0000 0.9417 0.000 0.000 1.000
#> aberrant_ERR2585297 1 0.0000 0.9663 1.000 0.000 0.000
#> aberrant_ERR2585337 2 0.0237 0.8861 0.000 0.996 0.004
#> aberrant_ERR2585319 2 0.5178 0.6499 0.000 0.744 0.256
#> aberrant_ERR2585315 2 0.4452 0.7325 0.000 0.808 0.192
#> aberrant_ERR2585336 2 0.0237 0.8861 0.000 0.996 0.004
#> aberrant_ERR2585307 2 0.0237 0.8861 0.000 0.996 0.004
#> aberrant_ERR2585301 2 0.5560 0.5839 0.000 0.700 0.300
#> aberrant_ERR2585326 2 0.0237 0.8861 0.000 0.996 0.004
#> aberrant_ERR2585331 2 0.0237 0.8861 0.000 0.996 0.004
#> aberrant_ERR2585346 3 0.0237 0.9404 0.004 0.000 0.996
#> aberrant_ERR2585314 2 0.0237 0.8861 0.000 0.996 0.004
#> aberrant_ERR2585298 1 0.5810 0.5384 0.664 0.336 0.000
#> aberrant_ERR2585345 2 0.0237 0.8861 0.000 0.996 0.004
#> aberrant_ERR2585299 1 0.0000 0.9663 1.000 0.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.9663 1.000 0.000 0.000
#> aberrant_ERR2585303 2 0.4504 0.7340 0.000 0.804 0.196
#> aberrant_ERR2585313 2 0.1031 0.8752 0.000 0.976 0.024
#> aberrant_ERR2585318 3 0.3192 0.8403 0.000 0.112 0.888
#> aberrant_ERR2585328 3 0.0237 0.9404 0.000 0.004 0.996
#> aberrant_ERR2585330 3 0.5905 0.4260 0.000 0.352 0.648
#> aberrant_ERR2585293 3 0.0237 0.9404 0.004 0.000 0.996
#> aberrant_ERR2585342 3 0.0237 0.9404 0.000 0.004 0.996
#> aberrant_ERR2585348 3 0.0000 0.9417 0.000 0.000 1.000
#> aberrant_ERR2585352 2 0.5431 0.6070 0.000 0.716 0.284
#> aberrant_ERR2585308 1 0.0000 0.9663 1.000 0.000 0.000
#> aberrant_ERR2585349 2 0.0237 0.8861 0.000 0.996 0.004
#> aberrant_ERR2585316 3 0.0237 0.9404 0.004 0.000 0.996
#> aberrant_ERR2585306 3 0.0237 0.9404 0.004 0.000 0.996
#> aberrant_ERR2585324 2 0.5178 0.6499 0.000 0.744 0.256
#> aberrant_ERR2585310 2 0.0237 0.8835 0.004 0.996 0.000
#> aberrant_ERR2585296 2 0.6309 0.0206 0.500 0.500 0.000
#> aberrant_ERR2585275 3 0.0237 0.9404 0.004 0.000 0.996
#> aberrant_ERR2585311 3 0.0000 0.9417 0.000 0.000 1.000
#> aberrant_ERR2585292 3 0.0237 0.9404 0.004 0.000 0.996
#> aberrant_ERR2585282 3 0.0000 0.9417 0.000 0.000 1.000
#> aberrant_ERR2585305 3 0.5905 0.4290 0.000 0.352 0.648
#> aberrant_ERR2585278 2 0.4702 0.7076 0.000 0.788 0.212
#> aberrant_ERR2585347 3 0.0000 0.9417 0.000 0.000 1.000
#> aberrant_ERR2585332 3 0.0000 0.9417 0.000 0.000 1.000
#> aberrant_ERR2585280 3 0.0592 0.9356 0.000 0.012 0.988
#> aberrant_ERR2585304 2 0.0000 0.8849 0.000 1.000 0.000
#> aberrant_ERR2585322 2 0.0237 0.8861 0.000 0.996 0.004
#> aberrant_ERR2585279 2 0.0000 0.8849 0.000 1.000 0.000
#> aberrant_ERR2585277 2 0.0237 0.8861 0.000 0.996 0.004
#> aberrant_ERR2585295 3 0.3686 0.7924 0.000 0.140 0.860
#> aberrant_ERR2585333 3 0.0000 0.9417 0.000 0.000 1.000
#> aberrant_ERR2585285 3 0.6309 -0.0498 0.000 0.496 0.504
#> aberrant_ERR2585286 2 0.0237 0.8861 0.000 0.996 0.004
#> aberrant_ERR2585294 2 0.6305 0.0948 0.000 0.516 0.484
#> aberrant_ERR2585300 3 0.0000 0.9417 0.000 0.000 1.000
#> aberrant_ERR2585334 2 0.0237 0.8861 0.000 0.996 0.004
#> aberrant_ERR2585361 3 0.0000 0.9417 0.000 0.000 1.000
#> aberrant_ERR2585372 3 0.0000 0.9417 0.000 0.000 1.000
#> round_ERR2585217 2 0.0424 0.8804 0.008 0.992 0.000
#> round_ERR2585205 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585214 2 0.0000 0.8849 0.000 1.000 0.000
#> round_ERR2585202 2 0.0000 0.8849 0.000 1.000 0.000
#> aberrant_ERR2585367 3 0.0424 0.9385 0.000 0.008 0.992
#> round_ERR2585220 1 0.0237 0.9651 0.996 0.004 0.000
#> round_ERR2585238 1 0.0000 0.9663 1.000 0.000 0.000
#> aberrant_ERR2585276 3 0.2625 0.8716 0.000 0.084 0.916
#> round_ERR2585218 1 0.0000 0.9663 1.000 0.000 0.000
#> aberrant_ERR2585363 2 0.5497 0.5960 0.000 0.708 0.292
#> round_ERR2585201 2 0.6204 0.2012 0.424 0.576 0.000
#> round_ERR2585210 1 0.0000 0.9663 1.000 0.000 0.000
#> aberrant_ERR2585362 3 0.1643 0.9105 0.000 0.044 0.956
#> aberrant_ERR2585360 3 0.0237 0.9404 0.000 0.004 0.996
#> round_ERR2585209 1 0.1163 0.9465 0.972 0.028 0.000
#> round_ERR2585242 1 0.6079 0.4171 0.612 0.388 0.000
#> round_ERR2585216 1 0.0237 0.9651 0.996 0.004 0.000
#> round_ERR2585219 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585237 2 0.0000 0.8849 0.000 1.000 0.000
#> round_ERR2585198 2 0.0000 0.8849 0.000 1.000 0.000
#> round_ERR2585211 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.9663 1.000 0.000 0.000
#> aberrant_ERR2585281 2 0.5882 0.4745 0.000 0.652 0.348
#> round_ERR2585212 1 0.0237 0.9651 0.996 0.004 0.000
#> round_ERR2585221 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585243 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585204 2 0.0000 0.8849 0.000 1.000 0.000
#> round_ERR2585213 2 0.0000 0.8849 0.000 1.000 0.000
#> aberrant_ERR2585373 3 0.0000 0.9417 0.000 0.000 1.000
#> aberrant_ERR2585358 3 0.0000 0.9417 0.000 0.000 1.000
#> aberrant_ERR2585365 2 0.4654 0.7178 0.000 0.792 0.208
#> aberrant_ERR2585359 3 0.0000 0.9417 0.000 0.000 1.000
#> aberrant_ERR2585370 2 0.0237 0.8861 0.000 0.996 0.004
#> round_ERR2585215 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585262 2 0.4194 0.8070 0.060 0.876 0.064
#> round_ERR2585199 2 0.0000 0.8849 0.000 1.000 0.000
#> aberrant_ERR2585369 3 0.1753 0.9069 0.000 0.048 0.952
#> round_ERR2585208 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585236 1 0.0000 0.9663 1.000 0.000 0.000
#> aberrant_ERR2585284 3 0.0237 0.9404 0.004 0.000 0.996
#> round_ERR2585224 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585260 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585229 1 0.0000 0.9663 1.000 0.000 0.000
#> aberrant_ERR2585364 3 0.0237 0.9404 0.004 0.000 0.996
#> round_ERR2585253 1 0.0000 0.9663 1.000 0.000 0.000
#> aberrant_ERR2585368 2 0.0237 0.8861 0.000 0.996 0.004
#> aberrant_ERR2585371 2 0.0237 0.8861 0.000 0.996 0.004
#> round_ERR2585239 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585273 1 0.0237 0.9651 0.996 0.004 0.000
#> round_ERR2585256 1 0.3879 0.8257 0.848 0.152 0.000
#> round_ERR2585272 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585246 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585261 2 0.6267 0.1135 0.452 0.548 0.000
#> round_ERR2585254 2 0.4887 0.6757 0.228 0.772 0.000
#> round_ERR2585225 1 0.4504 0.7704 0.804 0.196 0.000
#> round_ERR2585235 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585271 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585251 1 0.0237 0.9651 0.996 0.004 0.000
#> round_ERR2585255 1 0.5216 0.6788 0.740 0.260 0.000
#> round_ERR2585257 1 0.4555 0.7670 0.800 0.200 0.000
#> round_ERR2585226 1 0.0237 0.9651 0.996 0.004 0.000
#> round_ERR2585265 1 0.0237 0.9651 0.996 0.004 0.000
#> round_ERR2585259 1 0.0237 0.9651 0.996 0.004 0.000
#> round_ERR2585247 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585241 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585263 1 0.0237 0.9651 0.996 0.004 0.000
#> round_ERR2585264 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585233 1 0.0237 0.9651 0.996 0.004 0.000
#> round_ERR2585223 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585234 2 0.0000 0.8849 0.000 1.000 0.000
#> round_ERR2585222 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585228 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585248 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585240 1 0.4399 0.7806 0.812 0.188 0.000
#> round_ERR2585270 1 0.0237 0.9651 0.996 0.004 0.000
#> round_ERR2585232 1 0.0237 0.9651 0.996 0.004 0.000
#> aberrant_ERR2585341 2 0.3412 0.8077 0.000 0.876 0.124
#> aberrant_ERR2585355 2 0.0237 0.8861 0.000 0.996 0.004
#> round_ERR2585227 1 0.0237 0.9651 0.996 0.004 0.000
#> aberrant_ERR2585351 3 0.6267 0.1246 0.000 0.452 0.548
#> round_ERR2585269 1 0.0000 0.9663 1.000 0.000 0.000
#> aberrant_ERR2585357 2 0.0237 0.8861 0.000 0.996 0.004
#> aberrant_ERR2585350 2 0.0237 0.8861 0.000 0.996 0.004
#> round_ERR2585250 1 0.0237 0.9651 0.996 0.004 0.000
#> round_ERR2585245 1 0.0000 0.9663 1.000 0.000 0.000
#> aberrant_ERR2585353 3 0.0000 0.9417 0.000 0.000 1.000
#> round_ERR2585258 1 0.0237 0.9651 0.996 0.004 0.000
#> aberrant_ERR2585354 3 0.0000 0.9417 0.000 0.000 1.000
#> round_ERR2585249 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585268 1 0.0237 0.9651 0.996 0.004 0.000
#> aberrant_ERR2585356 3 0.0237 0.9404 0.004 0.000 0.996
#> round_ERR2585266 1 0.5138 0.6917 0.748 0.252 0.000
#> round_ERR2585231 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585230 1 0.0000 0.9663 1.000 0.000 0.000
#> round_ERR2585267 1 0.0000 0.9663 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 4 0.4933 0.358 0.000 0.432 0.000 0.568
#> aberrant_ERR2585338 2 0.0336 0.924 0.000 0.992 0.008 0.000
#> aberrant_ERR2585325 4 0.4961 0.312 0.000 0.448 0.000 0.552
#> aberrant_ERR2585283 4 0.0188 0.865 0.004 0.000 0.000 0.996
#> aberrant_ERR2585343 4 0.0000 0.865 0.000 0.000 0.000 1.000
#> aberrant_ERR2585329 2 0.0469 0.925 0.000 0.988 0.012 0.000
#> aberrant_ERR2585317 2 0.0469 0.925 0.000 0.988 0.012 0.000
#> aberrant_ERR2585339 2 0.0469 0.925 0.000 0.988 0.012 0.000
#> aberrant_ERR2585335 2 0.1118 0.904 0.000 0.964 0.000 0.036
#> aberrant_ERR2585287 4 0.0000 0.865 0.000 0.000 0.000 1.000
#> aberrant_ERR2585321 4 0.0469 0.864 0.000 0.012 0.000 0.988
#> aberrant_ERR2585297 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> aberrant_ERR2585337 2 0.0469 0.925 0.000 0.988 0.012 0.000
#> aberrant_ERR2585319 2 0.2530 0.838 0.000 0.888 0.000 0.112
#> aberrant_ERR2585315 2 0.0376 0.922 0.000 0.992 0.004 0.004
#> aberrant_ERR2585336 2 0.0469 0.925 0.000 0.988 0.012 0.000
#> aberrant_ERR2585307 2 0.0469 0.925 0.000 0.988 0.012 0.000
#> aberrant_ERR2585301 2 0.2654 0.847 0.000 0.888 0.004 0.108
#> aberrant_ERR2585326 2 0.0469 0.925 0.000 0.988 0.012 0.000
#> aberrant_ERR2585331 2 0.0707 0.922 0.000 0.980 0.020 0.000
#> aberrant_ERR2585346 4 0.0188 0.865 0.004 0.000 0.000 0.996
#> aberrant_ERR2585314 2 0.0469 0.925 0.000 0.988 0.012 0.000
#> aberrant_ERR2585298 3 0.0188 0.947 0.004 0.000 0.996 0.000
#> aberrant_ERR2585345 2 0.0469 0.925 0.000 0.988 0.012 0.000
#> aberrant_ERR2585299 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> aberrant_ERR2585303 2 0.0336 0.920 0.000 0.992 0.000 0.008
#> aberrant_ERR2585313 2 0.0336 0.924 0.000 0.992 0.008 0.000
#> aberrant_ERR2585318 4 0.4994 0.203 0.000 0.480 0.000 0.520
#> aberrant_ERR2585328 4 0.3219 0.779 0.000 0.164 0.000 0.836
#> aberrant_ERR2585330 2 0.4382 0.535 0.000 0.704 0.000 0.296
#> aberrant_ERR2585293 4 0.0188 0.865 0.004 0.000 0.000 0.996
#> aberrant_ERR2585342 4 0.4134 0.677 0.000 0.260 0.000 0.740
#> aberrant_ERR2585348 4 0.0469 0.865 0.000 0.012 0.000 0.988
#> aberrant_ERR2585352 2 0.1109 0.911 0.000 0.968 0.004 0.028
#> aberrant_ERR2585308 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> aberrant_ERR2585349 2 0.3569 0.755 0.000 0.804 0.196 0.000
#> aberrant_ERR2585316 4 0.0000 0.865 0.000 0.000 0.000 1.000
#> aberrant_ERR2585306 4 0.1022 0.848 0.032 0.000 0.000 0.968
#> aberrant_ERR2585324 2 0.2530 0.838 0.000 0.888 0.000 0.112
#> aberrant_ERR2585310 2 0.4978 0.510 0.012 0.664 0.324 0.000
#> aberrant_ERR2585296 3 0.0592 0.942 0.016 0.000 0.984 0.000
#> aberrant_ERR2585275 4 0.0188 0.865 0.004 0.000 0.000 0.996
#> aberrant_ERR2585311 4 0.1302 0.856 0.000 0.044 0.000 0.956
#> aberrant_ERR2585292 4 0.0188 0.865 0.004 0.000 0.000 0.996
#> aberrant_ERR2585282 4 0.0188 0.865 0.000 0.004 0.000 0.996
#> aberrant_ERR2585305 2 0.4804 0.305 0.000 0.616 0.000 0.384
#> aberrant_ERR2585278 2 0.0672 0.923 0.000 0.984 0.008 0.008
#> aberrant_ERR2585347 4 0.0000 0.865 0.000 0.000 0.000 1.000
#> aberrant_ERR2585332 4 0.0336 0.865 0.000 0.008 0.000 0.992
#> aberrant_ERR2585280 4 0.4564 0.573 0.000 0.328 0.000 0.672
#> aberrant_ERR2585304 2 0.1474 0.901 0.000 0.948 0.052 0.000
#> aberrant_ERR2585322 2 0.0336 0.924 0.000 0.992 0.008 0.000
#> aberrant_ERR2585279 2 0.2704 0.838 0.000 0.876 0.124 0.000
#> aberrant_ERR2585277 2 0.0469 0.925 0.000 0.988 0.012 0.000
#> aberrant_ERR2585295 4 0.4888 0.389 0.000 0.412 0.000 0.588
#> aberrant_ERR2585333 4 0.0000 0.865 0.000 0.000 0.000 1.000
#> aberrant_ERR2585285 2 0.3610 0.723 0.000 0.800 0.000 0.200
#> aberrant_ERR2585286 2 0.0469 0.925 0.000 0.988 0.012 0.000
#> aberrant_ERR2585294 2 0.3123 0.804 0.000 0.844 0.000 0.156
#> aberrant_ERR2585300 4 0.0000 0.865 0.000 0.000 0.000 1.000
#> aberrant_ERR2585334 2 0.0707 0.922 0.000 0.980 0.020 0.000
#> aberrant_ERR2585361 4 0.3907 0.714 0.000 0.232 0.000 0.768
#> aberrant_ERR2585372 4 0.1022 0.861 0.000 0.032 0.000 0.968
#> round_ERR2585217 3 0.0188 0.946 0.000 0.004 0.996 0.000
#> round_ERR2585205 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> round_ERR2585214 3 0.0188 0.946 0.000 0.004 0.996 0.000
#> round_ERR2585202 3 0.2814 0.828 0.000 0.132 0.868 0.000
#> aberrant_ERR2585367 4 0.5000 0.177 0.000 0.496 0.000 0.504
#> round_ERR2585220 1 0.1940 0.896 0.924 0.000 0.076 0.000
#> round_ERR2585238 1 0.0188 0.947 0.996 0.000 0.004 0.000
#> aberrant_ERR2585276 4 0.4933 0.301 0.000 0.432 0.000 0.568
#> round_ERR2585218 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> aberrant_ERR2585363 2 0.2125 0.875 0.000 0.920 0.004 0.076
#> round_ERR2585201 3 0.0000 0.947 0.000 0.000 1.000 0.000
#> round_ERR2585210 1 0.0188 0.947 0.996 0.000 0.004 0.000
#> aberrant_ERR2585362 4 0.4193 0.670 0.000 0.268 0.000 0.732
#> aberrant_ERR2585360 4 0.3024 0.791 0.000 0.148 0.000 0.852
#> round_ERR2585209 3 0.0469 0.944 0.012 0.000 0.988 0.000
#> round_ERR2585242 3 0.0336 0.946 0.008 0.000 0.992 0.000
#> round_ERR2585216 1 0.1118 0.928 0.964 0.000 0.036 0.000
#> round_ERR2585219 1 0.0188 0.947 0.996 0.000 0.004 0.000
#> round_ERR2585237 3 0.0188 0.946 0.000 0.004 0.996 0.000
#> round_ERR2585198 3 0.0188 0.946 0.000 0.004 0.996 0.000
#> round_ERR2585211 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> aberrant_ERR2585281 2 0.2593 0.853 0.000 0.892 0.004 0.104
#> round_ERR2585212 1 0.4500 0.569 0.684 0.000 0.316 0.000
#> round_ERR2585221 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> round_ERR2585243 1 0.0188 0.947 0.996 0.000 0.004 0.000
#> round_ERR2585204 3 0.0188 0.946 0.000 0.004 0.996 0.000
#> round_ERR2585213 3 0.4040 0.658 0.000 0.248 0.752 0.000
#> aberrant_ERR2585373 4 0.0336 0.865 0.000 0.008 0.000 0.992
#> aberrant_ERR2585358 4 0.0188 0.865 0.000 0.004 0.000 0.996
#> aberrant_ERR2585365 2 0.0469 0.919 0.000 0.988 0.000 0.012
#> aberrant_ERR2585359 4 0.0188 0.865 0.000 0.004 0.000 0.996
#> aberrant_ERR2585370 2 0.0469 0.925 0.000 0.988 0.012 0.000
#> round_ERR2585215 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> round_ERR2585262 3 0.0927 0.941 0.008 0.016 0.976 0.000
#> round_ERR2585199 3 0.1302 0.918 0.000 0.044 0.956 0.000
#> aberrant_ERR2585369 4 0.4843 0.439 0.000 0.396 0.000 0.604
#> round_ERR2585208 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> round_ERR2585236 1 0.1411 0.924 0.960 0.000 0.020 0.020
#> aberrant_ERR2585284 4 0.0188 0.865 0.004 0.000 0.000 0.996
#> round_ERR2585224 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> round_ERR2585260 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> round_ERR2585229 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> aberrant_ERR2585364 4 0.0188 0.865 0.004 0.000 0.000 0.996
#> round_ERR2585253 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> aberrant_ERR2585368 2 0.0469 0.925 0.000 0.988 0.012 0.000
#> aberrant_ERR2585371 2 0.0469 0.925 0.000 0.988 0.012 0.000
#> round_ERR2585239 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> round_ERR2585273 1 0.0188 0.947 0.996 0.000 0.004 0.000
#> round_ERR2585256 3 0.0469 0.944 0.012 0.000 0.988 0.000
#> round_ERR2585272 1 0.0188 0.947 0.996 0.000 0.004 0.000
#> round_ERR2585246 1 0.0188 0.947 0.996 0.000 0.004 0.000
#> round_ERR2585261 3 0.0000 0.947 0.000 0.000 1.000 0.000
#> round_ERR2585254 3 0.0000 0.947 0.000 0.000 1.000 0.000
#> round_ERR2585225 3 0.0817 0.937 0.024 0.000 0.976 0.000
#> round_ERR2585235 1 0.2011 0.891 0.920 0.000 0.080 0.000
#> round_ERR2585271 1 0.0188 0.947 0.996 0.000 0.004 0.000
#> round_ERR2585251 1 0.4679 0.491 0.648 0.000 0.352 0.000
#> round_ERR2585255 3 0.0188 0.947 0.004 0.000 0.996 0.000
#> round_ERR2585257 3 0.0336 0.946 0.008 0.000 0.992 0.000
#> round_ERR2585226 1 0.1022 0.931 0.968 0.000 0.032 0.000
#> round_ERR2585265 1 0.0817 0.936 0.976 0.000 0.024 0.000
#> round_ERR2585259 1 0.4961 0.226 0.552 0.000 0.448 0.000
#> round_ERR2585247 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> round_ERR2585241 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> round_ERR2585263 1 0.4996 0.111 0.516 0.000 0.484 0.000
#> round_ERR2585264 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> round_ERR2585233 3 0.4776 0.365 0.376 0.000 0.624 0.000
#> round_ERR2585223 1 0.0188 0.947 0.996 0.000 0.004 0.000
#> round_ERR2585234 3 0.0000 0.947 0.000 0.000 1.000 0.000
#> round_ERR2585222 1 0.0188 0.947 0.996 0.000 0.004 0.000
#> round_ERR2585228 1 0.0188 0.947 0.996 0.000 0.004 0.000
#> round_ERR2585248 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> round_ERR2585240 3 0.0469 0.944 0.012 0.000 0.988 0.000
#> round_ERR2585270 1 0.2011 0.893 0.920 0.000 0.080 0.000
#> round_ERR2585232 3 0.3172 0.791 0.160 0.000 0.840 0.000
#> aberrant_ERR2585341 2 0.0927 0.920 0.000 0.976 0.008 0.016
#> aberrant_ERR2585355 2 0.0469 0.925 0.000 0.988 0.012 0.000
#> round_ERR2585227 1 0.3942 0.701 0.764 0.000 0.236 0.000
#> aberrant_ERR2585351 2 0.3942 0.661 0.000 0.764 0.000 0.236
#> round_ERR2585269 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> aberrant_ERR2585357 2 0.0469 0.925 0.000 0.988 0.012 0.000
#> aberrant_ERR2585350 2 0.0469 0.925 0.000 0.988 0.012 0.000
#> round_ERR2585250 1 0.3649 0.752 0.796 0.000 0.204 0.000
#> round_ERR2585245 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> aberrant_ERR2585353 4 0.0336 0.865 0.000 0.008 0.000 0.992
#> round_ERR2585258 1 0.0469 0.943 0.988 0.000 0.012 0.000
#> aberrant_ERR2585354 4 0.1302 0.857 0.000 0.044 0.000 0.956
#> round_ERR2585249 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> round_ERR2585268 1 0.3569 0.763 0.804 0.000 0.196 0.000
#> aberrant_ERR2585356 4 0.0000 0.865 0.000 0.000 0.000 1.000
#> round_ERR2585266 3 0.0469 0.945 0.012 0.000 0.988 0.000
#> round_ERR2585231 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> round_ERR2585230 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> round_ERR2585267 1 0.0000 0.948 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 5 0.5460 0.5883 0.000 0.148 0.000 0.196 0.656
#> aberrant_ERR2585338 2 0.0880 0.7994 0.000 0.968 0.000 0.000 0.032
#> aberrant_ERR2585325 5 0.5480 0.5954 0.000 0.168 0.000 0.176 0.656
#> aberrant_ERR2585283 4 0.0000 0.6915 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585343 4 0.2329 0.6819 0.000 0.000 0.000 0.876 0.124
#> aberrant_ERR2585329 2 0.0703 0.8017 0.000 0.976 0.000 0.000 0.024
#> aberrant_ERR2585317 2 0.0609 0.8010 0.000 0.980 0.000 0.000 0.020
#> aberrant_ERR2585339 2 0.0880 0.7983 0.000 0.968 0.000 0.000 0.032
#> aberrant_ERR2585335 2 0.4088 0.4131 0.000 0.632 0.000 0.000 0.368
#> aberrant_ERR2585287 4 0.0609 0.6930 0.000 0.000 0.000 0.980 0.020
#> aberrant_ERR2585321 4 0.3857 0.5419 0.000 0.000 0.000 0.688 0.312
#> aberrant_ERR2585297 1 0.0898 0.9213 0.972 0.000 0.000 0.008 0.020
#> aberrant_ERR2585337 2 0.0794 0.8016 0.000 0.972 0.000 0.000 0.028
#> aberrant_ERR2585319 2 0.4552 0.1608 0.000 0.524 0.000 0.008 0.468
#> aberrant_ERR2585315 2 0.2561 0.7408 0.000 0.856 0.000 0.000 0.144
#> aberrant_ERR2585336 2 0.0510 0.8026 0.000 0.984 0.000 0.000 0.016
#> aberrant_ERR2585307 2 0.0771 0.7998 0.000 0.976 0.004 0.000 0.020
#> aberrant_ERR2585301 2 0.4827 0.0937 0.000 0.504 0.000 0.020 0.476
#> aberrant_ERR2585326 2 0.0510 0.8013 0.000 0.984 0.000 0.000 0.016
#> aberrant_ERR2585331 2 0.0912 0.7952 0.000 0.972 0.012 0.000 0.016
#> aberrant_ERR2585346 4 0.0000 0.6915 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585314 2 0.2124 0.7746 0.000 0.900 0.004 0.000 0.096
#> aberrant_ERR2585298 3 0.0880 0.8761 0.000 0.000 0.968 0.000 0.032
#> aberrant_ERR2585345 2 0.0000 0.8008 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585299 1 0.1285 0.9206 0.956 0.000 0.004 0.004 0.036
#> aberrant_ERR2585309 1 0.0451 0.9190 0.988 0.000 0.000 0.008 0.004
#> aberrant_ERR2585303 2 0.3282 0.6932 0.000 0.804 0.000 0.008 0.188
#> aberrant_ERR2585313 2 0.1410 0.7879 0.000 0.940 0.000 0.000 0.060
#> aberrant_ERR2585318 5 0.5946 0.5687 0.000 0.184 0.000 0.224 0.592
#> aberrant_ERR2585328 4 0.6392 -0.2350 0.000 0.172 0.000 0.456 0.372
#> aberrant_ERR2585330 5 0.5785 0.3277 0.000 0.404 0.000 0.092 0.504
#> aberrant_ERR2585293 4 0.0000 0.6915 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585342 5 0.5967 0.4845 0.000 0.136 0.000 0.308 0.556
#> aberrant_ERR2585348 4 0.4538 0.3963 0.000 0.016 0.000 0.620 0.364
#> aberrant_ERR2585352 2 0.4238 0.4125 0.000 0.628 0.000 0.004 0.368
#> aberrant_ERR2585308 1 0.0693 0.9198 0.980 0.000 0.000 0.008 0.012
#> aberrant_ERR2585349 2 0.4923 0.4986 0.000 0.680 0.252 0.000 0.068
#> aberrant_ERR2585316 4 0.1270 0.6938 0.000 0.000 0.000 0.948 0.052
#> aberrant_ERR2585306 4 0.3359 0.6340 0.020 0.000 0.000 0.816 0.164
#> aberrant_ERR2585324 2 0.4440 0.1763 0.000 0.528 0.000 0.004 0.468
#> aberrant_ERR2585310 2 0.5908 0.3964 0.000 0.588 0.256 0.000 0.156
#> aberrant_ERR2585296 3 0.2367 0.8558 0.020 0.004 0.904 0.000 0.072
#> aberrant_ERR2585275 4 0.0000 0.6915 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585311 5 0.5153 0.0932 0.000 0.040 0.000 0.436 0.524
#> aberrant_ERR2585292 4 0.0000 0.6915 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585282 4 0.3895 0.5258 0.000 0.000 0.000 0.680 0.320
#> aberrant_ERR2585305 5 0.6391 0.5097 0.008 0.284 0.000 0.168 0.540
#> aberrant_ERR2585278 2 0.3521 0.6379 0.000 0.764 0.000 0.004 0.232
#> aberrant_ERR2585347 4 0.2471 0.6719 0.000 0.000 0.000 0.864 0.136
#> aberrant_ERR2585332 4 0.3857 0.5459 0.000 0.000 0.000 0.688 0.312
#> aberrant_ERR2585280 4 0.6483 -0.2673 0.000 0.192 0.000 0.452 0.356
#> aberrant_ERR2585304 2 0.2574 0.7213 0.000 0.876 0.112 0.000 0.012
#> aberrant_ERR2585322 2 0.0794 0.8018 0.000 0.972 0.000 0.000 0.028
#> aberrant_ERR2585279 2 0.2464 0.7349 0.000 0.888 0.096 0.000 0.016
#> aberrant_ERR2585277 2 0.0162 0.8003 0.000 0.996 0.000 0.000 0.004
#> aberrant_ERR2585295 4 0.6785 -0.2965 0.000 0.340 0.000 0.376 0.284
#> aberrant_ERR2585333 4 0.3561 0.5962 0.000 0.000 0.000 0.740 0.260
#> aberrant_ERR2585285 2 0.5454 -0.0674 0.000 0.488 0.000 0.060 0.452
#> aberrant_ERR2585286 2 0.0510 0.8004 0.000 0.984 0.000 0.000 0.016
#> aberrant_ERR2585294 2 0.5757 0.0362 0.000 0.496 0.000 0.088 0.416
#> aberrant_ERR2585300 4 0.2179 0.6843 0.000 0.000 0.000 0.888 0.112
#> aberrant_ERR2585334 2 0.0798 0.7977 0.000 0.976 0.008 0.000 0.016
#> aberrant_ERR2585361 5 0.6506 0.4615 0.000 0.200 0.000 0.344 0.456
#> aberrant_ERR2585372 5 0.4546 0.0357 0.000 0.008 0.000 0.460 0.532
#> round_ERR2585217 3 0.0798 0.8775 0.000 0.008 0.976 0.000 0.016
#> round_ERR2585205 1 0.0963 0.9191 0.964 0.000 0.000 0.000 0.036
#> round_ERR2585214 3 0.1300 0.8748 0.000 0.028 0.956 0.000 0.016
#> round_ERR2585202 3 0.4823 0.5109 0.000 0.316 0.644 0.000 0.040
#> aberrant_ERR2585367 5 0.6497 0.5178 0.000 0.312 0.000 0.212 0.476
#> round_ERR2585220 1 0.3719 0.8321 0.816 0.000 0.116 0.000 0.068
#> round_ERR2585238 1 0.0771 0.9215 0.976 0.000 0.000 0.004 0.020
#> aberrant_ERR2585276 4 0.6692 -0.2639 0.000 0.296 0.000 0.432 0.272
#> round_ERR2585218 1 0.0609 0.9210 0.980 0.000 0.000 0.000 0.020
#> aberrant_ERR2585363 2 0.4494 0.3346 0.000 0.608 0.000 0.012 0.380
#> round_ERR2585201 3 0.0609 0.8762 0.000 0.000 0.980 0.000 0.020
#> round_ERR2585210 1 0.1041 0.9206 0.964 0.000 0.000 0.004 0.032
#> aberrant_ERR2585362 5 0.5703 0.5631 0.000 0.140 0.000 0.244 0.616
#> aberrant_ERR2585360 5 0.5732 0.5138 0.000 0.116 0.000 0.296 0.588
#> round_ERR2585209 3 0.1430 0.8691 0.004 0.000 0.944 0.000 0.052
#> round_ERR2585242 3 0.0880 0.8761 0.000 0.000 0.968 0.000 0.032
#> round_ERR2585216 1 0.3354 0.8545 0.844 0.000 0.088 0.000 0.068
#> round_ERR2585219 1 0.1557 0.9146 0.940 0.000 0.008 0.000 0.052
#> round_ERR2585237 3 0.1117 0.8742 0.000 0.020 0.964 0.000 0.016
#> round_ERR2585198 3 0.1469 0.8658 0.000 0.036 0.948 0.000 0.016
#> round_ERR2585211 1 0.0794 0.9207 0.972 0.000 0.000 0.000 0.028
#> round_ERR2585206 1 0.0404 0.9200 0.988 0.000 0.000 0.000 0.012
#> aberrant_ERR2585281 2 0.5018 0.5380 0.000 0.716 0.004 0.164 0.116
#> round_ERR2585212 1 0.5515 0.5780 0.624 0.000 0.268 0.000 0.108
#> round_ERR2585221 1 0.0566 0.9207 0.984 0.000 0.000 0.004 0.012
#> round_ERR2585243 1 0.0963 0.9210 0.964 0.000 0.000 0.000 0.036
#> round_ERR2585204 3 0.1914 0.8505 0.000 0.060 0.924 0.000 0.016
#> round_ERR2585213 3 0.4738 0.1371 0.000 0.464 0.520 0.000 0.016
#> aberrant_ERR2585373 4 0.4074 0.4435 0.000 0.000 0.000 0.636 0.364
#> aberrant_ERR2585358 4 0.4161 0.4016 0.000 0.000 0.000 0.608 0.392
#> aberrant_ERR2585365 2 0.3756 0.6077 0.000 0.744 0.000 0.008 0.248
#> aberrant_ERR2585359 4 0.3242 0.6392 0.000 0.000 0.000 0.784 0.216
#> aberrant_ERR2585370 2 0.0000 0.8008 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585215 1 0.0566 0.9214 0.984 0.000 0.000 0.004 0.012
#> round_ERR2585262 3 0.3968 0.7855 0.000 0.072 0.816 0.012 0.100
#> round_ERR2585199 3 0.4065 0.6245 0.000 0.264 0.720 0.000 0.016
#> aberrant_ERR2585369 5 0.5673 0.6069 0.000 0.184 0.000 0.184 0.632
#> round_ERR2585208 1 0.0404 0.9213 0.988 0.000 0.000 0.000 0.012
#> round_ERR2585252 1 0.0798 0.9210 0.976 0.000 0.000 0.008 0.016
#> round_ERR2585236 1 0.4091 0.7927 0.804 0.000 0.044 0.132 0.020
#> aberrant_ERR2585284 4 0.0162 0.6910 0.000 0.000 0.000 0.996 0.004
#> round_ERR2585224 1 0.0451 0.9190 0.988 0.000 0.000 0.008 0.004
#> round_ERR2585260 1 0.0955 0.9208 0.968 0.000 0.004 0.000 0.028
#> round_ERR2585229 1 0.0865 0.9213 0.972 0.000 0.000 0.004 0.024
#> aberrant_ERR2585364 4 0.0703 0.6923 0.000 0.000 0.000 0.976 0.024
#> round_ERR2585253 1 0.0451 0.9190 0.988 0.000 0.000 0.008 0.004
#> aberrant_ERR2585368 2 0.0404 0.7996 0.000 0.988 0.000 0.000 0.012
#> aberrant_ERR2585371 2 0.0404 0.7996 0.000 0.988 0.000 0.000 0.012
#> round_ERR2585239 1 0.1205 0.9203 0.956 0.000 0.000 0.004 0.040
#> round_ERR2585273 1 0.1653 0.9157 0.944 0.000 0.024 0.004 0.028
#> round_ERR2585256 3 0.0703 0.8778 0.000 0.000 0.976 0.000 0.024
#> round_ERR2585272 1 0.2359 0.9027 0.904 0.000 0.036 0.000 0.060
#> round_ERR2585246 1 0.0992 0.9216 0.968 0.000 0.000 0.008 0.024
#> round_ERR2585261 3 0.0794 0.8776 0.000 0.000 0.972 0.000 0.028
#> round_ERR2585254 3 0.1386 0.8751 0.000 0.016 0.952 0.000 0.032
#> round_ERR2585225 3 0.1701 0.8682 0.016 0.000 0.936 0.000 0.048
#> round_ERR2585235 1 0.2792 0.8766 0.884 0.000 0.072 0.004 0.040
#> round_ERR2585271 1 0.0963 0.9212 0.964 0.000 0.000 0.000 0.036
#> round_ERR2585251 1 0.5351 0.3912 0.560 0.000 0.380 0.000 0.060
#> round_ERR2585255 3 0.0880 0.8758 0.000 0.000 0.968 0.000 0.032
#> round_ERR2585257 3 0.1357 0.8738 0.004 0.000 0.948 0.000 0.048
#> round_ERR2585226 1 0.2139 0.9076 0.916 0.000 0.032 0.000 0.052
#> round_ERR2585265 1 0.3043 0.8804 0.864 0.000 0.056 0.000 0.080
#> round_ERR2585259 1 0.5338 0.3171 0.544 0.000 0.400 0.000 0.056
#> round_ERR2585247 1 0.0566 0.9206 0.984 0.000 0.000 0.004 0.012
#> round_ERR2585241 1 0.1270 0.9181 0.948 0.000 0.000 0.000 0.052
#> round_ERR2585263 1 0.5726 0.3686 0.536 0.000 0.372 0.000 0.092
#> round_ERR2585264 1 0.0451 0.9190 0.988 0.000 0.000 0.008 0.004
#> round_ERR2585233 3 0.5297 0.3319 0.360 0.000 0.580 0.000 0.060
#> round_ERR2585223 1 0.0794 0.9214 0.972 0.000 0.000 0.000 0.028
#> round_ERR2585234 3 0.0671 0.8766 0.000 0.016 0.980 0.000 0.004
#> round_ERR2585222 1 0.1638 0.9147 0.932 0.000 0.004 0.000 0.064
#> round_ERR2585228 1 0.1410 0.9169 0.940 0.000 0.000 0.000 0.060
#> round_ERR2585248 1 0.0451 0.9190 0.988 0.000 0.000 0.008 0.004
#> round_ERR2585240 3 0.1444 0.8721 0.012 0.000 0.948 0.000 0.040
#> round_ERR2585270 1 0.4038 0.8159 0.792 0.000 0.128 0.000 0.080
#> round_ERR2585232 3 0.4519 0.6282 0.228 0.000 0.720 0.000 0.052
#> aberrant_ERR2585341 2 0.3452 0.7020 0.000 0.820 0.000 0.032 0.148
#> aberrant_ERR2585355 2 0.1270 0.7931 0.000 0.948 0.000 0.000 0.052
#> round_ERR2585227 1 0.4840 0.6060 0.676 0.000 0.268 0.000 0.056
#> aberrant_ERR2585351 5 0.5092 0.1920 0.000 0.440 0.000 0.036 0.524
#> round_ERR2585269 1 0.0324 0.9193 0.992 0.000 0.000 0.004 0.004
#> aberrant_ERR2585357 2 0.0290 0.8010 0.000 0.992 0.000 0.000 0.008
#> aberrant_ERR2585350 2 0.0404 0.8013 0.000 0.988 0.000 0.000 0.012
#> round_ERR2585250 1 0.4933 0.6993 0.704 0.000 0.200 0.000 0.096
#> round_ERR2585245 1 0.0451 0.9190 0.988 0.000 0.000 0.008 0.004
#> aberrant_ERR2585353 4 0.4533 0.2282 0.000 0.008 0.000 0.544 0.448
#> round_ERR2585258 1 0.2859 0.8839 0.876 0.000 0.056 0.000 0.068
#> aberrant_ERR2585354 5 0.4798 0.0981 0.000 0.020 0.000 0.440 0.540
#> round_ERR2585249 1 0.0451 0.9200 0.988 0.000 0.000 0.004 0.008
#> round_ERR2585268 1 0.4660 0.7308 0.728 0.000 0.192 0.000 0.080
#> aberrant_ERR2585356 4 0.3074 0.6480 0.000 0.000 0.000 0.804 0.196
#> round_ERR2585266 3 0.1124 0.8756 0.004 0.000 0.960 0.000 0.036
#> round_ERR2585231 1 0.0451 0.9190 0.988 0.000 0.000 0.008 0.004
#> round_ERR2585230 1 0.1357 0.9176 0.948 0.000 0.004 0.000 0.048
#> round_ERR2585267 1 0.0693 0.9200 0.980 0.000 0.000 0.008 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 5 0.5418 0.47727 0.000 0.064 0.000 0.092 0.668 NA
#> aberrant_ERR2585338 2 0.1625 0.76917 0.000 0.928 0.000 0.000 0.060 NA
#> aberrant_ERR2585325 5 0.5425 0.48159 0.000 0.068 0.000 0.088 0.668 NA
#> aberrant_ERR2585283 4 0.0000 0.70103 0.000 0.000 0.000 1.000 0.000 NA
#> aberrant_ERR2585343 4 0.3523 0.65971 0.000 0.000 0.000 0.780 0.180 NA
#> aberrant_ERR2585329 2 0.1297 0.77681 0.000 0.948 0.000 0.000 0.040 NA
#> aberrant_ERR2585317 2 0.0806 0.77953 0.000 0.972 0.000 0.000 0.020 NA
#> aberrant_ERR2585339 2 0.1333 0.77572 0.000 0.944 0.000 0.000 0.048 NA
#> aberrant_ERR2585335 2 0.4813 0.35268 0.000 0.608 0.000 0.000 0.316 NA
#> aberrant_ERR2585287 4 0.0717 0.69952 0.000 0.000 0.000 0.976 0.016 NA
#> aberrant_ERR2585321 4 0.4767 0.50289 0.000 0.000 0.000 0.620 0.304 NA
#> aberrant_ERR2585297 1 0.1501 0.86496 0.924 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585337 2 0.0806 0.78003 0.000 0.972 0.000 0.000 0.020 NA
#> aberrant_ERR2585319 5 0.5700 0.26776 0.000 0.372 0.000 0.004 0.480 NA
#> aberrant_ERR2585315 2 0.3727 0.62133 0.000 0.748 0.000 0.000 0.216 NA
#> aberrant_ERR2585336 2 0.0632 0.78021 0.000 0.976 0.000 0.000 0.024 NA
#> aberrant_ERR2585307 2 0.1262 0.77984 0.000 0.956 0.008 0.000 0.020 NA
#> aberrant_ERR2585301 5 0.5914 0.24281 0.000 0.388 0.000 0.008 0.444 NA
#> aberrant_ERR2585326 2 0.0603 0.77914 0.000 0.980 0.000 0.000 0.016 NA
#> aberrant_ERR2585331 2 0.1511 0.76747 0.000 0.944 0.032 0.000 0.012 NA
#> aberrant_ERR2585346 4 0.0146 0.70030 0.000 0.000 0.000 0.996 0.004 NA
#> aberrant_ERR2585314 2 0.3389 0.72431 0.000 0.832 0.020 0.000 0.100 NA
#> aberrant_ERR2585298 3 0.1812 0.84068 0.000 0.000 0.912 0.000 0.008 NA
#> aberrant_ERR2585345 2 0.0692 0.78104 0.000 0.976 0.000 0.000 0.020 NA
#> aberrant_ERR2585299 1 0.1714 0.86891 0.908 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585309 1 0.1141 0.86070 0.948 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585303 2 0.3727 0.62235 0.000 0.748 0.000 0.000 0.216 NA
#> aberrant_ERR2585313 2 0.2255 0.75572 0.000 0.892 0.000 0.000 0.080 NA
#> aberrant_ERR2585318 5 0.6706 0.48911 0.000 0.156 0.000 0.180 0.532 NA
#> aberrant_ERR2585328 4 0.6883 -0.20372 0.000 0.144 0.000 0.396 0.368 NA
#> aberrant_ERR2585330 5 0.6020 0.38661 0.000 0.340 0.000 0.064 0.520 NA
#> aberrant_ERR2585293 4 0.0000 0.70103 0.000 0.000 0.000 1.000 0.000 NA
#> aberrant_ERR2585342 5 0.6726 0.40995 0.000 0.128 0.000 0.260 0.500 NA
#> aberrant_ERR2585348 4 0.5417 0.17295 0.000 0.004 0.000 0.472 0.424 NA
#> aberrant_ERR2585352 2 0.4941 0.01400 0.000 0.492 0.000 0.000 0.444 NA
#> aberrant_ERR2585308 1 0.0937 0.86449 0.960 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585349 2 0.5031 0.50291 0.000 0.668 0.236 0.000 0.044 NA
#> aberrant_ERR2585316 4 0.2164 0.69609 0.000 0.000 0.000 0.900 0.068 NA
#> aberrant_ERR2585306 4 0.5174 0.56947 0.036 0.000 0.000 0.684 0.160 NA
#> aberrant_ERR2585324 5 0.5700 0.26776 0.000 0.372 0.000 0.004 0.480 NA
#> aberrant_ERR2585310 2 0.6936 0.22580 0.004 0.484 0.176 0.000 0.092 NA
#> aberrant_ERR2585296 3 0.4209 0.76530 0.012 0.016 0.736 0.000 0.020 NA
#> aberrant_ERR2585275 4 0.0000 0.70103 0.000 0.000 0.000 1.000 0.000 NA
#> aberrant_ERR2585311 5 0.6005 0.24552 0.000 0.024 0.000 0.284 0.532 NA
#> aberrant_ERR2585292 4 0.0000 0.70103 0.000 0.000 0.000 1.000 0.000 NA
#> aberrant_ERR2585282 4 0.5440 0.29068 0.000 0.000 0.000 0.520 0.348 NA
#> aberrant_ERR2585305 5 0.7463 0.47399 0.000 0.204 0.008 0.128 0.412 NA
#> aberrant_ERR2585278 2 0.4473 0.49902 0.000 0.676 0.000 0.000 0.252 NA
#> aberrant_ERR2585347 4 0.3585 0.63113 0.000 0.000 0.000 0.780 0.172 NA
#> aberrant_ERR2585332 4 0.4868 0.45676 0.000 0.000 0.000 0.592 0.332 NA
#> aberrant_ERR2585280 5 0.6891 0.38832 0.000 0.128 0.000 0.272 0.472 NA
#> aberrant_ERR2585304 2 0.2108 0.75500 0.000 0.912 0.056 0.000 0.016 NA
#> aberrant_ERR2585322 2 0.1686 0.76953 0.000 0.924 0.000 0.000 0.064 NA
#> aberrant_ERR2585279 2 0.2834 0.69231 0.000 0.852 0.120 0.000 0.008 NA
#> aberrant_ERR2585277 2 0.0405 0.77917 0.000 0.988 0.000 0.000 0.004 NA
#> aberrant_ERR2585295 4 0.7599 -0.31823 0.000 0.284 0.004 0.296 0.288 NA
#> aberrant_ERR2585333 4 0.5182 0.44609 0.000 0.008 0.000 0.596 0.304 NA
#> aberrant_ERR2585285 2 0.5875 -0.16496 0.000 0.444 0.000 0.032 0.432 NA
#> aberrant_ERR2585286 2 0.0622 0.77952 0.000 0.980 0.000 0.000 0.008 NA
#> aberrant_ERR2585294 2 0.6964 -0.17843 0.000 0.436 0.000 0.096 0.300 NA
#> aberrant_ERR2585300 4 0.3825 0.64462 0.000 0.000 0.000 0.768 0.160 NA
#> aberrant_ERR2585334 2 0.1605 0.76620 0.000 0.940 0.032 0.000 0.012 NA
#> aberrant_ERR2585361 5 0.6091 0.44387 0.000 0.144 0.000 0.232 0.572 NA
#> aberrant_ERR2585372 5 0.5426 0.13073 0.000 0.008 0.000 0.344 0.544 NA
#> round_ERR2585217 3 0.2126 0.83569 0.000 0.020 0.904 0.000 0.004 NA
#> round_ERR2585205 1 0.2003 0.86453 0.884 0.000 0.000 0.000 0.000 NA
#> round_ERR2585214 3 0.1755 0.83845 0.000 0.032 0.932 0.000 0.008 NA
#> round_ERR2585202 3 0.5735 0.42055 0.000 0.316 0.552 0.000 0.028 NA
#> aberrant_ERR2585367 5 0.6540 0.48028 0.000 0.264 0.000 0.116 0.520 NA
#> round_ERR2585220 1 0.5354 0.69562 0.604 0.000 0.132 0.000 0.008 NA
#> round_ERR2585238 1 0.1863 0.86951 0.896 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585276 5 0.7487 0.37323 0.000 0.236 0.000 0.292 0.332 NA
#> round_ERR2585218 1 0.1765 0.86765 0.904 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585363 2 0.4649 -0.00816 0.000 0.492 0.000 0.000 0.468 NA
#> round_ERR2585201 3 0.1542 0.84196 0.000 0.004 0.936 0.000 0.008 NA
#> round_ERR2585210 1 0.2558 0.86235 0.840 0.000 0.000 0.000 0.004 NA
#> aberrant_ERR2585362 5 0.5916 0.49249 0.000 0.128 0.004 0.140 0.640 NA
#> aberrant_ERR2585360 5 0.6682 0.31867 0.000 0.096 0.000 0.312 0.472 NA
#> round_ERR2585209 3 0.2692 0.82597 0.000 0.000 0.840 0.000 0.012 NA
#> round_ERR2585242 3 0.2357 0.83568 0.000 0.000 0.872 0.000 0.012 NA
#> round_ERR2585216 1 0.4602 0.77631 0.668 0.000 0.068 0.000 0.004 NA
#> round_ERR2585219 1 0.2838 0.85104 0.808 0.000 0.000 0.000 0.004 NA
#> round_ERR2585237 3 0.2113 0.83346 0.000 0.028 0.908 0.000 0.004 NA
#> round_ERR2585198 3 0.1921 0.82368 0.000 0.056 0.920 0.000 0.012 NA
#> round_ERR2585211 1 0.2377 0.86313 0.868 0.000 0.004 0.000 0.004 NA
#> round_ERR2585206 1 0.2053 0.86694 0.888 0.000 0.000 0.000 0.004 NA
#> aberrant_ERR2585281 2 0.6038 0.41362 0.000 0.628 0.004 0.136 0.136 NA
#> round_ERR2585212 1 0.5751 0.51788 0.472 0.000 0.180 0.000 0.000 NA
#> round_ERR2585221 1 0.1471 0.86218 0.932 0.000 0.000 0.004 0.000 NA
#> round_ERR2585243 1 0.2420 0.86690 0.864 0.000 0.004 0.000 0.004 NA
#> round_ERR2585204 3 0.2326 0.79970 0.000 0.092 0.888 0.000 0.008 NA
#> round_ERR2585213 2 0.4684 0.08674 0.000 0.520 0.444 0.000 0.008 NA
#> aberrant_ERR2585373 4 0.5402 0.29205 0.000 0.004 0.000 0.512 0.380 NA
#> aberrant_ERR2585358 5 0.5105 -0.12654 0.000 0.004 0.000 0.428 0.500 NA
#> aberrant_ERR2585365 2 0.4748 0.40181 0.000 0.624 0.000 0.008 0.316 NA
#> aberrant_ERR2585359 4 0.3983 0.62839 0.000 0.000 0.000 0.736 0.208 NA
#> aberrant_ERR2585370 2 0.0291 0.77924 0.000 0.992 0.004 0.000 0.004 NA
#> round_ERR2585215 1 0.1327 0.86672 0.936 0.000 0.000 0.000 0.000 NA
#> round_ERR2585262 3 0.5769 0.71528 0.004 0.052 0.676 0.024 0.088 NA
#> round_ERR2585199 3 0.4490 0.42651 0.000 0.348 0.616 0.000 0.008 NA
#> aberrant_ERR2585369 5 0.5904 0.54591 0.000 0.188 0.000 0.112 0.620 NA
#> round_ERR2585208 1 0.1285 0.86635 0.944 0.000 0.000 0.000 0.004 NA
#> round_ERR2585252 1 0.0603 0.85909 0.980 0.000 0.000 0.000 0.004 NA
#> round_ERR2585236 1 0.5809 0.69551 0.672 0.000 0.064 0.112 0.024 NA
#> aberrant_ERR2585284 4 0.0291 0.70060 0.000 0.000 0.000 0.992 0.004 NA
#> round_ERR2585224 1 0.1196 0.85801 0.952 0.000 0.000 0.008 0.000 NA
#> round_ERR2585260 1 0.2243 0.86769 0.880 0.000 0.004 0.000 0.004 NA
#> round_ERR2585229 1 0.1588 0.86602 0.924 0.000 0.000 0.000 0.004 NA
#> aberrant_ERR2585364 4 0.1003 0.70015 0.000 0.000 0.000 0.964 0.020 NA
#> round_ERR2585253 1 0.0937 0.85977 0.960 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585368 2 0.0405 0.77946 0.000 0.988 0.000 0.000 0.008 NA
#> aberrant_ERR2585371 2 0.0405 0.77946 0.000 0.988 0.000 0.000 0.008 NA
#> round_ERR2585239 1 0.2793 0.85432 0.800 0.000 0.000 0.000 0.000 NA
#> round_ERR2585273 1 0.3245 0.84787 0.796 0.000 0.016 0.000 0.004 NA
#> round_ERR2585256 3 0.2544 0.83164 0.004 0.000 0.864 0.000 0.012 NA
#> round_ERR2585272 1 0.3520 0.83308 0.776 0.000 0.036 0.000 0.000 NA
#> round_ERR2585246 1 0.2001 0.86625 0.900 0.000 0.000 0.004 0.004 NA
#> round_ERR2585261 3 0.2118 0.84002 0.000 0.000 0.888 0.000 0.008 NA
#> round_ERR2585254 3 0.2834 0.82420 0.000 0.020 0.852 0.000 0.008 NA
#> round_ERR2585225 3 0.3141 0.81995 0.020 0.000 0.828 0.000 0.012 NA
#> round_ERR2585235 1 0.4838 0.75827 0.696 0.000 0.112 0.004 0.008 NA
#> round_ERR2585271 1 0.2669 0.86199 0.836 0.000 0.008 0.000 0.000 NA
#> round_ERR2585251 1 0.6140 0.39769 0.460 0.000 0.272 0.000 0.008 NA
#> round_ERR2585255 3 0.1757 0.84019 0.000 0.000 0.916 0.000 0.008 NA
#> round_ERR2585257 3 0.2744 0.83220 0.000 0.000 0.840 0.000 0.016 NA
#> round_ERR2585226 1 0.3794 0.81154 0.744 0.000 0.040 0.000 0.000 NA
#> round_ERR2585265 1 0.4722 0.77160 0.640 0.000 0.056 0.000 0.008 NA
#> round_ERR2585259 1 0.6431 0.13167 0.404 0.000 0.360 0.000 0.024 NA
#> round_ERR2585247 1 0.1863 0.86420 0.896 0.000 0.000 0.000 0.000 NA
#> round_ERR2585241 1 0.2668 0.85695 0.828 0.000 0.004 0.000 0.000 NA
#> round_ERR2585263 1 0.6325 0.23450 0.368 0.000 0.308 0.000 0.008 NA
#> round_ERR2585264 1 0.0508 0.85810 0.984 0.000 0.000 0.004 0.000 NA
#> round_ERR2585233 3 0.6115 0.33534 0.284 0.000 0.496 0.000 0.016 NA
#> round_ERR2585223 1 0.2615 0.86540 0.852 0.000 0.004 0.000 0.008 NA
#> round_ERR2585234 3 0.0881 0.83978 0.000 0.008 0.972 0.000 0.008 NA
#> round_ERR2585222 1 0.3243 0.84924 0.780 0.000 0.008 0.000 0.004 NA
#> round_ERR2585228 1 0.2762 0.84996 0.804 0.000 0.000 0.000 0.000 NA
#> round_ERR2585248 1 0.0405 0.85857 0.988 0.000 0.000 0.004 0.000 NA
#> round_ERR2585240 3 0.2531 0.83447 0.000 0.000 0.856 0.000 0.012 NA
#> round_ERR2585270 1 0.4777 0.77186 0.660 0.000 0.088 0.000 0.004 NA
#> round_ERR2585232 3 0.5372 0.59720 0.184 0.000 0.628 0.000 0.012 NA
#> aberrant_ERR2585341 2 0.4046 0.63322 0.000 0.752 0.000 0.004 0.176 NA
#> aberrant_ERR2585355 2 0.1745 0.77002 0.000 0.920 0.000 0.000 0.068 NA
#> round_ERR2585227 1 0.5539 0.61626 0.592 0.000 0.192 0.000 0.008 NA
#> aberrant_ERR2585351 5 0.5697 0.32220 0.000 0.376 0.000 0.040 0.516 NA
#> round_ERR2585269 1 0.0790 0.85990 0.968 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585357 2 0.0363 0.77924 0.000 0.988 0.000 0.000 0.012 NA
#> aberrant_ERR2585350 2 0.0858 0.77970 0.000 0.968 0.000 0.000 0.028 NA
#> round_ERR2585250 1 0.5625 0.63073 0.556 0.000 0.132 0.000 0.012 NA
#> round_ERR2585245 1 0.0865 0.85658 0.964 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585353 5 0.5178 -0.07643 0.000 0.000 0.000 0.424 0.488 NA
#> round_ERR2585258 1 0.4420 0.76568 0.644 0.000 0.048 0.000 0.000 NA
#> aberrant_ERR2585354 5 0.5853 0.17113 0.000 0.024 0.000 0.332 0.524 NA
#> round_ERR2585249 1 0.0937 0.85998 0.960 0.000 0.000 0.000 0.000 NA
#> round_ERR2585268 1 0.5901 0.54209 0.516 0.000 0.200 0.000 0.008 NA
#> aberrant_ERR2585356 4 0.4091 0.61472 0.000 0.000 0.000 0.732 0.200 NA
#> round_ERR2585266 3 0.2450 0.83295 0.000 0.000 0.868 0.000 0.016 NA
#> round_ERR2585231 1 0.1007 0.85991 0.956 0.000 0.000 0.000 0.000 NA
#> round_ERR2585230 1 0.3301 0.84283 0.772 0.000 0.008 0.000 0.004 NA
#> round_ERR2585267 1 0.1757 0.86617 0.916 0.000 0.000 0.000 0.008 NA
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> SD:skmeans 158 4.65e-19 2
#> SD:skmeans 150 7.32e-22 3
#> SD:skmeans 148 4.34e-27 4
#> SD:skmeans 130 1.99e-22 5
#> SD:skmeans 116 2.14e-19 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "pam"]
# you can also extract it by
# res = res_list["SD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'SD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.401 0.798 0.863 0.2311 0.904 0.904
#> 3 3 0.324 0.781 0.845 1.1708 0.575 0.532
#> 4 4 0.306 0.687 0.805 0.0967 0.974 0.948
#> 5 5 0.330 0.361 0.777 0.0649 0.996 0.991
#> 6 6 0.513 0.524 0.807 0.0771 0.951 0.895
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 1 0.8955 0.726 0.688 0.312
#> aberrant_ERR2585338 1 0.5408 0.827 0.876 0.124
#> aberrant_ERR2585325 1 0.8555 0.752 0.720 0.280
#> aberrant_ERR2585283 2 0.1184 0.906 0.016 0.984
#> aberrant_ERR2585343 1 0.9323 0.690 0.652 0.348
#> aberrant_ERR2585329 1 0.7745 0.785 0.772 0.228
#> aberrant_ERR2585317 1 0.7883 0.780 0.764 0.236
#> aberrant_ERR2585339 1 0.7299 0.799 0.796 0.204
#> aberrant_ERR2585335 1 0.8661 0.746 0.712 0.288
#> aberrant_ERR2585287 2 0.3584 0.882 0.068 0.932
#> aberrant_ERR2585321 1 0.9358 0.690 0.648 0.352
#> aberrant_ERR2585297 1 0.1184 0.839 0.984 0.016
#> aberrant_ERR2585337 1 0.7602 0.789 0.780 0.220
#> aberrant_ERR2585319 1 0.9286 0.694 0.656 0.344
#> aberrant_ERR2585315 1 0.9286 0.694 0.656 0.344
#> aberrant_ERR2585336 1 0.8016 0.776 0.756 0.244
#> aberrant_ERR2585307 1 0.5629 0.825 0.868 0.132
#> aberrant_ERR2585301 1 0.7219 0.799 0.800 0.200
#> aberrant_ERR2585326 1 0.8016 0.777 0.756 0.244
#> aberrant_ERR2585331 1 0.4298 0.833 0.912 0.088
#> aberrant_ERR2585346 2 0.5737 0.839 0.136 0.864
#> aberrant_ERR2585314 1 0.7815 0.782 0.768 0.232
#> aberrant_ERR2585298 1 0.0000 0.842 1.000 0.000
#> aberrant_ERR2585345 1 0.7815 0.782 0.768 0.232
#> aberrant_ERR2585299 1 0.1184 0.839 0.984 0.016
#> aberrant_ERR2585309 1 0.0938 0.839 0.988 0.012
#> aberrant_ERR2585303 1 0.8267 0.768 0.740 0.260
#> aberrant_ERR2585313 1 0.9209 0.703 0.664 0.336
#> aberrant_ERR2585318 1 0.9323 0.690 0.652 0.348
#> aberrant_ERR2585328 1 0.9286 0.694 0.656 0.344
#> aberrant_ERR2585330 1 0.9323 0.690 0.652 0.348
#> aberrant_ERR2585293 2 0.4298 0.889 0.088 0.912
#> aberrant_ERR2585342 1 0.9323 0.690 0.652 0.348
#> aberrant_ERR2585348 1 0.9323 0.690 0.652 0.348
#> aberrant_ERR2585352 1 0.9286 0.694 0.656 0.344
#> aberrant_ERR2585308 1 0.0938 0.839 0.988 0.012
#> aberrant_ERR2585349 1 0.5946 0.820 0.856 0.144
#> aberrant_ERR2585316 1 0.9323 0.690 0.652 0.348
#> aberrant_ERR2585306 1 0.8763 0.739 0.704 0.296
#> aberrant_ERR2585324 1 0.9286 0.694 0.656 0.344
#> aberrant_ERR2585310 1 0.0376 0.843 0.996 0.004
#> aberrant_ERR2585296 1 0.0000 0.842 1.000 0.000
#> aberrant_ERR2585275 2 0.0000 0.903 0.000 1.000
#> aberrant_ERR2585311 1 0.9323 0.690 0.652 0.348
#> aberrant_ERR2585292 2 0.4298 0.889 0.088 0.912
#> aberrant_ERR2585282 1 0.8555 0.744 0.720 0.280
#> aberrant_ERR2585305 1 0.8661 0.747 0.712 0.288
#> aberrant_ERR2585278 1 0.7528 0.795 0.784 0.216
#> aberrant_ERR2585347 1 0.9323 0.690 0.652 0.348
#> aberrant_ERR2585332 1 0.9323 0.690 0.652 0.348
#> aberrant_ERR2585280 1 0.9248 0.699 0.660 0.340
#> aberrant_ERR2585304 1 0.3274 0.838 0.940 0.060
#> aberrant_ERR2585322 1 0.7883 0.781 0.764 0.236
#> aberrant_ERR2585279 1 0.4298 0.833 0.912 0.088
#> aberrant_ERR2585277 1 0.5737 0.823 0.864 0.136
#> aberrant_ERR2585295 1 0.6343 0.817 0.840 0.160
#> aberrant_ERR2585333 1 0.9323 0.690 0.652 0.348
#> aberrant_ERR2585285 1 0.8327 0.763 0.736 0.264
#> aberrant_ERR2585286 1 0.5178 0.828 0.884 0.116
#> aberrant_ERR2585294 1 0.8608 0.750 0.716 0.284
#> aberrant_ERR2585300 1 0.9209 0.706 0.664 0.336
#> aberrant_ERR2585334 1 0.4298 0.833 0.912 0.088
#> aberrant_ERR2585361 1 0.9323 0.690 0.652 0.348
#> aberrant_ERR2585372 1 0.9323 0.690 0.652 0.348
#> round_ERR2585217 1 0.0672 0.843 0.992 0.008
#> round_ERR2585205 1 0.0938 0.839 0.988 0.012
#> round_ERR2585214 1 0.1414 0.843 0.980 0.020
#> round_ERR2585202 1 0.1414 0.843 0.980 0.020
#> aberrant_ERR2585367 1 0.8327 0.760 0.736 0.264
#> round_ERR2585220 1 0.0938 0.839 0.988 0.012
#> round_ERR2585238 1 0.0938 0.839 0.988 0.012
#> aberrant_ERR2585276 1 0.8909 0.732 0.692 0.308
#> round_ERR2585218 1 0.0938 0.839 0.988 0.012
#> aberrant_ERR2585363 1 0.8608 0.749 0.716 0.284
#> round_ERR2585201 1 0.0938 0.843 0.988 0.012
#> round_ERR2585210 1 0.0938 0.839 0.988 0.012
#> aberrant_ERR2585362 1 0.9044 0.720 0.680 0.320
#> aberrant_ERR2585360 1 0.9129 0.711 0.672 0.328
#> round_ERR2585209 1 0.0376 0.843 0.996 0.004
#> round_ERR2585242 1 0.0000 0.842 1.000 0.000
#> round_ERR2585216 1 0.0000 0.842 1.000 0.000
#> round_ERR2585219 1 0.0376 0.841 0.996 0.004
#> round_ERR2585237 1 0.1414 0.843 0.980 0.020
#> round_ERR2585198 1 0.1414 0.843 0.980 0.020
#> round_ERR2585211 1 0.0938 0.839 0.988 0.012
#> round_ERR2585206 1 0.0938 0.839 0.988 0.012
#> aberrant_ERR2585281 1 0.5629 0.825 0.868 0.132
#> round_ERR2585212 1 0.0376 0.841 0.996 0.004
#> round_ERR2585221 1 0.1184 0.839 0.984 0.016
#> round_ERR2585243 1 0.0672 0.840 0.992 0.008
#> round_ERR2585204 1 0.1414 0.843 0.980 0.020
#> round_ERR2585213 1 0.4022 0.835 0.920 0.080
#> aberrant_ERR2585373 1 0.9323 0.690 0.652 0.348
#> aberrant_ERR2585358 1 0.9323 0.690 0.652 0.348
#> aberrant_ERR2585365 1 0.8267 0.766 0.740 0.260
#> aberrant_ERR2585359 1 0.9358 0.686 0.648 0.352
#> aberrant_ERR2585370 1 0.7883 0.780 0.764 0.236
#> round_ERR2585215 1 0.0938 0.839 0.988 0.012
#> round_ERR2585262 1 0.1633 0.843 0.976 0.024
#> round_ERR2585199 1 0.1414 0.843 0.980 0.020
#> aberrant_ERR2585369 1 0.9323 0.690 0.652 0.348
#> round_ERR2585208 1 0.0938 0.839 0.988 0.012
#> round_ERR2585252 1 0.0938 0.839 0.988 0.012
#> round_ERR2585236 1 0.2423 0.837 0.960 0.040
#> aberrant_ERR2585284 2 0.0376 0.902 0.004 0.996
#> round_ERR2585224 1 0.1633 0.838 0.976 0.024
#> round_ERR2585260 1 0.0938 0.839 0.988 0.012
#> round_ERR2585229 1 0.0938 0.839 0.988 0.012
#> aberrant_ERR2585364 2 0.5629 0.779 0.132 0.868
#> round_ERR2585253 1 0.1184 0.839 0.984 0.016
#> aberrant_ERR2585368 1 0.4690 0.831 0.900 0.100
#> aberrant_ERR2585371 1 0.4431 0.832 0.908 0.092
#> round_ERR2585239 1 0.0938 0.839 0.988 0.012
#> round_ERR2585273 1 0.0938 0.839 0.988 0.012
#> round_ERR2585256 1 0.1184 0.843 0.984 0.016
#> round_ERR2585272 1 0.0376 0.841 0.996 0.004
#> round_ERR2585246 1 0.1184 0.839 0.984 0.016
#> round_ERR2585261 1 0.0000 0.842 1.000 0.000
#> round_ERR2585254 1 0.1184 0.843 0.984 0.016
#> round_ERR2585225 1 0.0000 0.842 1.000 0.000
#> round_ERR2585235 1 0.0000 0.842 1.000 0.000
#> round_ERR2585271 1 0.0938 0.839 0.988 0.012
#> round_ERR2585251 1 0.0938 0.839 0.988 0.012
#> round_ERR2585255 1 0.0938 0.843 0.988 0.012
#> round_ERR2585257 1 0.1414 0.843 0.980 0.020
#> round_ERR2585226 1 0.0000 0.842 1.000 0.000
#> round_ERR2585265 1 0.0938 0.839 0.988 0.012
#> round_ERR2585259 1 0.0672 0.840 0.992 0.008
#> round_ERR2585247 1 0.0938 0.839 0.988 0.012
#> round_ERR2585241 1 0.0938 0.839 0.988 0.012
#> round_ERR2585263 1 0.0000 0.842 1.000 0.000
#> round_ERR2585264 1 0.8081 0.506 0.752 0.248
#> round_ERR2585233 1 0.0000 0.842 1.000 0.000
#> round_ERR2585223 1 0.0938 0.839 0.988 0.012
#> round_ERR2585234 1 0.1184 0.843 0.984 0.016
#> round_ERR2585222 1 0.0672 0.840 0.992 0.008
#> round_ERR2585228 1 0.0938 0.839 0.988 0.012
#> round_ERR2585248 1 0.2603 0.822 0.956 0.044
#> round_ERR2585240 1 0.0376 0.842 0.996 0.004
#> round_ERR2585270 1 0.0938 0.839 0.988 0.012
#> round_ERR2585232 1 0.0000 0.842 1.000 0.000
#> aberrant_ERR2585341 1 0.6531 0.814 0.832 0.168
#> aberrant_ERR2585355 1 0.8144 0.773 0.748 0.252
#> round_ERR2585227 1 0.0672 0.840 0.992 0.008
#> aberrant_ERR2585351 1 0.8555 0.752 0.720 0.280
#> round_ERR2585269 1 0.0938 0.839 0.988 0.012
#> aberrant_ERR2585357 1 0.7815 0.782 0.768 0.232
#> aberrant_ERR2585350 1 0.7815 0.782 0.768 0.232
#> round_ERR2585250 1 0.0672 0.840 0.992 0.008
#> round_ERR2585245 1 0.2603 0.835 0.956 0.044
#> aberrant_ERR2585353 1 0.9323 0.690 0.652 0.348
#> round_ERR2585258 1 0.0938 0.839 0.988 0.012
#> aberrant_ERR2585354 1 0.8955 0.731 0.688 0.312
#> round_ERR2585249 1 0.1184 0.839 0.984 0.016
#> round_ERR2585268 1 0.0376 0.841 0.996 0.004
#> aberrant_ERR2585356 1 0.9427 0.689 0.640 0.360
#> round_ERR2585266 1 0.0672 0.843 0.992 0.008
#> round_ERR2585231 1 0.0938 0.839 0.988 0.012
#> round_ERR2585230 1 0.0938 0.839 0.988 0.012
#> round_ERR2585267 1 0.1414 0.840 0.980 0.020
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.2261 0.770 0.068 0.932 0.000
#> aberrant_ERR2585338 1 0.3192 0.807 0.888 0.112 0.000
#> aberrant_ERR2585325 2 0.3551 0.802 0.132 0.868 0.000
#> aberrant_ERR2585283 3 0.3619 0.809 0.000 0.136 0.864
#> aberrant_ERR2585343 2 0.1163 0.739 0.028 0.972 0.000
#> aberrant_ERR2585329 2 0.4931 0.821 0.232 0.768 0.000
#> aberrant_ERR2585317 2 0.4842 0.823 0.224 0.776 0.000
#> aberrant_ERR2585339 1 0.3879 0.767 0.848 0.152 0.000
#> aberrant_ERR2585335 2 0.4346 0.841 0.184 0.816 0.000
#> aberrant_ERR2585287 3 0.7061 0.656 0.036 0.332 0.632
#> aberrant_ERR2585321 2 0.2796 0.822 0.092 0.908 0.000
#> aberrant_ERR2585297 1 0.4452 0.846 0.808 0.000 0.192
#> aberrant_ERR2585337 2 0.6095 0.629 0.392 0.608 0.000
#> aberrant_ERR2585319 2 0.2165 0.794 0.064 0.936 0.000
#> aberrant_ERR2585315 2 0.4178 0.830 0.172 0.828 0.000
#> aberrant_ERR2585336 2 0.4887 0.823 0.228 0.772 0.000
#> aberrant_ERR2585307 1 0.4605 0.662 0.796 0.204 0.000
#> aberrant_ERR2585301 1 0.5465 0.601 0.712 0.288 0.000
#> aberrant_ERR2585326 2 0.4842 0.826 0.224 0.776 0.000
#> aberrant_ERR2585331 1 0.0237 0.852 0.996 0.004 0.000
#> aberrant_ERR2585346 3 0.3879 0.807 0.000 0.152 0.848
#> aberrant_ERR2585314 2 0.4974 0.817 0.236 0.764 0.000
#> aberrant_ERR2585298 1 0.0237 0.852 0.996 0.004 0.000
#> aberrant_ERR2585345 2 0.4887 0.821 0.228 0.772 0.000
#> aberrant_ERR2585299 1 0.5792 0.833 0.772 0.036 0.192
#> aberrant_ERR2585309 1 0.4682 0.846 0.804 0.004 0.192
#> aberrant_ERR2585303 2 0.6192 0.536 0.420 0.580 0.000
#> aberrant_ERR2585313 2 0.3482 0.838 0.128 0.872 0.000
#> aberrant_ERR2585318 2 0.2959 0.827 0.100 0.900 0.000
#> aberrant_ERR2585328 2 0.4842 0.783 0.224 0.776 0.000
#> aberrant_ERR2585330 2 0.3412 0.837 0.124 0.876 0.000
#> aberrant_ERR2585293 3 0.3038 0.797 0.000 0.104 0.896
#> aberrant_ERR2585342 2 0.2165 0.794 0.064 0.936 0.000
#> aberrant_ERR2585348 2 0.1964 0.767 0.056 0.944 0.000
#> aberrant_ERR2585352 2 0.3340 0.835 0.120 0.880 0.000
#> aberrant_ERR2585308 1 0.6662 0.807 0.736 0.072 0.192
#> aberrant_ERR2585349 1 0.5650 0.391 0.688 0.312 0.000
#> aberrant_ERR2585316 2 0.3941 0.829 0.156 0.844 0.000
#> aberrant_ERR2585306 1 0.6235 0.188 0.564 0.436 0.000
#> aberrant_ERR2585324 2 0.3116 0.813 0.108 0.892 0.000
#> aberrant_ERR2585310 1 0.2056 0.855 0.952 0.024 0.024
#> aberrant_ERR2585296 1 0.1315 0.851 0.972 0.020 0.008
#> aberrant_ERR2585275 3 0.4452 0.780 0.000 0.192 0.808
#> aberrant_ERR2585311 2 0.3116 0.832 0.108 0.892 0.000
#> aberrant_ERR2585292 3 0.3038 0.797 0.000 0.104 0.896
#> aberrant_ERR2585282 2 0.5431 0.643 0.284 0.716 0.000
#> aberrant_ERR2585305 2 0.4452 0.840 0.192 0.808 0.000
#> aberrant_ERR2585278 2 0.5926 0.654 0.356 0.644 0.000
#> aberrant_ERR2585347 2 0.3030 0.821 0.092 0.904 0.004
#> aberrant_ERR2585332 2 0.2066 0.789 0.060 0.940 0.000
#> aberrant_ERR2585280 2 0.6204 0.393 0.424 0.576 0.000
#> aberrant_ERR2585304 1 0.2625 0.801 0.916 0.084 0.000
#> aberrant_ERR2585322 1 0.6291 -0.233 0.532 0.468 0.000
#> aberrant_ERR2585279 1 0.0237 0.852 0.996 0.004 0.000
#> aberrant_ERR2585277 1 0.4605 0.679 0.796 0.204 0.000
#> aberrant_ERR2585295 1 0.4842 0.673 0.776 0.224 0.000
#> aberrant_ERR2585333 2 0.2625 0.816 0.084 0.916 0.000
#> aberrant_ERR2585285 2 0.4399 0.832 0.188 0.812 0.000
#> aberrant_ERR2585286 1 0.4452 0.700 0.808 0.192 0.000
#> aberrant_ERR2585294 1 0.6045 0.312 0.620 0.380 0.000
#> aberrant_ERR2585300 2 0.6108 0.740 0.240 0.732 0.028
#> aberrant_ERR2585334 1 0.0592 0.852 0.988 0.012 0.000
#> aberrant_ERR2585361 2 0.3482 0.837 0.128 0.872 0.000
#> aberrant_ERR2585372 2 0.2356 0.803 0.072 0.928 0.000
#> round_ERR2585217 1 0.0237 0.852 0.996 0.004 0.000
#> round_ERR2585205 1 0.4452 0.846 0.808 0.000 0.192
#> round_ERR2585214 1 0.0237 0.852 0.996 0.004 0.000
#> round_ERR2585202 1 0.0237 0.852 0.996 0.004 0.000
#> aberrant_ERR2585367 1 0.5431 0.628 0.716 0.284 0.000
#> round_ERR2585220 1 0.3816 0.858 0.852 0.000 0.148
#> round_ERR2585238 1 0.4452 0.846 0.808 0.000 0.192
#> aberrant_ERR2585276 2 0.5578 0.784 0.240 0.748 0.012
#> round_ERR2585218 1 0.4452 0.846 0.808 0.000 0.192
#> aberrant_ERR2585363 2 0.4291 0.839 0.180 0.820 0.000
#> round_ERR2585201 1 0.0237 0.852 0.996 0.004 0.000
#> round_ERR2585210 1 0.4399 0.848 0.812 0.000 0.188
#> aberrant_ERR2585362 2 0.4346 0.838 0.184 0.816 0.000
#> aberrant_ERR2585360 2 0.4399 0.835 0.188 0.812 0.000
#> round_ERR2585209 1 0.0237 0.852 0.996 0.004 0.000
#> round_ERR2585242 1 0.0237 0.852 0.996 0.004 0.000
#> round_ERR2585216 1 0.0983 0.856 0.980 0.004 0.016
#> round_ERR2585219 1 0.3619 0.859 0.864 0.000 0.136
#> round_ERR2585237 1 0.0237 0.852 0.996 0.004 0.000
#> round_ERR2585198 1 0.0237 0.852 0.996 0.004 0.000
#> round_ERR2585211 1 0.4452 0.846 0.808 0.000 0.192
#> round_ERR2585206 1 0.4452 0.846 0.808 0.000 0.192
#> aberrant_ERR2585281 1 0.3340 0.808 0.880 0.120 0.000
#> round_ERR2585212 1 0.1860 0.860 0.948 0.000 0.052
#> round_ERR2585221 1 0.4682 0.846 0.804 0.004 0.192
#> round_ERR2585243 1 0.4110 0.858 0.844 0.004 0.152
#> round_ERR2585204 1 0.0237 0.852 0.996 0.004 0.000
#> round_ERR2585213 1 0.0237 0.852 0.996 0.004 0.000
#> aberrant_ERR2585373 2 0.2356 0.804 0.072 0.928 0.000
#> aberrant_ERR2585358 2 0.0237 0.693 0.004 0.996 0.000
#> aberrant_ERR2585365 2 0.4931 0.821 0.232 0.768 0.000
#> aberrant_ERR2585359 2 0.2590 0.799 0.072 0.924 0.004
#> aberrant_ERR2585370 2 0.6062 0.637 0.384 0.616 0.000
#> round_ERR2585215 1 0.4452 0.846 0.808 0.000 0.192
#> round_ERR2585262 1 0.0237 0.852 0.996 0.004 0.000
#> round_ERR2585199 1 0.0237 0.852 0.996 0.004 0.000
#> aberrant_ERR2585369 2 0.3412 0.837 0.124 0.876 0.000
#> round_ERR2585208 1 0.4452 0.846 0.808 0.000 0.192
#> round_ERR2585252 1 0.4452 0.846 0.808 0.000 0.192
#> round_ERR2585236 1 0.4663 0.854 0.828 0.016 0.156
#> aberrant_ERR2585284 3 0.4452 0.780 0.000 0.192 0.808
#> round_ERR2585224 1 0.6632 0.805 0.732 0.064 0.204
#> round_ERR2585260 1 0.4682 0.846 0.804 0.004 0.192
#> round_ERR2585229 1 0.4682 0.846 0.804 0.004 0.192
#> aberrant_ERR2585364 2 0.7184 -0.346 0.024 0.504 0.472
#> round_ERR2585253 1 0.4504 0.844 0.804 0.000 0.196
#> aberrant_ERR2585368 1 0.4062 0.680 0.836 0.164 0.000
#> aberrant_ERR2585371 1 0.2261 0.811 0.932 0.068 0.000
#> round_ERR2585239 1 0.4452 0.846 0.808 0.000 0.192
#> round_ERR2585273 1 0.4682 0.846 0.804 0.004 0.192
#> round_ERR2585256 1 0.0237 0.852 0.996 0.004 0.000
#> round_ERR2585272 1 0.2301 0.859 0.936 0.004 0.060
#> round_ERR2585246 1 0.6304 0.819 0.752 0.056 0.192
#> round_ERR2585261 1 0.0475 0.853 0.992 0.004 0.004
#> round_ERR2585254 1 0.0237 0.852 0.996 0.004 0.000
#> round_ERR2585225 1 0.0237 0.852 0.996 0.004 0.000
#> round_ERR2585235 1 0.1015 0.853 0.980 0.012 0.008
#> round_ERR2585271 1 0.4452 0.846 0.808 0.000 0.192
#> round_ERR2585251 1 0.4235 0.851 0.824 0.000 0.176
#> round_ERR2585255 1 0.0237 0.852 0.996 0.004 0.000
#> round_ERR2585257 1 0.0237 0.852 0.996 0.004 0.000
#> round_ERR2585226 1 0.0424 0.851 0.992 0.008 0.000
#> round_ERR2585265 1 0.4452 0.846 0.808 0.000 0.192
#> round_ERR2585259 1 0.3715 0.860 0.868 0.004 0.128
#> round_ERR2585247 1 0.4452 0.846 0.808 0.000 0.192
#> round_ERR2585241 1 0.4178 0.853 0.828 0.000 0.172
#> round_ERR2585263 1 0.1765 0.859 0.956 0.004 0.040
#> round_ERR2585264 3 0.6215 -0.219 0.428 0.000 0.572
#> round_ERR2585233 1 0.0237 0.852 0.996 0.004 0.000
#> round_ERR2585223 1 0.4452 0.846 0.808 0.000 0.192
#> round_ERR2585234 1 0.0237 0.852 0.996 0.004 0.000
#> round_ERR2585222 1 0.3771 0.861 0.876 0.012 0.112
#> round_ERR2585228 1 0.4452 0.846 0.808 0.000 0.192
#> round_ERR2585248 1 0.5529 0.761 0.704 0.000 0.296
#> round_ERR2585240 1 0.0237 0.852 0.996 0.004 0.000
#> round_ERR2585270 1 0.5167 0.842 0.792 0.016 0.192
#> round_ERR2585232 1 0.0237 0.852 0.996 0.004 0.000
#> aberrant_ERR2585341 1 0.4887 0.658 0.772 0.228 0.000
#> aberrant_ERR2585355 2 0.5216 0.795 0.260 0.740 0.000
#> round_ERR2585227 1 0.2878 0.860 0.904 0.000 0.096
#> aberrant_ERR2585351 2 0.4399 0.838 0.188 0.812 0.000
#> round_ERR2585269 1 0.4452 0.846 0.808 0.000 0.192
#> aberrant_ERR2585357 2 0.5058 0.812 0.244 0.756 0.000
#> aberrant_ERR2585350 2 0.5216 0.799 0.260 0.740 0.000
#> round_ERR2585250 1 0.5147 0.850 0.800 0.020 0.180
#> round_ERR2585245 1 0.4504 0.845 0.804 0.000 0.196
#> aberrant_ERR2585353 2 0.3038 0.830 0.104 0.896 0.000
#> round_ERR2585258 1 0.4346 0.849 0.816 0.000 0.184
#> aberrant_ERR2585354 2 0.5024 0.816 0.220 0.776 0.004
#> round_ERR2585249 1 0.4452 0.846 0.808 0.000 0.192
#> round_ERR2585268 1 0.4458 0.823 0.864 0.080 0.056
#> aberrant_ERR2585356 2 0.4842 0.717 0.224 0.776 0.000
#> round_ERR2585266 1 0.0237 0.852 0.996 0.004 0.000
#> round_ERR2585231 1 0.4399 0.848 0.812 0.000 0.188
#> round_ERR2585230 1 0.3941 0.856 0.844 0.000 0.156
#> round_ERR2585267 1 0.4861 0.845 0.800 0.008 0.192
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 3 0.5498 0.226 0.020 0.404 0.576 0.000
#> aberrant_ERR2585338 1 0.2589 0.719 0.884 0.116 0.000 0.000
#> aberrant_ERR2585325 3 0.5805 0.231 0.036 0.388 0.576 0.000
#> aberrant_ERR2585283 4 0.0000 0.897 0.000 0.000 0.000 1.000
#> aberrant_ERR2585343 2 0.0817 0.631 0.000 0.976 0.024 0.000
#> aberrant_ERR2585329 2 0.3649 0.808 0.204 0.796 0.000 0.000
#> aberrant_ERR2585317 2 0.3610 0.808 0.200 0.800 0.000 0.000
#> aberrant_ERR2585339 1 0.2469 0.720 0.892 0.108 0.000 0.000
#> aberrant_ERR2585335 2 0.3266 0.816 0.168 0.832 0.000 0.000
#> aberrant_ERR2585287 3 0.6497 -0.434 0.004 0.076 0.580 0.340
#> aberrant_ERR2585321 2 0.1716 0.753 0.064 0.936 0.000 0.000
#> aberrant_ERR2585297 1 0.4855 0.660 0.600 0.000 0.400 0.000
#> aberrant_ERR2585337 2 0.4697 0.618 0.356 0.644 0.000 0.000
#> aberrant_ERR2585319 2 0.0469 0.641 0.000 0.988 0.012 0.000
#> aberrant_ERR2585315 2 0.3528 0.801 0.192 0.808 0.000 0.000
#> aberrant_ERR2585336 2 0.3649 0.808 0.204 0.796 0.000 0.000
#> aberrant_ERR2585307 1 0.4018 0.594 0.772 0.224 0.004 0.000
#> aberrant_ERR2585301 1 0.4584 0.498 0.696 0.300 0.004 0.000
#> aberrant_ERR2585326 2 0.3649 0.808 0.204 0.796 0.000 0.000
#> aberrant_ERR2585331 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> aberrant_ERR2585346 4 0.0188 0.893 0.000 0.004 0.000 0.996
#> aberrant_ERR2585314 2 0.3688 0.805 0.208 0.792 0.000 0.000
#> aberrant_ERR2585298 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> aberrant_ERR2585345 2 0.3610 0.808 0.200 0.800 0.000 0.000
#> aberrant_ERR2585299 1 0.6052 0.624 0.556 0.048 0.396 0.000
#> aberrant_ERR2585309 1 0.5060 0.648 0.584 0.004 0.412 0.000
#> aberrant_ERR2585303 2 0.4830 0.531 0.392 0.608 0.000 0.000
#> aberrant_ERR2585313 2 0.2921 0.811 0.140 0.860 0.000 0.000
#> aberrant_ERR2585318 2 0.2149 0.781 0.088 0.912 0.000 0.000
#> aberrant_ERR2585328 2 0.3837 0.758 0.224 0.776 0.000 0.000
#> aberrant_ERR2585330 2 0.3300 0.813 0.144 0.848 0.008 0.000
#> aberrant_ERR2585293 4 0.0000 0.897 0.000 0.000 0.000 1.000
#> aberrant_ERR2585342 2 0.0336 0.664 0.008 0.992 0.000 0.000
#> aberrant_ERR2585348 2 0.4197 0.621 0.036 0.808 0.156 0.000
#> aberrant_ERR2585352 2 0.2921 0.811 0.140 0.860 0.000 0.000
#> aberrant_ERR2585308 1 0.6521 0.570 0.512 0.076 0.412 0.000
#> aberrant_ERR2585349 1 0.4679 0.266 0.648 0.352 0.000 0.000
#> aberrant_ERR2585316 2 0.3172 0.797 0.160 0.840 0.000 0.000
#> aberrant_ERR2585306 1 0.5472 0.111 0.544 0.440 0.016 0.000
#> aberrant_ERR2585324 2 0.1584 0.666 0.036 0.952 0.012 0.000
#> aberrant_ERR2585310 1 0.1929 0.762 0.940 0.024 0.036 0.000
#> aberrant_ERR2585296 1 0.1042 0.754 0.972 0.020 0.008 0.000
#> aberrant_ERR2585275 4 0.0000 0.897 0.000 0.000 0.000 1.000
#> aberrant_ERR2585311 2 0.2408 0.793 0.104 0.896 0.000 0.000
#> aberrant_ERR2585292 4 0.0000 0.897 0.000 0.000 0.000 1.000
#> aberrant_ERR2585282 2 0.4606 0.602 0.264 0.724 0.012 0.000
#> aberrant_ERR2585305 2 0.3444 0.815 0.184 0.816 0.000 0.000
#> aberrant_ERR2585278 2 0.4643 0.628 0.344 0.656 0.000 0.000
#> aberrant_ERR2585347 2 0.2345 0.791 0.100 0.900 0.000 0.000
#> aberrant_ERR2585332 2 0.1388 0.695 0.028 0.960 0.012 0.000
#> aberrant_ERR2585280 2 0.6296 0.403 0.388 0.548 0.064 0.000
#> aberrant_ERR2585304 1 0.2149 0.696 0.912 0.088 0.000 0.000
#> aberrant_ERR2585322 2 0.4998 0.292 0.488 0.512 0.000 0.000
#> aberrant_ERR2585279 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> aberrant_ERR2585277 1 0.4103 0.560 0.744 0.256 0.000 0.000
#> aberrant_ERR2585295 1 0.4088 0.596 0.764 0.232 0.004 0.000
#> aberrant_ERR2585333 2 0.2179 0.750 0.064 0.924 0.012 0.000
#> aberrant_ERR2585285 2 0.2921 0.798 0.140 0.860 0.000 0.000
#> aberrant_ERR2585286 1 0.3975 0.590 0.760 0.240 0.000 0.000
#> aberrant_ERR2585294 1 0.4746 0.304 0.632 0.368 0.000 0.000
#> aberrant_ERR2585300 2 0.5723 0.650 0.244 0.684 0.072 0.000
#> aberrant_ERR2585334 1 0.0336 0.757 0.992 0.008 0.000 0.000
#> aberrant_ERR2585361 2 0.2868 0.811 0.136 0.864 0.000 0.000
#> aberrant_ERR2585372 2 0.2174 0.728 0.052 0.928 0.020 0.000
#> round_ERR2585217 1 0.0336 0.759 0.992 0.000 0.008 0.000
#> round_ERR2585205 1 0.4277 0.726 0.720 0.000 0.280 0.000
#> round_ERR2585214 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> round_ERR2585202 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> aberrant_ERR2585367 1 0.4277 0.580 0.720 0.280 0.000 0.000
#> round_ERR2585220 1 0.3649 0.753 0.796 0.000 0.204 0.000
#> round_ERR2585238 1 0.4888 0.650 0.588 0.000 0.412 0.000
#> aberrant_ERR2585276 2 0.4155 0.753 0.240 0.756 0.004 0.000
#> round_ERR2585218 1 0.4817 0.668 0.612 0.000 0.388 0.000
#> aberrant_ERR2585363 2 0.3311 0.814 0.172 0.828 0.000 0.000
#> round_ERR2585201 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> round_ERR2585210 1 0.4661 0.693 0.652 0.000 0.348 0.000
#> aberrant_ERR2585362 2 0.3486 0.810 0.188 0.812 0.000 0.000
#> aberrant_ERR2585360 2 0.3528 0.808 0.192 0.808 0.000 0.000
#> round_ERR2585209 1 0.0188 0.758 0.996 0.000 0.004 0.000
#> round_ERR2585242 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> round_ERR2585216 1 0.1557 0.767 0.944 0.000 0.056 0.000
#> round_ERR2585219 1 0.3266 0.761 0.832 0.000 0.168 0.000
#> round_ERR2585237 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> round_ERR2585198 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> round_ERR2585211 1 0.4661 0.694 0.652 0.000 0.348 0.000
#> round_ERR2585206 1 0.4888 0.650 0.588 0.000 0.412 0.000
#> aberrant_ERR2585281 1 0.2760 0.715 0.872 0.128 0.000 0.000
#> round_ERR2585212 1 0.2149 0.768 0.912 0.000 0.088 0.000
#> round_ERR2585221 1 0.5060 0.648 0.584 0.004 0.412 0.000
#> round_ERR2585243 1 0.3400 0.758 0.820 0.000 0.180 0.000
#> round_ERR2585204 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> round_ERR2585213 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> aberrant_ERR2585373 2 0.0817 0.693 0.024 0.976 0.000 0.000
#> aberrant_ERR2585358 2 0.0336 0.644 0.000 0.992 0.008 0.000
#> aberrant_ERR2585365 2 0.3801 0.795 0.220 0.780 0.000 0.000
#> aberrant_ERR2585359 2 0.3009 0.722 0.052 0.892 0.056 0.000
#> aberrant_ERR2585370 2 0.4661 0.618 0.348 0.652 0.000 0.000
#> round_ERR2585215 1 0.3837 0.746 0.776 0.000 0.224 0.000
#> round_ERR2585262 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> round_ERR2585199 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> aberrant_ERR2585369 2 0.2814 0.809 0.132 0.868 0.000 0.000
#> round_ERR2585208 1 0.4855 0.657 0.600 0.000 0.400 0.000
#> round_ERR2585252 1 0.4888 0.650 0.588 0.000 0.412 0.000
#> round_ERR2585236 1 0.3937 0.756 0.800 0.012 0.188 0.000
#> aberrant_ERR2585284 4 0.0000 0.897 0.000 0.000 0.000 1.000
#> round_ERR2585224 1 0.6465 0.576 0.516 0.072 0.412 0.000
#> round_ERR2585260 1 0.5039 0.655 0.592 0.004 0.404 0.000
#> round_ERR2585229 1 0.5050 0.652 0.588 0.004 0.408 0.000
#> aberrant_ERR2585364 4 0.5744 0.212 0.000 0.436 0.028 0.536
#> round_ERR2585253 1 0.4888 0.650 0.588 0.000 0.412 0.000
#> aberrant_ERR2585368 1 0.3024 0.615 0.852 0.148 0.000 0.000
#> aberrant_ERR2585371 1 0.1637 0.723 0.940 0.060 0.000 0.000
#> round_ERR2585239 1 0.4134 0.733 0.740 0.000 0.260 0.000
#> round_ERR2585273 1 0.5016 0.661 0.600 0.004 0.396 0.000
#> round_ERR2585256 1 0.0188 0.758 0.996 0.000 0.004 0.000
#> round_ERR2585272 1 0.1557 0.766 0.944 0.000 0.056 0.000
#> round_ERR2585246 1 0.6176 0.633 0.572 0.060 0.368 0.000
#> round_ERR2585261 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> round_ERR2585254 1 0.0469 0.760 0.988 0.000 0.012 0.000
#> round_ERR2585225 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> round_ERR2585235 1 0.2179 0.765 0.924 0.012 0.064 0.000
#> round_ERR2585271 1 0.4866 0.657 0.596 0.000 0.404 0.000
#> round_ERR2585251 1 0.3528 0.755 0.808 0.000 0.192 0.000
#> round_ERR2585255 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> round_ERR2585257 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> round_ERR2585226 1 0.0779 0.760 0.980 0.004 0.016 0.000
#> round_ERR2585265 1 0.4817 0.665 0.612 0.000 0.388 0.000
#> round_ERR2585259 1 0.3266 0.762 0.832 0.000 0.168 0.000
#> round_ERR2585247 1 0.4855 0.659 0.600 0.000 0.400 0.000
#> round_ERR2585241 1 0.3610 0.753 0.800 0.000 0.200 0.000
#> round_ERR2585263 1 0.1792 0.768 0.932 0.000 0.068 0.000
#> round_ERR2585264 3 0.7523 -0.477 0.404 0.000 0.412 0.184
#> round_ERR2585233 1 0.0469 0.760 0.988 0.000 0.012 0.000
#> round_ERR2585223 1 0.4713 0.683 0.640 0.000 0.360 0.000
#> round_ERR2585234 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> round_ERR2585222 1 0.3161 0.765 0.864 0.012 0.124 0.000
#> round_ERR2585228 1 0.4855 0.659 0.600 0.000 0.400 0.000
#> round_ERR2585248 1 0.5193 0.642 0.580 0.000 0.412 0.008
#> round_ERR2585240 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> round_ERR2585270 1 0.5149 0.695 0.648 0.016 0.336 0.000
#> round_ERR2585232 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> aberrant_ERR2585341 1 0.4164 0.564 0.736 0.264 0.000 0.000
#> aberrant_ERR2585355 2 0.3975 0.777 0.240 0.760 0.000 0.000
#> round_ERR2585227 1 0.2345 0.767 0.900 0.000 0.100 0.000
#> aberrant_ERR2585351 2 0.3356 0.814 0.176 0.824 0.000 0.000
#> round_ERR2585269 1 0.4888 0.650 0.588 0.000 0.412 0.000
#> aberrant_ERR2585357 2 0.3801 0.798 0.220 0.780 0.000 0.000
#> aberrant_ERR2585350 2 0.3907 0.787 0.232 0.768 0.000 0.000
#> round_ERR2585250 1 0.4095 0.755 0.792 0.016 0.192 0.000
#> round_ERR2585245 1 0.4888 0.650 0.588 0.000 0.412 0.000
#> aberrant_ERR2585353 2 0.2281 0.787 0.096 0.904 0.000 0.000
#> round_ERR2585258 1 0.4817 0.668 0.612 0.000 0.388 0.000
#> aberrant_ERR2585354 2 0.3982 0.777 0.220 0.776 0.004 0.000
#> round_ERR2585249 1 0.4888 0.650 0.588 0.000 0.412 0.000
#> round_ERR2585268 1 0.3894 0.717 0.844 0.088 0.068 0.000
#> aberrant_ERR2585356 2 0.3428 0.630 0.144 0.844 0.012 0.000
#> round_ERR2585266 1 0.0000 0.758 1.000 0.000 0.000 0.000
#> round_ERR2585231 1 0.4877 0.653 0.592 0.000 0.408 0.000
#> round_ERR2585230 1 0.3873 0.747 0.772 0.000 0.228 0.000
#> round_ERR2585267 1 0.5060 0.648 0.584 0.004 0.412 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 5 0.3910 0.4212 0.008 0.272 0.000 0.000 0.720
#> aberrant_ERR2585338 1 0.2488 0.4920 0.872 0.124 0.000 0.000 0.004
#> aberrant_ERR2585325 5 0.3992 0.4227 0.012 0.268 0.000 0.000 0.720
#> aberrant_ERR2585283 4 0.0000 0.8903 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585343 2 0.2236 0.5761 0.000 0.908 0.024 0.000 0.068
#> aberrant_ERR2585329 2 0.2605 0.7634 0.148 0.852 0.000 0.000 0.000
#> aberrant_ERR2585317 2 0.2516 0.7625 0.140 0.860 0.000 0.000 0.000
#> aberrant_ERR2585339 1 0.2573 0.4918 0.880 0.104 0.000 0.000 0.016
#> aberrant_ERR2585335 2 0.2798 0.7665 0.140 0.852 0.000 0.000 0.008
#> aberrant_ERR2585287 5 0.3530 -0.1100 0.000 0.012 0.000 0.204 0.784
#> aberrant_ERR2585321 2 0.4226 0.6071 0.060 0.764 0.000 0.000 0.176
#> aberrant_ERR2585297 1 0.4262 -0.3235 0.560 0.000 0.440 0.000 0.000
#> aberrant_ERR2585337 2 0.3913 0.5652 0.324 0.676 0.000 0.000 0.000
#> aberrant_ERR2585319 2 0.6777 -0.2567 0.000 0.372 0.352 0.000 0.276
#> aberrant_ERR2585315 2 0.3399 0.7455 0.168 0.812 0.000 0.000 0.020
#> aberrant_ERR2585336 2 0.2648 0.7634 0.152 0.848 0.000 0.000 0.000
#> aberrant_ERR2585307 1 0.3461 0.3557 0.772 0.224 0.004 0.000 0.000
#> aberrant_ERR2585301 1 0.5024 0.2714 0.692 0.212 0.000 0.000 0.096
#> aberrant_ERR2585326 2 0.2719 0.7645 0.144 0.852 0.000 0.000 0.004
#> aberrant_ERR2585331 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585346 4 0.0703 0.8740 0.000 0.000 0.000 0.976 0.024
#> aberrant_ERR2585314 2 0.2732 0.7618 0.160 0.840 0.000 0.000 0.000
#> aberrant_ERR2585298 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585345 2 0.2516 0.7625 0.140 0.860 0.000 0.000 0.000
#> aberrant_ERR2585299 1 0.5019 -0.4066 0.532 0.032 0.436 0.000 0.000
#> aberrant_ERR2585309 1 0.4278 -0.3638 0.548 0.000 0.452 0.000 0.000
#> aberrant_ERR2585303 2 0.4045 0.4733 0.356 0.644 0.000 0.000 0.000
#> aberrant_ERR2585313 2 0.2488 0.7626 0.124 0.872 0.000 0.000 0.004
#> aberrant_ERR2585318 2 0.2830 0.7331 0.080 0.876 0.000 0.000 0.044
#> aberrant_ERR2585328 2 0.3690 0.6766 0.224 0.764 0.000 0.000 0.012
#> aberrant_ERR2585330 2 0.5235 0.6883 0.120 0.716 0.016 0.000 0.148
#> aberrant_ERR2585293 4 0.0000 0.8903 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585342 2 0.1830 0.6081 0.008 0.924 0.000 0.000 0.068
#> aberrant_ERR2585348 2 0.4273 0.5629 0.036 0.748 0.004 0.000 0.212
#> aberrant_ERR2585352 2 0.2612 0.7637 0.124 0.868 0.000 0.000 0.008
#> aberrant_ERR2585308 1 0.5505 -0.5296 0.484 0.064 0.452 0.000 0.000
#> aberrant_ERR2585349 1 0.4138 0.1096 0.616 0.384 0.000 0.000 0.000
#> aberrant_ERR2585316 2 0.3154 0.7498 0.148 0.836 0.004 0.000 0.012
#> aberrant_ERR2585306 1 0.7513 -0.0892 0.476 0.212 0.068 0.000 0.244
#> aberrant_ERR2585324 2 0.6777 -0.2567 0.000 0.372 0.352 0.000 0.276
#> aberrant_ERR2585310 1 0.1568 0.5598 0.944 0.020 0.036 0.000 0.000
#> aberrant_ERR2585296 1 0.0798 0.5627 0.976 0.016 0.008 0.000 0.000
#> aberrant_ERR2585275 4 0.0000 0.8903 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585311 2 0.2464 0.7484 0.092 0.892 0.004 0.000 0.012
#> aberrant_ERR2585292 4 0.0000 0.8903 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585282 2 0.8028 0.0438 0.188 0.416 0.124 0.000 0.272
#> aberrant_ERR2585305 2 0.2648 0.7623 0.152 0.848 0.000 0.000 0.000
#> aberrant_ERR2585278 2 0.3816 0.5868 0.304 0.696 0.000 0.000 0.000
#> aberrant_ERR2585347 2 0.3743 0.7375 0.096 0.824 0.004 0.000 0.076
#> aberrant_ERR2585332 2 0.1617 0.6497 0.020 0.948 0.012 0.000 0.020
#> aberrant_ERR2585280 5 0.7932 -0.0876 0.332 0.172 0.108 0.000 0.388
#> aberrant_ERR2585304 1 0.1671 0.4997 0.924 0.076 0.000 0.000 0.000
#> aberrant_ERR2585322 2 0.4287 0.2347 0.460 0.540 0.000 0.000 0.000
#> aberrant_ERR2585279 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585277 1 0.3838 0.2950 0.716 0.280 0.000 0.000 0.004
#> aberrant_ERR2585295 1 0.3750 0.3492 0.756 0.232 0.000 0.000 0.012
#> aberrant_ERR2585333 2 0.5645 0.4488 0.064 0.636 0.024 0.000 0.276
#> aberrant_ERR2585285 2 0.2280 0.7554 0.120 0.880 0.000 0.000 0.000
#> aberrant_ERR2585286 1 0.3861 0.2828 0.712 0.284 0.000 0.000 0.004
#> aberrant_ERR2585294 1 0.5385 0.1375 0.624 0.288 0.000 0.000 0.088
#> aberrant_ERR2585300 2 0.6570 0.5201 0.224 0.568 0.024 0.000 0.184
#> aberrant_ERR2585334 1 0.0404 0.5666 0.988 0.012 0.000 0.000 0.000
#> aberrant_ERR2585361 2 0.4255 0.7324 0.128 0.776 0.000 0.000 0.096
#> aberrant_ERR2585372 2 0.2546 0.6857 0.048 0.904 0.012 0.000 0.036
#> round_ERR2585217 1 0.0290 0.5679 0.992 0.000 0.008 0.000 0.000
#> round_ERR2585205 1 0.3707 0.2534 0.716 0.000 0.284 0.000 0.000
#> round_ERR2585214 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585202 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585367 1 0.4219 0.2965 0.716 0.260 0.000 0.000 0.024
#> round_ERR2585220 1 0.3274 0.3843 0.780 0.000 0.220 0.000 0.000
#> round_ERR2585238 1 0.4278 -0.3638 0.548 0.000 0.452 0.000 0.000
#> aberrant_ERR2585276 2 0.3430 0.6920 0.220 0.776 0.000 0.000 0.004
#> round_ERR2585218 1 0.4227 -0.2617 0.580 0.000 0.420 0.000 0.000
#> aberrant_ERR2585363 2 0.2674 0.7618 0.120 0.868 0.012 0.000 0.000
#> round_ERR2585201 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585210 1 0.4074 -0.0526 0.636 0.000 0.364 0.000 0.000
#> aberrant_ERR2585362 2 0.2732 0.7544 0.160 0.840 0.000 0.000 0.000
#> aberrant_ERR2585360 2 0.5356 0.6969 0.176 0.700 0.016 0.000 0.108
#> round_ERR2585209 1 0.0162 0.5686 0.996 0.000 0.004 0.000 0.000
#> round_ERR2585242 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585216 1 0.1410 0.5521 0.940 0.000 0.060 0.000 0.000
#> round_ERR2585219 1 0.2891 0.4582 0.824 0.000 0.176 0.000 0.000
#> round_ERR2585237 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585198 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585211 1 0.4060 -0.0267 0.640 0.000 0.360 0.000 0.000
#> round_ERR2585206 1 0.4278 -0.3638 0.548 0.000 0.452 0.000 0.000
#> aberrant_ERR2585281 1 0.2719 0.4752 0.852 0.144 0.000 0.000 0.004
#> round_ERR2585212 1 0.1908 0.5351 0.908 0.000 0.092 0.000 0.000
#> round_ERR2585221 1 0.4278 -0.3638 0.548 0.000 0.452 0.000 0.000
#> round_ERR2585243 1 0.2966 0.4480 0.816 0.000 0.184 0.000 0.000
#> round_ERR2585204 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585213 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585373 2 0.0703 0.6667 0.024 0.976 0.000 0.000 0.000
#> aberrant_ERR2585358 2 0.5752 0.1727 0.000 0.620 0.172 0.000 0.208
#> aberrant_ERR2585365 2 0.2964 0.7571 0.152 0.840 0.004 0.000 0.004
#> aberrant_ERR2585359 2 0.3459 0.6710 0.052 0.832 0.000 0.000 0.116
#> aberrant_ERR2585370 2 0.3969 0.5640 0.304 0.692 0.000 0.000 0.004
#> round_ERR2585215 1 0.3452 0.3476 0.756 0.000 0.244 0.000 0.000
#> round_ERR2585262 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585199 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585369 2 0.2824 0.7632 0.116 0.864 0.000 0.000 0.020
#> round_ERR2585208 1 0.4262 -0.3464 0.560 0.000 0.440 0.000 0.000
#> round_ERR2585252 1 0.4278 -0.3638 0.548 0.000 0.452 0.000 0.000
#> round_ERR2585236 1 0.3391 0.4330 0.800 0.012 0.188 0.000 0.000
#> aberrant_ERR2585284 4 0.0000 0.8903 0.000 0.000 0.000 1.000 0.000
#> round_ERR2585224 1 0.5401 -0.5141 0.492 0.056 0.452 0.000 0.000
#> round_ERR2585260 1 0.4415 -0.3482 0.552 0.004 0.444 0.000 0.000
#> round_ERR2585229 1 0.4273 -0.3507 0.552 0.000 0.448 0.000 0.000
#> aberrant_ERR2585364 4 0.8271 0.2284 0.000 0.204 0.212 0.400 0.184
#> round_ERR2585253 1 0.4278 -0.3638 0.548 0.000 0.452 0.000 0.000
#> aberrant_ERR2585368 1 0.5684 0.0887 0.644 0.156 0.196 0.000 0.004
#> aberrant_ERR2585371 1 0.4555 0.2084 0.740 0.060 0.196 0.000 0.004
#> round_ERR2585239 1 0.3561 0.3037 0.740 0.000 0.260 0.000 0.000
#> round_ERR2585273 1 0.4397 -0.3123 0.564 0.004 0.432 0.000 0.000
#> round_ERR2585256 1 0.0162 0.5686 0.996 0.000 0.004 0.000 0.000
#> round_ERR2585272 1 0.1197 0.5580 0.952 0.000 0.048 0.000 0.000
#> round_ERR2585246 1 0.5221 -0.3518 0.552 0.048 0.400 0.000 0.000
#> round_ERR2585261 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585254 1 0.0404 0.5673 0.988 0.000 0.012 0.000 0.000
#> round_ERR2585225 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585235 1 0.1956 0.5361 0.916 0.008 0.076 0.000 0.000
#> round_ERR2585271 1 0.4268 -0.3375 0.556 0.000 0.444 0.000 0.000
#> round_ERR2585251 1 0.3074 0.4306 0.804 0.000 0.196 0.000 0.000
#> round_ERR2585255 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585257 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585226 1 0.0671 0.5667 0.980 0.004 0.016 0.000 0.000
#> round_ERR2585265 1 0.4242 -0.3057 0.572 0.000 0.428 0.000 0.000
#> round_ERR2585259 1 0.2891 0.4589 0.824 0.000 0.176 0.000 0.000
#> round_ERR2585247 1 0.4262 -0.3249 0.560 0.000 0.440 0.000 0.000
#> round_ERR2585241 1 0.3143 0.4189 0.796 0.000 0.204 0.000 0.000
#> round_ERR2585263 1 0.1544 0.5499 0.932 0.000 0.068 0.000 0.000
#> round_ERR2585264 3 0.6264 0.0000 0.400 0.000 0.452 0.148 0.000
#> round_ERR2585233 1 0.0404 0.5673 0.988 0.000 0.012 0.000 0.000
#> round_ERR2585223 1 0.4088 -0.0921 0.632 0.000 0.368 0.000 0.000
#> round_ERR2585234 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585222 1 0.2818 0.4985 0.856 0.012 0.132 0.000 0.000
#> round_ERR2585228 1 0.4262 -0.3249 0.560 0.000 0.440 0.000 0.000
#> round_ERR2585248 1 0.4538 -0.4037 0.540 0.000 0.452 0.008 0.000
#> round_ERR2585240 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585270 1 0.4402 -0.0272 0.636 0.012 0.352 0.000 0.000
#> round_ERR2585232 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585341 1 0.3796 0.2609 0.700 0.300 0.000 0.000 0.000
#> aberrant_ERR2585355 2 0.3439 0.7338 0.188 0.800 0.008 0.000 0.004
#> round_ERR2585227 1 0.1792 0.5397 0.916 0.000 0.084 0.000 0.000
#> aberrant_ERR2585351 2 0.2723 0.7622 0.124 0.864 0.012 0.000 0.000
#> round_ERR2585269 1 0.4278 -0.3638 0.548 0.000 0.452 0.000 0.000
#> aberrant_ERR2585357 2 0.2891 0.7566 0.176 0.824 0.000 0.000 0.000
#> aberrant_ERR2585350 2 0.2813 0.7538 0.168 0.832 0.000 0.000 0.000
#> round_ERR2585250 1 0.3527 0.4325 0.792 0.016 0.192 0.000 0.000
#> round_ERR2585245 1 0.4278 -0.3638 0.548 0.000 0.452 0.000 0.000
#> aberrant_ERR2585353 2 0.2597 0.7470 0.092 0.884 0.000 0.000 0.024
#> round_ERR2585258 1 0.4249 -0.3040 0.568 0.000 0.432 0.000 0.000
#> aberrant_ERR2585354 2 0.3123 0.7301 0.184 0.812 0.004 0.000 0.000
#> round_ERR2585249 1 0.4278 -0.3638 0.548 0.000 0.452 0.000 0.000
#> round_ERR2585268 1 0.3051 0.4928 0.864 0.076 0.060 0.000 0.000
#> aberrant_ERR2585356 2 0.7053 -0.2432 0.012 0.388 0.348 0.000 0.252
#> round_ERR2585266 1 0.0000 0.5691 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585231 1 0.4273 -0.3573 0.552 0.000 0.448 0.000 0.000
#> round_ERR2585230 1 0.3336 0.3742 0.772 0.000 0.228 0.000 0.000
#> round_ERR2585267 1 0.4278 -0.3638 0.548 0.000 0.452 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 3 0.0865 0.9244 0.000 0.000 0.964 0.000 0.036 0.000
#> aberrant_ERR2585338 1 0.2445 0.5445 0.868 0.000 0.004 0.000 0.120 0.008
#> aberrant_ERR2585325 3 0.0935 0.9255 0.004 0.000 0.964 0.000 0.032 0.000
#> aberrant_ERR2585283 4 0.0000 0.9938 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585343 5 0.2487 0.7741 0.000 0.000 0.032 0.000 0.876 0.092
#> aberrant_ERR2585329 5 0.0632 0.8261 0.024 0.000 0.000 0.000 0.976 0.000
#> aberrant_ERR2585317 5 0.0458 0.8228 0.016 0.000 0.000 0.000 0.984 0.000
#> aberrant_ERR2585339 1 0.2883 0.5413 0.864 0.000 0.032 0.000 0.088 0.016
#> aberrant_ERR2585335 5 0.0806 0.8268 0.020 0.000 0.000 0.000 0.972 0.008
#> aberrant_ERR2585287 3 0.2257 0.8480 0.000 0.000 0.876 0.116 0.008 0.000
#> aberrant_ERR2585321 5 0.2697 0.6811 0.000 0.000 0.000 0.000 0.812 0.188
#> aberrant_ERR2585297 1 0.3843 0.2248 0.548 0.452 0.000 0.000 0.000 0.000
#> aberrant_ERR2585337 5 0.3349 0.5684 0.244 0.000 0.008 0.000 0.748 0.000
#> aberrant_ERR2585319 6 0.0458 0.3649 0.000 0.000 0.000 0.000 0.016 0.984
#> aberrant_ERR2585315 5 0.2609 0.7647 0.096 0.000 0.000 0.000 0.868 0.036
#> aberrant_ERR2585336 5 0.0790 0.8255 0.032 0.000 0.000 0.000 0.968 0.000
#> aberrant_ERR2585307 1 0.3163 0.3841 0.764 0.004 0.000 0.000 0.232 0.000
#> aberrant_ERR2585301 1 0.4764 0.2753 0.660 0.000 0.000 0.000 0.232 0.108
#> aberrant_ERR2585326 5 0.0717 0.8257 0.016 0.000 0.000 0.000 0.976 0.008
#> aberrant_ERR2585331 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585346 4 0.0717 0.9688 0.000 0.000 0.008 0.976 0.000 0.016
#> aberrant_ERR2585314 5 0.0865 0.8253 0.036 0.000 0.000 0.000 0.964 0.000
#> aberrant_ERR2585298 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585345 5 0.0865 0.8258 0.036 0.000 0.000 0.000 0.964 0.000
#> aberrant_ERR2585299 1 0.4456 0.1613 0.524 0.448 0.000 0.000 0.028 0.000
#> aberrant_ERR2585309 1 0.3864 0.1600 0.520 0.480 0.000 0.000 0.000 0.000
#> aberrant_ERR2585303 5 0.3446 0.4219 0.308 0.000 0.000 0.000 0.692 0.000
#> aberrant_ERR2585313 5 0.0291 0.8205 0.004 0.000 0.000 0.000 0.992 0.004
#> aberrant_ERR2585318 5 0.1152 0.8157 0.004 0.000 0.000 0.000 0.952 0.044
#> aberrant_ERR2585328 5 0.3110 0.6161 0.196 0.000 0.000 0.000 0.792 0.012
#> aberrant_ERR2585330 5 0.3197 0.6966 0.012 0.008 0.000 0.000 0.804 0.176
#> aberrant_ERR2585293 4 0.0000 0.9938 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585342 5 0.1444 0.7984 0.000 0.000 0.000 0.000 0.928 0.072
#> aberrant_ERR2585348 5 0.3942 0.6843 0.012 0.000 0.084 0.000 0.784 0.120
#> aberrant_ERR2585352 5 0.0520 0.8243 0.008 0.000 0.000 0.000 0.984 0.008
#> aberrant_ERR2585308 2 0.5181 -0.0567 0.428 0.484 0.000 0.000 0.088 0.000
#> aberrant_ERR2585349 1 0.3717 0.1416 0.616 0.000 0.000 0.000 0.384 0.000
#> aberrant_ERR2585316 5 0.2164 0.8071 0.060 0.000 0.012 0.000 0.908 0.020
#> aberrant_ERR2585306 1 0.5765 -0.1351 0.416 0.000 0.000 0.000 0.172 0.412
#> aberrant_ERR2585324 6 0.0458 0.3649 0.000 0.000 0.000 0.000 0.016 0.984
#> aberrant_ERR2585310 1 0.1334 0.6085 0.948 0.032 0.000 0.000 0.020 0.000
#> aberrant_ERR2585296 1 0.0717 0.6031 0.976 0.008 0.000 0.000 0.016 0.000
#> aberrant_ERR2585275 4 0.0000 0.9938 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585311 5 0.0603 0.8220 0.004 0.000 0.000 0.000 0.980 0.016
#> aberrant_ERR2585292 4 0.0000 0.9938 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585282 6 0.5169 0.3678 0.136 0.000 0.000 0.000 0.260 0.604
#> aberrant_ERR2585305 5 0.1444 0.8073 0.072 0.000 0.000 0.000 0.928 0.000
#> aberrant_ERR2585278 5 0.2664 0.6727 0.184 0.000 0.000 0.000 0.816 0.000
#> aberrant_ERR2585347 5 0.2401 0.7898 0.004 0.000 0.044 0.000 0.892 0.060
#> aberrant_ERR2585332 5 0.1908 0.7878 0.004 0.000 0.000 0.000 0.900 0.096
#> aberrant_ERR2585280 6 0.6502 0.1251 0.296 0.000 0.052 0.000 0.168 0.484
#> aberrant_ERR2585304 1 0.1910 0.4826 0.892 0.000 0.000 0.000 0.108 0.000
#> aberrant_ERR2585322 5 0.3810 0.1592 0.428 0.000 0.000 0.000 0.572 0.000
#> aberrant_ERR2585279 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585277 1 0.4063 0.3642 0.712 0.000 0.028 0.000 0.252 0.008
#> aberrant_ERR2585295 1 0.3887 0.3437 0.724 0.000 0.008 0.000 0.248 0.020
#> aberrant_ERR2585333 5 0.4121 0.2857 0.016 0.000 0.000 0.000 0.604 0.380
#> aberrant_ERR2585285 5 0.0632 0.8254 0.024 0.000 0.000 0.000 0.976 0.000
#> aberrant_ERR2585286 1 0.3309 0.3604 0.720 0.000 0.000 0.000 0.280 0.000
#> aberrant_ERR2585294 1 0.5030 0.1142 0.588 0.000 0.000 0.000 0.316 0.096
#> aberrant_ERR2585300 5 0.5663 0.3957 0.176 0.024 0.000 0.000 0.608 0.192
#> aberrant_ERR2585334 1 0.0363 0.6070 0.988 0.000 0.000 0.000 0.012 0.000
#> aberrant_ERR2585361 5 0.3252 0.7513 0.068 0.000 0.000 0.000 0.824 0.108
#> aberrant_ERR2585372 5 0.1873 0.8112 0.008 0.000 0.020 0.000 0.924 0.048
#> round_ERR2585217 1 0.0260 0.6093 0.992 0.008 0.000 0.000 0.000 0.000
#> round_ERR2585205 1 0.3351 0.4815 0.712 0.288 0.000 0.000 0.000 0.000
#> round_ERR2585214 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585202 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585367 1 0.4399 0.3874 0.712 0.000 0.036 0.000 0.228 0.024
#> round_ERR2585220 1 0.2969 0.5393 0.776 0.224 0.000 0.000 0.000 0.000
#> round_ERR2585238 1 0.3993 0.1596 0.520 0.476 0.000 0.000 0.004 0.000
#> aberrant_ERR2585276 5 0.2738 0.6614 0.176 0.000 0.000 0.000 0.820 0.004
#> round_ERR2585218 1 0.3817 0.2605 0.568 0.432 0.000 0.000 0.000 0.000
#> aberrant_ERR2585363 5 0.0260 0.8210 0.008 0.000 0.000 0.000 0.992 0.000
#> round_ERR2585201 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585210 1 0.3672 0.3682 0.632 0.368 0.000 0.000 0.000 0.000
#> aberrant_ERR2585362 5 0.1714 0.7820 0.092 0.000 0.000 0.000 0.908 0.000
#> aberrant_ERR2585360 5 0.3534 0.7332 0.076 0.000 0.000 0.000 0.800 0.124
#> round_ERR2585209 1 0.0146 0.6091 0.996 0.004 0.000 0.000 0.000 0.000
#> round_ERR2585242 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585216 1 0.1267 0.6093 0.940 0.060 0.000 0.000 0.000 0.000
#> round_ERR2585219 1 0.2597 0.5738 0.824 0.176 0.000 0.000 0.000 0.000
#> round_ERR2585237 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585198 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585211 1 0.3672 0.3717 0.632 0.368 0.000 0.000 0.000 0.000
#> round_ERR2585206 1 0.3860 0.1802 0.528 0.472 0.000 0.000 0.000 0.000
#> aberrant_ERR2585281 1 0.3010 0.5298 0.836 0.000 0.028 0.000 0.132 0.004
#> round_ERR2585212 1 0.1814 0.6030 0.900 0.100 0.000 0.000 0.000 0.000
#> round_ERR2585221 1 0.3862 0.1704 0.524 0.476 0.000 0.000 0.000 0.000
#> round_ERR2585243 1 0.2664 0.5693 0.816 0.184 0.000 0.000 0.000 0.000
#> round_ERR2585204 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585213 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585373 5 0.0146 0.8188 0.000 0.000 0.004 0.000 0.996 0.000
#> aberrant_ERR2585358 6 0.3810 0.1801 0.000 0.000 0.000 0.000 0.428 0.572
#> aberrant_ERR2585365 5 0.2245 0.8061 0.052 0.004 0.028 0.000 0.908 0.008
#> aberrant_ERR2585359 5 0.2069 0.7967 0.004 0.000 0.020 0.000 0.908 0.068
#> aberrant_ERR2585370 5 0.3508 0.4609 0.292 0.000 0.000 0.000 0.704 0.004
#> round_ERR2585215 1 0.3126 0.5230 0.752 0.248 0.000 0.000 0.000 0.000
#> round_ERR2585262 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585199 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585369 5 0.0692 0.8212 0.004 0.000 0.000 0.000 0.976 0.020
#> round_ERR2585208 1 0.3854 0.1832 0.536 0.464 0.000 0.000 0.000 0.000
#> round_ERR2585252 1 0.3866 0.1495 0.516 0.484 0.000 0.000 0.000 0.000
#> round_ERR2585236 1 0.3121 0.5613 0.796 0.192 0.000 0.000 0.008 0.004
#> aberrant_ERR2585284 4 0.0000 0.9938 0.000 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585224 2 0.4988 -0.1059 0.448 0.484 0.000 0.000 0.068 0.000
#> round_ERR2585260 1 0.3979 0.2080 0.540 0.456 0.000 0.000 0.004 0.000
#> round_ERR2585229 1 0.3991 0.1695 0.524 0.472 0.000 0.000 0.004 0.000
#> aberrant_ERR2585364 6 0.5562 -0.0779 0.000 0.000 0.068 0.368 0.032 0.532
#> round_ERR2585253 1 0.3866 0.1495 0.516 0.484 0.000 0.000 0.000 0.000
#> aberrant_ERR2585368 2 0.5603 -0.1491 0.400 0.516 0.036 0.000 0.032 0.016
#> aberrant_ERR2585371 2 0.5418 -0.1306 0.412 0.516 0.036 0.000 0.020 0.016
#> round_ERR2585239 1 0.3198 0.5072 0.740 0.260 0.000 0.000 0.000 0.000
#> round_ERR2585273 1 0.3966 0.2309 0.552 0.444 0.000 0.000 0.004 0.000
#> round_ERR2585256 1 0.0146 0.6091 0.996 0.004 0.000 0.000 0.000 0.000
#> round_ERR2585272 1 0.0937 0.6124 0.960 0.040 0.000 0.000 0.000 0.000
#> round_ERR2585246 1 0.5007 0.0828 0.512 0.416 0.000 0.000 0.072 0.000
#> round_ERR2585261 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585254 1 0.0363 0.6097 0.988 0.012 0.000 0.000 0.000 0.000
#> round_ERR2585225 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585235 1 0.1757 0.5998 0.916 0.076 0.000 0.000 0.008 0.000
#> round_ERR2585271 1 0.3847 0.2159 0.544 0.456 0.000 0.000 0.000 0.000
#> round_ERR2585251 1 0.2762 0.5616 0.804 0.196 0.000 0.000 0.000 0.000
#> round_ERR2585255 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585257 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585226 1 0.0692 0.6086 0.976 0.020 0.000 0.000 0.004 0.000
#> round_ERR2585265 1 0.3828 0.2359 0.560 0.440 0.000 0.000 0.000 0.000
#> round_ERR2585259 1 0.2631 0.5732 0.820 0.180 0.000 0.000 0.000 0.000
#> round_ERR2585247 1 0.3843 0.2238 0.548 0.452 0.000 0.000 0.000 0.000
#> round_ERR2585241 1 0.2823 0.5562 0.796 0.204 0.000 0.000 0.000 0.000
#> round_ERR2585263 1 0.1387 0.6101 0.932 0.068 0.000 0.000 0.000 0.000
#> round_ERR2585264 2 0.5428 0.0183 0.396 0.484 0.000 0.120 0.000 0.000
#> round_ERR2585233 1 0.0363 0.6097 0.988 0.012 0.000 0.000 0.000 0.000
#> round_ERR2585223 1 0.3717 0.3328 0.616 0.384 0.000 0.000 0.000 0.000
#> round_ERR2585234 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585222 1 0.2489 0.5917 0.860 0.128 0.000 0.000 0.012 0.000
#> round_ERR2585228 1 0.3843 0.2236 0.548 0.452 0.000 0.000 0.000 0.000
#> round_ERR2585248 1 0.3866 0.1495 0.516 0.484 0.000 0.000 0.000 0.000
#> round_ERR2585240 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585270 1 0.4052 0.3703 0.628 0.356 0.000 0.000 0.016 0.000
#> round_ERR2585232 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585341 1 0.3428 0.3295 0.696 0.000 0.000 0.000 0.304 0.000
#> aberrant_ERR2585355 5 0.2302 0.7606 0.120 0.000 0.000 0.000 0.872 0.008
#> round_ERR2585227 1 0.1556 0.6077 0.920 0.080 0.000 0.000 0.000 0.000
#> aberrant_ERR2585351 5 0.0363 0.8221 0.012 0.000 0.000 0.000 0.988 0.000
#> round_ERR2585269 1 0.3866 0.1495 0.516 0.484 0.000 0.000 0.000 0.000
#> aberrant_ERR2585357 5 0.1075 0.8247 0.048 0.000 0.000 0.000 0.952 0.000
#> aberrant_ERR2585350 5 0.1610 0.8001 0.084 0.000 0.000 0.000 0.916 0.000
#> round_ERR2585250 1 0.3284 0.5570 0.784 0.196 0.000 0.000 0.020 0.000
#> round_ERR2585245 1 0.3866 0.1495 0.516 0.484 0.000 0.000 0.000 0.000
#> aberrant_ERR2585353 5 0.1151 0.8241 0.012 0.000 0.000 0.000 0.956 0.032
#> round_ERR2585258 1 0.3833 0.2368 0.556 0.444 0.000 0.000 0.000 0.000
#> aberrant_ERR2585354 5 0.2001 0.7799 0.092 0.004 0.004 0.000 0.900 0.000
#> round_ERR2585249 1 0.3866 0.1495 0.516 0.484 0.000 0.000 0.000 0.000
#> round_ERR2585268 1 0.3138 0.4937 0.832 0.060 0.000 0.000 0.108 0.000
#> aberrant_ERR2585356 6 0.1858 0.3953 0.004 0.000 0.000 0.000 0.092 0.904
#> round_ERR2585266 1 0.0000 0.6087 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585231 1 0.3864 0.1548 0.520 0.480 0.000 0.000 0.000 0.000
#> round_ERR2585230 1 0.3023 0.5352 0.768 0.232 0.000 0.000 0.000 0.000
#> round_ERR2585267 1 0.3866 0.1495 0.516 0.484 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> SD:pam 160 2.38e-02 2
#> SD:pam 153 8.80e-19 3
#> SD:pam 149 1.84e-18 4
#> SD:pam 85 1.98e-14 5
#> SD:pam 101 3.35e-15 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "mclust"]
# you can also extract it by
# res = res_list["SD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.988 0.995 0.5035 0.497 0.497
#> 3 3 0.801 0.865 0.913 0.1515 0.942 0.884
#> 4 4 0.835 0.843 0.919 0.1248 0.881 0.740
#> 5 5 0.771 0.751 0.881 0.1371 0.878 0.659
#> 6 6 0.730 0.674 0.833 0.0523 0.956 0.835
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585338 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585325 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585283 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585343 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585329 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585317 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585339 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585335 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585287 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585321 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585297 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585319 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585315 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585336 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585307 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585301 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585326 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585331 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585346 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585314 2 0.1633 0.972 0.024 0.976
#> aberrant_ERR2585298 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585345 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585299 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585303 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585313 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585318 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585328 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585330 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585293 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585342 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585348 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585352 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585308 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585349 2 0.8499 0.614 0.276 0.724
#> aberrant_ERR2585316 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585306 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585324 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585310 1 0.2236 0.959 0.964 0.036
#> aberrant_ERR2585296 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585275 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585311 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585292 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585282 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585305 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585278 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585347 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585332 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585280 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585304 1 0.6247 0.816 0.844 0.156
#> aberrant_ERR2585322 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585279 1 0.8661 0.598 0.712 0.288
#> aberrant_ERR2585277 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585295 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585333 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585285 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585286 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585294 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585300 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585334 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585361 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585372 2 0.0000 0.996 0.000 1.000
#> round_ERR2585217 1 0.0000 0.993 1.000 0.000
#> round_ERR2585205 1 0.0000 0.993 1.000 0.000
#> round_ERR2585214 1 0.0000 0.993 1.000 0.000
#> round_ERR2585202 1 0.0376 0.990 0.996 0.004
#> aberrant_ERR2585367 2 0.0000 0.996 0.000 1.000
#> round_ERR2585220 1 0.0000 0.993 1.000 0.000
#> round_ERR2585238 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585276 2 0.0000 0.996 0.000 1.000
#> round_ERR2585218 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.996 0.000 1.000
#> round_ERR2585201 1 0.0000 0.993 1.000 0.000
#> round_ERR2585210 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585362 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585360 2 0.0000 0.996 0.000 1.000
#> round_ERR2585209 1 0.0000 0.993 1.000 0.000
#> round_ERR2585242 1 0.0000 0.993 1.000 0.000
#> round_ERR2585216 1 0.0000 0.993 1.000 0.000
#> round_ERR2585219 1 0.0000 0.993 1.000 0.000
#> round_ERR2585237 1 0.0000 0.993 1.000 0.000
#> round_ERR2585198 1 0.0000 0.993 1.000 0.000
#> round_ERR2585211 1 0.0000 0.993 1.000 0.000
#> round_ERR2585206 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585281 2 0.0000 0.996 0.000 1.000
#> round_ERR2585212 1 0.0000 0.993 1.000 0.000
#> round_ERR2585221 1 0.0000 0.993 1.000 0.000
#> round_ERR2585243 1 0.0000 0.993 1.000 0.000
#> round_ERR2585204 1 0.0000 0.993 1.000 0.000
#> round_ERR2585213 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585373 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585358 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585365 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585359 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585370 2 0.0000 0.996 0.000 1.000
#> round_ERR2585215 1 0.0000 0.993 1.000 0.000
#> round_ERR2585262 1 0.2236 0.959 0.964 0.036
#> round_ERR2585199 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585369 2 0.0000 0.996 0.000 1.000
#> round_ERR2585208 1 0.0000 0.993 1.000 0.000
#> round_ERR2585252 1 0.0000 0.993 1.000 0.000
#> round_ERR2585236 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585284 2 0.0000 0.996 0.000 1.000
#> round_ERR2585224 1 0.0000 0.993 1.000 0.000
#> round_ERR2585260 1 0.0000 0.993 1.000 0.000
#> round_ERR2585229 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585364 2 0.0000 0.996 0.000 1.000
#> round_ERR2585253 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585371 2 0.0000 0.996 0.000 1.000
#> round_ERR2585239 1 0.0000 0.993 1.000 0.000
#> round_ERR2585273 1 0.0000 0.993 1.000 0.000
#> round_ERR2585256 1 0.0000 0.993 1.000 0.000
#> round_ERR2585272 1 0.0000 0.993 1.000 0.000
#> round_ERR2585246 1 0.0000 0.993 1.000 0.000
#> round_ERR2585261 1 0.0000 0.993 1.000 0.000
#> round_ERR2585254 1 0.0000 0.993 1.000 0.000
#> round_ERR2585225 1 0.0000 0.993 1.000 0.000
#> round_ERR2585235 1 0.0000 0.993 1.000 0.000
#> round_ERR2585271 1 0.0000 0.993 1.000 0.000
#> round_ERR2585251 1 0.0000 0.993 1.000 0.000
#> round_ERR2585255 1 0.0000 0.993 1.000 0.000
#> round_ERR2585257 1 0.0000 0.993 1.000 0.000
#> round_ERR2585226 1 0.0000 0.993 1.000 0.000
#> round_ERR2585265 1 0.0000 0.993 1.000 0.000
#> round_ERR2585259 1 0.0000 0.993 1.000 0.000
#> round_ERR2585247 1 0.0000 0.993 1.000 0.000
#> round_ERR2585241 1 0.0000 0.993 1.000 0.000
#> round_ERR2585263 1 0.0000 0.993 1.000 0.000
#> round_ERR2585264 1 0.0000 0.993 1.000 0.000
#> round_ERR2585233 1 0.0000 0.993 1.000 0.000
#> round_ERR2585223 1 0.0000 0.993 1.000 0.000
#> round_ERR2585234 1 0.0000 0.993 1.000 0.000
#> round_ERR2585222 1 0.0000 0.993 1.000 0.000
#> round_ERR2585228 1 0.0000 0.993 1.000 0.000
#> round_ERR2585248 1 0.0000 0.993 1.000 0.000
#> round_ERR2585240 1 0.0000 0.993 1.000 0.000
#> round_ERR2585270 1 0.0000 0.993 1.000 0.000
#> round_ERR2585232 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585341 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585355 2 0.0000 0.996 0.000 1.000
#> round_ERR2585227 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585351 2 0.0000 0.996 0.000 1.000
#> round_ERR2585269 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.996 0.000 1.000
#> aberrant_ERR2585350 2 0.0000 0.996 0.000 1.000
#> round_ERR2585250 1 0.0000 0.993 1.000 0.000
#> round_ERR2585245 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.996 0.000 1.000
#> round_ERR2585258 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.996 0.000 1.000
#> round_ERR2585249 1 0.0000 0.993 1.000 0.000
#> round_ERR2585268 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585356 2 0.0000 0.996 0.000 1.000
#> round_ERR2585266 1 0.0000 0.993 1.000 0.000
#> round_ERR2585231 1 0.0000 0.993 1.000 0.000
#> round_ERR2585230 1 0.0000 0.993 1.000 0.000
#> round_ERR2585267 1 0.0000 0.993 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.1643 0.918 0.000 0.956 0.044
#> aberrant_ERR2585338 2 0.1289 0.924 0.000 0.968 0.032
#> aberrant_ERR2585325 2 0.1411 0.920 0.000 0.964 0.036
#> aberrant_ERR2585283 3 0.4796 0.893 0.000 0.220 0.780
#> aberrant_ERR2585343 2 0.1964 0.912 0.000 0.944 0.056
#> aberrant_ERR2585329 2 0.0237 0.923 0.000 0.996 0.004
#> aberrant_ERR2585317 2 0.1643 0.920 0.000 0.956 0.044
#> aberrant_ERR2585339 2 0.0747 0.921 0.000 0.984 0.016
#> aberrant_ERR2585335 2 0.1411 0.921 0.000 0.964 0.036
#> aberrant_ERR2585287 3 0.4887 0.887 0.000 0.228 0.772
#> aberrant_ERR2585321 2 0.1031 0.924 0.000 0.976 0.024
#> aberrant_ERR2585297 1 0.0000 0.910 1.000 0.000 0.000
#> aberrant_ERR2585337 2 0.0747 0.921 0.000 0.984 0.016
#> aberrant_ERR2585319 2 0.2066 0.909 0.000 0.940 0.060
#> aberrant_ERR2585315 2 0.1031 0.925 0.000 0.976 0.024
#> aberrant_ERR2585336 2 0.0747 0.921 0.000 0.984 0.016
#> aberrant_ERR2585307 2 0.4750 0.668 0.000 0.784 0.216
#> aberrant_ERR2585301 2 0.1411 0.910 0.000 0.964 0.036
#> aberrant_ERR2585326 2 0.1529 0.924 0.000 0.960 0.040
#> aberrant_ERR2585331 2 0.3619 0.800 0.000 0.864 0.136
#> aberrant_ERR2585346 3 0.4974 0.881 0.000 0.236 0.764
#> aberrant_ERR2585314 2 0.5956 0.419 0.004 0.672 0.324
#> aberrant_ERR2585298 1 0.4887 0.816 0.772 0.000 0.228
#> aberrant_ERR2585345 2 0.0592 0.923 0.000 0.988 0.012
#> aberrant_ERR2585299 1 0.0000 0.910 1.000 0.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.910 1.000 0.000 0.000
#> aberrant_ERR2585303 2 0.1643 0.922 0.000 0.956 0.044
#> aberrant_ERR2585313 2 0.0424 0.924 0.000 0.992 0.008
#> aberrant_ERR2585318 2 0.0592 0.921 0.000 0.988 0.012
#> aberrant_ERR2585328 2 0.2165 0.914 0.000 0.936 0.064
#> aberrant_ERR2585330 2 0.0592 0.925 0.000 0.988 0.012
#> aberrant_ERR2585293 3 0.4796 0.893 0.000 0.220 0.780
#> aberrant_ERR2585342 2 0.0592 0.925 0.000 0.988 0.012
#> aberrant_ERR2585348 2 0.2537 0.898 0.000 0.920 0.080
#> aberrant_ERR2585352 2 0.0892 0.925 0.000 0.980 0.020
#> aberrant_ERR2585308 1 0.0000 0.910 1.000 0.000 0.000
#> aberrant_ERR2585349 2 0.7903 0.128 0.068 0.576 0.356
#> aberrant_ERR2585316 2 0.2448 0.899 0.000 0.924 0.076
#> aberrant_ERR2585306 2 0.3851 0.787 0.004 0.860 0.136
#> aberrant_ERR2585324 2 0.2066 0.909 0.000 0.940 0.060
#> aberrant_ERR2585310 1 0.8649 0.430 0.528 0.112 0.360
#> aberrant_ERR2585296 1 0.4235 0.852 0.824 0.000 0.176
#> aberrant_ERR2585275 3 0.4796 0.893 0.000 0.220 0.780
#> aberrant_ERR2585311 2 0.0424 0.922 0.000 0.992 0.008
#> aberrant_ERR2585292 3 0.4796 0.893 0.000 0.220 0.780
#> aberrant_ERR2585282 2 0.1411 0.920 0.000 0.964 0.036
#> aberrant_ERR2585305 2 0.4733 0.702 0.004 0.800 0.196
#> aberrant_ERR2585278 2 0.1529 0.918 0.000 0.960 0.040
#> aberrant_ERR2585347 2 0.2878 0.886 0.000 0.904 0.096
#> aberrant_ERR2585332 2 0.2066 0.909 0.000 0.940 0.060
#> aberrant_ERR2585280 2 0.2066 0.911 0.000 0.940 0.060
#> aberrant_ERR2585304 3 0.9985 0.183 0.316 0.324 0.360
#> aberrant_ERR2585322 2 0.0747 0.921 0.000 0.984 0.016
#> aberrant_ERR2585279 2 0.8435 0.178 0.124 0.592 0.284
#> aberrant_ERR2585277 2 0.1411 0.915 0.000 0.964 0.036
#> aberrant_ERR2585295 2 0.1860 0.919 0.000 0.948 0.052
#> aberrant_ERR2585333 2 0.0592 0.924 0.000 0.988 0.012
#> aberrant_ERR2585285 2 0.0424 0.924 0.000 0.992 0.008
#> aberrant_ERR2585286 2 0.1411 0.915 0.000 0.964 0.036
#> aberrant_ERR2585294 2 0.1031 0.917 0.000 0.976 0.024
#> aberrant_ERR2585300 2 0.2066 0.913 0.000 0.940 0.060
#> aberrant_ERR2585334 2 0.3551 0.806 0.000 0.868 0.132
#> aberrant_ERR2585361 2 0.2066 0.911 0.000 0.940 0.060
#> aberrant_ERR2585372 2 0.1643 0.917 0.000 0.956 0.044
#> round_ERR2585217 1 0.4504 0.838 0.804 0.000 0.196
#> round_ERR2585205 1 0.0237 0.910 0.996 0.000 0.004
#> round_ERR2585214 1 0.4887 0.816 0.772 0.000 0.228
#> round_ERR2585202 1 0.7481 0.584 0.596 0.048 0.356
#> aberrant_ERR2585367 2 0.2165 0.908 0.000 0.936 0.064
#> round_ERR2585220 1 0.0237 0.910 0.996 0.000 0.004
#> round_ERR2585238 1 0.0237 0.910 0.996 0.000 0.004
#> aberrant_ERR2585276 2 0.0424 0.923 0.000 0.992 0.008
#> round_ERR2585218 1 0.0237 0.910 0.996 0.000 0.004
#> aberrant_ERR2585363 2 0.0592 0.924 0.000 0.988 0.012
#> round_ERR2585201 1 0.4887 0.816 0.772 0.000 0.228
#> round_ERR2585210 1 0.0237 0.910 0.996 0.000 0.004
#> aberrant_ERR2585362 2 0.1529 0.925 0.000 0.960 0.040
#> aberrant_ERR2585360 2 0.0424 0.922 0.000 0.992 0.008
#> round_ERR2585209 1 0.4062 0.859 0.836 0.000 0.164
#> round_ERR2585242 1 0.4887 0.816 0.772 0.000 0.228
#> round_ERR2585216 1 0.0237 0.910 0.996 0.000 0.004
#> round_ERR2585219 1 0.0237 0.910 0.996 0.000 0.004
#> round_ERR2585237 1 0.4452 0.841 0.808 0.000 0.192
#> round_ERR2585198 1 0.4887 0.816 0.772 0.000 0.228
#> round_ERR2585211 1 0.0000 0.910 1.000 0.000 0.000
#> round_ERR2585206 1 0.0237 0.910 0.996 0.000 0.004
#> aberrant_ERR2585281 2 0.2356 0.910 0.000 0.928 0.072
#> round_ERR2585212 1 0.1163 0.906 0.972 0.000 0.028
#> round_ERR2585221 1 0.0000 0.910 1.000 0.000 0.000
#> round_ERR2585243 1 0.0000 0.910 1.000 0.000 0.000
#> round_ERR2585204 1 0.4887 0.816 0.772 0.000 0.228
#> round_ERR2585213 1 0.6773 0.656 0.636 0.024 0.340
#> aberrant_ERR2585373 2 0.1163 0.925 0.000 0.972 0.028
#> aberrant_ERR2585358 2 0.0592 0.923 0.000 0.988 0.012
#> aberrant_ERR2585365 2 0.1411 0.924 0.000 0.964 0.036
#> aberrant_ERR2585359 2 0.1860 0.914 0.000 0.948 0.052
#> aberrant_ERR2585370 2 0.0892 0.920 0.000 0.980 0.020
#> round_ERR2585215 1 0.0237 0.910 0.996 0.000 0.004
#> round_ERR2585262 1 0.7116 0.651 0.636 0.040 0.324
#> round_ERR2585199 1 0.5325 0.794 0.748 0.004 0.248
#> aberrant_ERR2585369 2 0.0592 0.925 0.000 0.988 0.012
#> round_ERR2585208 1 0.0237 0.910 0.996 0.000 0.004
#> round_ERR2585252 1 0.0237 0.910 0.996 0.000 0.004
#> round_ERR2585236 1 0.5365 0.790 0.744 0.004 0.252
#> aberrant_ERR2585284 3 0.4796 0.893 0.000 0.220 0.780
#> round_ERR2585224 1 0.0000 0.910 1.000 0.000 0.000
#> round_ERR2585260 1 0.0237 0.910 0.996 0.000 0.004
#> round_ERR2585229 1 0.0000 0.910 1.000 0.000 0.000
#> aberrant_ERR2585364 2 0.5926 0.309 0.000 0.644 0.356
#> round_ERR2585253 1 0.0237 0.910 0.996 0.000 0.004
#> aberrant_ERR2585368 2 0.1753 0.901 0.000 0.952 0.048
#> aberrant_ERR2585371 2 0.2066 0.889 0.000 0.940 0.060
#> round_ERR2585239 1 0.0000 0.910 1.000 0.000 0.000
#> round_ERR2585273 1 0.0237 0.910 0.996 0.000 0.004
#> round_ERR2585256 1 0.3752 0.866 0.856 0.000 0.144
#> round_ERR2585272 1 0.2625 0.892 0.916 0.000 0.084
#> round_ERR2585246 1 0.1643 0.887 0.956 0.000 0.044
#> round_ERR2585261 1 0.4121 0.857 0.832 0.000 0.168
#> round_ERR2585254 1 0.3816 0.866 0.852 0.000 0.148
#> round_ERR2585225 1 0.4887 0.816 0.772 0.000 0.228
#> round_ERR2585235 1 0.3038 0.883 0.896 0.000 0.104
#> round_ERR2585271 1 0.0237 0.910 0.996 0.000 0.004
#> round_ERR2585251 1 0.0237 0.910 0.996 0.000 0.004
#> round_ERR2585255 1 0.4887 0.816 0.772 0.000 0.228
#> round_ERR2585257 1 0.4555 0.836 0.800 0.000 0.200
#> round_ERR2585226 1 0.0237 0.910 0.996 0.000 0.004
#> round_ERR2585265 1 0.0237 0.910 0.996 0.000 0.004
#> round_ERR2585259 1 0.2448 0.893 0.924 0.000 0.076
#> round_ERR2585247 1 0.0000 0.910 1.000 0.000 0.000
#> round_ERR2585241 1 0.0237 0.910 0.996 0.000 0.004
#> round_ERR2585263 1 0.0424 0.910 0.992 0.000 0.008
#> round_ERR2585264 1 0.0000 0.910 1.000 0.000 0.000
#> round_ERR2585233 1 0.4796 0.822 0.780 0.000 0.220
#> round_ERR2585223 1 0.0237 0.910 0.996 0.000 0.004
#> round_ERR2585234 1 0.4887 0.816 0.772 0.000 0.228
#> round_ERR2585222 1 0.0000 0.910 1.000 0.000 0.000
#> round_ERR2585228 1 0.0237 0.910 0.996 0.000 0.004
#> round_ERR2585248 1 0.0237 0.910 0.996 0.000 0.004
#> round_ERR2585240 1 0.4555 0.839 0.800 0.000 0.200
#> round_ERR2585270 1 0.0237 0.910 0.996 0.000 0.004
#> round_ERR2585232 1 0.4235 0.853 0.824 0.000 0.176
#> aberrant_ERR2585341 2 0.2165 0.912 0.000 0.936 0.064
#> aberrant_ERR2585355 2 0.1289 0.917 0.000 0.968 0.032
#> round_ERR2585227 1 0.2261 0.896 0.932 0.000 0.068
#> aberrant_ERR2585351 2 0.0237 0.923 0.000 0.996 0.004
#> round_ERR2585269 1 0.0237 0.910 0.996 0.000 0.004
#> aberrant_ERR2585357 2 0.1289 0.918 0.000 0.968 0.032
#> aberrant_ERR2585350 2 0.1031 0.917 0.000 0.976 0.024
#> round_ERR2585250 1 0.2356 0.894 0.928 0.000 0.072
#> round_ERR2585245 1 0.0000 0.910 1.000 0.000 0.000
#> aberrant_ERR2585353 2 0.2066 0.909 0.000 0.940 0.060
#> round_ERR2585258 1 0.0237 0.910 0.996 0.000 0.004
#> aberrant_ERR2585354 2 0.0892 0.923 0.000 0.980 0.020
#> round_ERR2585249 1 0.0000 0.910 1.000 0.000 0.000
#> round_ERR2585268 1 0.3116 0.882 0.892 0.000 0.108
#> aberrant_ERR2585356 2 0.1031 0.924 0.000 0.976 0.024
#> round_ERR2585266 1 0.4887 0.816 0.772 0.000 0.228
#> round_ERR2585231 1 0.0000 0.910 1.000 0.000 0.000
#> round_ERR2585230 1 0.0237 0.910 0.996 0.000 0.004
#> round_ERR2585267 1 0.0000 0.910 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 2 0.0188 0.9555 0.000 0.996 0.000 0.004
#> aberrant_ERR2585338 2 0.2053 0.9331 0.000 0.924 0.004 0.072
#> aberrant_ERR2585325 2 0.0000 0.9550 0.000 1.000 0.000 0.000
#> aberrant_ERR2585283 4 0.0376 0.9251 0.000 0.004 0.004 0.992
#> aberrant_ERR2585343 2 0.0592 0.9535 0.000 0.984 0.000 0.016
#> aberrant_ERR2585329 2 0.0592 0.9528 0.000 0.984 0.016 0.000
#> aberrant_ERR2585317 2 0.0592 0.9528 0.000 0.984 0.016 0.000
#> aberrant_ERR2585339 2 0.1716 0.9383 0.000 0.936 0.000 0.064
#> aberrant_ERR2585335 2 0.0000 0.9550 0.000 1.000 0.000 0.000
#> aberrant_ERR2585287 4 0.1867 0.8882 0.000 0.072 0.000 0.928
#> aberrant_ERR2585321 2 0.0336 0.9550 0.000 0.992 0.000 0.008
#> aberrant_ERR2585297 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> aberrant_ERR2585337 2 0.0779 0.9530 0.000 0.980 0.016 0.004
#> aberrant_ERR2585319 2 0.0592 0.9535 0.000 0.984 0.000 0.016
#> aberrant_ERR2585315 2 0.0469 0.9551 0.000 0.988 0.000 0.012
#> aberrant_ERR2585336 2 0.1610 0.9470 0.000 0.952 0.016 0.032
#> aberrant_ERR2585307 2 0.3052 0.8495 0.000 0.860 0.136 0.004
#> aberrant_ERR2585301 2 0.0188 0.9553 0.000 0.996 0.000 0.004
#> aberrant_ERR2585326 2 0.1297 0.9505 0.000 0.964 0.016 0.020
#> aberrant_ERR2585331 2 0.4937 0.7610 0.000 0.764 0.172 0.064
#> aberrant_ERR2585346 4 0.3311 0.7904 0.000 0.172 0.000 0.828
#> aberrant_ERR2585314 2 0.5016 0.4168 0.000 0.600 0.396 0.004
#> aberrant_ERR2585298 3 0.3688 0.7772 0.208 0.000 0.792 0.000
#> aberrant_ERR2585345 2 0.0927 0.9528 0.000 0.976 0.016 0.008
#> aberrant_ERR2585299 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> aberrant_ERR2585303 2 0.1792 0.9364 0.000 0.932 0.000 0.068
#> aberrant_ERR2585313 2 0.0817 0.9546 0.000 0.976 0.000 0.024
#> aberrant_ERR2585318 2 0.0000 0.9550 0.000 1.000 0.000 0.000
#> aberrant_ERR2585328 2 0.1474 0.9449 0.000 0.948 0.000 0.052
#> aberrant_ERR2585330 2 0.0469 0.9539 0.000 0.988 0.000 0.012
#> aberrant_ERR2585293 4 0.0188 0.9252 0.000 0.000 0.004 0.996
#> aberrant_ERR2585342 2 0.0188 0.9553 0.000 0.996 0.000 0.004
#> aberrant_ERR2585348 2 0.1792 0.9383 0.000 0.932 0.000 0.068
#> aberrant_ERR2585352 2 0.0336 0.9550 0.000 0.992 0.000 0.008
#> aberrant_ERR2585308 1 0.0188 0.9225 0.996 0.000 0.004 0.000
#> aberrant_ERR2585349 3 0.5832 0.0414 0.032 0.368 0.596 0.004
#> aberrant_ERR2585316 2 0.1118 0.9442 0.000 0.964 0.000 0.036
#> aberrant_ERR2585306 2 0.1042 0.9484 0.000 0.972 0.008 0.020
#> aberrant_ERR2585324 2 0.0592 0.9535 0.000 0.984 0.000 0.016
#> aberrant_ERR2585310 3 0.1994 0.5977 0.052 0.008 0.936 0.004
#> aberrant_ERR2585296 3 0.4817 0.5790 0.388 0.000 0.612 0.000
#> aberrant_ERR2585275 4 0.1661 0.9108 0.000 0.052 0.004 0.944
#> aberrant_ERR2585311 2 0.0188 0.9553 0.000 0.996 0.000 0.004
#> aberrant_ERR2585292 4 0.0188 0.9252 0.000 0.000 0.004 0.996
#> aberrant_ERR2585282 2 0.0188 0.9553 0.000 0.996 0.000 0.004
#> aberrant_ERR2585305 2 0.1743 0.9250 0.000 0.940 0.056 0.004
#> aberrant_ERR2585278 2 0.0188 0.9553 0.000 0.996 0.000 0.004
#> aberrant_ERR2585347 2 0.1792 0.9417 0.000 0.932 0.000 0.068
#> aberrant_ERR2585332 2 0.0469 0.9548 0.000 0.988 0.000 0.012
#> aberrant_ERR2585280 2 0.0336 0.9554 0.000 0.992 0.000 0.008
#> aberrant_ERR2585304 3 0.2310 0.5730 0.040 0.028 0.928 0.004
#> aberrant_ERR2585322 2 0.1004 0.9523 0.000 0.972 0.004 0.024
#> aberrant_ERR2585279 3 0.5754 0.0722 0.000 0.316 0.636 0.048
#> aberrant_ERR2585277 2 0.2450 0.9273 0.000 0.912 0.016 0.072
#> aberrant_ERR2585295 2 0.1716 0.9376 0.000 0.936 0.000 0.064
#> aberrant_ERR2585333 2 0.0469 0.9539 0.000 0.988 0.000 0.012
#> aberrant_ERR2585285 2 0.0336 0.9550 0.000 0.992 0.000 0.008
#> aberrant_ERR2585286 2 0.2198 0.9314 0.000 0.920 0.008 0.072
#> aberrant_ERR2585294 2 0.0188 0.9553 0.000 0.996 0.000 0.004
#> aberrant_ERR2585300 2 0.0592 0.9530 0.000 0.984 0.000 0.016
#> aberrant_ERR2585334 2 0.4804 0.7779 0.000 0.776 0.160 0.064
#> aberrant_ERR2585361 2 0.1716 0.9383 0.000 0.936 0.000 0.064
#> aberrant_ERR2585372 2 0.0336 0.9550 0.000 0.992 0.000 0.008
#> round_ERR2585217 3 0.4040 0.7699 0.248 0.000 0.752 0.000
#> round_ERR2585205 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> round_ERR2585214 3 0.3400 0.7666 0.180 0.000 0.820 0.000
#> round_ERR2585202 3 0.1909 0.5951 0.048 0.008 0.940 0.004
#> aberrant_ERR2585367 2 0.1792 0.9364 0.000 0.932 0.000 0.068
#> round_ERR2585220 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> round_ERR2585238 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> aberrant_ERR2585276 2 0.0188 0.9553 0.000 0.996 0.000 0.004
#> round_ERR2585218 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> aberrant_ERR2585363 2 0.0188 0.9553 0.000 0.996 0.000 0.004
#> round_ERR2585201 3 0.3688 0.7772 0.208 0.000 0.792 0.000
#> round_ERR2585210 1 0.0707 0.9136 0.980 0.000 0.020 0.000
#> aberrant_ERR2585362 2 0.0000 0.9550 0.000 1.000 0.000 0.000
#> aberrant_ERR2585360 2 0.0000 0.9550 0.000 1.000 0.000 0.000
#> round_ERR2585209 1 0.4103 0.5778 0.744 0.000 0.256 0.000
#> round_ERR2585242 3 0.3726 0.7779 0.212 0.000 0.788 0.000
#> round_ERR2585216 1 0.0188 0.9224 0.996 0.000 0.004 0.000
#> round_ERR2585219 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> round_ERR2585237 3 0.4431 0.7155 0.304 0.000 0.696 0.000
#> round_ERR2585198 3 0.4193 0.7559 0.268 0.000 0.732 0.000
#> round_ERR2585211 1 0.0469 0.9188 0.988 0.000 0.012 0.000
#> round_ERR2585206 1 0.0188 0.9225 0.996 0.000 0.004 0.000
#> aberrant_ERR2585281 2 0.1867 0.9345 0.000 0.928 0.000 0.072
#> round_ERR2585212 1 0.1716 0.8729 0.936 0.000 0.064 0.000
#> round_ERR2585221 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> round_ERR2585243 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> round_ERR2585204 3 0.3486 0.7711 0.188 0.000 0.812 0.000
#> round_ERR2585213 3 0.0376 0.5700 0.004 0.000 0.992 0.004
#> aberrant_ERR2585373 2 0.0469 0.9539 0.000 0.988 0.000 0.012
#> aberrant_ERR2585358 2 0.0000 0.9550 0.000 1.000 0.000 0.000
#> aberrant_ERR2585365 2 0.1474 0.9444 0.000 0.948 0.000 0.052
#> aberrant_ERR2585359 2 0.0188 0.9553 0.000 0.996 0.000 0.004
#> aberrant_ERR2585370 2 0.2300 0.9308 0.000 0.920 0.016 0.064
#> round_ERR2585215 1 0.0921 0.9079 0.972 0.000 0.028 0.000
#> round_ERR2585262 3 0.2365 0.6161 0.064 0.012 0.920 0.004
#> round_ERR2585199 3 0.3024 0.7321 0.148 0.000 0.852 0.000
#> aberrant_ERR2585369 2 0.0469 0.9539 0.000 0.988 0.000 0.012
#> round_ERR2585208 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> round_ERR2585236 3 0.4855 0.6510 0.352 0.000 0.644 0.004
#> aberrant_ERR2585284 4 0.0188 0.9252 0.000 0.000 0.004 0.996
#> round_ERR2585224 1 0.1118 0.9015 0.964 0.000 0.036 0.000
#> round_ERR2585260 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> round_ERR2585229 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> aberrant_ERR2585364 2 0.4008 0.6775 0.000 0.756 0.000 0.244
#> round_ERR2585253 1 0.1022 0.9048 0.968 0.000 0.032 0.000
#> aberrant_ERR2585368 2 0.2521 0.9262 0.000 0.912 0.024 0.064
#> aberrant_ERR2585371 2 0.2521 0.9262 0.000 0.912 0.024 0.064
#> round_ERR2585239 1 0.0336 0.9199 0.992 0.000 0.008 0.000
#> round_ERR2585273 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> round_ERR2585256 3 0.4981 0.4174 0.464 0.000 0.536 0.000
#> round_ERR2585272 1 0.2814 0.7980 0.868 0.000 0.132 0.000
#> round_ERR2585246 1 0.0336 0.9209 0.992 0.000 0.008 0.000
#> round_ERR2585261 3 0.4855 0.5552 0.400 0.000 0.600 0.000
#> round_ERR2585254 1 0.4994 -0.2659 0.520 0.000 0.480 0.000
#> round_ERR2585225 3 0.3837 0.7744 0.224 0.000 0.776 0.000
#> round_ERR2585235 1 0.4164 0.5632 0.736 0.000 0.264 0.000
#> round_ERR2585271 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> round_ERR2585251 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> round_ERR2585255 3 0.3444 0.7689 0.184 0.000 0.816 0.000
#> round_ERR2585257 3 0.4103 0.7670 0.256 0.000 0.744 0.000
#> round_ERR2585226 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> round_ERR2585265 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> round_ERR2585259 1 0.4941 -0.1079 0.564 0.000 0.436 0.000
#> round_ERR2585247 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> round_ERR2585241 1 0.0188 0.9224 0.996 0.000 0.004 0.000
#> round_ERR2585263 1 0.0188 0.9225 0.996 0.000 0.004 0.000
#> round_ERR2585264 1 0.1022 0.9048 0.968 0.000 0.032 0.000
#> round_ERR2585233 3 0.4992 0.3094 0.476 0.000 0.524 0.000
#> round_ERR2585223 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> round_ERR2585234 3 0.3801 0.7782 0.220 0.000 0.780 0.000
#> round_ERR2585222 1 0.0336 0.9204 0.992 0.000 0.008 0.000
#> round_ERR2585228 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> round_ERR2585248 1 0.1022 0.9048 0.968 0.000 0.032 0.000
#> round_ERR2585240 1 0.4605 0.3746 0.664 0.000 0.336 0.000
#> round_ERR2585270 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> round_ERR2585232 1 0.3266 0.7464 0.832 0.000 0.168 0.000
#> aberrant_ERR2585341 2 0.1792 0.9364 0.000 0.932 0.000 0.068
#> aberrant_ERR2585355 2 0.2053 0.9331 0.000 0.924 0.004 0.072
#> round_ERR2585227 1 0.2647 0.8117 0.880 0.000 0.120 0.000
#> aberrant_ERR2585351 2 0.0000 0.9550 0.000 1.000 0.000 0.000
#> round_ERR2585269 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> aberrant_ERR2585357 2 0.1798 0.9440 0.000 0.944 0.016 0.040
#> aberrant_ERR2585350 2 0.1970 0.9371 0.000 0.932 0.008 0.060
#> round_ERR2585250 1 0.4103 0.5481 0.744 0.000 0.256 0.000
#> round_ERR2585245 1 0.0336 0.9209 0.992 0.000 0.008 0.000
#> aberrant_ERR2585353 2 0.0336 0.9555 0.000 0.992 0.000 0.008
#> round_ERR2585258 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.9550 0.000 1.000 0.000 0.000
#> round_ERR2585249 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> round_ERR2585268 1 0.4948 -0.1206 0.560 0.000 0.440 0.000
#> aberrant_ERR2585356 2 0.0336 0.9550 0.000 0.992 0.000 0.008
#> round_ERR2585266 3 0.3764 0.7781 0.216 0.000 0.784 0.000
#> round_ERR2585231 1 0.0469 0.9190 0.988 0.000 0.012 0.000
#> round_ERR2585230 1 0.0000 0.9236 1.000 0.000 0.000 0.000
#> round_ERR2585267 1 0.0000 0.9236 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 5 0.2612 0.7290 0.000 0.124 0.000 0.008 0.868
#> aberrant_ERR2585338 2 0.5605 0.4430 0.000 0.520 0.000 0.076 0.404
#> aberrant_ERR2585325 5 0.2886 0.7033 0.000 0.148 0.000 0.008 0.844
#> aberrant_ERR2585283 4 0.0162 0.9611 0.000 0.004 0.000 0.996 0.000
#> aberrant_ERR2585343 5 0.0794 0.8099 0.000 0.000 0.000 0.028 0.972
#> aberrant_ERR2585329 2 0.3796 0.6947 0.000 0.700 0.000 0.000 0.300
#> aberrant_ERR2585317 2 0.3895 0.6707 0.000 0.680 0.000 0.000 0.320
#> aberrant_ERR2585339 2 0.5771 0.3312 0.000 0.480 0.000 0.088 0.432
#> aberrant_ERR2585335 5 0.1792 0.7504 0.000 0.084 0.000 0.000 0.916
#> aberrant_ERR2585287 4 0.0898 0.9483 0.000 0.020 0.000 0.972 0.008
#> aberrant_ERR2585321 5 0.0162 0.8139 0.000 0.000 0.000 0.004 0.996
#> aberrant_ERR2585297 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585337 2 0.3684 0.7202 0.000 0.720 0.000 0.000 0.280
#> aberrant_ERR2585319 5 0.0579 0.8133 0.000 0.008 0.000 0.008 0.984
#> aberrant_ERR2585315 5 0.2390 0.7667 0.000 0.084 0.000 0.020 0.896
#> aberrant_ERR2585336 2 0.3395 0.7438 0.000 0.764 0.000 0.000 0.236
#> aberrant_ERR2585307 2 0.1831 0.5905 0.000 0.920 0.004 0.000 0.076
#> aberrant_ERR2585301 5 0.2605 0.6620 0.000 0.148 0.000 0.000 0.852
#> aberrant_ERR2585326 2 0.3242 0.7482 0.000 0.784 0.000 0.000 0.216
#> aberrant_ERR2585331 2 0.1341 0.6281 0.000 0.944 0.000 0.000 0.056
#> aberrant_ERR2585346 4 0.2424 0.8131 0.000 0.000 0.000 0.868 0.132
#> aberrant_ERR2585314 2 0.3768 0.4664 0.000 0.760 0.008 0.004 0.228
#> aberrant_ERR2585298 3 0.0162 0.7857 0.004 0.000 0.996 0.000 0.000
#> aberrant_ERR2585345 2 0.3586 0.7293 0.000 0.736 0.000 0.000 0.264
#> aberrant_ERR2585299 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585309 1 0.0162 0.9716 0.996 0.004 0.000 0.000 0.000
#> aberrant_ERR2585303 5 0.5938 -0.0220 0.000 0.376 0.000 0.112 0.512
#> aberrant_ERR2585313 5 0.5066 0.2475 0.000 0.344 0.000 0.048 0.608
#> aberrant_ERR2585318 5 0.0162 0.8133 0.000 0.004 0.000 0.000 0.996
#> aberrant_ERR2585328 5 0.4029 0.4193 0.000 0.316 0.000 0.004 0.680
#> aberrant_ERR2585330 5 0.0000 0.8140 0.000 0.000 0.000 0.000 1.000
#> aberrant_ERR2585293 4 0.0162 0.9611 0.000 0.004 0.000 0.996 0.000
#> aberrant_ERR2585342 5 0.0000 0.8140 0.000 0.000 0.000 0.000 1.000
#> aberrant_ERR2585348 5 0.5699 0.3528 0.000 0.264 0.000 0.128 0.608
#> aberrant_ERR2585352 5 0.1205 0.7991 0.000 0.040 0.000 0.004 0.956
#> aberrant_ERR2585308 1 0.0162 0.9716 0.996 0.004 0.000 0.000 0.000
#> aberrant_ERR2585349 2 0.2452 0.4996 0.000 0.896 0.084 0.004 0.016
#> aberrant_ERR2585316 5 0.0703 0.8114 0.000 0.000 0.000 0.024 0.976
#> aberrant_ERR2585306 5 0.0963 0.7934 0.000 0.036 0.000 0.000 0.964
#> aberrant_ERR2585324 5 0.0579 0.8133 0.000 0.008 0.000 0.008 0.984
#> aberrant_ERR2585310 3 0.6800 0.4650 0.088 0.332 0.524 0.004 0.052
#> aberrant_ERR2585296 3 0.3607 0.6571 0.244 0.000 0.752 0.004 0.000
#> aberrant_ERR2585275 4 0.0609 0.9507 0.000 0.000 0.000 0.980 0.020
#> aberrant_ERR2585311 5 0.0000 0.8140 0.000 0.000 0.000 0.000 1.000
#> aberrant_ERR2585292 4 0.0162 0.9611 0.000 0.004 0.000 0.996 0.000
#> aberrant_ERR2585282 5 0.0000 0.8140 0.000 0.000 0.000 0.000 1.000
#> aberrant_ERR2585305 5 0.4161 0.2257 0.000 0.392 0.000 0.000 0.608
#> aberrant_ERR2585278 5 0.1043 0.7989 0.000 0.040 0.000 0.000 0.960
#> aberrant_ERR2585347 5 0.4796 0.5984 0.000 0.152 0.000 0.120 0.728
#> aberrant_ERR2585332 5 0.0794 0.8104 0.000 0.000 0.000 0.028 0.972
#> aberrant_ERR2585280 5 0.3667 0.6854 0.000 0.140 0.000 0.048 0.812
#> aberrant_ERR2585304 2 0.4414 -0.0921 0.000 0.616 0.376 0.004 0.004
#> aberrant_ERR2585322 2 0.4383 0.4796 0.000 0.572 0.000 0.004 0.424
#> aberrant_ERR2585279 2 0.2249 0.4775 0.000 0.896 0.096 0.000 0.008
#> aberrant_ERR2585277 2 0.4949 0.6738 0.000 0.656 0.000 0.056 0.288
#> aberrant_ERR2585295 5 0.4898 0.1851 0.000 0.376 0.000 0.032 0.592
#> aberrant_ERR2585333 5 0.0404 0.8124 0.000 0.000 0.000 0.012 0.988
#> aberrant_ERR2585285 5 0.0162 0.8140 0.000 0.000 0.000 0.004 0.996
#> aberrant_ERR2585286 2 0.5456 0.5987 0.000 0.592 0.000 0.080 0.328
#> aberrant_ERR2585294 5 0.1544 0.7679 0.000 0.068 0.000 0.000 0.932
#> aberrant_ERR2585300 5 0.0290 0.8143 0.000 0.000 0.000 0.008 0.992
#> aberrant_ERR2585334 2 0.1908 0.6715 0.000 0.908 0.000 0.000 0.092
#> aberrant_ERR2585361 5 0.5277 0.4641 0.000 0.228 0.000 0.108 0.664
#> aberrant_ERR2585372 5 0.0162 0.8140 0.000 0.000 0.000 0.004 0.996
#> round_ERR2585217 3 0.0833 0.7862 0.016 0.004 0.976 0.004 0.000
#> round_ERR2585205 1 0.0162 0.9716 0.996 0.004 0.000 0.000 0.000
#> round_ERR2585214 3 0.0000 0.7832 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585202 3 0.4182 0.5474 0.000 0.352 0.644 0.004 0.000
#> aberrant_ERR2585367 5 0.5941 0.1411 0.000 0.332 0.000 0.124 0.544
#> round_ERR2585220 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585238 1 0.0162 0.9716 0.996 0.004 0.000 0.000 0.000
#> aberrant_ERR2585276 5 0.0000 0.8140 0.000 0.000 0.000 0.000 1.000
#> round_ERR2585218 1 0.0162 0.9716 0.996 0.004 0.000 0.000 0.000
#> aberrant_ERR2585363 5 0.3574 0.6659 0.000 0.168 0.000 0.028 0.804
#> round_ERR2585201 3 0.0162 0.7857 0.004 0.000 0.996 0.000 0.000
#> round_ERR2585210 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585362 5 0.3109 0.6431 0.000 0.200 0.000 0.000 0.800
#> aberrant_ERR2585360 5 0.0000 0.8140 0.000 0.000 0.000 0.000 1.000
#> round_ERR2585209 3 0.4225 0.5063 0.364 0.000 0.632 0.004 0.000
#> round_ERR2585242 3 0.0162 0.7857 0.004 0.000 0.996 0.000 0.000
#> round_ERR2585216 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585219 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585237 3 0.0671 0.7866 0.016 0.000 0.980 0.004 0.000
#> round_ERR2585198 3 0.0000 0.7832 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585211 1 0.0162 0.9716 0.996 0.004 0.000 0.000 0.000
#> round_ERR2585206 1 0.0162 0.9716 0.996 0.004 0.000 0.000 0.000
#> aberrant_ERR2585281 5 0.5965 -0.0866 0.000 0.392 0.000 0.112 0.496
#> round_ERR2585212 1 0.1121 0.9343 0.956 0.000 0.044 0.000 0.000
#> round_ERR2585221 1 0.0162 0.9702 0.996 0.000 0.004 0.000 0.000
#> round_ERR2585243 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585204 3 0.0000 0.7832 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585213 3 0.3876 0.5806 0.000 0.316 0.684 0.000 0.000
#> aberrant_ERR2585373 5 0.0162 0.8139 0.000 0.000 0.000 0.004 0.996
#> aberrant_ERR2585358 5 0.0162 0.8142 0.000 0.000 0.000 0.004 0.996
#> aberrant_ERR2585365 5 0.5644 0.2101 0.000 0.328 0.000 0.096 0.576
#> aberrant_ERR2585359 5 0.0609 0.8117 0.000 0.000 0.000 0.020 0.980
#> aberrant_ERR2585370 2 0.3835 0.7345 0.000 0.744 0.000 0.012 0.244
#> round_ERR2585215 1 0.0451 0.9683 0.988 0.008 0.004 0.000 0.000
#> round_ERR2585262 3 0.3491 0.6609 0.004 0.228 0.768 0.000 0.000
#> round_ERR2585199 3 0.2295 0.7525 0.008 0.088 0.900 0.004 0.000
#> aberrant_ERR2585369 5 0.0000 0.8140 0.000 0.000 0.000 0.000 1.000
#> round_ERR2585208 1 0.0162 0.9716 0.996 0.004 0.000 0.000 0.000
#> round_ERR2585252 1 0.0162 0.9716 0.996 0.004 0.000 0.000 0.000
#> round_ERR2585236 3 0.4184 0.6605 0.232 0.024 0.740 0.004 0.000
#> aberrant_ERR2585284 4 0.0162 0.9611 0.000 0.004 0.000 0.996 0.000
#> round_ERR2585224 1 0.0324 0.9699 0.992 0.004 0.004 0.000 0.000
#> round_ERR2585260 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585229 1 0.0162 0.9716 0.996 0.004 0.000 0.000 0.000
#> aberrant_ERR2585364 5 0.2516 0.7184 0.000 0.000 0.000 0.140 0.860
#> round_ERR2585253 1 0.0451 0.9683 0.988 0.008 0.004 0.000 0.000
#> aberrant_ERR2585368 2 0.3177 0.7482 0.000 0.792 0.000 0.000 0.208
#> aberrant_ERR2585371 2 0.3177 0.7482 0.000 0.792 0.000 0.000 0.208
#> round_ERR2585239 1 0.0290 0.9671 0.992 0.000 0.008 0.000 0.000
#> round_ERR2585273 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585256 3 0.3048 0.7200 0.176 0.000 0.820 0.004 0.000
#> round_ERR2585272 1 0.1430 0.9259 0.944 0.004 0.052 0.000 0.000
#> round_ERR2585246 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585261 3 0.2124 0.7619 0.096 0.000 0.900 0.004 0.000
#> round_ERR2585254 3 0.3550 0.6734 0.236 0.000 0.760 0.004 0.000
#> round_ERR2585225 3 0.0290 0.7860 0.008 0.000 0.992 0.000 0.000
#> round_ERR2585235 1 0.4218 0.4553 0.668 0.004 0.324 0.004 0.000
#> round_ERR2585271 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585251 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585255 3 0.0000 0.7832 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585257 3 0.0510 0.7868 0.016 0.000 0.984 0.000 0.000
#> round_ERR2585226 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585265 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585259 3 0.4060 0.5371 0.360 0.000 0.640 0.000 0.000
#> round_ERR2585247 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585241 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585263 1 0.0703 0.9526 0.976 0.000 0.024 0.000 0.000
#> round_ERR2585264 1 0.0451 0.9683 0.988 0.008 0.004 0.000 0.000
#> round_ERR2585233 3 0.3003 0.6803 0.188 0.000 0.812 0.000 0.000
#> round_ERR2585223 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585234 3 0.0162 0.7857 0.004 0.000 0.996 0.000 0.000
#> round_ERR2585222 1 0.0162 0.9701 0.996 0.000 0.004 0.000 0.000
#> round_ERR2585228 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585248 1 0.0290 0.9703 0.992 0.008 0.000 0.000 0.000
#> round_ERR2585240 3 0.4151 0.5134 0.344 0.000 0.652 0.004 0.000
#> round_ERR2585270 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585232 1 0.4029 0.4863 0.680 0.000 0.316 0.004 0.000
#> aberrant_ERR2585341 5 0.5959 -0.0692 0.000 0.388 0.000 0.112 0.500
#> aberrant_ERR2585355 2 0.5736 0.4318 0.000 0.512 0.000 0.088 0.400
#> round_ERR2585227 1 0.1638 0.9086 0.932 0.000 0.064 0.004 0.000
#> aberrant_ERR2585351 5 0.0000 0.8140 0.000 0.000 0.000 0.000 1.000
#> round_ERR2585269 1 0.0162 0.9716 0.996 0.004 0.000 0.000 0.000
#> aberrant_ERR2585357 2 0.3242 0.7482 0.000 0.784 0.000 0.000 0.216
#> aberrant_ERR2585350 2 0.3857 0.6752 0.000 0.688 0.000 0.000 0.312
#> round_ERR2585250 1 0.3906 0.5211 0.704 0.000 0.292 0.004 0.000
#> round_ERR2585245 1 0.0290 0.9703 0.992 0.008 0.000 0.000 0.000
#> aberrant_ERR2585353 5 0.0671 0.8129 0.000 0.004 0.000 0.016 0.980
#> round_ERR2585258 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585354 5 0.0000 0.8140 0.000 0.000 0.000 0.000 1.000
#> round_ERR2585249 1 0.0162 0.9716 0.996 0.004 0.000 0.000 0.000
#> round_ERR2585268 3 0.4450 0.1854 0.488 0.000 0.508 0.004 0.000
#> aberrant_ERR2585356 5 0.0609 0.8122 0.000 0.000 0.000 0.020 0.980
#> round_ERR2585266 3 0.0162 0.7857 0.004 0.000 0.996 0.000 0.000
#> round_ERR2585231 1 0.0324 0.9700 0.992 0.004 0.004 0.000 0.000
#> round_ERR2585230 1 0.0000 0.9719 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585267 1 0.0162 0.9716 0.996 0.004 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 5 0.3859 0.69124 0.000 0.036 0.000 0.016 0.772 0.176
#> aberrant_ERR2585338 2 0.4551 0.63929 0.000 0.724 0.000 0.012 0.104 0.160
#> aberrant_ERR2585325 5 0.4051 0.67368 0.000 0.056 0.000 0.012 0.760 0.172
#> aberrant_ERR2585283 4 0.0000 0.92222 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585343 5 0.0935 0.84691 0.000 0.000 0.000 0.032 0.964 0.004
#> aberrant_ERR2585329 2 0.3314 0.57404 0.000 0.764 0.000 0.000 0.224 0.012
#> aberrant_ERR2585317 2 0.3394 0.56377 0.000 0.752 0.000 0.000 0.236 0.012
#> aberrant_ERR2585339 2 0.5920 0.58539 0.000 0.592 0.000 0.040 0.168 0.200
#> aberrant_ERR2585335 5 0.2320 0.77641 0.000 0.132 0.000 0.000 0.864 0.004
#> aberrant_ERR2585287 4 0.1536 0.88491 0.000 0.016 0.000 0.940 0.040 0.004
#> aberrant_ERR2585321 5 0.0000 0.85019 0.000 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585297 1 0.0547 0.88817 0.980 0.000 0.000 0.000 0.000 0.020
#> aberrant_ERR2585337 2 0.3014 0.61216 0.000 0.804 0.000 0.000 0.184 0.012
#> aberrant_ERR2585319 5 0.1225 0.84699 0.000 0.036 0.000 0.012 0.952 0.000
#> aberrant_ERR2585315 5 0.2402 0.80263 0.000 0.120 0.000 0.012 0.868 0.000
#> aberrant_ERR2585336 2 0.1398 0.65977 0.000 0.940 0.000 0.000 0.052 0.008
#> aberrant_ERR2585307 2 0.4106 0.47111 0.000 0.736 0.000 0.000 0.076 0.188
#> aberrant_ERR2585301 5 0.2841 0.74220 0.000 0.164 0.000 0.000 0.824 0.012
#> aberrant_ERR2585326 2 0.1333 0.65919 0.000 0.944 0.000 0.000 0.048 0.008
#> aberrant_ERR2585331 2 0.2263 0.58431 0.000 0.884 0.000 0.000 0.016 0.100
#> aberrant_ERR2585346 4 0.2730 0.67371 0.000 0.000 0.000 0.808 0.192 0.000
#> aberrant_ERR2585314 2 0.5507 0.22207 0.000 0.548 0.000 0.000 0.168 0.284
#> aberrant_ERR2585298 3 0.0000 0.62439 0.000 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585345 2 0.3078 0.60253 0.000 0.796 0.000 0.000 0.192 0.012
#> aberrant_ERR2585299 1 0.1003 0.88631 0.964 0.000 0.016 0.000 0.000 0.020
#> aberrant_ERR2585309 1 0.1714 0.87777 0.908 0.000 0.000 0.000 0.000 0.092
#> aberrant_ERR2585303 2 0.6501 0.45858 0.000 0.472 0.000 0.040 0.284 0.204
#> aberrant_ERR2585313 5 0.6448 -0.06193 0.000 0.344 0.000 0.040 0.448 0.168
#> aberrant_ERR2585318 5 0.1285 0.83731 0.000 0.052 0.000 0.000 0.944 0.004
#> aberrant_ERR2585328 2 0.5917 0.25563 0.000 0.404 0.000 0.000 0.388 0.208
#> aberrant_ERR2585330 5 0.0405 0.85140 0.000 0.004 0.000 0.008 0.988 0.000
#> aberrant_ERR2585293 4 0.0000 0.92222 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585342 5 0.0363 0.85026 0.000 0.000 0.000 0.012 0.988 0.000
#> aberrant_ERR2585348 5 0.6711 0.00669 0.000 0.280 0.000 0.056 0.456 0.208
#> aberrant_ERR2585352 5 0.1887 0.83961 0.000 0.048 0.000 0.012 0.924 0.016
#> aberrant_ERR2585308 1 0.1714 0.87808 0.908 0.000 0.000 0.000 0.000 0.092
#> aberrant_ERR2585349 2 0.4594 -0.25620 0.000 0.484 0.036 0.000 0.000 0.480
#> aberrant_ERR2585316 5 0.0363 0.85026 0.000 0.000 0.000 0.012 0.988 0.000
#> aberrant_ERR2585306 5 0.1500 0.83781 0.000 0.052 0.000 0.000 0.936 0.012
#> aberrant_ERR2585324 5 0.1367 0.84411 0.000 0.044 0.000 0.012 0.944 0.000
#> aberrant_ERR2585310 6 0.5616 0.64241 0.012 0.104 0.332 0.000 0.004 0.548
#> aberrant_ERR2585296 3 0.5515 0.22518 0.184 0.000 0.556 0.000 0.000 0.260
#> aberrant_ERR2585275 4 0.0713 0.91219 0.000 0.000 0.000 0.972 0.028 0.000
#> aberrant_ERR2585311 5 0.0777 0.84721 0.000 0.024 0.000 0.000 0.972 0.004
#> aberrant_ERR2585292 4 0.0000 0.92222 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585282 5 0.0146 0.85046 0.000 0.004 0.000 0.000 0.996 0.000
#> aberrant_ERR2585305 5 0.4079 0.65010 0.000 0.112 0.000 0.000 0.752 0.136
#> aberrant_ERR2585278 5 0.1863 0.80634 0.000 0.104 0.000 0.000 0.896 0.000
#> aberrant_ERR2585347 5 0.5522 0.53466 0.000 0.084 0.000 0.064 0.648 0.204
#> aberrant_ERR2585332 5 0.1845 0.82901 0.000 0.000 0.000 0.028 0.920 0.052
#> aberrant_ERR2585280 5 0.4741 0.64082 0.000 0.056 0.000 0.048 0.720 0.176
#> aberrant_ERR2585304 6 0.5879 0.56834 0.000 0.316 0.220 0.000 0.000 0.464
#> aberrant_ERR2585322 2 0.3344 0.66589 0.000 0.804 0.000 0.000 0.152 0.044
#> aberrant_ERR2585279 2 0.4837 0.08623 0.000 0.616 0.068 0.000 0.004 0.312
#> aberrant_ERR2585277 2 0.2591 0.67065 0.000 0.880 0.000 0.004 0.064 0.052
#> aberrant_ERR2585295 2 0.5867 0.39186 0.000 0.456 0.000 0.000 0.336 0.208
#> aberrant_ERR2585333 5 0.0458 0.85002 0.000 0.000 0.000 0.016 0.984 0.000
#> aberrant_ERR2585285 5 0.0405 0.85132 0.000 0.004 0.000 0.008 0.988 0.000
#> aberrant_ERR2585286 2 0.3742 0.65670 0.000 0.796 0.000 0.008 0.076 0.120
#> aberrant_ERR2585294 5 0.1970 0.81219 0.000 0.092 0.000 0.000 0.900 0.008
#> aberrant_ERR2585300 5 0.0146 0.85080 0.000 0.000 0.000 0.004 0.996 0.000
#> aberrant_ERR2585334 2 0.2094 0.60536 0.000 0.900 0.000 0.000 0.020 0.080
#> aberrant_ERR2585361 5 0.6543 0.13123 0.000 0.244 0.000 0.052 0.496 0.208
#> aberrant_ERR2585372 5 0.0508 0.85059 0.000 0.000 0.000 0.012 0.984 0.004
#> round_ERR2585217 3 0.3408 0.50290 0.048 0.000 0.800 0.000 0.000 0.152
#> round_ERR2585205 1 0.1141 0.88774 0.948 0.000 0.000 0.000 0.000 0.052
#> round_ERR2585214 3 0.0260 0.61814 0.000 0.000 0.992 0.000 0.000 0.008
#> round_ERR2585202 6 0.4850 0.48913 0.000 0.056 0.448 0.000 0.000 0.496
#> aberrant_ERR2585367 2 0.6755 0.36297 0.000 0.416 0.000 0.052 0.324 0.208
#> round_ERR2585220 1 0.2006 0.86597 0.892 0.000 0.004 0.000 0.000 0.104
#> round_ERR2585238 1 0.1387 0.88665 0.932 0.000 0.000 0.000 0.000 0.068
#> aberrant_ERR2585276 5 0.1285 0.83818 0.000 0.052 0.000 0.000 0.944 0.004
#> round_ERR2585218 1 0.1387 0.88517 0.932 0.000 0.000 0.000 0.000 0.068
#> aberrant_ERR2585363 5 0.4171 0.68603 0.000 0.092 0.000 0.020 0.772 0.116
#> round_ERR2585201 3 0.0000 0.62439 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585210 1 0.0547 0.88947 0.980 0.000 0.000 0.000 0.000 0.020
#> aberrant_ERR2585362 5 0.5195 0.46144 0.000 0.208 0.000 0.000 0.616 0.176
#> aberrant_ERR2585360 5 0.0713 0.84718 0.000 0.028 0.000 0.000 0.972 0.000
#> round_ERR2585209 3 0.5071 0.28328 0.376 0.000 0.540 0.000 0.000 0.084
#> round_ERR2585242 3 0.0000 0.62439 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585216 1 0.1753 0.87470 0.912 0.000 0.004 0.000 0.000 0.084
#> round_ERR2585219 1 0.1858 0.87472 0.912 0.000 0.012 0.000 0.000 0.076
#> round_ERR2585237 3 0.3551 0.47818 0.048 0.000 0.784 0.000 0.000 0.168
#> round_ERR2585198 3 0.0146 0.62243 0.000 0.000 0.996 0.000 0.000 0.004
#> round_ERR2585211 1 0.1910 0.87276 0.892 0.000 0.000 0.000 0.000 0.108
#> round_ERR2585206 1 0.1663 0.88092 0.912 0.000 0.000 0.000 0.000 0.088
#> aberrant_ERR2585281 2 0.6366 0.52404 0.000 0.512 0.000 0.040 0.240 0.208
#> round_ERR2585212 1 0.2509 0.85446 0.876 0.000 0.036 0.000 0.000 0.088
#> round_ERR2585221 1 0.1007 0.88788 0.956 0.000 0.000 0.000 0.000 0.044
#> round_ERR2585243 1 0.1176 0.88413 0.956 0.000 0.024 0.000 0.000 0.020
#> round_ERR2585204 3 0.0260 0.61814 0.000 0.000 0.992 0.000 0.000 0.008
#> round_ERR2585213 3 0.5173 -0.51319 0.000 0.092 0.520 0.000 0.000 0.388
#> aberrant_ERR2585373 5 0.0146 0.85056 0.000 0.000 0.000 0.004 0.996 0.000
#> aberrant_ERR2585358 5 0.0717 0.85141 0.000 0.000 0.000 0.008 0.976 0.016
#> aberrant_ERR2585365 5 0.6539 -0.24869 0.000 0.380 0.000 0.040 0.400 0.180
#> aberrant_ERR2585359 5 0.0547 0.85003 0.000 0.000 0.000 0.020 0.980 0.000
#> aberrant_ERR2585370 2 0.1049 0.66000 0.000 0.960 0.000 0.000 0.032 0.008
#> round_ERR2585215 1 0.1863 0.87673 0.896 0.000 0.000 0.000 0.000 0.104
#> round_ERR2585262 3 0.4591 -0.50256 0.000 0.036 0.500 0.000 0.000 0.464
#> round_ERR2585199 3 0.3710 0.19144 0.000 0.012 0.696 0.000 0.000 0.292
#> aberrant_ERR2585369 5 0.0405 0.85091 0.000 0.004 0.000 0.008 0.988 0.000
#> round_ERR2585208 1 0.1910 0.87425 0.892 0.000 0.000 0.000 0.000 0.108
#> round_ERR2585252 1 0.2340 0.85392 0.852 0.000 0.000 0.000 0.000 0.148
#> round_ERR2585236 3 0.5461 0.13458 0.132 0.004 0.556 0.000 0.000 0.308
#> aberrant_ERR2585284 4 0.0000 0.92222 0.000 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585224 1 0.1814 0.87497 0.900 0.000 0.000 0.000 0.000 0.100
#> round_ERR2585260 1 0.1644 0.87747 0.920 0.000 0.004 0.000 0.000 0.076
#> round_ERR2585229 1 0.1204 0.88683 0.944 0.000 0.000 0.000 0.000 0.056
#> aberrant_ERR2585364 5 0.1910 0.80509 0.000 0.000 0.000 0.108 0.892 0.000
#> round_ERR2585253 1 0.2527 0.84220 0.832 0.000 0.000 0.000 0.000 0.168
#> aberrant_ERR2585368 2 0.0713 0.65542 0.000 0.972 0.000 0.000 0.028 0.000
#> aberrant_ERR2585371 2 0.0713 0.65542 0.000 0.972 0.000 0.000 0.028 0.000
#> round_ERR2585239 1 0.1492 0.88113 0.940 0.000 0.024 0.000 0.000 0.036
#> round_ERR2585273 1 0.1644 0.87714 0.920 0.000 0.004 0.000 0.000 0.076
#> round_ERR2585256 3 0.5036 0.37576 0.228 0.000 0.632 0.000 0.000 0.140
#> round_ERR2585272 1 0.1780 0.87815 0.924 0.000 0.048 0.000 0.000 0.028
#> round_ERR2585246 1 0.0603 0.88783 0.980 0.000 0.016 0.000 0.000 0.004
#> round_ERR2585261 3 0.3455 0.52367 0.144 0.000 0.800 0.000 0.000 0.056
#> round_ERR2585254 3 0.4957 0.31469 0.332 0.000 0.584 0.000 0.000 0.084
#> round_ERR2585225 3 0.0146 0.62471 0.004 0.000 0.996 0.000 0.000 0.000
#> round_ERR2585235 1 0.4468 0.20754 0.560 0.000 0.408 0.000 0.000 0.032
#> round_ERR2585271 1 0.0405 0.88851 0.988 0.000 0.004 0.000 0.000 0.008
#> round_ERR2585251 1 0.2165 0.86259 0.884 0.000 0.008 0.000 0.000 0.108
#> round_ERR2585255 3 0.0000 0.62439 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585257 3 0.0993 0.62010 0.024 0.000 0.964 0.000 0.000 0.012
#> round_ERR2585226 1 0.1908 0.86942 0.900 0.000 0.004 0.000 0.000 0.096
#> round_ERR2585265 1 0.1958 0.86927 0.896 0.000 0.004 0.000 0.000 0.100
#> round_ERR2585259 3 0.4178 0.31570 0.372 0.000 0.608 0.000 0.000 0.020
#> round_ERR2585247 1 0.0717 0.88892 0.976 0.000 0.008 0.000 0.000 0.016
#> round_ERR2585241 1 0.0937 0.88866 0.960 0.000 0.000 0.000 0.000 0.040
#> round_ERR2585263 1 0.3167 0.81811 0.832 0.000 0.072 0.000 0.000 0.096
#> round_ERR2585264 1 0.2527 0.84220 0.832 0.000 0.000 0.000 0.000 0.168
#> round_ERR2585233 3 0.1501 0.58093 0.076 0.000 0.924 0.000 0.000 0.000
#> round_ERR2585223 1 0.0713 0.88969 0.972 0.000 0.000 0.000 0.000 0.028
#> round_ERR2585234 3 0.0000 0.62439 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585222 1 0.1970 0.87266 0.912 0.000 0.028 0.000 0.000 0.060
#> round_ERR2585228 1 0.1411 0.88478 0.936 0.000 0.004 0.000 0.000 0.060
#> round_ERR2585248 1 0.2491 0.84488 0.836 0.000 0.000 0.000 0.000 0.164
#> round_ERR2585240 3 0.3694 0.40258 0.232 0.000 0.740 0.000 0.000 0.028
#> round_ERR2585270 1 0.2053 0.86431 0.888 0.000 0.004 0.000 0.000 0.108
#> round_ERR2585232 1 0.4634 0.26902 0.556 0.000 0.400 0.000 0.000 0.044
#> aberrant_ERR2585341 2 0.6366 0.52479 0.000 0.512 0.000 0.040 0.240 0.208
#> aberrant_ERR2585355 2 0.5201 0.61188 0.000 0.668 0.000 0.032 0.100 0.200
#> round_ERR2585227 1 0.3078 0.83396 0.836 0.000 0.056 0.000 0.000 0.108
#> aberrant_ERR2585351 5 0.1010 0.84705 0.000 0.036 0.000 0.000 0.960 0.004
#> round_ERR2585269 1 0.2300 0.85616 0.856 0.000 0.000 0.000 0.000 0.144
#> aberrant_ERR2585357 2 0.1151 0.65493 0.000 0.956 0.000 0.000 0.032 0.012
#> aberrant_ERR2585350 2 0.2046 0.67314 0.000 0.908 0.000 0.000 0.060 0.032
#> round_ERR2585250 1 0.4573 0.55246 0.676 0.000 0.236 0.000 0.000 0.088
#> round_ERR2585245 1 0.2527 0.84220 0.832 0.000 0.000 0.000 0.000 0.168
#> aberrant_ERR2585353 5 0.1826 0.82935 0.000 0.004 0.000 0.020 0.924 0.052
#> round_ERR2585258 1 0.1700 0.87675 0.916 0.000 0.004 0.000 0.000 0.080
#> aberrant_ERR2585354 5 0.1082 0.84239 0.000 0.040 0.000 0.000 0.956 0.004
#> round_ERR2585249 1 0.2378 0.85145 0.848 0.000 0.000 0.000 0.000 0.152
#> round_ERR2585268 1 0.5213 0.17238 0.544 0.000 0.352 0.000 0.000 0.104
#> aberrant_ERR2585356 5 0.0632 0.84904 0.000 0.000 0.000 0.024 0.976 0.000
#> round_ERR2585266 3 0.0146 0.62471 0.004 0.000 0.996 0.000 0.000 0.000
#> round_ERR2585231 1 0.2003 0.86939 0.884 0.000 0.000 0.000 0.000 0.116
#> round_ERR2585230 1 0.1753 0.87443 0.912 0.000 0.004 0.000 0.000 0.084
#> round_ERR2585267 1 0.2003 0.87131 0.884 0.000 0.000 0.000 0.000 0.116
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> SD:mclust 160 7.97e-29 2
#> SD:mclust 154 2.46e-29 3
#> SD:mclust 151 2.03e-26 4
#> SD:mclust 137 6.49e-24 5
#> SD:mclust 132 8.15e-23 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "NMF"]
# you can also extract it by
# res = res_list["SD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'SD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.975 0.989 0.5008 0.500 0.500
#> 3 3 0.742 0.810 0.902 0.2748 0.813 0.643
#> 4 4 0.652 0.769 0.870 0.0619 0.961 0.894
#> 5 5 0.600 0.635 0.800 0.0715 0.957 0.881
#> 6 6 0.609 0.553 0.765 0.0555 0.915 0.748
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585338 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585325 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585283 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585343 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585329 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585317 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585339 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585335 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585287 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585321 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585297 1 0.0000 0.990 1.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585319 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585315 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585336 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585307 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585301 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585326 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585331 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585346 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585314 2 0.0672 0.981 0.008 0.992
#> aberrant_ERR2585298 1 0.0672 0.984 0.992 0.008
#> aberrant_ERR2585345 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585299 1 0.0000 0.990 1.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.990 1.000 0.000
#> aberrant_ERR2585303 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585313 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585318 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585328 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585330 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585293 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585342 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585348 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585352 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585308 1 0.0000 0.990 1.000 0.000
#> aberrant_ERR2585349 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585316 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585306 2 0.3733 0.917 0.072 0.928
#> aberrant_ERR2585324 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585310 1 0.2603 0.950 0.956 0.044
#> aberrant_ERR2585296 1 0.0000 0.990 1.000 0.000
#> aberrant_ERR2585275 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585311 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585292 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585282 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585305 2 0.1414 0.970 0.020 0.980
#> aberrant_ERR2585278 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585347 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585332 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585280 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585304 2 0.0376 0.984 0.004 0.996
#> aberrant_ERR2585322 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585279 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585277 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585295 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585333 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585285 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585286 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585294 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585300 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585334 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585361 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585372 2 0.0000 0.988 0.000 1.000
#> round_ERR2585217 1 0.0000 0.990 1.000 0.000
#> round_ERR2585205 1 0.0000 0.990 1.000 0.000
#> round_ERR2585214 2 0.9815 0.274 0.420 0.580
#> round_ERR2585202 2 0.5737 0.841 0.136 0.864
#> aberrant_ERR2585367 2 0.0000 0.988 0.000 1.000
#> round_ERR2585220 1 0.0000 0.990 1.000 0.000
#> round_ERR2585238 1 0.0000 0.990 1.000 0.000
#> aberrant_ERR2585276 2 0.0000 0.988 0.000 1.000
#> round_ERR2585218 1 0.0000 0.990 1.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.988 0.000 1.000
#> round_ERR2585201 1 0.0376 0.987 0.996 0.004
#> round_ERR2585210 1 0.0000 0.990 1.000 0.000
#> aberrant_ERR2585362 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585360 2 0.0000 0.988 0.000 1.000
#> round_ERR2585209 1 0.0000 0.990 1.000 0.000
#> round_ERR2585242 1 0.0376 0.987 0.996 0.004
#> round_ERR2585216 1 0.0000 0.990 1.000 0.000
#> round_ERR2585219 1 0.0000 0.990 1.000 0.000
#> round_ERR2585237 1 0.0000 0.990 1.000 0.000
#> round_ERR2585198 1 0.0376 0.987 0.996 0.004
#> round_ERR2585211 1 0.0000 0.990 1.000 0.000
#> round_ERR2585206 1 0.0000 0.990 1.000 0.000
#> aberrant_ERR2585281 2 0.0000 0.988 0.000 1.000
#> round_ERR2585212 1 0.0000 0.990 1.000 0.000
#> round_ERR2585221 1 0.0000 0.990 1.000 0.000
#> round_ERR2585243 1 0.0000 0.990 1.000 0.000
#> round_ERR2585204 2 0.8909 0.554 0.308 0.692
#> round_ERR2585213 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585373 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585358 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585365 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585359 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585370 2 0.0000 0.988 0.000 1.000
#> round_ERR2585215 1 0.0000 0.990 1.000 0.000
#> round_ERR2585262 2 0.2778 0.942 0.048 0.952
#> round_ERR2585199 1 0.7602 0.721 0.780 0.220
#> aberrant_ERR2585369 2 0.0000 0.988 0.000 1.000
#> round_ERR2585208 1 0.0000 0.990 1.000 0.000
#> round_ERR2585252 1 0.0000 0.990 1.000 0.000
#> round_ERR2585236 1 0.0000 0.990 1.000 0.000
#> aberrant_ERR2585284 2 0.0000 0.988 0.000 1.000
#> round_ERR2585224 1 0.0000 0.990 1.000 0.000
#> round_ERR2585260 1 0.0000 0.990 1.000 0.000
#> round_ERR2585229 1 0.0000 0.990 1.000 0.000
#> aberrant_ERR2585364 2 0.0000 0.988 0.000 1.000
#> round_ERR2585253 1 0.0000 0.990 1.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585371 2 0.0000 0.988 0.000 1.000
#> round_ERR2585239 1 0.0000 0.990 1.000 0.000
#> round_ERR2585273 1 0.0000 0.990 1.000 0.000
#> round_ERR2585256 1 0.0000 0.990 1.000 0.000
#> round_ERR2585272 1 0.0000 0.990 1.000 0.000
#> round_ERR2585246 1 0.0000 0.990 1.000 0.000
#> round_ERR2585261 1 0.0000 0.990 1.000 0.000
#> round_ERR2585254 1 0.0000 0.990 1.000 0.000
#> round_ERR2585225 1 0.5408 0.859 0.876 0.124
#> round_ERR2585235 1 0.0000 0.990 1.000 0.000
#> round_ERR2585271 1 0.0000 0.990 1.000 0.000
#> round_ERR2585251 1 0.0000 0.990 1.000 0.000
#> round_ERR2585255 1 0.7883 0.696 0.764 0.236
#> round_ERR2585257 1 0.0000 0.990 1.000 0.000
#> round_ERR2585226 1 0.0000 0.990 1.000 0.000
#> round_ERR2585265 1 0.0000 0.990 1.000 0.000
#> round_ERR2585259 1 0.0000 0.990 1.000 0.000
#> round_ERR2585247 1 0.0000 0.990 1.000 0.000
#> round_ERR2585241 1 0.0000 0.990 1.000 0.000
#> round_ERR2585263 1 0.0000 0.990 1.000 0.000
#> round_ERR2585264 1 0.0000 0.990 1.000 0.000
#> round_ERR2585233 1 0.0000 0.990 1.000 0.000
#> round_ERR2585223 1 0.0000 0.990 1.000 0.000
#> round_ERR2585234 1 0.2423 0.954 0.960 0.040
#> round_ERR2585222 1 0.0000 0.990 1.000 0.000
#> round_ERR2585228 1 0.0000 0.990 1.000 0.000
#> round_ERR2585248 1 0.0000 0.990 1.000 0.000
#> round_ERR2585240 1 0.0000 0.990 1.000 0.000
#> round_ERR2585270 1 0.0000 0.990 1.000 0.000
#> round_ERR2585232 1 0.0000 0.990 1.000 0.000
#> aberrant_ERR2585341 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585355 2 0.0000 0.988 0.000 1.000
#> round_ERR2585227 1 0.0000 0.990 1.000 0.000
#> aberrant_ERR2585351 2 0.0000 0.988 0.000 1.000
#> round_ERR2585269 1 0.0000 0.990 1.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.988 0.000 1.000
#> aberrant_ERR2585350 2 0.0000 0.988 0.000 1.000
#> round_ERR2585250 1 0.0000 0.990 1.000 0.000
#> round_ERR2585245 1 0.0000 0.990 1.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.988 0.000 1.000
#> round_ERR2585258 1 0.0000 0.990 1.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.988 0.000 1.000
#> round_ERR2585249 1 0.0000 0.990 1.000 0.000
#> round_ERR2585268 1 0.0000 0.990 1.000 0.000
#> aberrant_ERR2585356 2 0.0000 0.988 0.000 1.000
#> round_ERR2585266 1 0.1184 0.977 0.984 0.016
#> round_ERR2585231 1 0.0000 0.990 1.000 0.000
#> round_ERR2585230 1 0.0000 0.990 1.000 0.000
#> round_ERR2585267 1 0.0000 0.990 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.3116 0.836 0.000 0.892 0.108
#> aberrant_ERR2585338 3 0.3412 0.735 0.000 0.124 0.876
#> aberrant_ERR2585325 2 0.2356 0.849 0.000 0.928 0.072
#> aberrant_ERR2585283 2 0.0237 0.846 0.000 0.996 0.004
#> aberrant_ERR2585343 2 0.0592 0.851 0.000 0.988 0.012
#> aberrant_ERR2585329 2 0.5835 0.623 0.000 0.660 0.340
#> aberrant_ERR2585317 2 0.4796 0.765 0.000 0.780 0.220
#> aberrant_ERR2585339 2 0.5178 0.737 0.000 0.744 0.256
#> aberrant_ERR2585335 2 0.0747 0.852 0.000 0.984 0.016
#> aberrant_ERR2585287 2 0.2625 0.825 0.000 0.916 0.084
#> aberrant_ERR2585321 2 0.0000 0.848 0.000 1.000 0.000
#> aberrant_ERR2585297 1 0.0000 0.968 1.000 0.000 0.000
#> aberrant_ERR2585337 2 0.4974 0.752 0.000 0.764 0.236
#> aberrant_ERR2585319 2 0.0000 0.848 0.000 1.000 0.000
#> aberrant_ERR2585315 2 0.0424 0.851 0.000 0.992 0.008
#> aberrant_ERR2585336 2 0.6267 0.384 0.000 0.548 0.452
#> aberrant_ERR2585307 2 0.5465 0.698 0.000 0.712 0.288
#> aberrant_ERR2585301 2 0.2261 0.849 0.000 0.932 0.068
#> aberrant_ERR2585326 2 0.4974 0.754 0.000 0.764 0.236
#> aberrant_ERR2585331 3 0.0592 0.795 0.000 0.012 0.988
#> aberrant_ERR2585346 2 0.0237 0.846 0.000 0.996 0.004
#> aberrant_ERR2585314 2 0.4178 0.799 0.000 0.828 0.172
#> aberrant_ERR2585298 3 0.2860 0.797 0.084 0.004 0.912
#> aberrant_ERR2585345 2 0.5497 0.691 0.000 0.708 0.292
#> aberrant_ERR2585299 1 0.0237 0.968 0.996 0.000 0.004
#> aberrant_ERR2585309 1 0.0000 0.968 1.000 0.000 0.000
#> aberrant_ERR2585303 2 0.6291 0.342 0.000 0.532 0.468
#> aberrant_ERR2585313 2 0.4796 0.766 0.000 0.780 0.220
#> aberrant_ERR2585318 2 0.0747 0.852 0.000 0.984 0.016
#> aberrant_ERR2585328 2 0.6180 0.478 0.000 0.584 0.416
#> aberrant_ERR2585330 2 0.0747 0.852 0.000 0.984 0.016
#> aberrant_ERR2585293 2 0.3116 0.789 0.000 0.892 0.108
#> aberrant_ERR2585342 2 0.1031 0.853 0.000 0.976 0.024
#> aberrant_ERR2585348 2 0.5882 0.611 0.000 0.652 0.348
#> aberrant_ERR2585352 2 0.3267 0.832 0.000 0.884 0.116
#> aberrant_ERR2585308 1 0.0000 0.968 1.000 0.000 0.000
#> aberrant_ERR2585349 3 0.1765 0.793 0.004 0.040 0.956
#> aberrant_ERR2585316 2 0.0424 0.851 0.000 0.992 0.008
#> aberrant_ERR2585306 2 0.1643 0.825 0.044 0.956 0.000
#> aberrant_ERR2585324 2 0.0000 0.848 0.000 1.000 0.000
#> aberrant_ERR2585310 1 0.2356 0.880 0.928 0.072 0.000
#> aberrant_ERR2585296 1 0.0592 0.966 0.988 0.000 0.012
#> aberrant_ERR2585275 2 0.0237 0.846 0.000 0.996 0.004
#> aberrant_ERR2585311 2 0.0424 0.851 0.000 0.992 0.008
#> aberrant_ERR2585292 2 0.3116 0.789 0.000 0.892 0.108
#> aberrant_ERR2585282 2 0.2261 0.849 0.000 0.932 0.068
#> aberrant_ERR2585305 2 0.3686 0.744 0.140 0.860 0.000
#> aberrant_ERR2585278 2 0.1411 0.853 0.000 0.964 0.036
#> aberrant_ERR2585347 2 0.2959 0.838 0.000 0.900 0.100
#> aberrant_ERR2585332 2 0.3482 0.827 0.000 0.872 0.128
#> aberrant_ERR2585280 2 0.0892 0.853 0.000 0.980 0.020
#> aberrant_ERR2585304 1 0.9479 -0.221 0.460 0.192 0.348
#> aberrant_ERR2585322 2 0.5621 0.671 0.000 0.692 0.308
#> aberrant_ERR2585279 3 0.0237 0.796 0.000 0.004 0.996
#> aberrant_ERR2585277 3 0.5529 0.447 0.000 0.296 0.704
#> aberrant_ERR2585295 2 0.6252 0.408 0.000 0.556 0.444
#> aberrant_ERR2585333 2 0.0237 0.846 0.000 0.996 0.004
#> aberrant_ERR2585285 2 0.1289 0.853 0.000 0.968 0.032
#> aberrant_ERR2585286 3 0.2448 0.774 0.000 0.076 0.924
#> aberrant_ERR2585294 2 0.0000 0.848 0.000 1.000 0.000
#> aberrant_ERR2585300 2 0.0237 0.846 0.000 0.996 0.004
#> aberrant_ERR2585334 3 0.0424 0.796 0.000 0.008 0.992
#> aberrant_ERR2585361 2 0.4931 0.756 0.000 0.768 0.232
#> aberrant_ERR2585372 2 0.1031 0.853 0.000 0.976 0.024
#> round_ERR2585217 3 0.6314 0.449 0.392 0.004 0.604
#> round_ERR2585205 1 0.0237 0.968 0.996 0.000 0.004
#> round_ERR2585214 3 0.1399 0.805 0.028 0.004 0.968
#> round_ERR2585202 3 0.5443 0.677 0.260 0.004 0.736
#> aberrant_ERR2585367 3 0.6295 -0.174 0.000 0.472 0.528
#> round_ERR2585220 1 0.0237 0.968 0.996 0.000 0.004
#> round_ERR2585238 1 0.0000 0.968 1.000 0.000 0.000
#> aberrant_ERR2585276 2 0.0000 0.848 0.000 1.000 0.000
#> round_ERR2585218 1 0.0237 0.968 0.996 0.000 0.004
#> aberrant_ERR2585363 2 0.5216 0.728 0.000 0.740 0.260
#> round_ERR2585201 3 0.2400 0.803 0.064 0.004 0.932
#> round_ERR2585210 1 0.0592 0.966 0.988 0.000 0.012
#> aberrant_ERR2585362 2 0.6045 0.555 0.000 0.620 0.380
#> aberrant_ERR2585360 2 0.1529 0.853 0.000 0.960 0.040
#> round_ERR2585209 1 0.1753 0.940 0.952 0.000 0.048
#> round_ERR2585242 3 0.6140 0.352 0.404 0.000 0.596
#> round_ERR2585216 1 0.0892 0.962 0.980 0.000 0.020
#> round_ERR2585219 1 0.0000 0.968 1.000 0.000 0.000
#> round_ERR2585237 3 0.6398 0.394 0.416 0.004 0.580
#> round_ERR2585198 3 0.5158 0.702 0.232 0.004 0.764
#> round_ERR2585211 1 0.0424 0.968 0.992 0.000 0.008
#> round_ERR2585206 1 0.0237 0.968 0.996 0.000 0.004
#> aberrant_ERR2585281 3 0.2959 0.758 0.000 0.100 0.900
#> round_ERR2585212 1 0.1031 0.960 0.976 0.000 0.024
#> round_ERR2585221 1 0.0000 0.968 1.000 0.000 0.000
#> round_ERR2585243 1 0.0000 0.968 1.000 0.000 0.000
#> round_ERR2585204 3 0.0829 0.801 0.012 0.004 0.984
#> round_ERR2585213 3 0.0237 0.796 0.000 0.004 0.996
#> aberrant_ERR2585373 2 0.0424 0.851 0.000 0.992 0.008
#> aberrant_ERR2585358 2 0.0424 0.851 0.000 0.992 0.008
#> aberrant_ERR2585365 2 0.5948 0.594 0.000 0.640 0.360
#> aberrant_ERR2585359 2 0.0000 0.848 0.000 1.000 0.000
#> aberrant_ERR2585370 3 0.5810 0.334 0.000 0.336 0.664
#> round_ERR2585215 1 0.0592 0.966 0.988 0.000 0.012
#> round_ERR2585262 3 0.1399 0.805 0.028 0.004 0.968
#> round_ERR2585199 3 0.5244 0.702 0.240 0.004 0.756
#> aberrant_ERR2585369 2 0.1860 0.851 0.000 0.948 0.052
#> round_ERR2585208 1 0.0000 0.968 1.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.968 1.000 0.000 0.000
#> round_ERR2585236 1 0.1647 0.946 0.960 0.004 0.036
#> aberrant_ERR2585284 2 0.6079 0.503 0.000 0.612 0.388
#> round_ERR2585224 1 0.0237 0.965 0.996 0.004 0.000
#> round_ERR2585260 1 0.0000 0.968 1.000 0.000 0.000
#> round_ERR2585229 1 0.0000 0.968 1.000 0.000 0.000
#> aberrant_ERR2585364 2 0.0237 0.846 0.000 0.996 0.004
#> round_ERR2585253 1 0.0000 0.968 1.000 0.000 0.000
#> aberrant_ERR2585368 3 0.2261 0.779 0.000 0.068 0.932
#> aberrant_ERR2585371 3 0.2165 0.780 0.000 0.064 0.936
#> round_ERR2585239 1 0.0237 0.968 0.996 0.000 0.004
#> round_ERR2585273 1 0.0000 0.968 1.000 0.000 0.000
#> round_ERR2585256 1 0.0892 0.962 0.980 0.000 0.020
#> round_ERR2585272 1 0.1031 0.960 0.976 0.000 0.024
#> round_ERR2585246 1 0.0237 0.965 0.996 0.004 0.000
#> round_ERR2585261 1 0.1860 0.937 0.948 0.000 0.052
#> round_ERR2585254 1 0.1031 0.960 0.976 0.000 0.024
#> round_ERR2585225 3 0.1878 0.805 0.044 0.004 0.952
#> round_ERR2585235 1 0.1163 0.957 0.972 0.000 0.028
#> round_ERR2585271 1 0.0592 0.966 0.988 0.000 0.012
#> round_ERR2585251 1 0.0237 0.968 0.996 0.000 0.004
#> round_ERR2585255 3 0.1765 0.806 0.040 0.004 0.956
#> round_ERR2585257 1 0.6495 -0.035 0.536 0.004 0.460
#> round_ERR2585226 1 0.0000 0.968 1.000 0.000 0.000
#> round_ERR2585265 1 0.0237 0.968 0.996 0.000 0.004
#> round_ERR2585259 1 0.3686 0.818 0.860 0.000 0.140
#> round_ERR2585247 1 0.0000 0.968 1.000 0.000 0.000
#> round_ERR2585241 1 0.0592 0.966 0.988 0.000 0.012
#> round_ERR2585263 1 0.0747 0.964 0.984 0.000 0.016
#> round_ERR2585264 1 0.0000 0.968 1.000 0.000 0.000
#> round_ERR2585233 3 0.5404 0.684 0.256 0.004 0.740
#> round_ERR2585223 1 0.0000 0.968 1.000 0.000 0.000
#> round_ERR2585234 3 0.3193 0.791 0.100 0.004 0.896
#> round_ERR2585222 1 0.0424 0.968 0.992 0.000 0.008
#> round_ERR2585228 1 0.0237 0.968 0.996 0.000 0.004
#> round_ERR2585248 1 0.0000 0.968 1.000 0.000 0.000
#> round_ERR2585240 1 0.2356 0.915 0.928 0.000 0.072
#> round_ERR2585270 1 0.0747 0.964 0.984 0.000 0.016
#> round_ERR2585232 1 0.1163 0.957 0.972 0.000 0.028
#> aberrant_ERR2585341 3 0.4452 0.648 0.000 0.192 0.808
#> aberrant_ERR2585355 3 0.3619 0.725 0.000 0.136 0.864
#> round_ERR2585227 1 0.0424 0.968 0.992 0.000 0.008
#> aberrant_ERR2585351 2 0.3551 0.823 0.000 0.868 0.132
#> round_ERR2585269 1 0.0000 0.968 1.000 0.000 0.000
#> aberrant_ERR2585357 2 0.6192 0.474 0.000 0.580 0.420
#> aberrant_ERR2585350 2 0.5988 0.580 0.000 0.632 0.368
#> round_ERR2585250 1 0.0424 0.968 0.992 0.000 0.008
#> round_ERR2585245 1 0.0000 0.968 1.000 0.000 0.000
#> aberrant_ERR2585353 2 0.2165 0.849 0.000 0.936 0.064
#> round_ERR2585258 1 0.0237 0.968 0.996 0.000 0.004
#> aberrant_ERR2585354 2 0.1529 0.853 0.000 0.960 0.040
#> round_ERR2585249 1 0.0000 0.968 1.000 0.000 0.000
#> round_ERR2585268 1 0.0747 0.965 0.984 0.000 0.016
#> aberrant_ERR2585356 2 0.0237 0.846 0.000 0.996 0.004
#> round_ERR2585266 3 0.5905 0.482 0.352 0.000 0.648
#> round_ERR2585231 1 0.0000 0.968 1.000 0.000 0.000
#> round_ERR2585230 1 0.0592 0.966 0.988 0.000 0.012
#> round_ERR2585267 1 0.0424 0.968 0.992 0.000 0.008
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 2 0.3215 0.790 0.000 0.876 0.032 0.092
#> aberrant_ERR2585338 3 0.3529 0.654 0.000 0.152 0.836 0.012
#> aberrant_ERR2585325 2 0.3523 0.784 0.000 0.856 0.032 0.112
#> aberrant_ERR2585283 2 0.4857 0.486 0.000 0.668 0.008 0.324
#> aberrant_ERR2585343 2 0.0469 0.805 0.000 0.988 0.000 0.012
#> aberrant_ERR2585329 2 0.3205 0.782 0.000 0.872 0.104 0.024
#> aberrant_ERR2585317 2 0.2036 0.805 0.000 0.936 0.032 0.032
#> aberrant_ERR2585339 2 0.4957 0.562 0.000 0.668 0.320 0.012
#> aberrant_ERR2585335 2 0.0469 0.804 0.000 0.988 0.000 0.012
#> aberrant_ERR2585287 2 0.7091 0.350 0.000 0.564 0.248 0.188
#> aberrant_ERR2585321 2 0.1211 0.804 0.000 0.960 0.000 0.040
#> aberrant_ERR2585297 1 0.2888 0.898 0.872 0.004 0.000 0.124
#> aberrant_ERR2585337 2 0.3554 0.762 0.000 0.844 0.136 0.020
#> aberrant_ERR2585319 2 0.0895 0.804 0.000 0.976 0.004 0.020
#> aberrant_ERR2585315 2 0.3610 0.699 0.000 0.800 0.000 0.200
#> aberrant_ERR2585336 2 0.4936 0.510 0.000 0.652 0.340 0.008
#> aberrant_ERR2585307 2 0.4974 0.659 0.000 0.736 0.224 0.040
#> aberrant_ERR2585301 2 0.1305 0.805 0.000 0.960 0.004 0.036
#> aberrant_ERR2585326 2 0.4295 0.666 0.000 0.752 0.240 0.008
#> aberrant_ERR2585331 3 0.1452 0.754 0.000 0.036 0.956 0.008
#> aberrant_ERR2585346 2 0.4391 0.622 0.000 0.740 0.008 0.252
#> aberrant_ERR2585314 2 0.2222 0.804 0.000 0.924 0.016 0.060
#> aberrant_ERR2585298 3 0.1022 0.763 0.032 0.000 0.968 0.000
#> aberrant_ERR2585345 2 0.2944 0.772 0.000 0.868 0.128 0.004
#> aberrant_ERR2585299 1 0.0592 0.930 0.984 0.000 0.000 0.016
#> aberrant_ERR2585309 1 0.3450 0.877 0.836 0.008 0.000 0.156
#> aberrant_ERR2585303 2 0.4989 0.274 0.000 0.528 0.472 0.000
#> aberrant_ERR2585313 2 0.2675 0.785 0.000 0.892 0.100 0.008
#> aberrant_ERR2585318 2 0.1118 0.804 0.000 0.964 0.000 0.036
#> aberrant_ERR2585328 2 0.4983 0.606 0.000 0.704 0.272 0.024
#> aberrant_ERR2585330 2 0.1302 0.805 0.000 0.956 0.000 0.044
#> aberrant_ERR2585293 4 0.4655 1.000 0.000 0.208 0.032 0.760
#> aberrant_ERR2585342 2 0.0592 0.806 0.000 0.984 0.000 0.016
#> aberrant_ERR2585348 2 0.4212 0.688 0.000 0.772 0.216 0.012
#> aberrant_ERR2585352 2 0.1510 0.808 0.000 0.956 0.028 0.016
#> aberrant_ERR2585308 1 0.2345 0.913 0.900 0.000 0.000 0.100
#> aberrant_ERR2585349 3 0.4286 0.722 0.056 0.072 0.844 0.028
#> aberrant_ERR2585316 2 0.0921 0.806 0.000 0.972 0.000 0.028
#> aberrant_ERR2585306 2 0.5713 0.343 0.040 0.620 0.000 0.340
#> aberrant_ERR2585324 2 0.1661 0.796 0.000 0.944 0.004 0.052
#> aberrant_ERR2585310 1 0.5113 0.727 0.760 0.152 0.000 0.088
#> aberrant_ERR2585296 1 0.2364 0.923 0.928 0.008 0.028 0.036
#> aberrant_ERR2585275 2 0.4483 0.584 0.000 0.712 0.004 0.284
#> aberrant_ERR2585311 2 0.1211 0.803 0.000 0.960 0.000 0.040
#> aberrant_ERR2585292 4 0.4655 1.000 0.000 0.208 0.032 0.760
#> aberrant_ERR2585282 2 0.1489 0.807 0.000 0.952 0.004 0.044
#> aberrant_ERR2585305 2 0.2741 0.763 0.012 0.892 0.000 0.096
#> aberrant_ERR2585278 2 0.1022 0.806 0.000 0.968 0.000 0.032
#> aberrant_ERR2585347 2 0.3708 0.768 0.000 0.832 0.020 0.148
#> aberrant_ERR2585332 2 0.2002 0.807 0.000 0.936 0.020 0.044
#> aberrant_ERR2585280 2 0.4671 0.667 0.000 0.752 0.028 0.220
#> aberrant_ERR2585304 3 0.8539 0.158 0.380 0.184 0.392 0.044
#> aberrant_ERR2585322 2 0.4549 0.711 0.000 0.776 0.188 0.036
#> aberrant_ERR2585279 3 0.0672 0.758 0.008 0.008 0.984 0.000
#> aberrant_ERR2585277 3 0.3873 0.561 0.000 0.228 0.772 0.000
#> aberrant_ERR2585295 3 0.5938 -0.193 0.000 0.480 0.484 0.036
#> aberrant_ERR2585333 2 0.3791 0.692 0.000 0.796 0.004 0.200
#> aberrant_ERR2585285 2 0.0469 0.806 0.000 0.988 0.000 0.012
#> aberrant_ERR2585286 3 0.2179 0.741 0.000 0.064 0.924 0.012
#> aberrant_ERR2585294 2 0.2814 0.764 0.000 0.868 0.000 0.132
#> aberrant_ERR2585300 2 0.3610 0.710 0.000 0.800 0.000 0.200
#> aberrant_ERR2585334 3 0.1452 0.754 0.000 0.036 0.956 0.008
#> aberrant_ERR2585361 2 0.4197 0.740 0.000 0.808 0.156 0.036
#> aberrant_ERR2585372 2 0.1022 0.806 0.000 0.968 0.000 0.032
#> round_ERR2585217 3 0.5708 0.478 0.340 0.016 0.628 0.016
#> round_ERR2585205 1 0.0895 0.928 0.976 0.000 0.004 0.020
#> round_ERR2585214 3 0.1302 0.763 0.044 0.000 0.956 0.000
#> round_ERR2585202 3 0.4871 0.661 0.176 0.036 0.776 0.012
#> aberrant_ERR2585367 2 0.4967 0.334 0.000 0.548 0.452 0.000
#> round_ERR2585220 1 0.0336 0.929 0.992 0.000 0.000 0.008
#> round_ERR2585238 1 0.1118 0.929 0.964 0.000 0.000 0.036
#> aberrant_ERR2585276 2 0.3791 0.712 0.000 0.796 0.004 0.200
#> round_ERR2585218 1 0.0336 0.929 0.992 0.000 0.000 0.008
#> aberrant_ERR2585363 2 0.2385 0.800 0.000 0.920 0.052 0.028
#> round_ERR2585201 3 0.2611 0.741 0.096 0.000 0.896 0.008
#> round_ERR2585210 1 0.2297 0.912 0.932 0.024 0.012 0.032
#> aberrant_ERR2585362 2 0.4257 0.740 0.000 0.812 0.140 0.048
#> aberrant_ERR2585360 2 0.1211 0.805 0.000 0.960 0.000 0.040
#> round_ERR2585209 1 0.2988 0.858 0.876 0.000 0.112 0.012
#> round_ERR2585242 3 0.4137 0.626 0.208 0.000 0.780 0.012
#> round_ERR2585216 1 0.1962 0.919 0.944 0.008 0.024 0.024
#> round_ERR2585219 1 0.0895 0.928 0.976 0.000 0.004 0.020
#> round_ERR2585237 3 0.5787 0.405 0.392 0.012 0.580 0.016
#> round_ERR2585198 3 0.3074 0.704 0.152 0.000 0.848 0.000
#> round_ERR2585211 1 0.1256 0.926 0.964 0.000 0.008 0.028
#> round_ERR2585206 1 0.0336 0.929 0.992 0.000 0.000 0.008
#> aberrant_ERR2585281 3 0.2473 0.729 0.000 0.080 0.908 0.012
#> round_ERR2585212 1 0.1733 0.919 0.948 0.000 0.028 0.024
#> round_ERR2585221 1 0.3479 0.877 0.840 0.012 0.000 0.148
#> round_ERR2585243 1 0.1635 0.930 0.948 0.008 0.000 0.044
#> round_ERR2585204 3 0.1022 0.763 0.032 0.000 0.968 0.000
#> round_ERR2585213 3 0.1004 0.763 0.024 0.004 0.972 0.000
#> aberrant_ERR2585373 2 0.0707 0.805 0.000 0.980 0.000 0.020
#> aberrant_ERR2585358 2 0.1211 0.806 0.000 0.960 0.000 0.040
#> aberrant_ERR2585365 2 0.3324 0.766 0.000 0.852 0.136 0.012
#> aberrant_ERR2585359 2 0.1305 0.805 0.000 0.960 0.004 0.036
#> aberrant_ERR2585370 3 0.4855 0.161 0.000 0.400 0.600 0.000
#> round_ERR2585215 1 0.1182 0.930 0.968 0.000 0.016 0.016
#> round_ERR2585262 3 0.1985 0.764 0.024 0.012 0.944 0.020
#> round_ERR2585199 3 0.4173 0.682 0.172 0.004 0.804 0.020
#> aberrant_ERR2585369 2 0.0921 0.804 0.000 0.972 0.000 0.028
#> round_ERR2585208 1 0.0921 0.930 0.972 0.000 0.000 0.028
#> round_ERR2585252 1 0.2334 0.917 0.908 0.004 0.000 0.088
#> round_ERR2585236 1 0.2207 0.913 0.928 0.004 0.056 0.012
#> aberrant_ERR2585284 2 0.5957 0.316 0.000 0.540 0.420 0.040
#> round_ERR2585224 1 0.4375 0.830 0.788 0.032 0.000 0.180
#> round_ERR2585260 1 0.0921 0.930 0.972 0.000 0.000 0.028
#> round_ERR2585229 1 0.0817 0.930 0.976 0.000 0.000 0.024
#> aberrant_ERR2585364 2 0.3074 0.738 0.000 0.848 0.000 0.152
#> round_ERR2585253 1 0.1792 0.923 0.932 0.000 0.000 0.068
#> aberrant_ERR2585368 3 0.1557 0.746 0.000 0.056 0.944 0.000
#> aberrant_ERR2585371 3 0.1557 0.746 0.000 0.056 0.944 0.000
#> round_ERR2585239 1 0.0469 0.930 0.988 0.000 0.000 0.012
#> round_ERR2585273 1 0.2704 0.903 0.876 0.000 0.000 0.124
#> round_ERR2585256 1 0.1297 0.924 0.964 0.000 0.020 0.016
#> round_ERR2585272 1 0.1059 0.925 0.972 0.000 0.016 0.012
#> round_ERR2585246 1 0.3881 0.857 0.812 0.016 0.000 0.172
#> round_ERR2585261 1 0.2654 0.874 0.888 0.000 0.108 0.004
#> round_ERR2585254 1 0.1151 0.924 0.968 0.000 0.024 0.008
#> round_ERR2585225 3 0.1118 0.763 0.036 0.000 0.964 0.000
#> round_ERR2585235 1 0.2131 0.928 0.932 0.000 0.036 0.032
#> round_ERR2585271 1 0.0188 0.929 0.996 0.000 0.000 0.004
#> round_ERR2585251 1 0.1557 0.925 0.944 0.000 0.000 0.056
#> round_ERR2585255 3 0.1488 0.762 0.032 0.000 0.956 0.012
#> round_ERR2585257 3 0.5193 0.348 0.412 0.000 0.580 0.008
#> round_ERR2585226 1 0.2530 0.905 0.888 0.000 0.000 0.112
#> round_ERR2585265 1 0.0707 0.930 0.980 0.000 0.000 0.020
#> round_ERR2585259 1 0.3946 0.781 0.812 0.000 0.168 0.020
#> round_ERR2585247 1 0.2654 0.906 0.888 0.004 0.000 0.108
#> round_ERR2585241 1 0.1406 0.924 0.960 0.000 0.016 0.024
#> round_ERR2585263 1 0.5679 0.731 0.768 0.108 0.068 0.056
#> round_ERR2585264 1 0.2081 0.918 0.916 0.000 0.000 0.084
#> round_ERR2585233 3 0.2469 0.740 0.108 0.000 0.892 0.000
#> round_ERR2585223 1 0.2149 0.916 0.912 0.000 0.000 0.088
#> round_ERR2585234 3 0.1302 0.761 0.044 0.000 0.956 0.000
#> round_ERR2585222 1 0.1510 0.930 0.956 0.000 0.016 0.028
#> round_ERR2585228 1 0.0779 0.928 0.980 0.000 0.004 0.016
#> round_ERR2585248 1 0.0817 0.930 0.976 0.000 0.000 0.024
#> round_ERR2585240 1 0.4267 0.779 0.788 0.000 0.188 0.024
#> round_ERR2585270 1 0.0524 0.930 0.988 0.000 0.004 0.008
#> round_ERR2585232 1 0.1256 0.923 0.964 0.000 0.028 0.008
#> aberrant_ERR2585341 3 0.4319 0.544 0.000 0.228 0.760 0.012
#> aberrant_ERR2585355 3 0.3074 0.663 0.000 0.152 0.848 0.000
#> round_ERR2585227 1 0.2281 0.912 0.904 0.000 0.000 0.096
#> aberrant_ERR2585351 2 0.2089 0.804 0.000 0.932 0.020 0.048
#> round_ERR2585269 1 0.2760 0.900 0.872 0.000 0.000 0.128
#> aberrant_ERR2585357 2 0.4972 0.318 0.000 0.544 0.456 0.000
#> aberrant_ERR2585350 2 0.4889 0.506 0.000 0.636 0.360 0.004
#> round_ERR2585250 1 0.1707 0.922 0.952 0.004 0.020 0.024
#> round_ERR2585245 1 0.3577 0.870 0.832 0.012 0.000 0.156
#> aberrant_ERR2585353 2 0.0817 0.806 0.000 0.976 0.000 0.024
#> round_ERR2585258 1 0.1867 0.921 0.928 0.000 0.000 0.072
#> aberrant_ERR2585354 2 0.0921 0.806 0.000 0.972 0.000 0.028
#> round_ERR2585249 1 0.3123 0.879 0.844 0.000 0.000 0.156
#> round_ERR2585268 1 0.1297 0.927 0.964 0.000 0.020 0.016
#> aberrant_ERR2585356 2 0.2081 0.786 0.000 0.916 0.000 0.084
#> round_ERR2585266 3 0.3768 0.661 0.184 0.000 0.808 0.008
#> round_ERR2585231 1 0.3448 0.868 0.828 0.004 0.000 0.168
#> round_ERR2585230 1 0.1004 0.928 0.972 0.000 0.004 0.024
#> round_ERR2585267 1 0.2530 0.910 0.888 0.000 0.000 0.112
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 2 0.5127 0.68064 0.004 0.680 0.012 0.044 0.260
#> aberrant_ERR2585338 3 0.2293 0.75797 0.000 0.084 0.900 0.000 0.016
#> aberrant_ERR2585325 2 0.5197 0.69516 0.004 0.688 0.012 0.056 0.240
#> aberrant_ERR2585283 2 0.5687 0.53323 0.000 0.580 0.000 0.316 0.104
#> aberrant_ERR2585343 2 0.1701 0.82688 0.000 0.936 0.000 0.016 0.048
#> aberrant_ERR2585329 2 0.3027 0.82421 0.000 0.876 0.072 0.012 0.040
#> aberrant_ERR2585317 2 0.2295 0.81679 0.000 0.900 0.004 0.008 0.088
#> aberrant_ERR2585339 2 0.4743 0.58300 0.000 0.640 0.332 0.004 0.024
#> aberrant_ERR2585335 2 0.2450 0.82612 0.000 0.900 0.000 0.048 0.052
#> aberrant_ERR2585287 2 0.7608 0.40548 0.000 0.488 0.248 0.160 0.104
#> aberrant_ERR2585321 2 0.2278 0.82781 0.000 0.908 0.000 0.032 0.060
#> aberrant_ERR2585297 1 0.4288 0.24541 0.612 0.004 0.000 0.000 0.384
#> aberrant_ERR2585337 2 0.3893 0.80626 0.000 0.824 0.096 0.064 0.016
#> aberrant_ERR2585319 2 0.2554 0.82266 0.000 0.892 0.000 0.036 0.072
#> aberrant_ERR2585315 2 0.3857 0.79413 0.000 0.808 0.000 0.108 0.084
#> aberrant_ERR2585336 2 0.4625 0.61260 0.000 0.660 0.316 0.012 0.012
#> aberrant_ERR2585307 2 0.5503 0.61632 0.000 0.644 0.276 0.020 0.060
#> aberrant_ERR2585301 2 0.1942 0.82936 0.000 0.920 0.000 0.012 0.068
#> aberrant_ERR2585326 2 0.3910 0.74768 0.000 0.772 0.196 0.000 0.032
#> aberrant_ERR2585331 3 0.0162 0.81063 0.000 0.000 0.996 0.000 0.004
#> aberrant_ERR2585346 2 0.5444 0.67738 0.000 0.656 0.000 0.140 0.204
#> aberrant_ERR2585314 2 0.3808 0.75403 0.012 0.780 0.004 0.004 0.200
#> aberrant_ERR2585298 3 0.0404 0.81179 0.012 0.000 0.988 0.000 0.000
#> aberrant_ERR2585345 2 0.2635 0.81919 0.000 0.888 0.088 0.016 0.008
#> aberrant_ERR2585299 1 0.1341 0.70260 0.944 0.000 0.000 0.000 0.056
#> aberrant_ERR2585309 1 0.4161 0.23372 0.608 0.000 0.000 0.000 0.392
#> aberrant_ERR2585303 3 0.4968 -0.07518 0.000 0.456 0.516 0.000 0.028
#> aberrant_ERR2585313 2 0.3310 0.82338 0.000 0.868 0.056 0.036 0.040
#> aberrant_ERR2585318 2 0.2006 0.82435 0.000 0.916 0.000 0.012 0.072
#> aberrant_ERR2585328 2 0.5020 0.72177 0.000 0.712 0.180 0.004 0.104
#> aberrant_ERR2585330 2 0.2708 0.82049 0.000 0.884 0.000 0.044 0.072
#> aberrant_ERR2585293 4 0.0955 1.00000 0.000 0.028 0.004 0.968 0.000
#> aberrant_ERR2585342 2 0.2054 0.82041 0.000 0.920 0.000 0.028 0.052
#> aberrant_ERR2585348 2 0.4622 0.77193 0.000 0.764 0.088 0.012 0.136
#> aberrant_ERR2585352 2 0.2227 0.82630 0.000 0.916 0.004 0.032 0.048
#> aberrant_ERR2585308 1 0.3684 0.50139 0.720 0.000 0.000 0.000 0.280
#> aberrant_ERR2585349 3 0.6364 0.53220 0.076 0.092 0.668 0.012 0.152
#> aberrant_ERR2585316 2 0.2344 0.83066 0.000 0.904 0.000 0.032 0.064
#> aberrant_ERR2585306 5 0.5380 -0.24809 0.016 0.424 0.000 0.028 0.532
#> aberrant_ERR2585324 2 0.3359 0.81244 0.000 0.840 0.000 0.052 0.108
#> aberrant_ERR2585310 1 0.6199 -0.00696 0.548 0.116 0.000 0.012 0.324
#> aberrant_ERR2585296 1 0.5268 0.57771 0.720 0.048 0.032 0.008 0.192
#> aberrant_ERR2585275 2 0.5578 0.65706 0.000 0.644 0.000 0.180 0.176
#> aberrant_ERR2585311 2 0.1942 0.82343 0.000 0.920 0.000 0.012 0.068
#> aberrant_ERR2585292 4 0.0955 1.00000 0.000 0.028 0.004 0.968 0.000
#> aberrant_ERR2585282 2 0.3409 0.78789 0.000 0.816 0.000 0.024 0.160
#> aberrant_ERR2585305 2 0.3439 0.77658 0.004 0.800 0.000 0.008 0.188
#> aberrant_ERR2585278 2 0.1894 0.82352 0.000 0.920 0.000 0.008 0.072
#> aberrant_ERR2585347 2 0.4093 0.80736 0.000 0.808 0.012 0.088 0.092
#> aberrant_ERR2585332 2 0.3670 0.77837 0.000 0.796 0.004 0.020 0.180
#> aberrant_ERR2585280 2 0.5449 0.74974 0.000 0.720 0.048 0.140 0.092
#> aberrant_ERR2585304 3 0.7585 0.29499 0.176 0.116 0.528 0.004 0.176
#> aberrant_ERR2585322 2 0.4189 0.79968 0.000 0.808 0.108 0.056 0.028
#> aberrant_ERR2585279 3 0.0000 0.81060 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585277 3 0.2463 0.74572 0.000 0.100 0.888 0.004 0.008
#> aberrant_ERR2585295 3 0.5987 -0.10935 0.000 0.440 0.476 0.016 0.068
#> aberrant_ERR2585333 2 0.3970 0.78631 0.000 0.800 0.000 0.104 0.096
#> aberrant_ERR2585285 2 0.1670 0.82479 0.000 0.936 0.000 0.012 0.052
#> aberrant_ERR2585286 3 0.1018 0.80379 0.000 0.016 0.968 0.000 0.016
#> aberrant_ERR2585294 2 0.3602 0.78036 0.000 0.796 0.000 0.024 0.180
#> aberrant_ERR2585300 2 0.4150 0.73831 0.000 0.748 0.000 0.036 0.216
#> aberrant_ERR2585334 3 0.0162 0.81063 0.000 0.000 0.996 0.000 0.004
#> aberrant_ERR2585361 2 0.4095 0.81221 0.000 0.820 0.088 0.052 0.040
#> aberrant_ERR2585372 2 0.2237 0.81952 0.000 0.904 0.004 0.008 0.084
#> round_ERR2585217 1 0.6677 0.18947 0.504 0.016 0.312 0.000 0.168
#> round_ERR2585205 1 0.2020 0.67639 0.900 0.000 0.000 0.000 0.100
#> round_ERR2585214 3 0.0740 0.81116 0.008 0.000 0.980 0.004 0.008
#> round_ERR2585202 3 0.4604 0.67896 0.104 0.044 0.792 0.004 0.056
#> aberrant_ERR2585367 2 0.5508 0.17177 0.000 0.476 0.460 0.000 0.064
#> round_ERR2585220 1 0.1502 0.70278 0.940 0.000 0.004 0.000 0.056
#> round_ERR2585238 1 0.2561 0.65361 0.856 0.000 0.000 0.000 0.144
#> aberrant_ERR2585276 2 0.4444 0.75593 0.000 0.748 0.000 0.072 0.180
#> round_ERR2585218 1 0.0880 0.69971 0.968 0.000 0.000 0.000 0.032
#> aberrant_ERR2585363 2 0.3145 0.79598 0.000 0.844 0.008 0.012 0.136
#> round_ERR2585201 3 0.2464 0.75418 0.092 0.000 0.892 0.004 0.012
#> round_ERR2585210 1 0.3607 0.60648 0.804 0.008 0.004 0.008 0.176
#> aberrant_ERR2585362 2 0.4370 0.75791 0.000 0.768 0.040 0.016 0.176
#> aberrant_ERR2585360 2 0.2077 0.82724 0.000 0.908 0.000 0.008 0.084
#> round_ERR2585209 1 0.3497 0.64555 0.840 0.000 0.044 0.008 0.108
#> round_ERR2585242 3 0.0963 0.80477 0.036 0.000 0.964 0.000 0.000
#> round_ERR2585216 1 0.3312 0.63528 0.832 0.004 0.012 0.004 0.148
#> round_ERR2585219 1 0.1965 0.67648 0.904 0.000 0.000 0.000 0.096
#> round_ERR2585237 1 0.6258 0.27099 0.564 0.004 0.296 0.008 0.128
#> round_ERR2585198 3 0.1492 0.80180 0.040 0.000 0.948 0.004 0.008
#> round_ERR2585211 1 0.2377 0.66311 0.872 0.000 0.000 0.000 0.128
#> round_ERR2585206 1 0.0880 0.70247 0.968 0.000 0.000 0.000 0.032
#> aberrant_ERR2585281 3 0.1356 0.80031 0.000 0.028 0.956 0.004 0.012
#> round_ERR2585212 1 0.3107 0.65209 0.852 0.000 0.016 0.008 0.124
#> round_ERR2585221 1 0.4437 -0.12332 0.532 0.004 0.000 0.000 0.464
#> round_ERR2585243 1 0.3243 0.66383 0.812 0.004 0.000 0.004 0.180
#> round_ERR2585204 3 0.0486 0.81183 0.004 0.000 0.988 0.004 0.004
#> round_ERR2585213 3 0.0000 0.81060 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585373 2 0.1648 0.82727 0.000 0.940 0.000 0.020 0.040
#> aberrant_ERR2585358 2 0.1300 0.82374 0.000 0.956 0.000 0.016 0.028
#> aberrant_ERR2585365 2 0.3277 0.81720 0.000 0.856 0.072 0.004 0.068
#> aberrant_ERR2585359 2 0.2130 0.82073 0.000 0.908 0.000 0.012 0.080
#> aberrant_ERR2585370 3 0.4213 0.44217 0.000 0.308 0.680 0.000 0.012
#> round_ERR2585215 1 0.2293 0.70227 0.900 0.000 0.016 0.000 0.084
#> round_ERR2585262 3 0.0833 0.81052 0.004 0.004 0.976 0.000 0.016
#> round_ERR2585199 3 0.4528 0.60697 0.172 0.000 0.756 0.008 0.064
#> aberrant_ERR2585369 2 0.1430 0.82484 0.000 0.944 0.000 0.004 0.052
#> round_ERR2585208 1 0.1671 0.69441 0.924 0.000 0.000 0.000 0.076
#> round_ERR2585252 1 0.4009 0.43940 0.684 0.000 0.000 0.004 0.312
#> round_ERR2585236 1 0.4177 0.65483 0.808 0.004 0.072 0.012 0.104
#> aberrant_ERR2585284 2 0.6417 0.56036 0.000 0.588 0.268 0.044 0.100
#> round_ERR2585224 5 0.4637 0.06308 0.452 0.012 0.000 0.000 0.536
#> round_ERR2585260 1 0.3109 0.60141 0.800 0.000 0.000 0.000 0.200
#> round_ERR2585229 1 0.1357 0.70386 0.948 0.000 0.000 0.004 0.048
#> aberrant_ERR2585364 2 0.3631 0.80189 0.000 0.824 0.000 0.104 0.072
#> round_ERR2585253 1 0.2773 0.64259 0.836 0.000 0.000 0.000 0.164
#> aberrant_ERR2585368 3 0.0404 0.81019 0.000 0.012 0.988 0.000 0.000
#> aberrant_ERR2585371 3 0.0290 0.81019 0.000 0.008 0.992 0.000 0.000
#> round_ERR2585239 1 0.1357 0.69891 0.948 0.000 0.000 0.004 0.048
#> round_ERR2585273 1 0.4060 0.32856 0.640 0.000 0.000 0.000 0.360
#> round_ERR2585256 1 0.1670 0.70292 0.936 0.000 0.012 0.000 0.052
#> round_ERR2585272 1 0.1892 0.70144 0.916 0.000 0.004 0.000 0.080
#> round_ERR2585246 5 0.4451 -0.07877 0.492 0.004 0.000 0.000 0.504
#> round_ERR2585261 1 0.3145 0.63110 0.844 0.000 0.136 0.008 0.012
#> round_ERR2585254 1 0.1386 0.69793 0.952 0.000 0.016 0.000 0.032
#> round_ERR2585225 3 0.0290 0.81160 0.008 0.000 0.992 0.000 0.000
#> round_ERR2585235 1 0.4538 0.59654 0.752 0.000 0.060 0.008 0.180
#> round_ERR2585271 1 0.1430 0.70042 0.944 0.000 0.000 0.004 0.052
#> round_ERR2585251 1 0.3196 0.61361 0.804 0.000 0.004 0.000 0.192
#> round_ERR2585255 3 0.0290 0.81160 0.008 0.000 0.992 0.000 0.000
#> round_ERR2585257 3 0.4576 0.48440 0.268 0.000 0.692 0.000 0.040
#> round_ERR2585226 1 0.4045 0.33452 0.644 0.000 0.000 0.000 0.356
#> round_ERR2585265 1 0.1502 0.69550 0.940 0.000 0.000 0.004 0.056
#> round_ERR2585259 1 0.3876 0.60823 0.812 0.000 0.116 0.004 0.068
#> round_ERR2585247 1 0.4101 0.38922 0.664 0.000 0.000 0.004 0.332
#> round_ERR2585241 1 0.2989 0.65538 0.852 0.000 0.008 0.008 0.132
#> round_ERR2585263 1 0.5775 0.43154 0.660 0.052 0.024 0.016 0.248
#> round_ERR2585264 1 0.3421 0.60832 0.788 0.000 0.000 0.008 0.204
#> round_ERR2585233 3 0.0880 0.80817 0.032 0.000 0.968 0.000 0.000
#> round_ERR2585223 1 0.3730 0.47902 0.712 0.000 0.000 0.000 0.288
#> round_ERR2585234 3 0.0833 0.81036 0.016 0.000 0.976 0.004 0.004
#> round_ERR2585222 1 0.2886 0.70054 0.864 0.000 0.004 0.016 0.116
#> round_ERR2585228 1 0.1341 0.69760 0.944 0.000 0.000 0.000 0.056
#> round_ERR2585248 1 0.2798 0.68220 0.852 0.000 0.000 0.008 0.140
#> round_ERR2585240 3 0.6132 -0.20277 0.428 0.000 0.444 0.000 0.128
#> round_ERR2585270 1 0.2457 0.70408 0.900 0.000 0.016 0.008 0.076
#> round_ERR2585232 1 0.1942 0.70220 0.920 0.000 0.012 0.000 0.068
#> aberrant_ERR2585341 3 0.3073 0.71757 0.000 0.116 0.856 0.004 0.024
#> aberrant_ERR2585355 3 0.2358 0.74554 0.000 0.104 0.888 0.000 0.008
#> round_ERR2585227 1 0.4045 0.33235 0.644 0.000 0.000 0.000 0.356
#> aberrant_ERR2585351 2 0.3396 0.78715 0.004 0.824 0.004 0.012 0.156
#> round_ERR2585269 1 0.4074 0.31274 0.636 0.000 0.000 0.000 0.364
#> aberrant_ERR2585357 2 0.4824 0.23820 0.000 0.512 0.468 0.000 0.020
#> aberrant_ERR2585350 2 0.4654 0.54886 0.000 0.628 0.348 0.000 0.024
#> round_ERR2585250 1 0.3660 0.67867 0.844 0.020 0.016 0.016 0.104
#> round_ERR2585245 1 0.4297 -0.14610 0.528 0.000 0.000 0.000 0.472
#> aberrant_ERR2585353 2 0.1701 0.82412 0.000 0.936 0.000 0.016 0.048
#> round_ERR2585258 1 0.3707 0.48288 0.716 0.000 0.000 0.000 0.284
#> aberrant_ERR2585354 2 0.1768 0.82399 0.000 0.924 0.000 0.004 0.072
#> round_ERR2585249 1 0.4367 0.12316 0.580 0.000 0.000 0.004 0.416
#> round_ERR2585268 1 0.3688 0.67941 0.828 0.020 0.008 0.012 0.132
#> aberrant_ERR2585356 2 0.3053 0.80311 0.000 0.828 0.000 0.008 0.164
#> round_ERR2585266 3 0.1270 0.79409 0.052 0.000 0.948 0.000 0.000
#> round_ERR2585231 1 0.4287 -0.08694 0.540 0.000 0.000 0.000 0.460
#> round_ERR2585230 1 0.2859 0.67210 0.876 0.000 0.016 0.012 0.096
#> round_ERR2585267 1 0.3752 0.47178 0.708 0.000 0.000 0.000 0.292
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 6 0.5109 0.9280 0.020 0.000 0.036 0.004 0.388 0.552
#> aberrant_ERR2585338 2 0.2106 0.7704 0.000 0.904 0.000 0.000 0.064 0.032
#> aberrant_ERR2585325 6 0.5249 0.9266 0.020 0.000 0.036 0.008 0.412 0.524
#> aberrant_ERR2585283 5 0.6158 0.2448 0.000 0.004 0.060 0.148 0.592 0.196
#> aberrant_ERR2585343 5 0.2766 0.6041 0.000 0.000 0.020 0.004 0.852 0.124
#> aberrant_ERR2585329 5 0.2544 0.6128 0.000 0.012 0.004 0.000 0.864 0.120
#> aberrant_ERR2585317 5 0.2730 0.5282 0.000 0.000 0.000 0.000 0.808 0.192
#> aberrant_ERR2585339 5 0.4831 0.1824 0.000 0.340 0.008 0.000 0.600 0.052
#> aberrant_ERR2585335 5 0.3239 0.5590 0.000 0.000 0.008 0.024 0.816 0.152
#> aberrant_ERR2585287 5 0.7502 -0.2742 0.000 0.228 0.044 0.060 0.436 0.232
#> aberrant_ERR2585321 5 0.2467 0.6059 0.004 0.000 0.008 0.008 0.880 0.100
#> aberrant_ERR2585297 3 0.3468 0.7710 0.284 0.000 0.712 0.000 0.000 0.004
#> aberrant_ERR2585337 5 0.3716 0.5880 0.000 0.056 0.004 0.028 0.820 0.092
#> aberrant_ERR2585319 5 0.4027 0.5466 0.000 0.000 0.064 0.012 0.768 0.156
#> aberrant_ERR2585315 5 0.3700 0.5666 0.000 0.000 0.032 0.032 0.804 0.132
#> aberrant_ERR2585336 5 0.4619 0.3514 0.000 0.192 0.000 0.008 0.704 0.096
#> aberrant_ERR2585307 5 0.6339 -0.0986 0.000 0.340 0.100 0.004 0.496 0.060
#> aberrant_ERR2585301 5 0.2865 0.6060 0.000 0.000 0.012 0.008 0.840 0.140
#> aberrant_ERR2585326 5 0.4193 0.4765 0.000 0.168 0.008 0.000 0.748 0.076
#> aberrant_ERR2585331 2 0.0508 0.8037 0.000 0.984 0.004 0.000 0.000 0.012
#> aberrant_ERR2585346 5 0.5879 0.2422 0.000 0.004 0.096 0.048 0.592 0.260
#> aberrant_ERR2585314 5 0.5004 -0.1716 0.028 0.000 0.028 0.000 0.548 0.396
#> aberrant_ERR2585298 2 0.0291 0.8045 0.004 0.992 0.004 0.000 0.000 0.000
#> aberrant_ERR2585345 5 0.2208 0.6150 0.000 0.032 0.004 0.004 0.908 0.052
#> aberrant_ERR2585299 1 0.3093 0.6744 0.816 0.000 0.164 0.008 0.000 0.012
#> aberrant_ERR2585309 3 0.3820 0.7516 0.332 0.000 0.660 0.004 0.000 0.004
#> aberrant_ERR2585303 2 0.4102 0.3037 0.000 0.628 0.004 0.000 0.356 0.012
#> aberrant_ERR2585313 5 0.2876 0.5678 0.000 0.000 0.016 0.008 0.844 0.132
#> aberrant_ERR2585318 5 0.2706 0.5506 0.000 0.000 0.008 0.000 0.832 0.160
#> aberrant_ERR2585328 5 0.5772 0.3068 0.032 0.092 0.032 0.004 0.668 0.172
#> aberrant_ERR2585330 5 0.2532 0.6184 0.000 0.000 0.024 0.012 0.884 0.080
#> aberrant_ERR2585293 4 0.0603 1.0000 0.000 0.000 0.000 0.980 0.016 0.004
#> aberrant_ERR2585342 5 0.1820 0.6147 0.000 0.000 0.008 0.012 0.924 0.056
#> aberrant_ERR2585348 5 0.4753 0.1074 0.000 0.044 0.008 0.004 0.632 0.312
#> aberrant_ERR2585352 5 0.2592 0.5844 0.000 0.000 0.004 0.016 0.864 0.116
#> aberrant_ERR2585308 3 0.3899 0.6514 0.404 0.000 0.592 0.004 0.000 0.000
#> aberrant_ERR2585349 2 0.7200 0.2672 0.180 0.500 0.020 0.008 0.068 0.224
#> aberrant_ERR2585316 5 0.3206 0.5878 0.000 0.000 0.028 0.004 0.816 0.152
#> aberrant_ERR2585306 3 0.5468 -0.2493 0.004 0.000 0.540 0.000 0.332 0.124
#> aberrant_ERR2585324 5 0.4525 0.4982 0.000 0.000 0.080 0.016 0.724 0.180
#> aberrant_ERR2585310 3 0.7209 0.3464 0.332 0.000 0.408 0.008 0.116 0.136
#> aberrant_ERR2585296 1 0.6530 0.3838 0.532 0.016 0.196 0.004 0.024 0.228
#> aberrant_ERR2585275 5 0.6084 0.2684 0.004 0.004 0.104 0.056 0.596 0.236
#> aberrant_ERR2585311 5 0.1785 0.6165 0.000 0.000 0.016 0.008 0.928 0.048
#> aberrant_ERR2585292 4 0.0603 1.0000 0.000 0.000 0.000 0.980 0.016 0.004
#> aberrant_ERR2585282 5 0.3874 0.3070 0.000 0.000 0.008 0.012 0.704 0.276
#> aberrant_ERR2585305 5 0.4519 0.4749 0.000 0.000 0.120 0.008 0.724 0.148
#> aberrant_ERR2585278 5 0.3346 0.5950 0.000 0.000 0.036 0.008 0.816 0.140
#> aberrant_ERR2585347 5 0.4583 0.4509 0.000 0.000 0.040 0.032 0.704 0.224
#> aberrant_ERR2585332 5 0.4374 0.1163 0.004 0.000 0.016 0.008 0.632 0.340
#> aberrant_ERR2585280 5 0.5864 0.3091 0.000 0.028 0.052 0.064 0.636 0.220
#> aberrant_ERR2585304 2 0.6735 0.3330 0.020 0.528 0.268 0.000 0.084 0.100
#> aberrant_ERR2585322 5 0.3983 0.5787 0.000 0.064 0.012 0.012 0.796 0.116
#> aberrant_ERR2585279 2 0.0405 0.8040 0.000 0.988 0.008 0.000 0.000 0.004
#> aberrant_ERR2585277 2 0.2202 0.7741 0.000 0.904 0.008 0.004 0.072 0.012
#> aberrant_ERR2585295 2 0.6796 -0.2446 0.000 0.400 0.044 0.004 0.348 0.204
#> aberrant_ERR2585333 5 0.3904 0.5605 0.000 0.000 0.044 0.032 0.792 0.132
#> aberrant_ERR2585285 5 0.2326 0.6194 0.000 0.000 0.012 0.008 0.888 0.092
#> aberrant_ERR2585286 2 0.0858 0.8010 0.000 0.968 0.000 0.000 0.004 0.028
#> aberrant_ERR2585294 5 0.4754 0.4632 0.000 0.000 0.080 0.020 0.700 0.200
#> aberrant_ERR2585300 5 0.4771 0.4522 0.000 0.000 0.120 0.016 0.708 0.156
#> aberrant_ERR2585334 2 0.0622 0.8026 0.000 0.980 0.000 0.008 0.000 0.012
#> aberrant_ERR2585361 5 0.2965 0.6082 0.000 0.020 0.008 0.024 0.868 0.080
#> aberrant_ERR2585372 5 0.3457 0.4261 0.000 0.000 0.016 0.000 0.752 0.232
#> round_ERR2585217 1 0.5775 0.4505 0.640 0.164 0.028 0.000 0.016 0.152
#> round_ERR2585205 1 0.1418 0.7104 0.944 0.000 0.024 0.000 0.000 0.032
#> round_ERR2585214 2 0.0622 0.8038 0.012 0.980 0.008 0.000 0.000 0.000
#> round_ERR2585202 2 0.5342 0.5959 0.136 0.704 0.016 0.004 0.032 0.108
#> aberrant_ERR2585367 2 0.5675 -0.0835 0.000 0.488 0.012 0.004 0.400 0.096
#> round_ERR2585220 1 0.2378 0.6823 0.848 0.000 0.152 0.000 0.000 0.000
#> round_ERR2585238 1 0.3861 0.3872 0.672 0.000 0.316 0.004 0.000 0.008
#> aberrant_ERR2585276 5 0.5259 0.3975 0.000 0.000 0.092 0.036 0.660 0.212
#> round_ERR2585218 1 0.1753 0.7116 0.912 0.000 0.084 0.004 0.000 0.000
#> aberrant_ERR2585363 5 0.3437 0.4308 0.000 0.000 0.008 0.004 0.752 0.236
#> round_ERR2585201 2 0.2070 0.7480 0.100 0.892 0.008 0.000 0.000 0.000
#> round_ERR2585210 1 0.3420 0.6611 0.824 0.004 0.060 0.004 0.000 0.108
#> aberrant_ERR2585362 5 0.4389 -0.1682 0.016 0.004 0.004 0.000 0.596 0.380
#> aberrant_ERR2585360 5 0.2747 0.5917 0.004 0.000 0.028 0.000 0.860 0.108
#> round_ERR2585209 1 0.2614 0.6939 0.888 0.036 0.024 0.000 0.000 0.052
#> round_ERR2585242 2 0.0551 0.8051 0.008 0.984 0.004 0.000 0.000 0.004
#> round_ERR2585216 1 0.3453 0.6436 0.808 0.008 0.040 0.000 0.000 0.144
#> round_ERR2585219 1 0.1003 0.7104 0.964 0.000 0.016 0.000 0.000 0.020
#> round_ERR2585237 1 0.4988 0.4843 0.684 0.196 0.024 0.000 0.000 0.096
#> round_ERR2585198 2 0.0806 0.8019 0.020 0.972 0.008 0.000 0.000 0.000
#> round_ERR2585211 1 0.1564 0.7045 0.936 0.000 0.040 0.000 0.000 0.024
#> round_ERR2585206 1 0.2858 0.6919 0.844 0.000 0.124 0.000 0.000 0.032
#> aberrant_ERR2585281 2 0.1684 0.7967 0.000 0.940 0.016 0.008 0.008 0.028
#> round_ERR2585212 1 0.2251 0.6916 0.904 0.008 0.036 0.000 0.000 0.052
#> round_ERR2585221 3 0.4050 0.7191 0.236 0.000 0.716 0.000 0.000 0.048
#> round_ERR2585243 1 0.5144 0.1962 0.548 0.004 0.368 0.000 0.000 0.080
#> round_ERR2585204 2 0.0622 0.8035 0.012 0.980 0.008 0.000 0.000 0.000
#> round_ERR2585213 2 0.0146 0.8039 0.000 0.996 0.004 0.000 0.000 0.000
#> aberrant_ERR2585373 5 0.2325 0.6162 0.000 0.000 0.008 0.008 0.884 0.100
#> aberrant_ERR2585358 5 0.1956 0.6028 0.000 0.000 0.008 0.004 0.908 0.080
#> aberrant_ERR2585365 5 0.3754 0.5044 0.000 0.072 0.000 0.000 0.776 0.152
#> aberrant_ERR2585359 5 0.3462 0.5188 0.008 0.000 0.016 0.004 0.792 0.180
#> aberrant_ERR2585370 2 0.2946 0.6823 0.000 0.824 0.004 0.000 0.160 0.012
#> round_ERR2585215 1 0.3833 0.6928 0.784 0.004 0.120 0.000 0.000 0.092
#> round_ERR2585262 2 0.1655 0.7967 0.004 0.936 0.012 0.000 0.004 0.044
#> round_ERR2585199 2 0.4034 0.5448 0.260 0.708 0.008 0.000 0.000 0.024
#> aberrant_ERR2585369 5 0.1806 0.5891 0.000 0.000 0.004 0.000 0.908 0.088
#> round_ERR2585208 1 0.3368 0.5826 0.756 0.000 0.232 0.000 0.000 0.012
#> round_ERR2585252 3 0.4136 0.5804 0.428 0.000 0.560 0.000 0.000 0.012
#> round_ERR2585236 1 0.5483 0.6299 0.680 0.056 0.180 0.008 0.004 0.072
#> aberrant_ERR2585284 5 0.6915 -0.2190 0.000 0.184 0.032 0.036 0.492 0.256
#> round_ERR2585224 3 0.4017 0.6595 0.168 0.000 0.764 0.000 0.012 0.056
#> round_ERR2585260 1 0.3961 -0.1200 0.556 0.000 0.440 0.004 0.000 0.000
#> round_ERR2585229 1 0.2917 0.6928 0.840 0.000 0.136 0.008 0.000 0.016
#> aberrant_ERR2585364 5 0.3374 0.5994 0.000 0.000 0.012 0.044 0.824 0.120
#> round_ERR2585253 1 0.4034 0.3707 0.652 0.000 0.328 0.000 0.000 0.020
#> aberrant_ERR2585368 2 0.0260 0.8045 0.000 0.992 0.000 0.000 0.008 0.000
#> aberrant_ERR2585371 2 0.0260 0.8045 0.000 0.992 0.000 0.000 0.008 0.000
#> round_ERR2585239 1 0.2706 0.6898 0.832 0.000 0.160 0.000 0.000 0.008
#> round_ERR2585273 3 0.3652 0.7551 0.324 0.000 0.672 0.000 0.000 0.004
#> round_ERR2585256 1 0.2887 0.7144 0.856 0.008 0.104 0.000 0.000 0.032
#> round_ERR2585272 1 0.2266 0.7159 0.880 0.000 0.108 0.000 0.000 0.012
#> round_ERR2585246 3 0.3648 0.7429 0.240 0.000 0.740 0.004 0.000 0.016
#> round_ERR2585261 1 0.4606 0.5596 0.708 0.208 0.064 0.000 0.000 0.020
#> round_ERR2585254 1 0.2595 0.7160 0.880 0.016 0.084 0.000 0.000 0.020
#> round_ERR2585225 2 0.0551 0.8052 0.008 0.984 0.004 0.000 0.000 0.004
#> round_ERR2585235 1 0.5520 -0.1034 0.516 0.088 0.380 0.000 0.000 0.016
#> round_ERR2585271 1 0.2588 0.7174 0.876 0.008 0.092 0.000 0.000 0.024
#> round_ERR2585251 1 0.3728 0.3463 0.652 0.000 0.344 0.000 0.000 0.004
#> round_ERR2585255 2 0.0665 0.8045 0.000 0.980 0.004 0.008 0.000 0.008
#> round_ERR2585257 2 0.5124 0.4751 0.188 0.664 0.136 0.004 0.000 0.008
#> round_ERR2585226 3 0.3619 0.7652 0.316 0.000 0.680 0.000 0.000 0.004
#> round_ERR2585265 1 0.3301 0.6051 0.772 0.000 0.216 0.004 0.000 0.008
#> round_ERR2585259 1 0.4435 0.6578 0.772 0.104 0.064 0.004 0.000 0.056
#> round_ERR2585247 3 0.4106 0.7438 0.312 0.000 0.664 0.004 0.000 0.020
#> round_ERR2585241 1 0.1552 0.7073 0.940 0.000 0.020 0.004 0.000 0.036
#> round_ERR2585263 1 0.4693 0.5268 0.708 0.012 0.056 0.000 0.012 0.212
#> round_ERR2585264 1 0.4227 0.2985 0.632 0.000 0.344 0.004 0.000 0.020
#> round_ERR2585233 2 0.1370 0.7930 0.036 0.948 0.012 0.000 0.000 0.004
#> round_ERR2585223 3 0.4002 0.6424 0.404 0.000 0.588 0.000 0.000 0.008
#> round_ERR2585234 2 0.0806 0.8014 0.020 0.972 0.008 0.000 0.000 0.000
#> round_ERR2585222 1 0.4264 0.6620 0.752 0.000 0.148 0.012 0.000 0.088
#> round_ERR2585228 1 0.2356 0.7148 0.884 0.000 0.096 0.004 0.000 0.016
#> round_ERR2585248 1 0.3934 0.5057 0.676 0.000 0.304 0.000 0.000 0.020
#> round_ERR2585240 2 0.5373 0.3518 0.144 0.608 0.240 0.000 0.000 0.008
#> round_ERR2585270 1 0.3899 0.6877 0.788 0.016 0.152 0.008 0.000 0.036
#> round_ERR2585232 1 0.3453 0.6564 0.788 0.028 0.180 0.000 0.000 0.004
#> aberrant_ERR2585341 2 0.3162 0.7351 0.000 0.844 0.008 0.000 0.068 0.080
#> aberrant_ERR2585355 2 0.2056 0.7669 0.000 0.904 0.004 0.000 0.080 0.012
#> round_ERR2585227 3 0.3835 0.7602 0.320 0.000 0.668 0.000 0.000 0.012
#> aberrant_ERR2585351 5 0.4194 0.3151 0.012 0.000 0.016 0.004 0.692 0.276
#> round_ERR2585269 3 0.3766 0.7670 0.304 0.000 0.684 0.000 0.000 0.012
#> aberrant_ERR2585357 2 0.4927 0.0639 0.000 0.540 0.004 0.000 0.400 0.056
#> aberrant_ERR2585350 5 0.4638 0.1075 0.000 0.368 0.004 0.000 0.588 0.040
#> round_ERR2585250 1 0.4758 0.6338 0.736 0.008 0.148 0.012 0.008 0.088
#> round_ERR2585245 3 0.3875 0.7692 0.280 0.000 0.700 0.004 0.000 0.016
#> aberrant_ERR2585353 5 0.2778 0.5642 0.000 0.000 0.000 0.008 0.824 0.168
#> round_ERR2585258 3 0.4025 0.6073 0.416 0.000 0.576 0.000 0.000 0.008
#> aberrant_ERR2585354 5 0.2773 0.5450 0.004 0.000 0.008 0.000 0.836 0.152
#> round_ERR2585249 3 0.3808 0.7682 0.284 0.000 0.700 0.004 0.000 0.012
#> round_ERR2585268 1 0.5102 0.6271 0.720 0.020 0.132 0.008 0.012 0.108
#> aberrant_ERR2585356 5 0.2904 0.6048 0.000 0.000 0.028 0.008 0.852 0.112
#> round_ERR2585266 2 0.1414 0.7980 0.020 0.952 0.012 0.004 0.000 0.012
#> round_ERR2585231 3 0.3680 0.7446 0.232 0.000 0.744 0.004 0.000 0.020
#> round_ERR2585230 1 0.2591 0.7040 0.880 0.004 0.064 0.000 0.000 0.052
#> round_ERR2585267 3 0.4625 0.6156 0.388 0.000 0.572 0.004 0.000 0.036
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> SD:NMF 159 1.02e-26 2
#> SD:NMF 146 1.88e-22 3
#> SD:NMF 147 3.76e-21 4
#> SD:NMF 129 2.91e-19 5
#> SD:NMF 114 2.48e-14 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "hclust"]
# you can also extract it by
# res = res_list["CV:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'CV' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.222 0.648 0.824 0.4617 0.497 0.497
#> 3 3 0.342 0.639 0.782 0.3493 0.794 0.606
#> 4 4 0.523 0.632 0.760 0.1291 0.933 0.806
#> 5 5 0.597 0.558 0.718 0.0732 0.927 0.754
#> 6 6 0.602 0.527 0.721 0.0330 0.951 0.813
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.8443 0.6849 0.272 0.728
#> aberrant_ERR2585338 2 0.1843 0.7434 0.028 0.972
#> aberrant_ERR2585325 2 0.8443 0.6849 0.272 0.728
#> aberrant_ERR2585283 1 0.5059 0.7679 0.888 0.112
#> aberrant_ERR2585343 1 0.9732 0.2421 0.596 0.404
#> aberrant_ERR2585329 2 0.2603 0.7459 0.044 0.956
#> aberrant_ERR2585317 2 0.3584 0.7505 0.068 0.932
#> aberrant_ERR2585339 2 0.0672 0.7337 0.008 0.992
#> aberrant_ERR2585335 2 0.7815 0.7095 0.232 0.768
#> aberrant_ERR2585287 1 0.9044 0.4826 0.680 0.320
#> aberrant_ERR2585321 1 0.9970 -0.0307 0.532 0.468
#> aberrant_ERR2585297 1 0.0938 0.8148 0.988 0.012
#> aberrant_ERR2585337 2 0.2603 0.7466 0.044 0.956
#> aberrant_ERR2585319 2 0.7219 0.7305 0.200 0.800
#> aberrant_ERR2585315 2 0.2603 0.7460 0.044 0.956
#> aberrant_ERR2585336 2 0.2423 0.7450 0.040 0.960
#> aberrant_ERR2585307 2 0.2423 0.7473 0.040 0.960
#> aberrant_ERR2585301 2 0.9850 0.4006 0.428 0.572
#> aberrant_ERR2585326 2 0.0938 0.7362 0.012 0.988
#> aberrant_ERR2585331 2 0.0000 0.7299 0.000 1.000
#> aberrant_ERR2585346 1 0.4815 0.7748 0.896 0.104
#> aberrant_ERR2585314 2 0.5737 0.7498 0.136 0.864
#> aberrant_ERR2585298 2 0.9686 0.4747 0.396 0.604
#> aberrant_ERR2585345 2 0.4161 0.7522 0.084 0.916
#> aberrant_ERR2585299 1 0.1184 0.8169 0.984 0.016
#> aberrant_ERR2585309 1 0.0000 0.8117 1.000 0.000
#> aberrant_ERR2585303 2 0.4815 0.7470 0.104 0.896
#> aberrant_ERR2585313 2 0.1633 0.7409 0.024 0.976
#> aberrant_ERR2585318 2 0.9552 0.5333 0.376 0.624
#> aberrant_ERR2585328 1 0.9427 0.3836 0.640 0.360
#> aberrant_ERR2585330 2 0.7376 0.7266 0.208 0.792
#> aberrant_ERR2585293 1 0.5059 0.7679 0.888 0.112
#> aberrant_ERR2585342 1 0.9850 0.1534 0.572 0.428
#> aberrant_ERR2585348 2 0.9209 0.5918 0.336 0.664
#> aberrant_ERR2585352 2 0.6712 0.7391 0.176 0.824
#> aberrant_ERR2585308 1 0.0376 0.8136 0.996 0.004
#> aberrant_ERR2585349 2 0.0938 0.7369 0.012 0.988
#> aberrant_ERR2585316 1 0.8861 0.5053 0.696 0.304
#> aberrant_ERR2585306 1 0.9044 0.4754 0.680 0.320
#> aberrant_ERR2585324 2 0.7219 0.7305 0.200 0.800
#> aberrant_ERR2585310 2 0.7602 0.7252 0.220 0.780
#> aberrant_ERR2585296 1 0.8813 0.5061 0.700 0.300
#> aberrant_ERR2585275 1 0.6887 0.7025 0.816 0.184
#> aberrant_ERR2585311 1 0.9909 0.0779 0.556 0.444
#> aberrant_ERR2585292 1 0.5059 0.7679 0.888 0.112
#> aberrant_ERR2585282 1 0.9977 -0.0783 0.528 0.472
#> aberrant_ERR2585305 1 0.9988 -0.0861 0.520 0.480
#> aberrant_ERR2585278 2 0.7299 0.7280 0.204 0.796
#> aberrant_ERR2585347 1 0.8713 0.5292 0.708 0.292
#> aberrant_ERR2585332 1 0.9393 0.3894 0.644 0.356
#> aberrant_ERR2585280 2 0.8661 0.6677 0.288 0.712
#> aberrant_ERR2585304 2 0.7815 0.6915 0.232 0.768
#> aberrant_ERR2585322 2 0.3733 0.7538 0.072 0.928
#> aberrant_ERR2585279 2 0.0000 0.7299 0.000 1.000
#> aberrant_ERR2585277 2 0.0376 0.7319 0.004 0.996
#> aberrant_ERR2585295 2 0.9661 0.4772 0.392 0.608
#> aberrant_ERR2585333 1 0.9977 -0.0653 0.528 0.472
#> aberrant_ERR2585285 2 0.8909 0.6366 0.308 0.692
#> aberrant_ERR2585286 2 0.1414 0.7375 0.020 0.980
#> aberrant_ERR2585294 2 0.9775 0.4280 0.412 0.588
#> aberrant_ERR2585300 1 0.9710 0.2546 0.600 0.400
#> aberrant_ERR2585334 2 0.0000 0.7299 0.000 1.000
#> aberrant_ERR2585361 2 0.6801 0.7396 0.180 0.820
#> aberrant_ERR2585372 2 0.9580 0.5223 0.380 0.620
#> round_ERR2585217 2 0.7815 0.6865 0.232 0.768
#> round_ERR2585205 1 0.1414 0.8178 0.980 0.020
#> round_ERR2585214 2 0.9044 0.5982 0.320 0.680
#> round_ERR2585202 2 0.8327 0.6724 0.264 0.736
#> aberrant_ERR2585367 2 0.7299 0.7257 0.204 0.796
#> round_ERR2585220 1 0.3879 0.8050 0.924 0.076
#> round_ERR2585238 1 0.0938 0.8161 0.988 0.012
#> aberrant_ERR2585276 2 0.9983 0.2564 0.476 0.524
#> round_ERR2585218 1 0.1184 0.8172 0.984 0.016
#> aberrant_ERR2585363 2 0.4431 0.7502 0.092 0.908
#> round_ERR2585201 2 0.9661 0.4834 0.392 0.608
#> round_ERR2585210 1 0.0000 0.8117 1.000 0.000
#> aberrant_ERR2585362 2 0.9977 0.2869 0.472 0.528
#> aberrant_ERR2585360 2 0.9944 0.3193 0.456 0.544
#> round_ERR2585209 1 0.9635 0.2706 0.612 0.388
#> round_ERR2585242 2 0.9775 0.4423 0.412 0.588
#> round_ERR2585216 1 0.5946 0.7560 0.856 0.144
#> round_ERR2585219 1 0.2043 0.8180 0.968 0.032
#> round_ERR2585237 2 0.9815 0.4345 0.420 0.580
#> round_ERR2585198 2 0.7674 0.6927 0.224 0.776
#> round_ERR2585211 1 0.1184 0.8168 0.984 0.016
#> round_ERR2585206 1 0.0672 0.8153 0.992 0.008
#> aberrant_ERR2585281 2 0.4298 0.7485 0.088 0.912
#> round_ERR2585212 1 0.3584 0.8087 0.932 0.068
#> round_ERR2585221 1 0.0376 0.8139 0.996 0.004
#> round_ERR2585243 1 0.0938 0.8165 0.988 0.012
#> round_ERR2585204 2 0.5294 0.7360 0.120 0.880
#> round_ERR2585213 2 0.2948 0.7446 0.052 0.948
#> aberrant_ERR2585373 2 0.9944 0.3210 0.456 0.544
#> aberrant_ERR2585358 1 0.9686 0.2702 0.604 0.396
#> aberrant_ERR2585365 2 0.6343 0.7463 0.160 0.840
#> aberrant_ERR2585359 1 0.9044 0.4727 0.680 0.320
#> aberrant_ERR2585370 2 0.0000 0.7299 0.000 1.000
#> round_ERR2585215 1 0.0000 0.8117 1.000 0.000
#> round_ERR2585262 2 0.9580 0.5164 0.380 0.620
#> round_ERR2585199 2 0.6801 0.7102 0.180 0.820
#> aberrant_ERR2585369 2 0.9580 0.5184 0.380 0.620
#> round_ERR2585208 1 0.0938 0.8160 0.988 0.012
#> round_ERR2585252 1 0.0376 0.8139 0.996 0.004
#> round_ERR2585236 1 0.4431 0.7973 0.908 0.092
#> aberrant_ERR2585284 1 0.4815 0.7748 0.896 0.104
#> round_ERR2585224 1 0.0000 0.8117 1.000 0.000
#> round_ERR2585260 1 0.2778 0.8141 0.952 0.048
#> round_ERR2585229 1 0.1184 0.8172 0.984 0.016
#> aberrant_ERR2585364 1 0.6438 0.7222 0.836 0.164
#> round_ERR2585253 1 0.0000 0.8117 1.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.7299 0.000 1.000
#> aberrant_ERR2585371 2 0.0000 0.7299 0.000 1.000
#> round_ERR2585239 1 0.2043 0.8176 0.968 0.032
#> round_ERR2585273 1 0.0672 0.8156 0.992 0.008
#> round_ERR2585256 1 0.8267 0.6067 0.740 0.260
#> round_ERR2585272 1 0.5842 0.7611 0.860 0.140
#> round_ERR2585246 1 0.0376 0.8136 0.996 0.004
#> round_ERR2585261 2 0.9833 0.4198 0.424 0.576
#> round_ERR2585254 2 0.9552 0.5575 0.376 0.624
#> round_ERR2585225 2 0.9754 0.4541 0.408 0.592
#> round_ERR2585235 1 0.6801 0.7079 0.820 0.180
#> round_ERR2585271 1 0.2236 0.8170 0.964 0.036
#> round_ERR2585251 1 0.4022 0.8032 0.920 0.080
#> round_ERR2585255 2 0.9754 0.4521 0.408 0.592
#> round_ERR2585257 2 0.9909 0.3695 0.444 0.556
#> round_ERR2585226 1 0.4161 0.8007 0.916 0.084
#> round_ERR2585265 1 0.3274 0.8118 0.940 0.060
#> round_ERR2585259 1 0.7139 0.6895 0.804 0.196
#> round_ERR2585247 1 0.0672 0.8153 0.992 0.008
#> round_ERR2585241 1 0.1414 0.8177 0.980 0.020
#> round_ERR2585263 1 0.6343 0.7458 0.840 0.160
#> round_ERR2585264 1 0.0000 0.8117 1.000 0.000
#> round_ERR2585233 2 0.9815 0.4268 0.420 0.580
#> round_ERR2585223 1 0.3114 0.8117 0.944 0.056
#> round_ERR2585234 2 0.8608 0.6450 0.284 0.716
#> round_ERR2585222 1 0.1843 0.8174 0.972 0.028
#> round_ERR2585228 1 0.1414 0.8176 0.980 0.020
#> round_ERR2585248 1 0.0000 0.8117 1.000 0.000
#> round_ERR2585240 1 0.9522 0.3355 0.628 0.372
#> round_ERR2585270 1 0.2948 0.8135 0.948 0.052
#> round_ERR2585232 1 0.8713 0.5344 0.708 0.292
#> aberrant_ERR2585341 2 0.6531 0.7407 0.168 0.832
#> aberrant_ERR2585355 2 0.0938 0.7371 0.012 0.988
#> round_ERR2585227 1 0.3431 0.8074 0.936 0.064
#> aberrant_ERR2585351 2 0.9323 0.5774 0.348 0.652
#> round_ERR2585269 1 0.0000 0.8117 1.000 0.000
#> aberrant_ERR2585357 2 0.0938 0.7368 0.012 0.988
#> aberrant_ERR2585350 2 0.1843 0.7434 0.028 0.972
#> round_ERR2585250 1 0.3733 0.8088 0.928 0.072
#> round_ERR2585245 1 0.0000 0.8117 1.000 0.000
#> aberrant_ERR2585353 2 0.9944 0.3358 0.456 0.544
#> round_ERR2585258 1 0.3733 0.8067 0.928 0.072
#> aberrant_ERR2585354 2 0.9866 0.3892 0.432 0.568
#> round_ERR2585249 1 0.0000 0.8117 1.000 0.000
#> round_ERR2585268 1 0.6887 0.7251 0.816 0.184
#> aberrant_ERR2585356 1 0.8909 0.5015 0.692 0.308
#> round_ERR2585266 2 0.9795 0.4324 0.416 0.584
#> round_ERR2585231 1 0.0000 0.8117 1.000 0.000
#> round_ERR2585230 1 0.1633 0.8177 0.976 0.024
#> round_ERR2585267 1 0.0000 0.8117 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.7476 -0.108 0.036 0.512 0.452
#> aberrant_ERR2585338 2 0.2625 0.618 0.000 0.916 0.084
#> aberrant_ERR2585325 2 0.7476 -0.108 0.036 0.512 0.452
#> aberrant_ERR2585283 3 0.5864 0.623 0.288 0.008 0.704
#> aberrant_ERR2585343 3 0.6823 0.732 0.108 0.152 0.740
#> aberrant_ERR2585329 2 0.3618 0.607 0.012 0.884 0.104
#> aberrant_ERR2585317 2 0.4277 0.595 0.016 0.852 0.132
#> aberrant_ERR2585339 2 0.2261 0.622 0.000 0.932 0.068
#> aberrant_ERR2585335 2 0.7263 0.167 0.036 0.592 0.372
#> aberrant_ERR2585287 3 0.8052 0.710 0.196 0.152 0.652
#> aberrant_ERR2585321 3 0.7256 0.714 0.088 0.216 0.696
#> aberrant_ERR2585297 1 0.1170 0.915 0.976 0.008 0.016
#> aberrant_ERR2585337 2 0.3618 0.614 0.012 0.884 0.104
#> aberrant_ERR2585319 2 0.6956 0.341 0.040 0.660 0.300
#> aberrant_ERR2585315 2 0.3618 0.608 0.012 0.884 0.104
#> aberrant_ERR2585336 2 0.3532 0.611 0.008 0.884 0.108
#> aberrant_ERR2585307 2 0.3784 0.603 0.004 0.864 0.132
#> aberrant_ERR2585301 3 0.7905 0.587 0.072 0.340 0.588
#> aberrant_ERR2585326 2 0.2165 0.622 0.000 0.936 0.064
#> aberrant_ERR2585331 2 0.0747 0.624 0.000 0.984 0.016
#> aberrant_ERR2585346 3 0.5815 0.605 0.304 0.004 0.692
#> aberrant_ERR2585314 2 0.6067 0.479 0.028 0.736 0.236
#> aberrant_ERR2585298 2 0.8295 0.359 0.364 0.548 0.088
#> aberrant_ERR2585345 2 0.4475 0.586 0.016 0.840 0.144
#> aberrant_ERR2585299 1 0.1751 0.915 0.960 0.012 0.028
#> aberrant_ERR2585309 1 0.0747 0.911 0.984 0.000 0.016
#> aberrant_ERR2585303 2 0.5404 0.456 0.004 0.740 0.256
#> aberrant_ERR2585313 2 0.2959 0.611 0.000 0.900 0.100
#> aberrant_ERR2585318 3 0.8034 0.469 0.068 0.392 0.540
#> aberrant_ERR2585328 3 0.8950 0.636 0.212 0.220 0.568
#> aberrant_ERR2585330 2 0.7559 0.237 0.056 0.608 0.336
#> aberrant_ERR2585293 3 0.5864 0.623 0.288 0.008 0.704
#> aberrant_ERR2585342 3 0.6783 0.724 0.088 0.176 0.736
#> aberrant_ERR2585348 3 0.7828 0.337 0.052 0.448 0.500
#> aberrant_ERR2585352 2 0.6967 0.383 0.044 0.668 0.288
#> aberrant_ERR2585308 1 0.0983 0.913 0.980 0.004 0.016
#> aberrant_ERR2585349 2 0.1482 0.628 0.012 0.968 0.020
#> aberrant_ERR2585316 3 0.7129 0.722 0.180 0.104 0.716
#> aberrant_ERR2585306 3 0.8397 0.630 0.296 0.116 0.588
#> aberrant_ERR2585324 2 0.6956 0.341 0.040 0.660 0.300
#> aberrant_ERR2585310 2 0.7860 0.364 0.088 0.628 0.284
#> aberrant_ERR2585296 1 0.7032 0.546 0.676 0.272 0.052
#> aberrant_ERR2585275 3 0.6025 0.663 0.232 0.028 0.740
#> aberrant_ERR2585311 3 0.6886 0.720 0.088 0.184 0.728
#> aberrant_ERR2585292 3 0.5864 0.623 0.288 0.008 0.704
#> aberrant_ERR2585282 3 0.8801 0.646 0.152 0.284 0.564
#> aberrant_ERR2585305 3 0.8117 0.695 0.128 0.236 0.636
#> aberrant_ERR2585278 2 0.7186 0.276 0.040 0.624 0.336
#> aberrant_ERR2585347 3 0.7391 0.712 0.196 0.108 0.696
#> aberrant_ERR2585332 3 0.6728 0.731 0.124 0.128 0.748
#> aberrant_ERR2585280 2 0.7647 -0.117 0.044 0.516 0.440
#> aberrant_ERR2585304 2 0.6968 0.539 0.204 0.716 0.080
#> aberrant_ERR2585322 2 0.4351 0.574 0.004 0.828 0.168
#> aberrant_ERR2585279 2 0.0747 0.624 0.000 0.984 0.016
#> aberrant_ERR2585277 2 0.0747 0.624 0.000 0.984 0.016
#> aberrant_ERR2585295 3 0.8310 0.441 0.080 0.420 0.500
#> aberrant_ERR2585333 3 0.7213 0.710 0.088 0.212 0.700
#> aberrant_ERR2585285 2 0.7974 -0.185 0.060 0.504 0.436
#> aberrant_ERR2585286 2 0.1964 0.623 0.000 0.944 0.056
#> aberrant_ERR2585294 3 0.7422 0.577 0.048 0.344 0.608
#> aberrant_ERR2585300 3 0.6157 0.724 0.092 0.128 0.780
#> aberrant_ERR2585334 2 0.0592 0.623 0.000 0.988 0.012
#> aberrant_ERR2585361 2 0.6819 0.308 0.028 0.644 0.328
#> aberrant_ERR2585372 3 0.7919 0.498 0.064 0.380 0.556
#> round_ERR2585217 2 0.7092 0.531 0.208 0.708 0.084
#> round_ERR2585205 1 0.1482 0.916 0.968 0.012 0.020
#> round_ERR2585214 2 0.7916 0.470 0.292 0.620 0.088
#> round_ERR2585202 2 0.6662 0.517 0.252 0.704 0.044
#> aberrant_ERR2585367 2 0.7065 0.222 0.032 0.616 0.352
#> round_ERR2585220 1 0.2749 0.890 0.924 0.064 0.012
#> round_ERR2585238 1 0.0848 0.915 0.984 0.008 0.008
#> aberrant_ERR2585276 3 0.7801 0.656 0.088 0.276 0.636
#> round_ERR2585218 1 0.1015 0.916 0.980 0.012 0.008
#> aberrant_ERR2585363 2 0.5639 0.501 0.016 0.752 0.232
#> round_ERR2585201 2 0.8361 0.356 0.364 0.544 0.092
#> round_ERR2585210 1 0.0747 0.911 0.984 0.000 0.016
#> aberrant_ERR2585362 3 0.8107 0.639 0.096 0.300 0.604
#> aberrant_ERR2585360 3 0.7749 0.636 0.072 0.312 0.616
#> round_ERR2585209 1 0.7705 0.374 0.604 0.332 0.064
#> round_ERR2585242 2 0.8414 0.318 0.380 0.528 0.092
#> round_ERR2585216 1 0.4059 0.824 0.860 0.128 0.012
#> round_ERR2585219 1 0.1620 0.915 0.964 0.024 0.012
#> round_ERR2585237 2 0.8250 0.307 0.392 0.528 0.080
#> round_ERR2585198 2 0.6804 0.541 0.204 0.724 0.072
#> round_ERR2585211 1 0.0829 0.915 0.984 0.012 0.004
#> round_ERR2585206 1 0.0661 0.914 0.988 0.004 0.008
#> aberrant_ERR2585281 2 0.4235 0.534 0.000 0.824 0.176
#> round_ERR2585212 1 0.2599 0.897 0.932 0.052 0.016
#> round_ERR2585221 1 0.1411 0.904 0.964 0.000 0.036
#> round_ERR2585243 1 0.1482 0.916 0.968 0.012 0.020
#> round_ERR2585204 2 0.5094 0.585 0.112 0.832 0.056
#> round_ERR2585213 2 0.3134 0.615 0.052 0.916 0.032
#> aberrant_ERR2585373 3 0.8173 0.648 0.100 0.300 0.600
#> aberrant_ERR2585358 3 0.6573 0.731 0.104 0.140 0.756
#> aberrant_ERR2585365 2 0.6501 0.353 0.020 0.664 0.316
#> aberrant_ERR2585359 3 0.6128 0.720 0.136 0.084 0.780
#> aberrant_ERR2585370 2 0.1753 0.624 0.000 0.952 0.048
#> round_ERR2585215 1 0.0747 0.911 0.984 0.000 0.016
#> round_ERR2585262 2 0.8390 0.409 0.340 0.560 0.100
#> round_ERR2585199 2 0.5743 0.562 0.172 0.784 0.044
#> aberrant_ERR2585369 3 0.8013 0.538 0.072 0.364 0.564
#> round_ERR2585208 1 0.1015 0.915 0.980 0.008 0.012
#> round_ERR2585252 1 0.0592 0.912 0.988 0.000 0.012
#> round_ERR2585236 1 0.5042 0.812 0.836 0.060 0.104
#> aberrant_ERR2585284 3 0.5845 0.594 0.308 0.004 0.688
#> round_ERR2585224 1 0.0892 0.910 0.980 0.000 0.020
#> round_ERR2585260 1 0.2152 0.910 0.948 0.036 0.016
#> round_ERR2585229 1 0.1170 0.915 0.976 0.008 0.016
#> aberrant_ERR2585364 3 0.5803 0.653 0.248 0.016 0.736
#> round_ERR2585253 1 0.0747 0.911 0.984 0.000 0.016
#> aberrant_ERR2585368 2 0.1289 0.625 0.000 0.968 0.032
#> aberrant_ERR2585371 2 0.1289 0.625 0.000 0.968 0.032
#> round_ERR2585239 1 0.1919 0.914 0.956 0.020 0.024
#> round_ERR2585273 1 0.1170 0.916 0.976 0.008 0.016
#> round_ERR2585256 1 0.6673 0.667 0.720 0.224 0.056
#> round_ERR2585272 1 0.4519 0.838 0.852 0.116 0.032
#> round_ERR2585246 1 0.1267 0.914 0.972 0.004 0.024
#> round_ERR2585261 2 0.8201 0.295 0.400 0.524 0.076
#> round_ERR2585254 2 0.7980 0.426 0.356 0.572 0.072
#> round_ERR2585225 2 0.8526 0.319 0.376 0.524 0.100
#> round_ERR2585235 1 0.6363 0.759 0.768 0.136 0.096
#> round_ERR2585271 1 0.1711 0.912 0.960 0.032 0.008
#> round_ERR2585251 1 0.3141 0.885 0.912 0.068 0.020
#> round_ERR2585255 2 0.8586 0.318 0.376 0.520 0.104
#> round_ERR2585257 2 0.8665 0.229 0.412 0.484 0.104
#> round_ERR2585226 1 0.2998 0.888 0.916 0.068 0.016
#> round_ERR2585265 1 0.2339 0.901 0.940 0.048 0.012
#> round_ERR2585259 1 0.6001 0.745 0.772 0.176 0.052
#> round_ERR2585247 1 0.1585 0.915 0.964 0.008 0.028
#> round_ERR2585241 1 0.1015 0.916 0.980 0.012 0.008
#> round_ERR2585263 1 0.5875 0.765 0.792 0.136 0.072
#> round_ERR2585264 1 0.0747 0.911 0.984 0.000 0.016
#> round_ERR2585233 2 0.8559 0.296 0.388 0.512 0.100
#> round_ERR2585223 1 0.2492 0.902 0.936 0.048 0.016
#> round_ERR2585234 2 0.7479 0.497 0.264 0.660 0.076
#> round_ERR2585222 1 0.1774 0.915 0.960 0.024 0.016
#> round_ERR2585228 1 0.0983 0.915 0.980 0.016 0.004
#> round_ERR2585248 1 0.0892 0.911 0.980 0.000 0.020
#> round_ERR2585240 1 0.7874 0.412 0.604 0.320 0.076
#> round_ERR2585270 1 0.1877 0.909 0.956 0.032 0.012
#> round_ERR2585232 1 0.7489 0.572 0.664 0.256 0.080
#> aberrant_ERR2585341 2 0.6357 0.368 0.020 0.684 0.296
#> aberrant_ERR2585355 2 0.2356 0.623 0.000 0.928 0.072
#> round_ERR2585227 1 0.3039 0.902 0.920 0.044 0.036
#> aberrant_ERR2585351 3 0.8884 0.382 0.120 0.420 0.460
#> round_ERR2585269 1 0.0747 0.911 0.984 0.000 0.016
#> aberrant_ERR2585357 2 0.2165 0.623 0.000 0.936 0.064
#> aberrant_ERR2585350 2 0.2774 0.623 0.008 0.920 0.072
#> round_ERR2585250 1 0.3083 0.890 0.916 0.060 0.024
#> round_ERR2585245 1 0.0747 0.911 0.984 0.000 0.016
#> aberrant_ERR2585353 3 0.8132 0.635 0.096 0.304 0.600
#> round_ERR2585258 1 0.2486 0.894 0.932 0.060 0.008
#> aberrant_ERR2585354 3 0.7901 0.629 0.080 0.312 0.608
#> round_ERR2585249 1 0.0592 0.912 0.988 0.000 0.012
#> round_ERR2585268 1 0.6001 0.771 0.784 0.144 0.072
#> aberrant_ERR2585356 3 0.6266 0.715 0.156 0.076 0.768
#> round_ERR2585266 2 0.8426 0.310 0.384 0.524 0.092
#> round_ERR2585231 1 0.0747 0.911 0.984 0.000 0.016
#> round_ERR2585230 1 0.1315 0.916 0.972 0.020 0.008
#> round_ERR2585267 1 0.0747 0.911 0.984 0.000 0.016
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 2 0.6082 -0.0621 0.000 0.480 0.044 0.476
#> aberrant_ERR2585338 2 0.3617 0.6885 0.000 0.860 0.064 0.076
#> aberrant_ERR2585325 2 0.6082 -0.0621 0.000 0.480 0.044 0.476
#> aberrant_ERR2585283 4 0.5678 0.6260 0.068 0.004 0.224 0.704
#> aberrant_ERR2585343 4 0.4800 0.7259 0.024 0.128 0.044 0.804
#> aberrant_ERR2585329 2 0.2843 0.6969 0.000 0.892 0.020 0.088
#> aberrant_ERR2585317 2 0.2926 0.6889 0.004 0.888 0.012 0.096
#> aberrant_ERR2585339 2 0.1510 0.6896 0.000 0.956 0.016 0.028
#> aberrant_ERR2585335 2 0.5865 0.2231 0.008 0.576 0.024 0.392
#> aberrant_ERR2585287 4 0.6767 0.6970 0.056 0.128 0.124 0.692
#> aberrant_ERR2585321 4 0.5321 0.7099 0.016 0.192 0.044 0.748
#> aberrant_ERR2585297 1 0.1356 0.8845 0.960 0.008 0.032 0.000
#> aberrant_ERR2585337 2 0.3100 0.7026 0.004 0.888 0.028 0.080
#> aberrant_ERR2585319 2 0.5487 0.3998 0.004 0.644 0.024 0.328
#> aberrant_ERR2585315 2 0.2271 0.6975 0.000 0.916 0.008 0.076
#> aberrant_ERR2585336 2 0.2821 0.7021 0.004 0.900 0.020 0.076
#> aberrant_ERR2585307 2 0.3372 0.6903 0.000 0.868 0.036 0.096
#> aberrant_ERR2585301 4 0.5907 0.5991 0.012 0.308 0.036 0.644
#> aberrant_ERR2585326 2 0.1042 0.6875 0.000 0.972 0.008 0.020
#> aberrant_ERR2585331 2 0.2412 0.6339 0.000 0.908 0.084 0.008
#> aberrant_ERR2585346 4 0.5790 0.6133 0.080 0.000 0.236 0.684
#> aberrant_ERR2585314 2 0.5199 0.5492 0.004 0.720 0.036 0.240
#> aberrant_ERR2585298 3 0.6501 0.8484 0.116 0.268 0.616 0.000
#> aberrant_ERR2585345 2 0.3725 0.6815 0.004 0.848 0.028 0.120
#> aberrant_ERR2585299 1 0.1847 0.8805 0.940 0.004 0.052 0.004
#> aberrant_ERR2585309 1 0.0592 0.8764 0.984 0.000 0.016 0.000
#> aberrant_ERR2585303 2 0.5458 0.5448 0.000 0.704 0.060 0.236
#> aberrant_ERR2585313 2 0.2402 0.7021 0.000 0.912 0.012 0.076
#> aberrant_ERR2585318 4 0.6123 0.4757 0.012 0.356 0.036 0.596
#> aberrant_ERR2585328 4 0.8000 0.6186 0.072 0.176 0.168 0.584
#> aberrant_ERR2585330 2 0.6249 0.3106 0.016 0.604 0.040 0.340
#> aberrant_ERR2585293 4 0.5678 0.6260 0.068 0.004 0.224 0.704
#> aberrant_ERR2585342 4 0.4527 0.7212 0.016 0.144 0.032 0.808
#> aberrant_ERR2585348 4 0.6766 0.2965 0.012 0.416 0.064 0.508
#> aberrant_ERR2585352 2 0.5622 0.4707 0.008 0.676 0.036 0.280
#> aberrant_ERR2585308 1 0.0469 0.8786 0.988 0.000 0.012 0.000
#> aberrant_ERR2585349 2 0.3161 0.6329 0.000 0.864 0.124 0.012
#> aberrant_ERR2585316 4 0.5361 0.7164 0.048 0.076 0.088 0.788
#> aberrant_ERR2585306 4 0.6892 0.6059 0.204 0.092 0.044 0.660
#> aberrant_ERR2585324 2 0.5487 0.3998 0.004 0.644 0.024 0.328
#> aberrant_ERR2585310 2 0.7172 0.3992 0.060 0.608 0.060 0.272
#> aberrant_ERR2585296 1 0.7425 0.1932 0.560 0.172 0.256 0.012
#> aberrant_ERR2585275 4 0.4912 0.6638 0.060 0.008 0.148 0.784
#> aberrant_ERR2585311 4 0.4686 0.7154 0.020 0.148 0.032 0.800
#> aberrant_ERR2585292 4 0.5678 0.6260 0.068 0.004 0.224 0.704
#> aberrant_ERR2585282 4 0.7329 0.6354 0.060 0.256 0.076 0.608
#> aberrant_ERR2585305 4 0.6256 0.6800 0.068 0.224 0.024 0.684
#> aberrant_ERR2585278 2 0.5904 0.3356 0.004 0.612 0.040 0.344
#> aberrant_ERR2585347 4 0.6166 0.7072 0.052 0.100 0.112 0.736
#> aberrant_ERR2585332 4 0.5304 0.7253 0.024 0.116 0.080 0.780
#> aberrant_ERR2585280 2 0.6275 -0.0672 0.000 0.484 0.056 0.460
#> aberrant_ERR2585304 2 0.7102 -0.3707 0.064 0.516 0.392 0.028
#> aberrant_ERR2585322 2 0.3196 0.6763 0.000 0.856 0.008 0.136
#> aberrant_ERR2585279 2 0.2480 0.6315 0.000 0.904 0.088 0.008
#> aberrant_ERR2585277 2 0.2480 0.6321 0.000 0.904 0.088 0.008
#> aberrant_ERR2585295 4 0.7214 0.4062 0.020 0.372 0.088 0.520
#> aberrant_ERR2585333 4 0.5001 0.7037 0.012 0.180 0.040 0.768
#> aberrant_ERR2585285 4 0.6369 0.1822 0.016 0.468 0.032 0.484
#> aberrant_ERR2585286 2 0.2908 0.6790 0.000 0.896 0.064 0.040
#> aberrant_ERR2585294 4 0.5662 0.5900 0.012 0.312 0.024 0.652
#> aberrant_ERR2585300 4 0.3974 0.7171 0.016 0.092 0.040 0.852
#> aberrant_ERR2585334 2 0.2799 0.6195 0.000 0.884 0.108 0.008
#> aberrant_ERR2585361 2 0.6004 0.3770 0.004 0.616 0.048 0.332
#> aberrant_ERR2585372 4 0.6025 0.4945 0.012 0.352 0.032 0.604
#> round_ERR2585217 2 0.6332 -0.4662 0.060 0.488 0.452 0.000
#> round_ERR2585205 1 0.1543 0.8846 0.956 0.008 0.032 0.004
#> round_ERR2585214 3 0.6396 0.7393 0.076 0.360 0.564 0.000
#> round_ERR2585202 2 0.7329 -0.3175 0.132 0.512 0.348 0.008
#> aberrant_ERR2585367 2 0.6449 0.2830 0.004 0.568 0.068 0.360
#> round_ERR2585220 1 0.3550 0.8303 0.860 0.044 0.096 0.000
#> round_ERR2585238 1 0.0779 0.8818 0.980 0.004 0.016 0.000
#> aberrant_ERR2585276 4 0.5962 0.6706 0.024 0.232 0.048 0.696
#> round_ERR2585218 1 0.1356 0.8841 0.960 0.008 0.032 0.000
#> aberrant_ERR2585363 2 0.4997 0.5828 0.004 0.744 0.036 0.216
#> round_ERR2585201 3 0.6500 0.8521 0.120 0.260 0.620 0.000
#> round_ERR2585210 1 0.0817 0.8826 0.976 0.000 0.024 0.000
#> aberrant_ERR2585362 4 0.6388 0.6377 0.028 0.268 0.052 0.652
#> aberrant_ERR2585360 4 0.5923 0.6348 0.020 0.288 0.032 0.660
#> round_ERR2585209 1 0.7301 -0.2623 0.452 0.152 0.396 0.000
#> round_ERR2585242 3 0.6414 0.8568 0.124 0.240 0.636 0.000
#> round_ERR2585216 1 0.4906 0.7212 0.776 0.084 0.140 0.000
#> round_ERR2585219 1 0.1767 0.8819 0.944 0.012 0.044 0.000
#> round_ERR2585237 3 0.7422 0.7760 0.180 0.292 0.524 0.004
#> round_ERR2585198 2 0.6835 -0.3819 0.064 0.524 0.396 0.016
#> round_ERR2585211 1 0.1042 0.8824 0.972 0.008 0.020 0.000
#> round_ERR2585206 1 0.0895 0.8820 0.976 0.004 0.020 0.000
#> aberrant_ERR2585281 2 0.5610 0.6050 0.000 0.720 0.104 0.176
#> round_ERR2585212 1 0.3082 0.8511 0.884 0.032 0.084 0.000
#> round_ERR2585221 1 0.1733 0.8690 0.948 0.000 0.028 0.024
#> round_ERR2585243 1 0.1256 0.8842 0.964 0.008 0.028 0.000
#> round_ERR2585204 2 0.5476 -0.1484 0.020 0.584 0.396 0.000
#> round_ERR2585213 2 0.4216 0.4688 0.008 0.788 0.196 0.008
#> aberrant_ERR2585373 4 0.6477 0.6491 0.032 0.268 0.052 0.648
#> aberrant_ERR2585358 4 0.4430 0.7238 0.016 0.100 0.056 0.828
#> aberrant_ERR2585365 2 0.6207 0.4024 0.008 0.620 0.056 0.316
#> aberrant_ERR2585359 4 0.5132 0.7175 0.040 0.080 0.080 0.800
#> aberrant_ERR2585370 2 0.0804 0.6804 0.000 0.980 0.012 0.008
#> round_ERR2585215 1 0.0469 0.8774 0.988 0.000 0.012 0.000
#> round_ERR2585262 3 0.6882 0.8089 0.100 0.296 0.592 0.012
#> round_ERR2585199 2 0.6322 -0.2493 0.060 0.576 0.360 0.004
#> aberrant_ERR2585369 4 0.6217 0.5618 0.016 0.316 0.044 0.624
#> round_ERR2585208 1 0.0779 0.8808 0.980 0.004 0.016 0.000
#> round_ERR2585252 1 0.0707 0.8783 0.980 0.000 0.020 0.000
#> round_ERR2585236 1 0.5694 0.6814 0.728 0.008 0.176 0.088
#> aberrant_ERR2585284 4 0.5998 0.5996 0.088 0.000 0.248 0.664
#> round_ERR2585224 1 0.0937 0.8750 0.976 0.000 0.012 0.012
#> round_ERR2585260 1 0.1888 0.8810 0.940 0.016 0.044 0.000
#> round_ERR2585229 1 0.1743 0.8816 0.940 0.004 0.056 0.000
#> aberrant_ERR2585364 4 0.5420 0.6517 0.076 0.008 0.168 0.748
#> round_ERR2585253 1 0.0469 0.8754 0.988 0.000 0.012 0.000
#> aberrant_ERR2585368 2 0.2376 0.6502 0.000 0.916 0.068 0.016
#> aberrant_ERR2585371 2 0.2376 0.6502 0.000 0.916 0.068 0.016
#> round_ERR2585239 1 0.2365 0.8787 0.920 0.012 0.064 0.004
#> round_ERR2585273 1 0.1109 0.8840 0.968 0.004 0.028 0.000
#> round_ERR2585256 1 0.6804 0.3153 0.572 0.092 0.328 0.008
#> round_ERR2585272 1 0.4590 0.7426 0.772 0.036 0.192 0.000
#> round_ERR2585246 1 0.1042 0.8802 0.972 0.000 0.020 0.008
#> round_ERR2585261 3 0.7105 0.8229 0.176 0.268 0.556 0.000
#> round_ERR2585254 3 0.8070 0.6142 0.196 0.376 0.412 0.016
#> round_ERR2585225 3 0.6178 0.8524 0.112 0.228 0.660 0.000
#> round_ERR2585235 1 0.6413 0.3541 0.588 0.036 0.352 0.024
#> round_ERR2585271 1 0.2002 0.8783 0.936 0.020 0.044 0.000
#> round_ERR2585251 1 0.3523 0.8315 0.856 0.032 0.112 0.000
#> round_ERR2585255 3 0.6127 0.8496 0.108 0.228 0.664 0.000
#> round_ERR2585257 3 0.6262 0.8377 0.132 0.208 0.660 0.000
#> round_ERR2585226 1 0.3156 0.8492 0.884 0.048 0.068 0.000
#> round_ERR2585265 1 0.2943 0.8567 0.892 0.032 0.076 0.000
#> round_ERR2585259 1 0.5840 0.5611 0.688 0.072 0.236 0.004
#> round_ERR2585247 1 0.1109 0.8832 0.968 0.004 0.028 0.000
#> round_ERR2585241 1 0.1722 0.8826 0.944 0.008 0.048 0.000
#> round_ERR2585263 1 0.6843 0.5771 0.672 0.076 0.192 0.060
#> round_ERR2585264 1 0.0469 0.8754 0.988 0.000 0.012 0.000
#> round_ERR2585233 3 0.6359 0.8506 0.132 0.220 0.648 0.000
#> round_ERR2585223 1 0.2644 0.8652 0.908 0.032 0.060 0.000
#> round_ERR2585234 3 0.6538 0.6698 0.080 0.392 0.528 0.000
#> round_ERR2585222 1 0.1722 0.8828 0.944 0.008 0.048 0.000
#> round_ERR2585228 1 0.1584 0.8825 0.952 0.012 0.036 0.000
#> round_ERR2585248 1 0.0707 0.8757 0.980 0.000 0.020 0.000
#> round_ERR2585240 3 0.6901 0.3273 0.404 0.108 0.488 0.000
#> round_ERR2585270 1 0.2730 0.8664 0.896 0.016 0.088 0.000
#> round_ERR2585232 1 0.7023 -0.0456 0.488 0.092 0.412 0.008
#> aberrant_ERR2585341 2 0.5569 0.4450 0.000 0.660 0.044 0.296
#> aberrant_ERR2585355 2 0.2623 0.6980 0.000 0.908 0.028 0.064
#> round_ERR2585227 1 0.2413 0.8700 0.916 0.020 0.064 0.000
#> aberrant_ERR2585351 4 0.7967 0.4116 0.072 0.368 0.076 0.484
#> round_ERR2585269 1 0.0469 0.8774 0.988 0.000 0.012 0.000
#> aberrant_ERR2585357 2 0.1724 0.6914 0.000 0.948 0.020 0.032
#> aberrant_ERR2585350 2 0.2089 0.6986 0.000 0.932 0.020 0.048
#> round_ERR2585250 1 0.3837 0.8415 0.860 0.032 0.088 0.020
#> round_ERR2585245 1 0.0469 0.8754 0.988 0.000 0.012 0.000
#> aberrant_ERR2585353 4 0.6668 0.6299 0.032 0.288 0.056 0.624
#> round_ERR2585258 1 0.3176 0.8485 0.880 0.036 0.084 0.000
#> aberrant_ERR2585354 4 0.6044 0.6359 0.016 0.272 0.048 0.664
#> round_ERR2585249 1 0.0707 0.8785 0.980 0.000 0.020 0.000
#> round_ERR2585268 1 0.6655 0.5462 0.652 0.072 0.244 0.032
#> aberrant_ERR2585356 4 0.4711 0.7100 0.048 0.048 0.080 0.824
#> round_ERR2585266 3 0.6549 0.8554 0.128 0.228 0.640 0.004
#> round_ERR2585231 1 0.0707 0.8771 0.980 0.000 0.020 0.000
#> round_ERR2585230 1 0.2021 0.8792 0.932 0.012 0.056 0.000
#> round_ERR2585267 1 0.0592 0.8766 0.984 0.000 0.016 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 2 0.6976 -0.2756 0.000 0.412 0.012 0.228 0.348
#> aberrant_ERR2585338 2 0.3648 0.6683 0.000 0.828 0.024 0.020 0.128
#> aberrant_ERR2585325 2 0.6976 -0.2756 0.000 0.412 0.012 0.228 0.348
#> aberrant_ERR2585283 4 0.0740 0.4850 0.008 0.000 0.008 0.980 0.004
#> aberrant_ERR2585343 4 0.5713 -0.0451 0.000 0.084 0.000 0.500 0.416
#> aberrant_ERR2585329 2 0.2908 0.6716 0.000 0.868 0.008 0.016 0.108
#> aberrant_ERR2585317 2 0.3063 0.6633 0.000 0.864 0.004 0.036 0.096
#> aberrant_ERR2585339 2 0.1757 0.6786 0.000 0.936 0.012 0.004 0.048
#> aberrant_ERR2585335 2 0.6494 0.0636 0.000 0.516 0.004 0.212 0.268
#> aberrant_ERR2585287 4 0.5421 0.3660 0.008 0.084 0.020 0.712 0.176
#> aberrant_ERR2585321 4 0.6207 -0.3085 0.000 0.140 0.000 0.460 0.400
#> aberrant_ERR2585297 1 0.1569 0.8927 0.944 0.000 0.044 0.004 0.008
#> aberrant_ERR2585337 2 0.3049 0.6798 0.000 0.872 0.012 0.032 0.084
#> aberrant_ERR2585319 2 0.5904 0.3705 0.000 0.600 0.004 0.136 0.260
#> aberrant_ERR2585315 2 0.2629 0.6733 0.000 0.880 0.004 0.012 0.104
#> aberrant_ERR2585336 2 0.2929 0.6748 0.000 0.856 0.012 0.004 0.128
#> aberrant_ERR2585307 2 0.3497 0.6616 0.000 0.828 0.020 0.012 0.140
#> aberrant_ERR2585301 5 0.6906 0.5501 0.004 0.236 0.004 0.320 0.436
#> aberrant_ERR2585326 2 0.1202 0.6770 0.000 0.960 0.004 0.004 0.032
#> aberrant_ERR2585331 2 0.3389 0.6350 0.000 0.836 0.048 0.000 0.116
#> aberrant_ERR2585346 4 0.1836 0.4690 0.016 0.000 0.008 0.936 0.040
#> aberrant_ERR2585314 2 0.5587 0.4942 0.000 0.656 0.016 0.088 0.240
#> aberrant_ERR2585298 3 0.2906 0.7635 0.028 0.080 0.880 0.000 0.012
#> aberrant_ERR2585345 2 0.3825 0.6588 0.000 0.828 0.020 0.048 0.104
#> aberrant_ERR2585299 1 0.2199 0.8859 0.916 0.000 0.060 0.008 0.016
#> aberrant_ERR2585309 1 0.0740 0.8833 0.980 0.000 0.008 0.004 0.008
#> aberrant_ERR2585303 2 0.5820 0.4729 0.000 0.628 0.024 0.080 0.268
#> aberrant_ERR2585313 2 0.2284 0.6774 0.000 0.896 0.004 0.004 0.096
#> aberrant_ERR2585318 5 0.6962 0.5408 0.008 0.292 0.000 0.284 0.416
#> aberrant_ERR2585328 4 0.6639 0.2563 0.008 0.112 0.044 0.600 0.236
#> aberrant_ERR2585330 2 0.6557 0.1877 0.000 0.528 0.012 0.180 0.280
#> aberrant_ERR2585293 4 0.0867 0.4856 0.008 0.000 0.008 0.976 0.008
#> aberrant_ERR2585342 4 0.5816 -0.1380 0.000 0.092 0.000 0.468 0.440
#> aberrant_ERR2585348 5 0.7410 0.3416 0.000 0.308 0.028 0.304 0.360
#> aberrant_ERR2585352 2 0.5895 0.4326 0.000 0.624 0.008 0.148 0.220
#> aberrant_ERR2585308 1 0.0579 0.8856 0.984 0.000 0.008 0.000 0.008
#> aberrant_ERR2585349 2 0.4449 0.6222 0.000 0.752 0.080 0.000 0.168
#> aberrant_ERR2585316 4 0.4843 0.3564 0.008 0.036 0.000 0.676 0.280
#> aberrant_ERR2585306 4 0.7477 0.0839 0.184 0.044 0.004 0.416 0.352
#> aberrant_ERR2585324 2 0.5904 0.3705 0.000 0.600 0.004 0.136 0.260
#> aberrant_ERR2585310 2 0.7483 0.2844 0.052 0.548 0.036 0.124 0.240
#> aberrant_ERR2585296 1 0.6845 0.1305 0.484 0.068 0.384 0.008 0.056
#> aberrant_ERR2585275 4 0.3559 0.4736 0.008 0.000 0.012 0.804 0.176
#> aberrant_ERR2585311 5 0.5725 0.1644 0.000 0.084 0.000 0.428 0.488
#> aberrant_ERR2585292 4 0.0867 0.4856 0.008 0.000 0.008 0.976 0.008
#> aberrant_ERR2585282 4 0.7184 -0.2511 0.012 0.168 0.020 0.456 0.344
#> aberrant_ERR2585305 5 0.7227 0.3792 0.044 0.144 0.004 0.348 0.460
#> aberrant_ERR2585278 2 0.6448 0.1913 0.000 0.524 0.016 0.132 0.328
#> aberrant_ERR2585347 4 0.4802 0.4098 0.008 0.072 0.004 0.744 0.172
#> aberrant_ERR2585332 4 0.5500 0.1309 0.000 0.072 0.000 0.552 0.376
#> aberrant_ERR2585280 2 0.6815 -0.2924 0.000 0.400 0.008 0.204 0.388
#> aberrant_ERR2585304 3 0.6182 0.5527 0.020 0.336 0.568 0.012 0.064
#> aberrant_ERR2585322 2 0.3807 0.6547 0.000 0.820 0.008 0.056 0.116
#> aberrant_ERR2585279 2 0.3323 0.6371 0.000 0.844 0.056 0.000 0.100
#> aberrant_ERR2585277 2 0.3289 0.6363 0.000 0.844 0.048 0.000 0.108
#> aberrant_ERR2585295 5 0.7538 0.3361 0.004 0.288 0.028 0.320 0.360
#> aberrant_ERR2585333 5 0.6269 0.3309 0.000 0.128 0.004 0.416 0.452
#> aberrant_ERR2585285 2 0.6612 -0.3836 0.000 0.412 0.000 0.216 0.372
#> aberrant_ERR2585286 2 0.4107 0.6568 0.000 0.804 0.040 0.024 0.132
#> aberrant_ERR2585294 5 0.6795 0.5443 0.004 0.244 0.004 0.268 0.480
#> aberrant_ERR2585300 5 0.5425 -0.0566 0.000 0.048 0.004 0.440 0.508
#> aberrant_ERR2585334 2 0.3932 0.6172 0.000 0.796 0.064 0.000 0.140
#> aberrant_ERR2585361 2 0.6503 0.2541 0.000 0.532 0.016 0.148 0.304
#> aberrant_ERR2585372 5 0.7029 0.5096 0.004 0.292 0.004 0.300 0.400
#> round_ERR2585217 3 0.5831 0.5357 0.004 0.304 0.584 0.000 0.108
#> round_ERR2585205 1 0.1644 0.8923 0.940 0.000 0.048 0.004 0.008
#> round_ERR2585214 3 0.4125 0.7238 0.008 0.172 0.780 0.000 0.040
#> round_ERR2585202 3 0.7340 0.3892 0.088 0.360 0.456 0.004 0.092
#> aberrant_ERR2585367 2 0.6808 0.1353 0.000 0.488 0.020 0.172 0.320
#> round_ERR2585220 1 0.3319 0.8258 0.820 0.000 0.160 0.000 0.020
#> round_ERR2585238 1 0.0865 0.8891 0.972 0.000 0.024 0.000 0.004
#> aberrant_ERR2585276 5 0.7114 0.5100 0.008 0.184 0.016 0.352 0.440
#> round_ERR2585218 1 0.1408 0.8918 0.948 0.000 0.044 0.000 0.008
#> aberrant_ERR2585363 2 0.5250 0.5412 0.000 0.668 0.008 0.072 0.252
#> round_ERR2585201 3 0.2760 0.7629 0.028 0.064 0.892 0.000 0.016
#> round_ERR2585210 1 0.1280 0.8911 0.960 0.000 0.024 0.008 0.008
#> aberrant_ERR2585362 4 0.6947 -0.4404 0.000 0.216 0.012 0.404 0.368
#> aberrant_ERR2585360 5 0.6660 0.4563 0.000 0.228 0.000 0.380 0.392
#> round_ERR2585209 3 0.6011 0.2764 0.380 0.048 0.536 0.000 0.036
#> round_ERR2585242 3 0.2299 0.7616 0.032 0.052 0.912 0.000 0.004
#> round_ERR2585216 1 0.4240 0.7134 0.732 0.004 0.240 0.000 0.024
#> round_ERR2585219 1 0.1764 0.8901 0.928 0.000 0.064 0.000 0.008
#> round_ERR2585237 3 0.5519 0.7308 0.108 0.116 0.720 0.000 0.056
#> round_ERR2585198 3 0.5790 0.5598 0.020 0.344 0.576 0.000 0.060
#> round_ERR2585211 1 0.1168 0.8900 0.960 0.000 0.032 0.000 0.008
#> round_ERR2585206 1 0.1026 0.8897 0.968 0.000 0.024 0.004 0.004
#> aberrant_ERR2585281 2 0.6359 0.4880 0.000 0.584 0.072 0.056 0.288
#> round_ERR2585212 1 0.3209 0.8529 0.848 0.004 0.120 0.000 0.028
#> round_ERR2585221 1 0.1836 0.8772 0.936 0.000 0.016 0.040 0.008
#> round_ERR2585243 1 0.1701 0.8925 0.936 0.000 0.048 0.000 0.016
#> round_ERR2585204 3 0.5881 0.4246 0.004 0.400 0.508 0.000 0.088
#> round_ERR2585213 2 0.5348 0.3668 0.000 0.656 0.232 0.000 0.112
#> aberrant_ERR2585373 5 0.7017 0.5207 0.008 0.200 0.008 0.352 0.432
#> aberrant_ERR2585358 4 0.5556 0.0316 0.000 0.072 0.000 0.524 0.404
#> aberrant_ERR2585365 2 0.6454 0.2813 0.000 0.544 0.020 0.132 0.304
#> aberrant_ERR2585359 4 0.5071 0.3014 0.004 0.040 0.000 0.616 0.340
#> aberrant_ERR2585370 2 0.1386 0.6729 0.000 0.952 0.016 0.000 0.032
#> round_ERR2585215 1 0.0854 0.8862 0.976 0.000 0.008 0.012 0.004
#> round_ERR2585262 3 0.4291 0.7386 0.012 0.092 0.812 0.016 0.068
#> round_ERR2585199 3 0.5723 0.5127 0.020 0.384 0.548 0.000 0.048
#> aberrant_ERR2585369 5 0.6731 0.5603 0.004 0.232 0.000 0.312 0.452
#> round_ERR2585208 1 0.0671 0.8889 0.980 0.000 0.016 0.000 0.004
#> round_ERR2585252 1 0.0854 0.8858 0.976 0.000 0.012 0.008 0.004
#> round_ERR2585236 1 0.5847 0.6308 0.668 0.000 0.184 0.116 0.032
#> aberrant_ERR2585284 4 0.2321 0.4509 0.024 0.000 0.016 0.916 0.044
#> round_ERR2585224 1 0.0968 0.8833 0.972 0.000 0.004 0.012 0.012
#> round_ERR2585260 1 0.1704 0.8887 0.928 0.000 0.068 0.000 0.004
#> round_ERR2585229 1 0.1628 0.8915 0.936 0.000 0.056 0.000 0.008
#> aberrant_ERR2585364 4 0.3124 0.4866 0.016 0.000 0.004 0.844 0.136
#> round_ERR2585253 1 0.0613 0.8820 0.984 0.000 0.004 0.004 0.008
#> aberrant_ERR2585368 2 0.3030 0.6499 0.000 0.868 0.040 0.004 0.088
#> aberrant_ERR2585371 2 0.3030 0.6499 0.000 0.868 0.040 0.004 0.088
#> round_ERR2585239 1 0.2260 0.8868 0.908 0.000 0.064 0.000 0.028
#> round_ERR2585273 1 0.1331 0.8923 0.952 0.000 0.040 0.000 0.008
#> round_ERR2585256 1 0.6349 0.2420 0.512 0.036 0.392 0.008 0.052
#> round_ERR2585272 1 0.4153 0.7322 0.736 0.000 0.240 0.004 0.020
#> round_ERR2585246 1 0.1267 0.8887 0.960 0.000 0.012 0.004 0.024
#> round_ERR2585261 3 0.4233 0.7571 0.084 0.092 0.804 0.000 0.020
#> round_ERR2585254 3 0.6862 0.6473 0.152 0.192 0.592 0.004 0.060
#> round_ERR2585225 3 0.2087 0.7514 0.020 0.032 0.928 0.000 0.020
#> round_ERR2585235 1 0.6100 0.2922 0.540 0.000 0.368 0.036 0.056
#> round_ERR2585271 1 0.1894 0.8859 0.920 0.000 0.072 0.000 0.008
#> round_ERR2585251 1 0.3726 0.8221 0.812 0.004 0.152 0.004 0.028
#> round_ERR2585255 3 0.2180 0.7500 0.020 0.032 0.924 0.000 0.024
#> round_ERR2585257 3 0.2767 0.7436 0.032 0.024 0.900 0.004 0.040
#> round_ERR2585226 1 0.2741 0.8578 0.860 0.000 0.132 0.004 0.004
#> round_ERR2585265 1 0.2886 0.8591 0.864 0.004 0.116 0.000 0.016
#> round_ERR2585259 1 0.5532 0.4989 0.632 0.024 0.304 0.008 0.032
#> round_ERR2585247 1 0.1251 0.8907 0.956 0.000 0.036 0.000 0.008
#> round_ERR2585241 1 0.2046 0.8879 0.916 0.000 0.068 0.000 0.016
#> round_ERR2585263 1 0.6684 0.4975 0.592 0.024 0.272 0.048 0.064
#> round_ERR2585264 1 0.0613 0.8820 0.984 0.000 0.004 0.004 0.008
#> round_ERR2585233 3 0.2529 0.7504 0.036 0.032 0.908 0.000 0.024
#> round_ERR2585223 1 0.2416 0.8708 0.888 0.000 0.100 0.000 0.012
#> round_ERR2585234 3 0.4784 0.7104 0.016 0.192 0.736 0.000 0.056
#> round_ERR2585222 1 0.2037 0.8898 0.920 0.000 0.064 0.004 0.012
#> round_ERR2585228 1 0.1731 0.8896 0.932 0.000 0.060 0.004 0.004
#> round_ERR2585248 1 0.0854 0.8821 0.976 0.000 0.008 0.004 0.012
#> round_ERR2585240 3 0.4989 0.3775 0.336 0.020 0.628 0.000 0.016
#> round_ERR2585270 1 0.2919 0.8700 0.868 0.000 0.104 0.004 0.024
#> round_ERR2585232 3 0.5454 0.0802 0.428 0.008 0.528 0.008 0.028
#> aberrant_ERR2585341 2 0.6114 0.3311 0.000 0.580 0.008 0.140 0.272
#> aberrant_ERR2585355 2 0.3387 0.6757 0.000 0.852 0.020 0.028 0.100
#> round_ERR2585227 1 0.2519 0.8732 0.884 0.000 0.100 0.000 0.016
#> aberrant_ERR2585351 5 0.8193 0.4790 0.048 0.308 0.036 0.204 0.404
#> round_ERR2585269 1 0.0290 0.8845 0.992 0.000 0.000 0.008 0.000
#> aberrant_ERR2585357 2 0.1809 0.6774 0.000 0.928 0.012 0.000 0.060
#> aberrant_ERR2585350 2 0.2634 0.6821 0.000 0.900 0.020 0.024 0.056
#> round_ERR2585250 1 0.3790 0.8338 0.816 0.000 0.136 0.012 0.036
#> round_ERR2585245 1 0.0740 0.8826 0.980 0.000 0.004 0.008 0.008
#> aberrant_ERR2585353 4 0.6963 -0.4491 0.004 0.240 0.004 0.400 0.352
#> round_ERR2585258 1 0.2646 0.8590 0.868 0.000 0.124 0.004 0.004
#> aberrant_ERR2585354 5 0.7036 0.5370 0.004 0.212 0.012 0.336 0.436
#> round_ERR2585249 1 0.0740 0.8860 0.980 0.000 0.008 0.004 0.008
#> round_ERR2585268 1 0.6426 0.4623 0.580 0.024 0.312 0.036 0.048
#> aberrant_ERR2585356 4 0.5082 0.3461 0.016 0.016 0.004 0.620 0.344
#> round_ERR2585266 3 0.2472 0.7599 0.036 0.044 0.908 0.000 0.012
#> round_ERR2585231 1 0.0727 0.8848 0.980 0.000 0.004 0.012 0.004
#> round_ERR2585230 1 0.2228 0.8846 0.908 0.000 0.076 0.004 0.012
#> round_ERR2585267 1 0.0451 0.8834 0.988 0.000 0.000 0.008 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 5 0.6814 0.25513 0.000 0.336 0.004 0.088 0.448 0.124
#> aberrant_ERR2585338 2 0.4656 0.26687 0.000 0.740 0.028 0.008 0.068 0.156
#> aberrant_ERR2585325 5 0.6814 0.25513 0.000 0.336 0.004 0.088 0.448 0.124
#> aberrant_ERR2585283 4 0.3198 0.75826 0.000 0.000 0.000 0.740 0.260 0.000
#> aberrant_ERR2585343 5 0.5279 0.28963 0.000 0.036 0.000 0.184 0.668 0.112
#> aberrant_ERR2585329 2 0.2956 0.47346 0.000 0.860 0.004 0.008 0.092 0.036
#> aberrant_ERR2585317 2 0.3020 0.47580 0.000 0.856 0.004 0.024 0.100 0.016
#> aberrant_ERR2585339 2 0.2345 0.41678 0.000 0.904 0.012 0.004 0.028 0.052
#> aberrant_ERR2585335 2 0.5944 0.05652 0.000 0.456 0.004 0.064 0.428 0.048
#> aberrant_ERR2585287 4 0.6455 0.39616 0.004 0.056 0.016 0.460 0.396 0.068
#> aberrant_ERR2585321 5 0.5736 0.42024 0.000 0.088 0.004 0.180 0.648 0.080
#> aberrant_ERR2585297 1 0.1679 0.88773 0.936 0.000 0.036 0.016 0.000 0.012
#> aberrant_ERR2585337 2 0.3507 0.46470 0.000 0.836 0.020 0.008 0.084 0.052
#> aberrant_ERR2585319 2 0.5346 0.31360 0.000 0.560 0.000 0.036 0.356 0.048
#> aberrant_ERR2585315 2 0.2795 0.47634 0.000 0.856 0.000 0.000 0.100 0.044
#> aberrant_ERR2585336 2 0.3175 0.46550 0.000 0.852 0.012 0.004 0.076 0.056
#> aberrant_ERR2585307 2 0.4057 0.44906 0.000 0.776 0.024 0.004 0.156 0.040
#> aberrant_ERR2585301 5 0.4450 0.54317 0.004 0.164 0.000 0.056 0.748 0.028
#> aberrant_ERR2585326 2 0.2256 0.44692 0.000 0.908 0.008 0.004 0.048 0.032
#> aberrant_ERR2585331 2 0.3830 0.10428 0.000 0.760 0.036 0.000 0.008 0.196
#> aberrant_ERR2585346 4 0.3627 0.74321 0.004 0.000 0.000 0.752 0.224 0.020
#> aberrant_ERR2585314 2 0.5543 0.37251 0.000 0.596 0.016 0.032 0.308 0.048
#> aberrant_ERR2585298 3 0.2326 0.70829 0.020 0.040 0.908 0.004 0.000 0.028
#> aberrant_ERR2585345 2 0.3992 0.46950 0.000 0.800 0.020 0.024 0.124 0.032
#> aberrant_ERR2585299 1 0.2818 0.87721 0.884 0.000 0.044 0.036 0.008 0.028
#> aberrant_ERR2585309 1 0.0862 0.87762 0.972 0.000 0.004 0.008 0.000 0.016
#> aberrant_ERR2585303 2 0.6477 -0.04327 0.000 0.548 0.012 0.044 0.212 0.184
#> aberrant_ERR2585313 2 0.2614 0.46620 0.000 0.884 0.000 0.012 0.060 0.044
#> aberrant_ERR2585318 5 0.4925 0.53399 0.004 0.216 0.000 0.048 0.692 0.040
#> aberrant_ERR2585328 4 0.7503 0.32125 0.008 0.072 0.024 0.432 0.288 0.176
#> aberrant_ERR2585330 2 0.6011 0.17952 0.000 0.472 0.008 0.040 0.408 0.072
#> aberrant_ERR2585293 4 0.3198 0.75875 0.000 0.000 0.000 0.740 0.260 0.000
#> aberrant_ERR2585342 5 0.5532 0.34859 0.000 0.052 0.000 0.180 0.652 0.116
#> aberrant_ERR2585348 5 0.7668 0.36193 0.000 0.228 0.020 0.148 0.420 0.184
#> aberrant_ERR2585352 2 0.5836 0.34841 0.000 0.588 0.008 0.064 0.284 0.056
#> aberrant_ERR2585308 1 0.0862 0.88139 0.972 0.000 0.004 0.008 0.000 0.016
#> aberrant_ERR2585349 2 0.5399 0.17847 0.000 0.668 0.072 0.012 0.040 0.208
#> aberrant_ERR2585316 5 0.5428 -0.25445 0.004 0.004 0.000 0.384 0.516 0.092
#> aberrant_ERR2585306 5 0.6588 0.09903 0.180 0.020 0.000 0.152 0.576 0.072
#> aberrant_ERR2585324 2 0.5346 0.31360 0.000 0.560 0.000 0.036 0.356 0.048
#> aberrant_ERR2585310 2 0.6597 0.22436 0.052 0.488 0.028 0.032 0.372 0.028
#> aberrant_ERR2585296 1 0.6734 0.12756 0.464 0.048 0.376 0.048 0.004 0.060
#> aberrant_ERR2585275 4 0.5514 0.65065 0.004 0.000 0.004 0.564 0.304 0.124
#> aberrant_ERR2585311 5 0.4242 0.42182 0.000 0.044 0.000 0.116 0.776 0.064
#> aberrant_ERR2585292 4 0.3198 0.75875 0.000 0.000 0.000 0.740 0.260 0.000
#> aberrant_ERR2585282 5 0.7211 0.24816 0.012 0.104 0.012 0.264 0.492 0.116
#> aberrant_ERR2585305 5 0.4835 0.48045 0.044 0.084 0.000 0.080 0.760 0.032
#> aberrant_ERR2585278 2 0.5703 0.13723 0.000 0.456 0.008 0.024 0.448 0.064
#> aberrant_ERR2585347 4 0.5876 0.44756 0.004 0.040 0.000 0.472 0.416 0.068
#> aberrant_ERR2585332 5 0.5870 0.13466 0.000 0.040 0.000 0.300 0.556 0.104
#> aberrant_ERR2585280 5 0.6556 0.27581 0.000 0.332 0.008 0.060 0.484 0.116
#> aberrant_ERR2585304 3 0.6231 0.46553 0.016 0.272 0.580 0.016 0.028 0.088
#> aberrant_ERR2585322 2 0.4331 0.43519 0.000 0.776 0.008 0.032 0.120 0.064
#> aberrant_ERR2585279 2 0.3837 0.12408 0.000 0.768 0.044 0.000 0.008 0.180
#> aberrant_ERR2585277 2 0.3525 0.12889 0.000 0.784 0.032 0.000 0.004 0.180
#> aberrant_ERR2585295 5 0.7520 0.35288 0.000 0.200 0.020 0.172 0.456 0.152
#> aberrant_ERR2585333 5 0.4845 0.48836 0.000 0.072 0.000 0.120 0.732 0.076
#> aberrant_ERR2585285 5 0.5201 0.28818 0.000 0.368 0.000 0.036 0.560 0.036
#> aberrant_ERR2585286 2 0.4864 0.09198 0.000 0.704 0.028 0.004 0.068 0.196
#> aberrant_ERR2585294 5 0.4616 0.54246 0.000 0.176 0.000 0.056 0.728 0.040
#> aberrant_ERR2585300 5 0.5365 0.25038 0.000 0.024 0.000 0.184 0.648 0.144
#> aberrant_ERR2585334 2 0.4364 -0.13331 0.000 0.688 0.052 0.000 0.004 0.256
#> aberrant_ERR2585361 2 0.7251 0.12147 0.000 0.428 0.016 0.088 0.308 0.160
#> aberrant_ERR2585372 5 0.6424 0.50751 0.004 0.224 0.000 0.104 0.564 0.104
#> round_ERR2585217 3 0.5906 0.43287 0.000 0.240 0.568 0.012 0.008 0.172
#> round_ERR2585205 1 0.2057 0.88755 0.920 0.000 0.044 0.016 0.004 0.016
#> round_ERR2585214 3 0.3684 0.66172 0.004 0.124 0.808 0.012 0.000 0.052
#> round_ERR2585202 3 0.7435 0.26136 0.072 0.296 0.452 0.020 0.016 0.144
#> aberrant_ERR2585367 2 0.7548 0.00454 0.000 0.388 0.020 0.104 0.308 0.180
#> round_ERR2585220 1 0.3505 0.82279 0.804 0.000 0.152 0.028 0.000 0.016
#> round_ERR2585238 1 0.0993 0.88433 0.964 0.000 0.024 0.012 0.000 0.000
#> aberrant_ERR2585276 5 0.4106 0.52618 0.000 0.112 0.008 0.084 0.784 0.012
#> round_ERR2585218 1 0.1726 0.88702 0.932 0.000 0.044 0.012 0.000 0.012
#> aberrant_ERR2585363 2 0.5966 0.37465 0.000 0.616 0.008 0.064 0.216 0.096
#> round_ERR2585201 3 0.1931 0.70704 0.016 0.020 0.928 0.004 0.000 0.032
#> round_ERR2585210 1 0.1875 0.88676 0.928 0.000 0.020 0.032 0.000 0.020
#> aberrant_ERR2585362 5 0.6535 0.47124 0.000 0.132 0.004 0.176 0.568 0.120
#> aberrant_ERR2585360 5 0.6484 0.45778 0.000 0.180 0.000 0.164 0.556 0.100
#> round_ERR2585209 3 0.5974 0.26512 0.368 0.020 0.516 0.028 0.000 0.068
#> round_ERR2585242 3 0.1774 0.70897 0.024 0.020 0.936 0.004 0.000 0.016
#> round_ERR2585216 1 0.4511 0.70501 0.712 0.000 0.224 0.036 0.004 0.024
#> round_ERR2585219 1 0.2001 0.88421 0.912 0.000 0.068 0.012 0.000 0.008
#> round_ERR2585237 3 0.5558 0.66298 0.100 0.072 0.712 0.032 0.004 0.080
#> round_ERR2585198 3 0.5995 0.47129 0.016 0.280 0.588 0.012 0.020 0.084
#> round_ERR2585211 1 0.1710 0.88646 0.936 0.000 0.028 0.020 0.000 0.016
#> round_ERR2585206 1 0.1036 0.88429 0.964 0.000 0.024 0.008 0.000 0.004
#> aberrant_ERR2585281 6 0.6086 0.00000 0.000 0.432 0.028 0.028 0.060 0.452
#> round_ERR2585212 1 0.3300 0.84927 0.832 0.000 0.116 0.032 0.000 0.020
#> round_ERR2585221 1 0.2030 0.87043 0.920 0.000 0.012 0.048 0.004 0.016
#> round_ERR2585243 1 0.2195 0.88695 0.912 0.000 0.036 0.028 0.000 0.024
#> round_ERR2585204 3 0.5736 0.23304 0.004 0.324 0.508 0.000 0.000 0.164
#> round_ERR2585213 2 0.5470 -0.27875 0.000 0.588 0.228 0.000 0.004 0.180
#> aberrant_ERR2585373 5 0.5311 0.52772 0.004 0.112 0.000 0.112 0.700 0.072
#> aberrant_ERR2585358 5 0.5101 0.31379 0.000 0.024 0.000 0.200 0.672 0.104
#> aberrant_ERR2585365 2 0.6989 0.14214 0.000 0.448 0.012 0.068 0.312 0.160
#> aberrant_ERR2585359 5 0.5102 -0.04104 0.000 0.016 0.000 0.308 0.608 0.068
#> aberrant_ERR2585370 2 0.2537 0.41330 0.000 0.896 0.020 0.004 0.032 0.048
#> round_ERR2585215 1 0.1426 0.88117 0.948 0.000 0.008 0.028 0.000 0.016
#> round_ERR2585262 3 0.4426 0.65090 0.008 0.036 0.764 0.052 0.000 0.140
#> round_ERR2585199 3 0.5779 0.40668 0.016 0.292 0.568 0.008 0.000 0.116
#> aberrant_ERR2585369 5 0.5352 0.54319 0.000 0.172 0.000 0.104 0.672 0.052
#> round_ERR2585208 1 0.0717 0.88342 0.976 0.000 0.016 0.000 0.000 0.008
#> round_ERR2585252 1 0.0881 0.88147 0.972 0.000 0.008 0.012 0.000 0.008
#> round_ERR2585236 1 0.6134 0.59809 0.636 0.000 0.148 0.136 0.036 0.044
#> aberrant_ERR2585284 4 0.3825 0.72432 0.012 0.000 0.008 0.764 0.200 0.016
#> round_ERR2585224 1 0.1261 0.87827 0.952 0.000 0.000 0.024 0.000 0.024
#> round_ERR2585260 1 0.2076 0.88543 0.912 0.000 0.060 0.016 0.000 0.012
#> round_ERR2585229 1 0.2101 0.88636 0.912 0.000 0.052 0.028 0.000 0.008
#> aberrant_ERR2585364 4 0.4956 0.66232 0.004 0.000 0.000 0.592 0.332 0.072
#> round_ERR2585253 1 0.0806 0.87649 0.972 0.000 0.000 0.008 0.000 0.020
#> aberrant_ERR2585368 2 0.3626 0.23051 0.000 0.800 0.032 0.000 0.020 0.148
#> aberrant_ERR2585371 2 0.3626 0.23051 0.000 0.800 0.032 0.000 0.020 0.148
#> round_ERR2585239 1 0.2404 0.88221 0.896 0.000 0.064 0.020 0.000 0.020
#> round_ERR2585273 1 0.1675 0.88717 0.936 0.000 0.024 0.008 0.000 0.032
#> round_ERR2585256 1 0.6429 0.26622 0.500 0.020 0.356 0.064 0.004 0.056
#> round_ERR2585272 1 0.4265 0.74124 0.732 0.000 0.208 0.024 0.000 0.036
#> round_ERR2585246 1 0.1629 0.88451 0.940 0.000 0.004 0.024 0.004 0.028
#> round_ERR2585261 3 0.3969 0.69921 0.076 0.048 0.816 0.016 0.000 0.044
#> round_ERR2585254 3 0.6718 0.57305 0.148 0.148 0.600 0.016 0.024 0.064
#> round_ERR2585225 3 0.1586 0.69899 0.012 0.004 0.940 0.004 0.000 0.040
#> round_ERR2585235 1 0.6177 0.25147 0.516 0.000 0.336 0.084 0.004 0.060
#> round_ERR2585271 1 0.2151 0.88068 0.904 0.000 0.072 0.016 0.000 0.008
#> round_ERR2585251 1 0.3939 0.82358 0.800 0.008 0.124 0.032 0.000 0.036
#> round_ERR2585255 3 0.1894 0.69796 0.012 0.004 0.928 0.016 0.000 0.040
#> round_ERR2585257 3 0.2966 0.68666 0.016 0.004 0.868 0.044 0.000 0.068
#> round_ERR2585226 1 0.3111 0.85191 0.840 0.000 0.120 0.020 0.000 0.020
#> round_ERR2585265 1 0.2969 0.85609 0.852 0.004 0.112 0.024 0.000 0.008
#> round_ERR2585259 1 0.5503 0.49797 0.620 0.004 0.268 0.048 0.000 0.060
#> round_ERR2585247 1 0.1710 0.88500 0.936 0.000 0.016 0.028 0.000 0.020
#> round_ERR2585241 1 0.2419 0.88328 0.896 0.000 0.060 0.028 0.000 0.016
#> round_ERR2585263 1 0.6398 0.50268 0.580 0.012 0.264 0.076 0.036 0.032
#> round_ERR2585264 1 0.0603 0.87617 0.980 0.000 0.000 0.004 0.000 0.016
#> round_ERR2585233 3 0.2228 0.69995 0.016 0.004 0.912 0.024 0.000 0.044
#> round_ERR2585223 1 0.2747 0.86507 0.868 0.000 0.096 0.016 0.000 0.020
#> round_ERR2585234 3 0.4442 0.64833 0.012 0.128 0.748 0.004 0.000 0.108
#> round_ERR2585222 1 0.2186 0.88599 0.908 0.000 0.056 0.012 0.000 0.024
#> round_ERR2585228 1 0.1976 0.88445 0.916 0.000 0.060 0.016 0.000 0.008
#> round_ERR2585248 1 0.1003 0.87645 0.964 0.000 0.000 0.016 0.000 0.020
#> round_ERR2585240 3 0.4439 0.41168 0.316 0.004 0.648 0.008 0.000 0.024
#> round_ERR2585270 1 0.2878 0.86570 0.860 0.000 0.100 0.024 0.000 0.016
#> round_ERR2585232 3 0.5105 0.08852 0.420 0.000 0.520 0.028 0.000 0.032
#> aberrant_ERR2585341 2 0.6955 -0.03111 0.000 0.476 0.016 0.064 0.284 0.160
#> aberrant_ERR2585355 2 0.3982 0.34385 0.000 0.796 0.012 0.016 0.052 0.124
#> round_ERR2585227 1 0.2790 0.86915 0.868 0.000 0.088 0.012 0.000 0.032
#> aberrant_ERR2585351 5 0.6903 0.49196 0.044 0.224 0.016 0.068 0.572 0.076
#> round_ERR2585269 1 0.0458 0.88058 0.984 0.000 0.000 0.016 0.000 0.000
#> aberrant_ERR2585357 2 0.2749 0.43182 0.000 0.884 0.020 0.004 0.048 0.044
#> aberrant_ERR2585350 2 0.3129 0.43706 0.000 0.864 0.024 0.008 0.060 0.044
#> round_ERR2585250 1 0.3833 0.83505 0.808 0.000 0.120 0.040 0.012 0.020
#> round_ERR2585245 1 0.0717 0.87691 0.976 0.000 0.000 0.008 0.000 0.016
#> aberrant_ERR2585353 5 0.6502 0.46436 0.000 0.156 0.000 0.192 0.552 0.100
#> round_ERR2585258 1 0.2859 0.85654 0.856 0.000 0.108 0.028 0.000 0.008
#> aberrant_ERR2585354 5 0.5794 0.53264 0.000 0.140 0.000 0.140 0.640 0.080
#> round_ERR2585249 1 0.0870 0.87989 0.972 0.000 0.004 0.012 0.000 0.012
#> round_ERR2585268 1 0.6347 0.47739 0.568 0.016 0.276 0.080 0.008 0.052
#> aberrant_ERR2585356 5 0.5255 -0.16409 0.004 0.000 0.000 0.320 0.572 0.104
#> round_ERR2585266 3 0.1854 0.70856 0.028 0.016 0.932 0.004 0.000 0.020
#> round_ERR2585231 1 0.0870 0.87957 0.972 0.000 0.004 0.012 0.000 0.012
#> round_ERR2585230 1 0.2265 0.88186 0.900 0.000 0.068 0.024 0.000 0.008
#> round_ERR2585267 1 0.0622 0.87842 0.980 0.000 0.000 0.008 0.000 0.012
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> CV:hclust 125 5.51e-11 2
#> CV:hclust 122 5.79e-19 3
#> CV:hclust 127 2.32e-23 4
#> CV:hclust 100 1.29e-16 5
#> CV:hclust 79 8.25e-12 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "kmeans"]
# you can also extract it by
# res = res_list["CV:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'CV' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.856 0.927 0.968 0.5017 0.498 0.498
#> 3 3 0.615 0.726 0.837 0.3009 0.769 0.568
#> 4 4 0.724 0.804 0.881 0.1184 0.864 0.629
#> 5 5 0.712 0.722 0.832 0.0568 0.957 0.841
#> 6 6 0.715 0.592 0.755 0.0438 0.953 0.808
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585338 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585325 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585283 1 0.8267 0.6632 0.740 0.260
#> aberrant_ERR2585343 2 0.0938 0.9687 0.012 0.988
#> aberrant_ERR2585329 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585317 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585339 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585335 2 0.0672 0.9700 0.008 0.992
#> aberrant_ERR2585287 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585321 2 0.1414 0.9635 0.020 0.980
#> aberrant_ERR2585297 1 0.0000 0.9597 1.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585319 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585315 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585336 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585307 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585301 2 0.0672 0.9700 0.008 0.992
#> aberrant_ERR2585326 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585331 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585346 1 0.5842 0.8376 0.860 0.140
#> aberrant_ERR2585314 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585298 1 0.5178 0.8721 0.884 0.116
#> aberrant_ERR2585345 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585299 1 0.0000 0.9597 1.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.9597 1.000 0.000
#> aberrant_ERR2585303 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585313 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585318 2 0.0938 0.9687 0.012 0.988
#> aberrant_ERR2585328 2 0.0672 0.9700 0.008 0.992
#> aberrant_ERR2585330 2 0.0938 0.9687 0.012 0.988
#> aberrant_ERR2585293 1 0.7299 0.7523 0.796 0.204
#> aberrant_ERR2585342 2 0.0938 0.9687 0.012 0.988
#> aberrant_ERR2585348 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585352 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585308 1 0.0000 0.9597 1.000 0.000
#> aberrant_ERR2585349 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585316 2 0.4939 0.8733 0.108 0.892
#> aberrant_ERR2585306 1 0.7299 0.7514 0.796 0.204
#> aberrant_ERR2585324 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585310 2 0.2603 0.9449 0.044 0.956
#> aberrant_ERR2585296 1 0.0672 0.9547 0.992 0.008
#> aberrant_ERR2585275 2 0.9993 0.0311 0.484 0.516
#> aberrant_ERR2585311 2 0.1184 0.9663 0.016 0.984
#> aberrant_ERR2585292 1 0.7299 0.7523 0.796 0.204
#> aberrant_ERR2585282 2 0.1184 0.9663 0.016 0.984
#> aberrant_ERR2585305 2 0.4690 0.8856 0.100 0.900
#> aberrant_ERR2585278 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585347 2 0.1633 0.9608 0.024 0.976
#> aberrant_ERR2585332 2 0.0938 0.9687 0.012 0.988
#> aberrant_ERR2585280 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585304 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585322 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585279 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585277 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585295 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585333 2 0.0938 0.9687 0.012 0.988
#> aberrant_ERR2585285 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585286 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585294 2 0.0938 0.9687 0.012 0.988
#> aberrant_ERR2585300 2 0.0938 0.9687 0.012 0.988
#> aberrant_ERR2585334 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585361 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585372 2 0.0938 0.9687 0.012 0.988
#> round_ERR2585217 1 0.9661 0.3832 0.608 0.392
#> round_ERR2585205 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585214 2 0.2778 0.9337 0.048 0.952
#> round_ERR2585202 2 0.3114 0.9257 0.056 0.944
#> aberrant_ERR2585367 2 0.0000 0.9718 0.000 1.000
#> round_ERR2585220 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585238 1 0.0000 0.9597 1.000 0.000
#> aberrant_ERR2585276 2 0.0938 0.9687 0.012 0.988
#> round_ERR2585218 1 0.0000 0.9597 1.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.9718 0.000 1.000
#> round_ERR2585201 1 0.5737 0.8518 0.864 0.136
#> round_ERR2585210 1 0.0000 0.9597 1.000 0.000
#> aberrant_ERR2585362 2 0.0938 0.9687 0.012 0.988
#> aberrant_ERR2585360 2 0.0938 0.9687 0.012 0.988
#> round_ERR2585209 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585242 1 0.3733 0.9099 0.928 0.072
#> round_ERR2585216 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585219 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585237 2 0.9286 0.4611 0.344 0.656
#> round_ERR2585198 2 0.4298 0.8906 0.088 0.912
#> round_ERR2585211 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585206 1 0.0000 0.9597 1.000 0.000
#> aberrant_ERR2585281 2 0.0000 0.9718 0.000 1.000
#> round_ERR2585212 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585221 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585243 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585204 2 0.0376 0.9701 0.004 0.996
#> round_ERR2585213 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585373 2 0.0938 0.9687 0.012 0.988
#> aberrant_ERR2585358 2 0.0938 0.9687 0.012 0.988
#> aberrant_ERR2585365 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585359 2 0.3584 0.9192 0.068 0.932
#> aberrant_ERR2585370 2 0.0000 0.9718 0.000 1.000
#> round_ERR2585215 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585262 1 0.7299 0.7623 0.796 0.204
#> round_ERR2585199 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585369 2 0.0938 0.9687 0.012 0.988
#> round_ERR2585208 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585252 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585236 1 0.0000 0.9597 1.000 0.000
#> aberrant_ERR2585284 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585224 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585260 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585229 1 0.0000 0.9597 1.000 0.000
#> aberrant_ERR2585364 1 0.9993 0.0768 0.516 0.484
#> round_ERR2585253 1 0.0000 0.9597 1.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585371 2 0.0000 0.9718 0.000 1.000
#> round_ERR2585239 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585273 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585256 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585272 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585246 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585261 1 0.1633 0.9456 0.976 0.024
#> round_ERR2585254 1 0.2778 0.9254 0.952 0.048
#> round_ERR2585225 1 0.5059 0.8762 0.888 0.112
#> round_ERR2585235 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585271 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585251 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585255 1 0.5629 0.8569 0.868 0.132
#> round_ERR2585257 1 0.1184 0.9497 0.984 0.016
#> round_ERR2585226 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585265 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585259 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585247 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585241 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585263 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585264 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585233 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585223 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585234 2 0.9963 0.0955 0.464 0.536
#> round_ERR2585222 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585228 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585248 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585240 1 0.4690 0.8860 0.900 0.100
#> round_ERR2585270 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585232 1 0.0000 0.9597 1.000 0.000
#> aberrant_ERR2585341 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585355 2 0.0000 0.9718 0.000 1.000
#> round_ERR2585227 1 0.0000 0.9597 1.000 0.000
#> aberrant_ERR2585351 2 0.1184 0.9663 0.016 0.984
#> round_ERR2585269 1 0.0000 0.9597 1.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.9718 0.000 1.000
#> aberrant_ERR2585350 2 0.0000 0.9718 0.000 1.000
#> round_ERR2585250 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585245 1 0.0000 0.9597 1.000 0.000
#> aberrant_ERR2585353 2 0.0938 0.9687 0.012 0.988
#> round_ERR2585258 1 0.0000 0.9597 1.000 0.000
#> aberrant_ERR2585354 2 0.0938 0.9687 0.012 0.988
#> round_ERR2585249 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585268 1 0.0000 0.9597 1.000 0.000
#> aberrant_ERR2585356 2 0.3879 0.9120 0.076 0.924
#> round_ERR2585266 1 0.5294 0.8686 0.880 0.120
#> round_ERR2585231 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585230 1 0.0000 0.9597 1.000 0.000
#> round_ERR2585267 1 0.0000 0.9597 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.4062 0.7056 0.000 0.836 0.164
#> aberrant_ERR2585338 3 0.5216 0.7553 0.000 0.260 0.740
#> aberrant_ERR2585325 2 0.4062 0.7056 0.000 0.836 0.164
#> aberrant_ERR2585283 2 0.4750 0.6456 0.216 0.784 0.000
#> aberrant_ERR2585343 2 0.0000 0.8321 0.000 1.000 0.000
#> aberrant_ERR2585329 3 0.5291 0.7479 0.000 0.268 0.732
#> aberrant_ERR2585317 3 0.5216 0.7553 0.000 0.260 0.740
#> aberrant_ERR2585339 3 0.5216 0.7553 0.000 0.260 0.740
#> aberrant_ERR2585335 2 0.6204 -0.0148 0.000 0.576 0.424
#> aberrant_ERR2585287 2 0.0000 0.8321 0.000 1.000 0.000
#> aberrant_ERR2585321 2 0.0000 0.8321 0.000 1.000 0.000
#> aberrant_ERR2585297 1 0.0000 0.9023 1.000 0.000 0.000
#> aberrant_ERR2585337 3 0.5216 0.7553 0.000 0.260 0.740
#> aberrant_ERR2585319 2 0.6295 -0.2112 0.000 0.528 0.472
#> aberrant_ERR2585315 3 0.5882 0.6421 0.000 0.348 0.652
#> aberrant_ERR2585336 3 0.5216 0.7553 0.000 0.260 0.740
#> aberrant_ERR2585307 3 0.5178 0.7559 0.000 0.256 0.744
#> aberrant_ERR2585301 2 0.3340 0.7502 0.000 0.880 0.120
#> aberrant_ERR2585326 3 0.5178 0.7559 0.000 0.256 0.744
#> aberrant_ERR2585331 3 0.4346 0.7404 0.000 0.184 0.816
#> aberrant_ERR2585346 2 0.4931 0.6274 0.232 0.768 0.000
#> aberrant_ERR2585314 3 0.6062 0.5759 0.000 0.384 0.616
#> aberrant_ERR2585298 3 0.6295 -0.3800 0.472 0.000 0.528
#> aberrant_ERR2585345 3 0.5216 0.7553 0.000 0.260 0.740
#> aberrant_ERR2585299 1 0.0000 0.9023 1.000 0.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.9023 1.000 0.000 0.000
#> aberrant_ERR2585303 3 0.6291 0.3996 0.000 0.468 0.532
#> aberrant_ERR2585313 3 0.5216 0.7553 0.000 0.260 0.740
#> aberrant_ERR2585318 2 0.3267 0.7530 0.000 0.884 0.116
#> aberrant_ERR2585328 2 0.0000 0.8321 0.000 1.000 0.000
#> aberrant_ERR2585330 2 0.4002 0.7010 0.000 0.840 0.160
#> aberrant_ERR2585293 2 0.4842 0.6370 0.224 0.776 0.000
#> aberrant_ERR2585342 2 0.0237 0.8306 0.000 0.996 0.004
#> aberrant_ERR2585348 2 0.0237 0.8303 0.000 0.996 0.004
#> aberrant_ERR2585352 3 0.6305 0.3221 0.000 0.484 0.516
#> aberrant_ERR2585308 1 0.0000 0.9023 1.000 0.000 0.000
#> aberrant_ERR2585349 3 0.4399 0.7416 0.000 0.188 0.812
#> aberrant_ERR2585316 2 0.1529 0.8043 0.040 0.960 0.000
#> aberrant_ERR2585306 2 0.4702 0.6497 0.212 0.788 0.000
#> aberrant_ERR2585324 2 0.6295 -0.2112 0.000 0.528 0.472
#> aberrant_ERR2585310 3 0.8519 0.4385 0.096 0.396 0.508
#> aberrant_ERR2585296 1 0.4974 0.8072 0.764 0.000 0.236
#> aberrant_ERR2585275 2 0.4121 0.6922 0.168 0.832 0.000
#> aberrant_ERR2585311 2 0.0000 0.8321 0.000 1.000 0.000
#> aberrant_ERR2585292 2 0.4842 0.6370 0.224 0.776 0.000
#> aberrant_ERR2585282 2 0.0000 0.8321 0.000 1.000 0.000
#> aberrant_ERR2585305 2 0.3038 0.7639 0.000 0.896 0.104
#> aberrant_ERR2585278 2 0.6309 -0.3006 0.000 0.500 0.500
#> aberrant_ERR2585347 2 0.0000 0.8321 0.000 1.000 0.000
#> aberrant_ERR2585332 2 0.0000 0.8321 0.000 1.000 0.000
#> aberrant_ERR2585280 2 0.3551 0.7369 0.000 0.868 0.132
#> aberrant_ERR2585304 3 0.4121 0.7345 0.000 0.168 0.832
#> aberrant_ERR2585322 3 0.5216 0.7553 0.000 0.260 0.740
#> aberrant_ERR2585279 3 0.4062 0.7324 0.000 0.164 0.836
#> aberrant_ERR2585277 3 0.5216 0.7553 0.000 0.260 0.740
#> aberrant_ERR2585295 2 0.4121 0.6480 0.000 0.832 0.168
#> aberrant_ERR2585333 2 0.0000 0.8321 0.000 1.000 0.000
#> aberrant_ERR2585285 2 0.4974 0.5667 0.000 0.764 0.236
#> aberrant_ERR2585286 3 0.5216 0.7553 0.000 0.260 0.740
#> aberrant_ERR2585294 2 0.2959 0.7682 0.000 0.900 0.100
#> aberrant_ERR2585300 2 0.0000 0.8321 0.000 1.000 0.000
#> aberrant_ERR2585334 3 0.4235 0.7375 0.000 0.176 0.824
#> aberrant_ERR2585361 2 0.0237 0.8306 0.000 0.996 0.004
#> aberrant_ERR2585372 2 0.0000 0.8321 0.000 1.000 0.000
#> round_ERR2585217 3 0.5325 0.3023 0.248 0.004 0.748
#> round_ERR2585205 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585214 3 0.0424 0.6490 0.000 0.008 0.992
#> round_ERR2585202 3 0.0424 0.6490 0.000 0.008 0.992
#> aberrant_ERR2585367 2 0.5138 0.5189 0.000 0.748 0.252
#> round_ERR2585220 1 0.3879 0.8571 0.848 0.000 0.152
#> round_ERR2585238 1 0.0000 0.9023 1.000 0.000 0.000
#> aberrant_ERR2585276 2 0.0000 0.8321 0.000 1.000 0.000
#> round_ERR2585218 1 0.0000 0.9023 1.000 0.000 0.000
#> aberrant_ERR2585363 3 0.6244 0.4487 0.000 0.440 0.560
#> round_ERR2585201 3 0.6305 -0.4085 0.484 0.000 0.516
#> round_ERR2585210 1 0.0000 0.9023 1.000 0.000 0.000
#> aberrant_ERR2585362 2 0.0000 0.8321 0.000 1.000 0.000
#> aberrant_ERR2585360 2 0.0592 0.8267 0.000 0.988 0.012
#> round_ERR2585209 1 0.5138 0.7946 0.748 0.000 0.252
#> round_ERR2585242 1 0.6299 0.4775 0.524 0.000 0.476
#> round_ERR2585216 1 0.3879 0.8571 0.848 0.000 0.152
#> round_ERR2585219 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585237 3 0.2173 0.6166 0.048 0.008 0.944
#> round_ERR2585198 3 0.0424 0.6490 0.000 0.008 0.992
#> round_ERR2585211 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.9023 1.000 0.000 0.000
#> aberrant_ERR2585281 3 0.5397 0.7379 0.000 0.280 0.720
#> round_ERR2585212 1 0.3879 0.8571 0.848 0.000 0.152
#> round_ERR2585221 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585243 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585204 3 0.0424 0.6490 0.000 0.008 0.992
#> round_ERR2585213 3 0.0424 0.6490 0.000 0.008 0.992
#> aberrant_ERR2585373 2 0.0000 0.8321 0.000 1.000 0.000
#> aberrant_ERR2585358 2 0.0000 0.8321 0.000 1.000 0.000
#> aberrant_ERR2585365 3 0.6305 0.3512 0.000 0.484 0.516
#> aberrant_ERR2585359 2 0.0000 0.8321 0.000 1.000 0.000
#> aberrant_ERR2585370 3 0.5138 0.7555 0.000 0.252 0.748
#> round_ERR2585215 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585262 1 0.6410 0.5800 0.576 0.004 0.420
#> round_ERR2585199 3 0.0424 0.6490 0.000 0.008 0.992
#> aberrant_ERR2585369 2 0.0000 0.8321 0.000 1.000 0.000
#> round_ERR2585208 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585236 1 0.0000 0.9023 1.000 0.000 0.000
#> aberrant_ERR2585284 2 0.5178 0.5971 0.256 0.744 0.000
#> round_ERR2585224 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585260 1 0.1529 0.8928 0.960 0.000 0.040
#> round_ERR2585229 1 0.0000 0.9023 1.000 0.000 0.000
#> aberrant_ERR2585364 2 0.4346 0.6774 0.184 0.816 0.000
#> round_ERR2585253 1 0.0000 0.9023 1.000 0.000 0.000
#> aberrant_ERR2585368 3 0.5178 0.7559 0.000 0.256 0.744
#> aberrant_ERR2585371 3 0.5178 0.7559 0.000 0.256 0.744
#> round_ERR2585239 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585273 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585256 1 0.4974 0.8075 0.764 0.000 0.236
#> round_ERR2585272 1 0.3116 0.8744 0.892 0.000 0.108
#> round_ERR2585246 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585261 1 0.5835 0.7074 0.660 0.000 0.340
#> round_ERR2585254 1 0.5926 0.6860 0.644 0.000 0.356
#> round_ERR2585225 1 0.6299 0.4778 0.524 0.000 0.476
#> round_ERR2585235 1 0.0237 0.9014 0.996 0.000 0.004
#> round_ERR2585271 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585251 1 0.4452 0.8365 0.808 0.000 0.192
#> round_ERR2585255 3 0.6309 -0.4358 0.496 0.000 0.504
#> round_ERR2585257 1 0.5529 0.7540 0.704 0.000 0.296
#> round_ERR2585226 1 0.4002 0.8533 0.840 0.000 0.160
#> round_ERR2585265 1 0.3879 0.8571 0.848 0.000 0.152
#> round_ERR2585259 1 0.3816 0.8593 0.852 0.000 0.148
#> round_ERR2585247 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585241 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585263 1 0.4887 0.8127 0.772 0.000 0.228
#> round_ERR2585264 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585233 1 0.5058 0.8012 0.756 0.000 0.244
#> round_ERR2585223 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585234 3 0.2496 0.5965 0.068 0.004 0.928
#> round_ERR2585222 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585228 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585248 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585240 1 0.6244 0.5487 0.560 0.000 0.440
#> round_ERR2585270 1 0.3816 0.8587 0.852 0.000 0.148
#> round_ERR2585232 1 0.4887 0.8136 0.772 0.000 0.228
#> aberrant_ERR2585341 3 0.5968 0.6266 0.000 0.364 0.636
#> aberrant_ERR2585355 3 0.5216 0.7553 0.000 0.260 0.740
#> round_ERR2585227 1 0.4178 0.8470 0.828 0.000 0.172
#> aberrant_ERR2585351 2 0.3686 0.7265 0.000 0.860 0.140
#> round_ERR2585269 1 0.0000 0.9023 1.000 0.000 0.000
#> aberrant_ERR2585357 3 0.5216 0.7553 0.000 0.260 0.740
#> aberrant_ERR2585350 3 0.5216 0.7553 0.000 0.260 0.740
#> round_ERR2585250 1 0.2625 0.8808 0.916 0.000 0.084
#> round_ERR2585245 1 0.0000 0.9023 1.000 0.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.8321 0.000 1.000 0.000
#> round_ERR2585258 1 0.4002 0.8533 0.840 0.000 0.160
#> aberrant_ERR2585354 2 0.0000 0.8321 0.000 1.000 0.000
#> round_ERR2585249 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585268 1 0.4555 0.8311 0.800 0.000 0.200
#> aberrant_ERR2585356 2 0.0237 0.8299 0.004 0.996 0.000
#> round_ERR2585266 1 0.6305 0.4605 0.516 0.000 0.484
#> round_ERR2585231 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585230 1 0.0000 0.9023 1.000 0.000 0.000
#> round_ERR2585267 1 0.0000 0.9023 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 4 0.5597 0.0831 0.000 0.464 0.020 0.516
#> aberrant_ERR2585338 2 0.0657 0.8964 0.000 0.984 0.012 0.004
#> aberrant_ERR2585325 4 0.5597 0.0831 0.000 0.464 0.020 0.516
#> aberrant_ERR2585283 4 0.3870 0.7839 0.004 0.000 0.208 0.788
#> aberrant_ERR2585343 4 0.0469 0.8608 0.000 0.000 0.012 0.988
#> aberrant_ERR2585329 2 0.0336 0.8963 0.000 0.992 0.000 0.008
#> aberrant_ERR2585317 2 0.0336 0.8963 0.000 0.992 0.000 0.008
#> aberrant_ERR2585339 2 0.0524 0.8970 0.000 0.988 0.008 0.004
#> aberrant_ERR2585335 2 0.4991 0.4017 0.000 0.608 0.004 0.388
#> aberrant_ERR2585287 4 0.3610 0.7906 0.000 0.000 0.200 0.800
#> aberrant_ERR2585321 4 0.0376 0.8609 0.000 0.004 0.004 0.992
#> aberrant_ERR2585297 1 0.0188 0.9389 0.996 0.000 0.004 0.000
#> aberrant_ERR2585337 2 0.0376 0.8970 0.000 0.992 0.004 0.004
#> aberrant_ERR2585319 2 0.4019 0.7535 0.000 0.792 0.012 0.196
#> aberrant_ERR2585315 2 0.1302 0.8858 0.000 0.956 0.000 0.044
#> aberrant_ERR2585336 2 0.0188 0.8969 0.000 0.996 0.000 0.004
#> aberrant_ERR2585307 2 0.0524 0.8970 0.000 0.988 0.008 0.004
#> aberrant_ERR2585301 4 0.4018 0.6723 0.000 0.224 0.004 0.772
#> aberrant_ERR2585326 2 0.0524 0.8970 0.000 0.988 0.008 0.004
#> aberrant_ERR2585331 2 0.0592 0.8938 0.000 0.984 0.016 0.000
#> aberrant_ERR2585346 4 0.4011 0.7816 0.008 0.000 0.208 0.784
#> aberrant_ERR2585314 2 0.1867 0.8679 0.000 0.928 0.000 0.072
#> aberrant_ERR2585298 3 0.4464 0.8159 0.208 0.024 0.768 0.000
#> aberrant_ERR2585345 2 0.0188 0.8969 0.000 0.996 0.000 0.004
#> aberrant_ERR2585299 1 0.0336 0.9387 0.992 0.000 0.008 0.000
#> aberrant_ERR2585309 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> aberrant_ERR2585303 2 0.4453 0.6889 0.000 0.744 0.012 0.244
#> aberrant_ERR2585313 2 0.0336 0.8967 0.000 0.992 0.000 0.008
#> aberrant_ERR2585318 4 0.3402 0.7468 0.000 0.164 0.004 0.832
#> aberrant_ERR2585328 4 0.1545 0.8593 0.000 0.008 0.040 0.952
#> aberrant_ERR2585330 4 0.4761 0.4723 0.000 0.332 0.004 0.664
#> aberrant_ERR2585293 4 0.4327 0.7721 0.016 0.000 0.216 0.768
#> aberrant_ERR2585342 4 0.1452 0.8502 0.000 0.036 0.008 0.956
#> aberrant_ERR2585348 4 0.1936 0.8540 0.000 0.032 0.028 0.940
#> aberrant_ERR2585352 2 0.3219 0.8018 0.000 0.836 0.000 0.164
#> aberrant_ERR2585308 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> aberrant_ERR2585349 2 0.3266 0.7402 0.000 0.832 0.168 0.000
#> aberrant_ERR2585316 4 0.1716 0.8509 0.000 0.000 0.064 0.936
#> aberrant_ERR2585306 4 0.0779 0.8608 0.004 0.000 0.016 0.980
#> aberrant_ERR2585324 2 0.4019 0.7535 0.000 0.792 0.012 0.196
#> aberrant_ERR2585310 2 0.5966 0.7012 0.056 0.728 0.040 0.176
#> aberrant_ERR2585296 3 0.4781 0.6779 0.336 0.004 0.660 0.000
#> aberrant_ERR2585275 4 0.3831 0.7863 0.004 0.000 0.204 0.792
#> aberrant_ERR2585311 4 0.0524 0.8595 0.000 0.008 0.004 0.988
#> aberrant_ERR2585292 4 0.4327 0.7721 0.016 0.000 0.216 0.768
#> aberrant_ERR2585282 4 0.1305 0.8590 0.000 0.004 0.036 0.960
#> aberrant_ERR2585305 4 0.3105 0.7718 0.000 0.140 0.004 0.856
#> aberrant_ERR2585278 2 0.4122 0.7116 0.000 0.760 0.004 0.236
#> aberrant_ERR2585347 4 0.1867 0.8483 0.000 0.000 0.072 0.928
#> aberrant_ERR2585332 4 0.0895 0.8611 0.000 0.004 0.020 0.976
#> aberrant_ERR2585280 2 0.5143 0.1793 0.000 0.540 0.004 0.456
#> aberrant_ERR2585304 2 0.0817 0.8899 0.000 0.976 0.024 0.000
#> aberrant_ERR2585322 2 0.0921 0.8913 0.000 0.972 0.000 0.028
#> aberrant_ERR2585279 2 0.0592 0.8938 0.000 0.984 0.016 0.000
#> aberrant_ERR2585277 2 0.0657 0.8964 0.000 0.984 0.012 0.004
#> aberrant_ERR2585295 4 0.4868 0.6273 0.000 0.256 0.024 0.720
#> aberrant_ERR2585333 4 0.0524 0.8608 0.000 0.004 0.008 0.988
#> aberrant_ERR2585285 4 0.5165 -0.0179 0.000 0.484 0.004 0.512
#> aberrant_ERR2585286 2 0.0657 0.8964 0.000 0.984 0.012 0.004
#> aberrant_ERR2585294 4 0.3448 0.7475 0.000 0.168 0.004 0.828
#> aberrant_ERR2585300 4 0.0336 0.8609 0.000 0.000 0.008 0.992
#> aberrant_ERR2585334 2 0.0921 0.8874 0.000 0.972 0.028 0.000
#> aberrant_ERR2585361 4 0.2198 0.8360 0.000 0.072 0.008 0.920
#> aberrant_ERR2585372 4 0.0188 0.8604 0.000 0.004 0.000 0.996
#> round_ERR2585217 3 0.4761 0.7502 0.048 0.184 0.768 0.000
#> round_ERR2585205 1 0.0336 0.9387 0.992 0.000 0.008 0.000
#> round_ERR2585214 3 0.3942 0.6971 0.000 0.236 0.764 0.000
#> round_ERR2585202 3 0.3942 0.6971 0.000 0.236 0.764 0.000
#> aberrant_ERR2585367 4 0.5388 0.1028 0.000 0.456 0.012 0.532
#> round_ERR2585220 1 0.3123 0.7926 0.844 0.000 0.156 0.000
#> round_ERR2585238 1 0.0336 0.9387 0.992 0.000 0.008 0.000
#> aberrant_ERR2585276 4 0.0657 0.8592 0.000 0.012 0.004 0.984
#> round_ERR2585218 1 0.0188 0.9388 0.996 0.000 0.004 0.000
#> aberrant_ERR2585363 2 0.3494 0.7849 0.000 0.824 0.004 0.172
#> round_ERR2585201 3 0.4677 0.8177 0.192 0.040 0.768 0.000
#> round_ERR2585210 1 0.0188 0.9389 0.996 0.000 0.004 0.000
#> aberrant_ERR2585362 4 0.0336 0.8612 0.000 0.000 0.008 0.992
#> aberrant_ERR2585360 4 0.1305 0.8497 0.000 0.036 0.004 0.960
#> round_ERR2585209 3 0.4008 0.7911 0.244 0.000 0.756 0.000
#> round_ERR2585242 3 0.4399 0.8149 0.212 0.020 0.768 0.000
#> round_ERR2585216 1 0.1792 0.8926 0.932 0.000 0.068 0.000
#> round_ERR2585219 1 0.0336 0.9387 0.992 0.000 0.008 0.000
#> round_ERR2585237 3 0.4158 0.7117 0.008 0.224 0.768 0.000
#> round_ERR2585198 3 0.3975 0.6919 0.000 0.240 0.760 0.000
#> round_ERR2585211 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> aberrant_ERR2585281 2 0.1722 0.8850 0.000 0.944 0.008 0.048
#> round_ERR2585212 1 0.3444 0.7521 0.816 0.000 0.184 0.000
#> round_ERR2585221 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> round_ERR2585243 1 0.0336 0.9387 0.992 0.000 0.008 0.000
#> round_ERR2585204 3 0.4072 0.6802 0.000 0.252 0.748 0.000
#> round_ERR2585213 3 0.4830 0.4679 0.000 0.392 0.608 0.000
#> aberrant_ERR2585373 4 0.0376 0.8599 0.000 0.004 0.004 0.992
#> aberrant_ERR2585358 4 0.0817 0.8603 0.000 0.000 0.024 0.976
#> aberrant_ERR2585365 2 0.4608 0.5875 0.000 0.692 0.004 0.304
#> aberrant_ERR2585359 4 0.0817 0.8601 0.000 0.000 0.024 0.976
#> aberrant_ERR2585370 2 0.0657 0.8964 0.000 0.984 0.012 0.004
#> round_ERR2585215 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> round_ERR2585262 3 0.4928 0.8107 0.188 0.016 0.768 0.028
#> round_ERR2585199 3 0.4250 0.6531 0.000 0.276 0.724 0.000
#> aberrant_ERR2585369 4 0.0657 0.8590 0.000 0.012 0.004 0.984
#> round_ERR2585208 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> round_ERR2585236 1 0.0672 0.9329 0.984 0.000 0.008 0.008
#> aberrant_ERR2585284 4 0.4253 0.7770 0.016 0.000 0.208 0.776
#> round_ERR2585224 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> round_ERR2585260 1 0.0469 0.9366 0.988 0.000 0.012 0.000
#> round_ERR2585229 1 0.0188 0.9389 0.996 0.000 0.004 0.000
#> aberrant_ERR2585364 4 0.3355 0.8089 0.004 0.000 0.160 0.836
#> round_ERR2585253 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> aberrant_ERR2585368 2 0.0779 0.8963 0.000 0.980 0.016 0.004
#> aberrant_ERR2585371 2 0.0779 0.8963 0.000 0.980 0.016 0.004
#> round_ERR2585239 1 0.0336 0.9387 0.992 0.000 0.008 0.000
#> round_ERR2585273 1 0.0336 0.9387 0.992 0.000 0.008 0.000
#> round_ERR2585256 3 0.4543 0.6953 0.324 0.000 0.676 0.000
#> round_ERR2585272 1 0.3528 0.7158 0.808 0.000 0.192 0.000
#> round_ERR2585246 1 0.0188 0.9389 0.996 0.000 0.004 0.000
#> round_ERR2585261 3 0.5056 0.8144 0.164 0.076 0.760 0.000
#> round_ERR2585254 3 0.5512 0.8065 0.172 0.100 0.728 0.000
#> round_ERR2585225 3 0.4248 0.8108 0.220 0.012 0.768 0.000
#> round_ERR2585235 1 0.1867 0.8830 0.928 0.000 0.072 0.000
#> round_ERR2585271 1 0.0188 0.9388 0.996 0.000 0.004 0.000
#> round_ERR2585251 1 0.4830 0.1985 0.608 0.000 0.392 0.000
#> round_ERR2585255 3 0.4399 0.8149 0.212 0.020 0.768 0.000
#> round_ERR2585257 3 0.3975 0.7948 0.240 0.000 0.760 0.000
#> round_ERR2585226 1 0.3311 0.7695 0.828 0.000 0.172 0.000
#> round_ERR2585265 1 0.3024 0.8039 0.852 0.000 0.148 0.000
#> round_ERR2585259 1 0.4382 0.5148 0.704 0.000 0.296 0.000
#> round_ERR2585247 1 0.0336 0.9387 0.992 0.000 0.008 0.000
#> round_ERR2585241 1 0.0336 0.9387 0.992 0.000 0.008 0.000
#> round_ERR2585263 3 0.4877 0.5295 0.408 0.000 0.592 0.000
#> round_ERR2585264 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> round_ERR2585233 3 0.4040 0.7882 0.248 0.000 0.752 0.000
#> round_ERR2585223 1 0.0336 0.9387 0.992 0.000 0.008 0.000
#> round_ERR2585234 3 0.4158 0.7118 0.008 0.224 0.768 0.000
#> round_ERR2585222 1 0.0336 0.9387 0.992 0.000 0.008 0.000
#> round_ERR2585228 1 0.0336 0.9387 0.992 0.000 0.008 0.000
#> round_ERR2585248 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> round_ERR2585240 3 0.4158 0.8083 0.224 0.008 0.768 0.000
#> round_ERR2585270 1 0.2760 0.8301 0.872 0.000 0.128 0.000
#> round_ERR2585232 3 0.4382 0.7360 0.296 0.000 0.704 0.000
#> aberrant_ERR2585341 2 0.3300 0.8144 0.000 0.848 0.008 0.144
#> aberrant_ERR2585355 2 0.0804 0.8972 0.000 0.980 0.012 0.008
#> round_ERR2585227 1 0.4304 0.5493 0.716 0.000 0.284 0.000
#> aberrant_ERR2585351 4 0.3945 0.6836 0.000 0.216 0.004 0.780
#> round_ERR2585269 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> aberrant_ERR2585357 2 0.0524 0.8970 0.000 0.988 0.008 0.004
#> aberrant_ERR2585350 2 0.0657 0.8964 0.000 0.984 0.012 0.004
#> round_ERR2585250 1 0.2345 0.8613 0.900 0.000 0.100 0.000
#> round_ERR2585245 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> aberrant_ERR2585353 4 0.0336 0.8602 0.000 0.008 0.000 0.992
#> round_ERR2585258 1 0.2647 0.8389 0.880 0.000 0.120 0.000
#> aberrant_ERR2585354 4 0.0188 0.8604 0.000 0.004 0.000 0.996
#> round_ERR2585249 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> round_ERR2585268 3 0.4817 0.5741 0.388 0.000 0.612 0.000
#> aberrant_ERR2585356 4 0.1118 0.8598 0.000 0.000 0.036 0.964
#> round_ERR2585266 3 0.4248 0.8108 0.220 0.012 0.768 0.000
#> round_ERR2585231 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> round_ERR2585230 1 0.0336 0.9387 0.992 0.000 0.008 0.000
#> round_ERR2585267 1 0.0000 0.9384 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 2 0.7433 0.00789 0.000 0.400 0.064 0.152 0.384
#> aberrant_ERR2585338 2 0.0609 0.85181 0.000 0.980 0.000 0.020 0.000
#> aberrant_ERR2585325 2 0.7433 0.00789 0.000 0.400 0.064 0.152 0.384
#> aberrant_ERR2585283 4 0.4192 0.91739 0.000 0.000 0.000 0.596 0.404
#> aberrant_ERR2585343 5 0.2079 0.68233 0.000 0.000 0.020 0.064 0.916
#> aberrant_ERR2585329 2 0.1399 0.84463 0.000 0.952 0.000 0.020 0.028
#> aberrant_ERR2585317 2 0.0771 0.85076 0.000 0.976 0.000 0.020 0.004
#> aberrant_ERR2585339 2 0.0290 0.85178 0.000 0.992 0.000 0.008 0.000
#> aberrant_ERR2585335 2 0.5099 0.14524 0.000 0.488 0.012 0.016 0.484
#> aberrant_ERR2585287 4 0.4727 0.82348 0.000 0.000 0.016 0.532 0.452
#> aberrant_ERR2585321 5 0.1444 0.69592 0.000 0.000 0.012 0.040 0.948
#> aberrant_ERR2585297 1 0.1965 0.86614 0.904 0.000 0.000 0.096 0.000
#> aberrant_ERR2585337 2 0.0404 0.85191 0.000 0.988 0.000 0.012 0.000
#> aberrant_ERR2585319 2 0.4969 0.62003 0.000 0.684 0.020 0.032 0.264
#> aberrant_ERR2585315 2 0.1978 0.83856 0.000 0.928 0.004 0.024 0.044
#> aberrant_ERR2585336 2 0.0510 0.85170 0.000 0.984 0.000 0.016 0.000
#> aberrant_ERR2585307 2 0.0404 0.85191 0.000 0.988 0.000 0.012 0.000
#> aberrant_ERR2585301 5 0.3333 0.63708 0.000 0.096 0.020 0.028 0.856
#> aberrant_ERR2585326 2 0.0290 0.85178 0.000 0.992 0.000 0.008 0.000
#> aberrant_ERR2585331 2 0.1399 0.83939 0.000 0.952 0.020 0.028 0.000
#> aberrant_ERR2585346 4 0.4182 0.91947 0.000 0.000 0.000 0.600 0.400
#> aberrant_ERR2585314 2 0.2685 0.81016 0.000 0.880 0.000 0.028 0.092
#> aberrant_ERR2585298 3 0.2922 0.85602 0.080 0.016 0.880 0.024 0.000
#> aberrant_ERR2585345 2 0.0510 0.85181 0.000 0.984 0.000 0.016 0.000
#> aberrant_ERR2585299 1 0.1270 0.86833 0.948 0.000 0.000 0.052 0.000
#> aberrant_ERR2585309 1 0.1965 0.85870 0.904 0.000 0.000 0.096 0.000
#> aberrant_ERR2585303 2 0.5523 0.39942 0.000 0.604 0.040 0.024 0.332
#> aberrant_ERR2585313 2 0.0609 0.85215 0.000 0.980 0.000 0.020 0.000
#> aberrant_ERR2585318 5 0.2689 0.65581 0.000 0.084 0.016 0.012 0.888
#> aberrant_ERR2585328 5 0.3890 0.54024 0.000 0.004 0.036 0.168 0.792
#> aberrant_ERR2585330 5 0.4231 0.45697 0.000 0.232 0.016 0.012 0.740
#> aberrant_ERR2585293 4 0.4564 0.90867 0.000 0.000 0.016 0.612 0.372
#> aberrant_ERR2585342 5 0.1836 0.70384 0.000 0.008 0.016 0.040 0.936
#> aberrant_ERR2585348 5 0.4518 0.56040 0.000 0.032 0.044 0.148 0.776
#> aberrant_ERR2585352 2 0.4145 0.73151 0.000 0.772 0.012 0.028 0.188
#> aberrant_ERR2585308 1 0.1851 0.86246 0.912 0.000 0.000 0.088 0.000
#> aberrant_ERR2585349 2 0.4441 0.60474 0.000 0.716 0.252 0.024 0.008
#> aberrant_ERR2585316 5 0.3909 0.39671 0.000 0.000 0.024 0.216 0.760
#> aberrant_ERR2585306 5 0.1597 0.69051 0.000 0.000 0.012 0.048 0.940
#> aberrant_ERR2585324 2 0.4969 0.62003 0.000 0.684 0.020 0.032 0.264
#> aberrant_ERR2585310 2 0.7522 0.43672 0.028 0.516 0.052 0.124 0.280
#> aberrant_ERR2585296 3 0.5025 0.73780 0.172 0.000 0.704 0.124 0.000
#> aberrant_ERR2585275 4 0.4256 0.88401 0.000 0.000 0.000 0.564 0.436
#> aberrant_ERR2585311 5 0.0912 0.70362 0.000 0.000 0.012 0.016 0.972
#> aberrant_ERR2585292 4 0.4564 0.90867 0.000 0.000 0.016 0.612 0.372
#> aberrant_ERR2585282 5 0.2597 0.66212 0.000 0.000 0.024 0.092 0.884
#> aberrant_ERR2585305 5 0.3221 0.65186 0.008 0.060 0.016 0.040 0.876
#> aberrant_ERR2585278 2 0.5210 0.42098 0.000 0.576 0.012 0.028 0.384
#> aberrant_ERR2585347 5 0.4540 -0.06059 0.000 0.000 0.024 0.320 0.656
#> aberrant_ERR2585332 5 0.2511 0.66708 0.000 0.000 0.028 0.080 0.892
#> aberrant_ERR2585280 5 0.6059 0.10843 0.000 0.404 0.044 0.040 0.512
#> aberrant_ERR2585304 2 0.1281 0.84934 0.000 0.956 0.012 0.032 0.000
#> aberrant_ERR2585322 2 0.0794 0.84838 0.000 0.972 0.000 0.000 0.028
#> aberrant_ERR2585279 2 0.1668 0.83283 0.000 0.940 0.032 0.028 0.000
#> aberrant_ERR2585277 2 0.0703 0.84829 0.000 0.976 0.000 0.024 0.000
#> aberrant_ERR2585295 5 0.6412 0.34190 0.000 0.212 0.048 0.124 0.616
#> aberrant_ERR2585333 5 0.1018 0.70160 0.000 0.000 0.016 0.016 0.968
#> aberrant_ERR2585285 5 0.4420 0.43089 0.000 0.260 0.016 0.012 0.712
#> aberrant_ERR2585286 2 0.0771 0.84962 0.000 0.976 0.004 0.020 0.000
#> aberrant_ERR2585294 5 0.2830 0.65538 0.000 0.080 0.016 0.020 0.884
#> aberrant_ERR2585300 5 0.1211 0.69960 0.000 0.000 0.016 0.024 0.960
#> aberrant_ERR2585334 2 0.1907 0.82541 0.000 0.928 0.044 0.028 0.000
#> aberrant_ERR2585361 5 0.4462 0.62124 0.000 0.060 0.044 0.100 0.796
#> aberrant_ERR2585372 5 0.1012 0.70779 0.000 0.000 0.012 0.020 0.968
#> round_ERR2585217 3 0.2585 0.83244 0.024 0.072 0.896 0.008 0.000
#> round_ERR2585205 1 0.1197 0.86537 0.952 0.000 0.000 0.048 0.000
#> round_ERR2585214 3 0.2561 0.81123 0.000 0.096 0.884 0.020 0.000
#> round_ERR2585202 3 0.2464 0.81250 0.000 0.096 0.888 0.016 0.000
#> aberrant_ERR2585367 5 0.5985 0.32487 0.000 0.344 0.044 0.044 0.568
#> round_ERR2585220 1 0.5233 0.65328 0.684 0.000 0.168 0.148 0.000
#> round_ERR2585238 1 0.0404 0.87113 0.988 0.000 0.000 0.012 0.000
#> aberrant_ERR2585276 5 0.1211 0.70237 0.000 0.000 0.016 0.024 0.960
#> round_ERR2585218 1 0.1197 0.86687 0.952 0.000 0.000 0.048 0.000
#> aberrant_ERR2585363 2 0.4255 0.73363 0.000 0.772 0.016 0.032 0.180
#> round_ERR2585201 3 0.2830 0.85734 0.080 0.016 0.884 0.020 0.000
#> round_ERR2585210 1 0.1121 0.86914 0.956 0.000 0.000 0.044 0.000
#> aberrant_ERR2585362 5 0.3164 0.63857 0.000 0.000 0.044 0.104 0.852
#> aberrant_ERR2585360 5 0.1393 0.70334 0.000 0.012 0.008 0.024 0.956
#> round_ERR2585209 3 0.2905 0.84796 0.096 0.000 0.868 0.036 0.000
#> round_ERR2585242 3 0.2689 0.85688 0.084 0.012 0.888 0.016 0.000
#> round_ERR2585216 1 0.3888 0.79272 0.800 0.000 0.064 0.136 0.000
#> round_ERR2585219 1 0.0880 0.86714 0.968 0.000 0.000 0.032 0.000
#> round_ERR2585237 3 0.2464 0.81786 0.000 0.096 0.888 0.016 0.000
#> round_ERR2585198 3 0.2561 0.81123 0.000 0.096 0.884 0.020 0.000
#> round_ERR2585211 1 0.1121 0.86902 0.956 0.000 0.000 0.044 0.000
#> round_ERR2585206 1 0.1341 0.86776 0.944 0.000 0.000 0.056 0.000
#> aberrant_ERR2585281 2 0.3013 0.81362 0.000 0.880 0.024 0.028 0.068
#> round_ERR2585212 1 0.5295 0.61726 0.672 0.000 0.200 0.128 0.000
#> round_ERR2585221 1 0.1851 0.85907 0.912 0.000 0.000 0.088 0.000
#> round_ERR2585243 1 0.2179 0.84439 0.888 0.000 0.000 0.112 0.000
#> round_ERR2585204 3 0.2813 0.80168 0.000 0.108 0.868 0.024 0.000
#> round_ERR2585213 3 0.4445 0.57662 0.000 0.300 0.676 0.024 0.000
#> aberrant_ERR2585373 5 0.0854 0.70694 0.000 0.004 0.012 0.008 0.976
#> aberrant_ERR2585358 5 0.3016 0.61702 0.000 0.000 0.020 0.132 0.848
#> aberrant_ERR2585365 5 0.5898 0.13136 0.000 0.432 0.040 0.032 0.496
#> aberrant_ERR2585359 5 0.2361 0.65293 0.000 0.000 0.012 0.096 0.892
#> aberrant_ERR2585370 2 0.0290 0.85178 0.000 0.992 0.000 0.008 0.000
#> round_ERR2585215 1 0.1908 0.85866 0.908 0.000 0.000 0.092 0.000
#> round_ERR2585262 3 0.2820 0.84293 0.052 0.004 0.892 0.044 0.008
#> round_ERR2585199 3 0.3106 0.77628 0.000 0.140 0.840 0.020 0.000
#> aberrant_ERR2585369 5 0.1186 0.70780 0.000 0.008 0.008 0.020 0.964
#> round_ERR2585208 1 0.1792 0.86223 0.916 0.000 0.000 0.084 0.000
#> round_ERR2585252 1 0.1965 0.85827 0.904 0.000 0.000 0.096 0.000
#> round_ERR2585236 1 0.2848 0.85273 0.840 0.000 0.004 0.156 0.000
#> aberrant_ERR2585284 4 0.4402 0.87704 0.012 0.000 0.000 0.636 0.352
#> round_ERR2585224 1 0.2074 0.85866 0.896 0.000 0.000 0.104 0.000
#> round_ERR2585260 1 0.2462 0.84132 0.880 0.000 0.008 0.112 0.000
#> round_ERR2585229 1 0.0963 0.86904 0.964 0.000 0.000 0.036 0.000
#> aberrant_ERR2585364 5 0.4249 -0.55661 0.000 0.000 0.000 0.432 0.568
#> round_ERR2585253 1 0.1908 0.85866 0.908 0.000 0.000 0.092 0.000
#> aberrant_ERR2585368 2 0.0671 0.85137 0.000 0.980 0.004 0.016 0.000
#> aberrant_ERR2585371 2 0.0671 0.85137 0.000 0.980 0.004 0.016 0.000
#> round_ERR2585239 1 0.1671 0.85885 0.924 0.000 0.000 0.076 0.000
#> round_ERR2585273 1 0.2389 0.86310 0.880 0.000 0.004 0.116 0.000
#> round_ERR2585256 3 0.5345 0.70556 0.196 0.000 0.668 0.136 0.000
#> round_ERR2585272 1 0.5200 0.65520 0.688 0.000 0.156 0.156 0.000
#> round_ERR2585246 1 0.2127 0.86750 0.892 0.000 0.000 0.108 0.000
#> round_ERR2585261 3 0.3129 0.85605 0.076 0.020 0.872 0.032 0.000
#> round_ERR2585254 3 0.4733 0.82522 0.076 0.040 0.776 0.108 0.000
#> round_ERR2585225 3 0.2756 0.85419 0.092 0.004 0.880 0.024 0.000
#> round_ERR2585235 1 0.4238 0.79619 0.756 0.000 0.052 0.192 0.000
#> round_ERR2585271 1 0.0880 0.86985 0.968 0.000 0.000 0.032 0.000
#> round_ERR2585251 1 0.6299 -0.08003 0.432 0.000 0.416 0.152 0.000
#> round_ERR2585255 3 0.2786 0.85635 0.084 0.012 0.884 0.020 0.000
#> round_ERR2585257 3 0.4357 0.81587 0.104 0.000 0.768 0.128 0.000
#> round_ERR2585226 1 0.5430 0.61342 0.660 0.000 0.192 0.148 0.000
#> round_ERR2585265 1 0.5010 0.68900 0.708 0.000 0.148 0.144 0.000
#> round_ERR2585259 1 0.6000 0.28141 0.540 0.000 0.328 0.132 0.000
#> round_ERR2585247 1 0.1544 0.86626 0.932 0.000 0.000 0.068 0.000
#> round_ERR2585241 1 0.1478 0.86283 0.936 0.000 0.000 0.064 0.000
#> round_ERR2585263 3 0.6119 0.49307 0.296 0.000 0.544 0.160 0.000
#> round_ERR2585264 1 0.1851 0.85889 0.912 0.000 0.000 0.088 0.000
#> round_ERR2585233 3 0.3427 0.84298 0.108 0.000 0.836 0.056 0.000
#> round_ERR2585223 1 0.1197 0.87040 0.952 0.000 0.000 0.048 0.000
#> round_ERR2585234 3 0.2249 0.81570 0.000 0.096 0.896 0.008 0.000
#> round_ERR2585222 1 0.1544 0.87190 0.932 0.000 0.000 0.068 0.000
#> round_ERR2585228 1 0.1043 0.86793 0.960 0.000 0.000 0.040 0.000
#> round_ERR2585248 1 0.1851 0.85889 0.912 0.000 0.000 0.088 0.000
#> round_ERR2585240 3 0.3008 0.85321 0.092 0.004 0.868 0.036 0.000
#> round_ERR2585270 1 0.4417 0.72739 0.760 0.000 0.148 0.092 0.000
#> round_ERR2585232 3 0.5516 0.68012 0.220 0.000 0.644 0.136 0.000
#> aberrant_ERR2585341 2 0.4395 0.73834 0.000 0.792 0.052 0.032 0.124
#> aberrant_ERR2585355 2 0.0566 0.85159 0.000 0.984 0.004 0.012 0.000
#> round_ERR2585227 1 0.5714 0.43719 0.592 0.000 0.292 0.116 0.000
#> aberrant_ERR2585351 5 0.3113 0.63690 0.000 0.100 0.016 0.020 0.864
#> round_ERR2585269 1 0.1908 0.85841 0.908 0.000 0.000 0.092 0.000
#> aberrant_ERR2585357 2 0.0290 0.85178 0.000 0.992 0.000 0.008 0.000
#> aberrant_ERR2585350 2 0.0000 0.85198 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585250 1 0.4262 0.76125 0.776 0.000 0.100 0.124 0.000
#> round_ERR2585245 1 0.1965 0.85870 0.904 0.000 0.000 0.096 0.000
#> aberrant_ERR2585353 5 0.2300 0.68227 0.000 0.000 0.024 0.072 0.904
#> round_ERR2585258 1 0.5006 0.69843 0.708 0.000 0.136 0.156 0.000
#> aberrant_ERR2585354 5 0.1630 0.70223 0.000 0.004 0.016 0.036 0.944
#> round_ERR2585249 1 0.1965 0.85878 0.904 0.000 0.000 0.096 0.000
#> round_ERR2585268 3 0.6072 0.50847 0.292 0.000 0.552 0.156 0.000
#> aberrant_ERR2585356 5 0.2753 0.58888 0.000 0.000 0.008 0.136 0.856
#> round_ERR2585266 3 0.2766 0.85561 0.084 0.008 0.884 0.024 0.000
#> round_ERR2585231 1 0.1965 0.85870 0.904 0.000 0.000 0.096 0.000
#> round_ERR2585230 1 0.1544 0.86103 0.932 0.000 0.000 0.068 0.000
#> round_ERR2585267 1 0.1965 0.85870 0.904 0.000 0.000 0.096 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 2 0.7596 -0.1497 0.000 0.316 0.004 0.136 0.308 0.236
#> aberrant_ERR2585338 2 0.1320 0.8428 0.000 0.948 0.000 0.016 0.000 0.036
#> aberrant_ERR2585325 2 0.7596 -0.1497 0.000 0.316 0.004 0.136 0.308 0.236
#> aberrant_ERR2585283 4 0.2941 0.8420 0.000 0.000 0.000 0.780 0.220 0.000
#> aberrant_ERR2585343 5 0.3633 0.5936 0.000 0.000 0.004 0.136 0.796 0.064
#> aberrant_ERR2585329 2 0.1780 0.8343 0.000 0.932 0.000 0.012 0.028 0.028
#> aberrant_ERR2585317 2 0.1116 0.8426 0.000 0.960 0.000 0.008 0.004 0.028
#> aberrant_ERR2585339 2 0.0520 0.8466 0.000 0.984 0.000 0.008 0.000 0.008
#> aberrant_ERR2585335 5 0.4860 0.0452 0.000 0.436 0.000 0.008 0.516 0.040
#> aberrant_ERR2585287 4 0.4099 0.7984 0.000 0.000 0.000 0.708 0.244 0.048
#> aberrant_ERR2585321 5 0.3347 0.6418 0.000 0.000 0.004 0.068 0.824 0.104
#> aberrant_ERR2585297 1 0.2163 0.6410 0.892 0.000 0.000 0.016 0.000 0.092
#> aberrant_ERR2585337 2 0.0891 0.8440 0.000 0.968 0.000 0.008 0.000 0.024
#> aberrant_ERR2585319 2 0.5101 0.6035 0.000 0.668 0.004 0.024 0.228 0.076
#> aberrant_ERR2585315 2 0.1693 0.8358 0.000 0.936 0.000 0.012 0.032 0.020
#> aberrant_ERR2585336 2 0.0862 0.8446 0.000 0.972 0.000 0.008 0.004 0.016
#> aberrant_ERR2585307 2 0.0405 0.8461 0.000 0.988 0.000 0.004 0.000 0.008
#> aberrant_ERR2585301 5 0.2668 0.6565 0.000 0.060 0.000 0.028 0.884 0.028
#> aberrant_ERR2585326 2 0.0622 0.8471 0.000 0.980 0.000 0.008 0.000 0.012
#> aberrant_ERR2585331 2 0.2198 0.8304 0.000 0.912 0.032 0.024 0.000 0.032
#> aberrant_ERR2585346 4 0.3103 0.8427 0.008 0.000 0.000 0.784 0.208 0.000
#> aberrant_ERR2585314 2 0.3626 0.7235 0.000 0.784 0.000 0.012 0.176 0.028
#> aberrant_ERR2585298 3 0.1406 0.8497 0.016 0.004 0.952 0.020 0.000 0.008
#> aberrant_ERR2585345 2 0.0717 0.8463 0.000 0.976 0.000 0.008 0.000 0.016
#> aberrant_ERR2585299 1 0.4482 0.4391 0.600 0.000 0.000 0.040 0.000 0.360
#> aberrant_ERR2585309 1 0.0458 0.6410 0.984 0.000 0.000 0.016 0.000 0.000
#> aberrant_ERR2585303 2 0.6094 0.1694 0.000 0.512 0.000 0.028 0.308 0.152
#> aberrant_ERR2585313 2 0.0622 0.8456 0.000 0.980 0.000 0.008 0.000 0.012
#> aberrant_ERR2585318 5 0.2593 0.6561 0.000 0.068 0.000 0.012 0.884 0.036
#> aberrant_ERR2585328 5 0.5150 0.3782 0.000 0.000 0.000 0.256 0.608 0.136
#> aberrant_ERR2585330 5 0.3385 0.5620 0.000 0.180 0.000 0.000 0.788 0.032
#> aberrant_ERR2585293 4 0.4516 0.8110 0.016 0.000 0.012 0.740 0.176 0.056
#> aberrant_ERR2585342 5 0.1980 0.6715 0.000 0.016 0.000 0.048 0.920 0.016
#> aberrant_ERR2585348 5 0.5956 0.3853 0.000 0.036 0.000 0.212 0.580 0.172
#> aberrant_ERR2585352 2 0.4492 0.6396 0.000 0.712 0.000 0.020 0.216 0.052
#> aberrant_ERR2585308 1 0.1257 0.6448 0.952 0.000 0.000 0.028 0.000 0.020
#> aberrant_ERR2585349 2 0.4727 0.5798 0.000 0.664 0.272 0.012 0.004 0.048
#> aberrant_ERR2585316 5 0.5146 0.3450 0.000 0.000 0.004 0.264 0.616 0.116
#> aberrant_ERR2585306 5 0.2288 0.6386 0.004 0.000 0.000 0.072 0.896 0.028
#> aberrant_ERR2585324 2 0.5101 0.6035 0.000 0.668 0.004 0.024 0.228 0.076
#> aberrant_ERR2585310 2 0.7048 0.1693 0.008 0.404 0.040 0.008 0.336 0.204
#> aberrant_ERR2585296 3 0.5034 0.4343 0.044 0.000 0.620 0.016 0.008 0.312
#> aberrant_ERR2585275 4 0.3483 0.8342 0.000 0.000 0.000 0.748 0.236 0.016
#> aberrant_ERR2585311 5 0.1092 0.6688 0.000 0.000 0.000 0.020 0.960 0.020
#> aberrant_ERR2585292 4 0.4516 0.8110 0.016 0.000 0.012 0.740 0.176 0.056
#> aberrant_ERR2585282 5 0.4455 0.5324 0.000 0.000 0.000 0.160 0.712 0.128
#> aberrant_ERR2585305 5 0.2875 0.6503 0.004 0.044 0.000 0.020 0.876 0.056
#> aberrant_ERR2585278 5 0.5072 0.0476 0.000 0.424 0.000 0.016 0.516 0.044
#> aberrant_ERR2585347 4 0.5342 0.3710 0.000 0.000 0.004 0.484 0.420 0.092
#> aberrant_ERR2585332 5 0.4224 0.5887 0.000 0.000 0.004 0.128 0.748 0.120
#> aberrant_ERR2585280 5 0.5718 0.3532 0.000 0.320 0.000 0.036 0.556 0.088
#> aberrant_ERR2585304 2 0.1679 0.8410 0.000 0.936 0.028 0.008 0.000 0.028
#> aberrant_ERR2585322 2 0.0551 0.8478 0.000 0.984 0.000 0.004 0.004 0.008
#> aberrant_ERR2585279 2 0.2415 0.8258 0.000 0.900 0.040 0.024 0.000 0.036
#> aberrant_ERR2585277 2 0.1480 0.8403 0.000 0.940 0.000 0.020 0.000 0.040
#> aberrant_ERR2585295 5 0.7128 0.2840 0.000 0.192 0.000 0.156 0.464 0.188
#> aberrant_ERR2585333 5 0.1713 0.6638 0.000 0.000 0.000 0.044 0.928 0.028
#> aberrant_ERR2585285 5 0.3772 0.5567 0.000 0.180 0.000 0.008 0.772 0.040
#> aberrant_ERR2585286 2 0.1549 0.8402 0.000 0.936 0.000 0.020 0.000 0.044
#> aberrant_ERR2585294 5 0.2322 0.6550 0.000 0.064 0.000 0.004 0.896 0.036
#> aberrant_ERR2585300 5 0.1334 0.6625 0.000 0.000 0.000 0.032 0.948 0.020
#> aberrant_ERR2585334 2 0.2554 0.8230 0.000 0.892 0.044 0.024 0.000 0.040
#> aberrant_ERR2585361 5 0.5318 0.5578 0.000 0.040 0.000 0.104 0.664 0.192
#> aberrant_ERR2585372 5 0.3050 0.6645 0.000 0.004 0.000 0.028 0.832 0.136
#> round_ERR2585217 3 0.1570 0.8469 0.004 0.016 0.944 0.008 0.000 0.028
#> round_ERR2585205 1 0.4094 0.5105 0.652 0.000 0.000 0.024 0.000 0.324
#> round_ERR2585214 3 0.1167 0.8429 0.000 0.020 0.960 0.008 0.000 0.012
#> round_ERR2585202 3 0.1515 0.8430 0.000 0.020 0.944 0.008 0.000 0.028
#> aberrant_ERR2585367 5 0.6626 0.3913 0.000 0.264 0.000 0.056 0.480 0.200
#> round_ERR2585220 6 0.5597 0.4956 0.376 0.000 0.116 0.008 0.000 0.500
#> round_ERR2585238 1 0.3745 0.5877 0.732 0.000 0.000 0.028 0.000 0.240
#> aberrant_ERR2585276 5 0.1552 0.6663 0.000 0.004 0.000 0.036 0.940 0.020
#> round_ERR2585218 1 0.3952 0.5319 0.672 0.000 0.000 0.020 0.000 0.308
#> aberrant_ERR2585363 2 0.4439 0.6676 0.000 0.724 0.000 0.016 0.196 0.064
#> round_ERR2585201 3 0.1774 0.8495 0.016 0.004 0.936 0.020 0.000 0.024
#> round_ERR2585210 1 0.3766 0.5930 0.736 0.000 0.000 0.032 0.000 0.232
#> aberrant_ERR2585362 5 0.4513 0.5692 0.000 0.000 0.000 0.124 0.704 0.172
#> aberrant_ERR2585360 5 0.1773 0.6793 0.000 0.016 0.000 0.016 0.932 0.036
#> round_ERR2585209 3 0.2723 0.7971 0.020 0.000 0.856 0.004 0.000 0.120
#> round_ERR2585242 3 0.2112 0.8462 0.020 0.000 0.916 0.028 0.000 0.036
#> round_ERR2585216 1 0.5116 -0.1142 0.472 0.000 0.040 0.020 0.000 0.468
#> round_ERR2585219 1 0.4083 0.5338 0.668 0.000 0.000 0.028 0.000 0.304
#> round_ERR2585237 3 0.1536 0.8461 0.000 0.020 0.944 0.012 0.000 0.024
#> round_ERR2585198 3 0.1434 0.8390 0.000 0.020 0.948 0.008 0.000 0.024
#> round_ERR2585211 1 0.3766 0.5876 0.736 0.000 0.000 0.032 0.000 0.232
#> round_ERR2585206 1 0.2831 0.6338 0.840 0.000 0.000 0.024 0.000 0.136
#> aberrant_ERR2585281 2 0.3078 0.8101 0.000 0.860 0.000 0.028 0.064 0.048
#> round_ERR2585212 6 0.6119 0.5627 0.328 0.000 0.148 0.028 0.000 0.496
#> round_ERR2585221 1 0.0820 0.6452 0.972 0.000 0.000 0.016 0.000 0.012
#> round_ERR2585243 1 0.4300 0.3619 0.608 0.000 0.000 0.028 0.000 0.364
#> round_ERR2585204 3 0.1679 0.8346 0.000 0.028 0.936 0.008 0.000 0.028
#> round_ERR2585213 3 0.4108 0.6128 0.000 0.232 0.724 0.012 0.000 0.032
#> aberrant_ERR2585373 5 0.1265 0.6758 0.000 0.000 0.000 0.008 0.948 0.044
#> aberrant_ERR2585358 5 0.4917 0.4976 0.000 0.000 0.004 0.188 0.668 0.140
#> aberrant_ERR2585365 5 0.6455 0.3716 0.000 0.304 0.000 0.040 0.472 0.184
#> aberrant_ERR2585359 5 0.4509 0.5231 0.000 0.000 0.004 0.180 0.712 0.104
#> aberrant_ERR2585370 2 0.0717 0.8458 0.000 0.976 0.000 0.008 0.000 0.016
#> round_ERR2585215 1 0.1168 0.6448 0.956 0.000 0.000 0.016 0.000 0.028
#> round_ERR2585262 3 0.2585 0.8407 0.012 0.000 0.880 0.024 0.000 0.084
#> round_ERR2585199 3 0.2206 0.8097 0.000 0.064 0.904 0.008 0.000 0.024
#> aberrant_ERR2585369 5 0.1616 0.6765 0.000 0.000 0.000 0.020 0.932 0.048
#> round_ERR2585208 1 0.1320 0.6493 0.948 0.000 0.000 0.016 0.000 0.036
#> round_ERR2585252 1 0.0622 0.6435 0.980 0.000 0.000 0.012 0.000 0.008
#> round_ERR2585236 1 0.4468 0.4384 0.660 0.000 0.004 0.048 0.000 0.288
#> aberrant_ERR2585284 4 0.3487 0.8187 0.044 0.000 0.000 0.788 0.168 0.000
#> round_ERR2585224 1 0.0632 0.6404 0.976 0.000 0.000 0.024 0.000 0.000
#> round_ERR2585260 1 0.4088 0.3520 0.616 0.000 0.000 0.016 0.000 0.368
#> round_ERR2585229 1 0.4040 0.5580 0.688 0.000 0.000 0.032 0.000 0.280
#> aberrant_ERR2585364 4 0.4697 0.6237 0.000 0.000 0.004 0.584 0.368 0.044
#> round_ERR2585253 1 0.1088 0.6441 0.960 0.000 0.000 0.016 0.000 0.024
#> aberrant_ERR2585368 2 0.1991 0.8385 0.000 0.920 0.012 0.024 0.000 0.044
#> aberrant_ERR2585371 2 0.1991 0.8385 0.000 0.920 0.012 0.024 0.000 0.044
#> round_ERR2585239 1 0.4228 0.3854 0.588 0.000 0.000 0.020 0.000 0.392
#> round_ERR2585273 1 0.3394 0.5725 0.776 0.000 0.000 0.024 0.000 0.200
#> round_ERR2585256 3 0.5312 -0.0729 0.056 0.000 0.488 0.020 0.000 0.436
#> round_ERR2585272 6 0.5793 0.2695 0.420 0.000 0.072 0.040 0.000 0.468
#> round_ERR2585246 1 0.2858 0.6153 0.844 0.000 0.000 0.032 0.000 0.124
#> round_ERR2585261 3 0.2138 0.8434 0.012 0.008 0.912 0.008 0.000 0.060
#> round_ERR2585254 3 0.3922 0.7065 0.016 0.012 0.760 0.012 0.000 0.200
#> round_ERR2585225 3 0.2252 0.8445 0.020 0.000 0.908 0.028 0.000 0.044
#> round_ERR2585235 1 0.5199 0.1699 0.584 0.000 0.024 0.056 0.000 0.336
#> round_ERR2585271 1 0.3840 0.5717 0.696 0.000 0.000 0.020 0.000 0.284
#> round_ERR2585251 6 0.6233 0.5901 0.208 0.000 0.324 0.016 0.000 0.452
#> round_ERR2585255 3 0.2228 0.8471 0.016 0.004 0.912 0.024 0.000 0.044
#> round_ERR2585257 3 0.4308 0.7081 0.020 0.000 0.724 0.040 0.000 0.216
#> round_ERR2585226 1 0.6055 -0.4945 0.424 0.000 0.132 0.024 0.000 0.420
#> round_ERR2585265 6 0.5502 0.4394 0.404 0.000 0.112 0.004 0.000 0.480
#> round_ERR2585259 6 0.6484 0.5992 0.272 0.000 0.208 0.040 0.000 0.480
#> round_ERR2585247 1 0.4060 0.5178 0.684 0.000 0.000 0.032 0.000 0.284
#> round_ERR2585241 1 0.4124 0.5116 0.644 0.000 0.000 0.024 0.000 0.332
#> round_ERR2585263 6 0.5820 0.4087 0.092 0.000 0.372 0.032 0.000 0.504
#> round_ERR2585264 1 0.0806 0.6436 0.972 0.000 0.000 0.008 0.000 0.020
#> round_ERR2585233 3 0.3051 0.8271 0.024 0.000 0.856 0.032 0.000 0.088
#> round_ERR2585223 1 0.3541 0.5740 0.728 0.000 0.000 0.012 0.000 0.260
#> round_ERR2585234 3 0.0951 0.8449 0.000 0.020 0.968 0.004 0.000 0.008
#> round_ERR2585222 1 0.3404 0.6013 0.760 0.000 0.000 0.016 0.000 0.224
#> round_ERR2585228 1 0.4065 0.5347 0.672 0.000 0.000 0.028 0.000 0.300
#> round_ERR2585248 1 0.0820 0.6449 0.972 0.000 0.000 0.012 0.000 0.016
#> round_ERR2585240 3 0.2252 0.8448 0.020 0.000 0.908 0.028 0.000 0.044
#> round_ERR2585270 1 0.5946 -0.3543 0.444 0.000 0.108 0.028 0.000 0.420
#> round_ERR2585232 3 0.5572 0.0440 0.060 0.000 0.512 0.036 0.000 0.392
#> aberrant_ERR2585341 2 0.4131 0.7379 0.000 0.784 0.000 0.036 0.072 0.108
#> aberrant_ERR2585355 2 0.1418 0.8424 0.000 0.944 0.000 0.024 0.000 0.032
#> round_ERR2585227 1 0.6623 -0.4899 0.408 0.000 0.224 0.036 0.000 0.332
#> aberrant_ERR2585351 5 0.2760 0.6585 0.000 0.076 0.000 0.004 0.868 0.052
#> round_ERR2585269 1 0.0603 0.6424 0.980 0.000 0.000 0.016 0.000 0.004
#> aberrant_ERR2585357 2 0.0508 0.8469 0.000 0.984 0.000 0.004 0.000 0.012
#> aberrant_ERR2585350 2 0.0520 0.8463 0.000 0.984 0.000 0.008 0.000 0.008
#> round_ERR2585250 6 0.5485 0.3279 0.424 0.000 0.072 0.020 0.000 0.484
#> round_ERR2585245 1 0.0547 0.6400 0.980 0.000 0.000 0.020 0.000 0.000
#> aberrant_ERR2585353 5 0.3928 0.6067 0.000 0.000 0.000 0.080 0.760 0.160
#> round_ERR2585258 1 0.5716 -0.3923 0.460 0.000 0.096 0.020 0.000 0.424
#> aberrant_ERR2585354 5 0.2972 0.6545 0.000 0.000 0.000 0.036 0.836 0.128
#> round_ERR2585249 1 0.0820 0.6462 0.972 0.000 0.000 0.016 0.000 0.012
#> round_ERR2585268 6 0.6022 0.4231 0.132 0.000 0.372 0.024 0.000 0.472
#> aberrant_ERR2585356 5 0.3809 0.4104 0.000 0.000 0.004 0.240 0.732 0.024
#> round_ERR2585266 3 0.2183 0.8453 0.020 0.000 0.912 0.028 0.000 0.040
#> round_ERR2585231 1 0.0547 0.6400 0.980 0.000 0.000 0.020 0.000 0.000
#> round_ERR2585230 1 0.4178 0.4289 0.608 0.000 0.000 0.020 0.000 0.372
#> round_ERR2585267 1 0.0291 0.6453 0.992 0.000 0.000 0.004 0.000 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> CV:kmeans 155 4.72e-21 2
#> CV:kmeans 144 1.58e-22 3
#> CV:kmeans 151 9.92e-28 4
#> CV:kmeans 141 9.08e-25 5
#> CV:kmeans 123 1.07e-21 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "skmeans"]
# you can also extract it by
# res = res_list["CV:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.875 0.936 0.971 0.5031 0.497 0.497
#> 3 3 0.801 0.841 0.934 0.3145 0.761 0.555
#> 4 4 0.786 0.819 0.909 0.1164 0.856 0.615
#> 5 5 0.709 0.658 0.812 0.0545 0.964 0.867
#> 6 6 0.649 0.514 0.734 0.0413 0.951 0.810
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585338 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585325 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585283 1 0.7219 0.759 0.800 0.200
#> aberrant_ERR2585343 2 0.0938 0.971 0.012 0.988
#> aberrant_ERR2585329 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585317 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585339 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585335 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585287 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585321 2 0.2603 0.946 0.044 0.956
#> aberrant_ERR2585297 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585319 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585315 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585336 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585307 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585301 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585326 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585331 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585346 1 0.6438 0.807 0.836 0.164
#> aberrant_ERR2585314 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585298 1 0.4161 0.899 0.916 0.084
#> aberrant_ERR2585345 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585299 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585303 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585313 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585318 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585328 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585330 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585293 1 0.6712 0.791 0.824 0.176
#> aberrant_ERR2585342 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585348 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585352 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585308 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585349 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585316 2 0.7528 0.721 0.216 0.784
#> aberrant_ERR2585306 1 0.5519 0.849 0.872 0.128
#> aberrant_ERR2585324 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585310 2 0.4939 0.886 0.108 0.892
#> aberrant_ERR2585296 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585275 1 0.9323 0.491 0.652 0.348
#> aberrant_ERR2585311 2 0.2236 0.952 0.036 0.964
#> aberrant_ERR2585292 1 0.6712 0.791 0.824 0.176
#> aberrant_ERR2585282 2 0.2948 0.938 0.052 0.948
#> aberrant_ERR2585305 2 0.5519 0.861 0.128 0.872
#> aberrant_ERR2585278 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585347 2 0.3584 0.925 0.068 0.932
#> aberrant_ERR2585332 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585280 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585304 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585322 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585279 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585277 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585295 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585333 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585285 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585286 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585294 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585300 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585334 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585361 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585372 2 0.0000 0.978 0.000 1.000
#> round_ERR2585217 1 0.8861 0.584 0.696 0.304
#> round_ERR2585205 1 0.0000 0.960 1.000 0.000
#> round_ERR2585214 2 0.3584 0.920 0.068 0.932
#> round_ERR2585202 2 0.5178 0.866 0.116 0.884
#> aberrant_ERR2585367 2 0.0000 0.978 0.000 1.000
#> round_ERR2585220 1 0.0000 0.960 1.000 0.000
#> round_ERR2585238 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585276 2 0.0376 0.976 0.004 0.996
#> round_ERR2585218 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.978 0.000 1.000
#> round_ERR2585201 1 0.4298 0.896 0.912 0.088
#> round_ERR2585210 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585362 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585360 2 0.0672 0.973 0.008 0.992
#> round_ERR2585209 1 0.0000 0.960 1.000 0.000
#> round_ERR2585242 1 0.2778 0.927 0.952 0.048
#> round_ERR2585216 1 0.0000 0.960 1.000 0.000
#> round_ERR2585219 1 0.0000 0.960 1.000 0.000
#> round_ERR2585237 2 0.9491 0.408 0.368 0.632
#> round_ERR2585198 2 0.5842 0.835 0.140 0.860
#> round_ERR2585211 1 0.0000 0.960 1.000 0.000
#> round_ERR2585206 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585281 2 0.0000 0.978 0.000 1.000
#> round_ERR2585212 1 0.0000 0.960 1.000 0.000
#> round_ERR2585221 1 0.0000 0.960 1.000 0.000
#> round_ERR2585243 1 0.0000 0.960 1.000 0.000
#> round_ERR2585204 2 0.1843 0.957 0.028 0.972
#> round_ERR2585213 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585373 2 0.0938 0.971 0.012 0.988
#> aberrant_ERR2585358 2 0.0672 0.973 0.008 0.992
#> aberrant_ERR2585365 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585359 2 0.3879 0.916 0.076 0.924
#> aberrant_ERR2585370 2 0.0000 0.978 0.000 1.000
#> round_ERR2585215 1 0.0000 0.960 1.000 0.000
#> round_ERR2585262 1 0.4939 0.875 0.892 0.108
#> round_ERR2585199 2 0.1633 0.961 0.024 0.976
#> aberrant_ERR2585369 2 0.0000 0.978 0.000 1.000
#> round_ERR2585208 1 0.0000 0.960 1.000 0.000
#> round_ERR2585252 1 0.0000 0.960 1.000 0.000
#> round_ERR2585236 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585284 1 0.0000 0.960 1.000 0.000
#> round_ERR2585224 1 0.0000 0.960 1.000 0.000
#> round_ERR2585260 1 0.0000 0.960 1.000 0.000
#> round_ERR2585229 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585364 1 0.9775 0.324 0.588 0.412
#> round_ERR2585253 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585371 2 0.0000 0.978 0.000 1.000
#> round_ERR2585239 1 0.0000 0.960 1.000 0.000
#> round_ERR2585273 1 0.0000 0.960 1.000 0.000
#> round_ERR2585256 1 0.0000 0.960 1.000 0.000
#> round_ERR2585272 1 0.0000 0.960 1.000 0.000
#> round_ERR2585246 1 0.0000 0.960 1.000 0.000
#> round_ERR2585261 1 0.0672 0.955 0.992 0.008
#> round_ERR2585254 1 0.1184 0.949 0.984 0.016
#> round_ERR2585225 1 0.3584 0.912 0.932 0.068
#> round_ERR2585235 1 0.0000 0.960 1.000 0.000
#> round_ERR2585271 1 0.0000 0.960 1.000 0.000
#> round_ERR2585251 1 0.0000 0.960 1.000 0.000
#> round_ERR2585255 1 0.4562 0.888 0.904 0.096
#> round_ERR2585257 1 0.1633 0.944 0.976 0.024
#> round_ERR2585226 1 0.0000 0.960 1.000 0.000
#> round_ERR2585265 1 0.0000 0.960 1.000 0.000
#> round_ERR2585259 1 0.0000 0.960 1.000 0.000
#> round_ERR2585247 1 0.0000 0.960 1.000 0.000
#> round_ERR2585241 1 0.0000 0.960 1.000 0.000
#> round_ERR2585263 1 0.0000 0.960 1.000 0.000
#> round_ERR2585264 1 0.0000 0.960 1.000 0.000
#> round_ERR2585233 1 0.0000 0.960 1.000 0.000
#> round_ERR2585223 1 0.0000 0.960 1.000 0.000
#> round_ERR2585234 1 0.9944 0.188 0.544 0.456
#> round_ERR2585222 1 0.0000 0.960 1.000 0.000
#> round_ERR2585228 1 0.0000 0.960 1.000 0.000
#> round_ERR2585248 1 0.0000 0.960 1.000 0.000
#> round_ERR2585240 1 0.3431 0.915 0.936 0.064
#> round_ERR2585270 1 0.0000 0.960 1.000 0.000
#> round_ERR2585232 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585341 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585355 2 0.0000 0.978 0.000 1.000
#> round_ERR2585227 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585351 2 0.1633 0.962 0.024 0.976
#> round_ERR2585269 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.978 0.000 1.000
#> aberrant_ERR2585350 2 0.0000 0.978 0.000 1.000
#> round_ERR2585250 1 0.0000 0.960 1.000 0.000
#> round_ERR2585245 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.978 0.000 1.000
#> round_ERR2585258 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.978 0.000 1.000
#> round_ERR2585249 1 0.0000 0.960 1.000 0.000
#> round_ERR2585268 1 0.0000 0.960 1.000 0.000
#> aberrant_ERR2585356 2 0.5629 0.854 0.132 0.868
#> round_ERR2585266 1 0.4022 0.902 0.920 0.080
#> round_ERR2585231 1 0.0000 0.960 1.000 0.000
#> round_ERR2585230 1 0.0000 0.960 1.000 0.000
#> round_ERR2585267 1 0.0000 0.960 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.1411 0.8900 0.000 0.964 0.036
#> aberrant_ERR2585338 3 0.0237 0.9084 0.000 0.004 0.996
#> aberrant_ERR2585325 2 0.1411 0.8900 0.000 0.964 0.036
#> aberrant_ERR2585283 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585343 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585329 3 0.0237 0.9084 0.000 0.004 0.996
#> aberrant_ERR2585317 3 0.0237 0.9084 0.000 0.004 0.996
#> aberrant_ERR2585339 3 0.0000 0.9087 0.000 0.000 1.000
#> aberrant_ERR2585335 3 0.6026 0.3321 0.000 0.376 0.624
#> aberrant_ERR2585287 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585321 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585297 1 0.0000 0.9501 1.000 0.000 0.000
#> aberrant_ERR2585337 3 0.0000 0.9087 0.000 0.000 1.000
#> aberrant_ERR2585319 3 0.5431 0.5583 0.000 0.284 0.716
#> aberrant_ERR2585315 3 0.1163 0.8941 0.000 0.028 0.972
#> aberrant_ERR2585336 3 0.0237 0.9084 0.000 0.004 0.996
#> aberrant_ERR2585307 3 0.0000 0.9087 0.000 0.000 1.000
#> aberrant_ERR2585301 2 0.5760 0.5592 0.000 0.672 0.328
#> aberrant_ERR2585326 3 0.0000 0.9087 0.000 0.000 1.000
#> aberrant_ERR2585331 3 0.0000 0.9087 0.000 0.000 1.000
#> aberrant_ERR2585346 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585314 3 0.1163 0.8938 0.000 0.028 0.972
#> aberrant_ERR2585298 3 0.6225 0.1830 0.432 0.000 0.568
#> aberrant_ERR2585345 3 0.0237 0.9084 0.000 0.004 0.996
#> aberrant_ERR2585299 1 0.0000 0.9501 1.000 0.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.9501 1.000 0.000 0.000
#> aberrant_ERR2585303 3 0.4750 0.6932 0.000 0.216 0.784
#> aberrant_ERR2585313 3 0.0237 0.9084 0.000 0.004 0.996
#> aberrant_ERR2585318 2 0.4974 0.7039 0.000 0.764 0.236
#> aberrant_ERR2585328 2 0.1031 0.8947 0.000 0.976 0.024
#> aberrant_ERR2585330 2 0.5363 0.6464 0.000 0.724 0.276
#> aberrant_ERR2585293 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585342 2 0.1643 0.8840 0.000 0.956 0.044
#> aberrant_ERR2585348 2 0.3038 0.8292 0.000 0.896 0.104
#> aberrant_ERR2585352 3 0.4605 0.7005 0.000 0.204 0.796
#> aberrant_ERR2585308 1 0.0000 0.9501 1.000 0.000 0.000
#> aberrant_ERR2585349 3 0.0000 0.9087 0.000 0.000 1.000
#> aberrant_ERR2585316 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585306 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585324 3 0.5431 0.5583 0.000 0.284 0.716
#> aberrant_ERR2585310 3 0.3587 0.8268 0.088 0.020 0.892
#> aberrant_ERR2585296 1 0.3551 0.8178 0.868 0.000 0.132
#> aberrant_ERR2585275 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585311 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585292 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585282 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585305 2 0.6420 0.6072 0.024 0.688 0.288
#> aberrant_ERR2585278 3 0.3816 0.7807 0.000 0.148 0.852
#> aberrant_ERR2585347 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585332 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585280 2 0.5098 0.6894 0.000 0.752 0.248
#> aberrant_ERR2585304 3 0.0000 0.9087 0.000 0.000 1.000
#> aberrant_ERR2585322 3 0.0237 0.9084 0.000 0.004 0.996
#> aberrant_ERR2585279 3 0.0000 0.9087 0.000 0.000 1.000
#> aberrant_ERR2585277 3 0.0000 0.9087 0.000 0.000 1.000
#> aberrant_ERR2585295 2 0.5905 0.4713 0.000 0.648 0.352
#> aberrant_ERR2585333 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585285 2 0.6286 0.2011 0.000 0.536 0.464
#> aberrant_ERR2585286 3 0.0237 0.9084 0.000 0.004 0.996
#> aberrant_ERR2585294 2 0.5905 0.5067 0.000 0.648 0.352
#> aberrant_ERR2585300 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585334 3 0.0000 0.9087 0.000 0.000 1.000
#> aberrant_ERR2585361 2 0.3686 0.8025 0.000 0.860 0.140
#> aberrant_ERR2585372 2 0.0000 0.9050 0.000 1.000 0.000
#> round_ERR2585217 3 0.3192 0.8099 0.112 0.000 0.888
#> round_ERR2585205 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585214 3 0.0000 0.9087 0.000 0.000 1.000
#> round_ERR2585202 3 0.0000 0.9087 0.000 0.000 1.000
#> aberrant_ERR2585367 2 0.6280 0.1910 0.000 0.540 0.460
#> round_ERR2585220 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585238 1 0.0000 0.9501 1.000 0.000 0.000
#> aberrant_ERR2585276 2 0.1289 0.8921 0.000 0.968 0.032
#> round_ERR2585218 1 0.0000 0.9501 1.000 0.000 0.000
#> aberrant_ERR2585363 3 0.3340 0.8119 0.000 0.120 0.880
#> round_ERR2585201 3 0.5926 0.4049 0.356 0.000 0.644
#> round_ERR2585210 1 0.0000 0.9501 1.000 0.000 0.000
#> aberrant_ERR2585362 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585360 2 0.1529 0.8867 0.000 0.960 0.040
#> round_ERR2585209 1 0.0237 0.9471 0.996 0.000 0.004
#> round_ERR2585242 1 0.6299 0.1331 0.524 0.000 0.476
#> round_ERR2585216 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585219 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585237 3 0.0237 0.9067 0.004 0.000 0.996
#> round_ERR2585198 3 0.0000 0.9087 0.000 0.000 1.000
#> round_ERR2585211 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.9501 1.000 0.000 0.000
#> aberrant_ERR2585281 3 0.2625 0.8510 0.000 0.084 0.916
#> round_ERR2585212 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585221 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585243 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585204 3 0.0000 0.9087 0.000 0.000 1.000
#> round_ERR2585213 3 0.0000 0.9087 0.000 0.000 1.000
#> aberrant_ERR2585373 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585358 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585365 3 0.4750 0.6953 0.000 0.216 0.784
#> aberrant_ERR2585359 2 0.0000 0.9050 0.000 1.000 0.000
#> aberrant_ERR2585370 3 0.0000 0.9087 0.000 0.000 1.000
#> round_ERR2585215 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585262 3 0.8283 0.2684 0.380 0.084 0.536
#> round_ERR2585199 3 0.0000 0.9087 0.000 0.000 1.000
#> aberrant_ERR2585369 2 0.0237 0.9037 0.000 0.996 0.004
#> round_ERR2585208 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585236 1 0.0237 0.9468 0.996 0.004 0.000
#> aberrant_ERR2585284 2 0.0592 0.8971 0.012 0.988 0.000
#> round_ERR2585224 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585260 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585229 1 0.0000 0.9501 1.000 0.000 0.000
#> aberrant_ERR2585364 2 0.0000 0.9050 0.000 1.000 0.000
#> round_ERR2585253 1 0.0000 0.9501 1.000 0.000 0.000
#> aberrant_ERR2585368 3 0.0237 0.9084 0.000 0.004 0.996
#> aberrant_ERR2585371 3 0.0237 0.9084 0.000 0.004 0.996
#> round_ERR2585239 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585273 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585256 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585272 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585246 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585261 1 0.6126 0.3405 0.600 0.000 0.400
#> round_ERR2585254 1 0.6305 0.0405 0.516 0.000 0.484
#> round_ERR2585225 1 0.5785 0.5158 0.668 0.000 0.332
#> round_ERR2585235 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585271 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585251 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585255 1 0.6302 0.1191 0.520 0.000 0.480
#> round_ERR2585257 1 0.3412 0.8301 0.876 0.000 0.124
#> round_ERR2585226 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585265 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585259 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585247 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585241 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585263 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585264 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585233 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585223 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585234 3 0.0237 0.9065 0.004 0.000 0.996
#> round_ERR2585222 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585228 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585248 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585240 1 0.5291 0.6331 0.732 0.000 0.268
#> round_ERR2585270 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585232 1 0.0000 0.9501 1.000 0.000 0.000
#> aberrant_ERR2585341 3 0.3752 0.7914 0.000 0.144 0.856
#> aberrant_ERR2585355 3 0.0237 0.9084 0.000 0.004 0.996
#> round_ERR2585227 1 0.0000 0.9501 1.000 0.000 0.000
#> aberrant_ERR2585351 2 0.5905 0.5074 0.000 0.648 0.352
#> round_ERR2585269 1 0.0000 0.9501 1.000 0.000 0.000
#> aberrant_ERR2585357 3 0.0000 0.9087 0.000 0.000 1.000
#> aberrant_ERR2585350 3 0.0237 0.9084 0.000 0.004 0.996
#> round_ERR2585250 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585245 1 0.0000 0.9501 1.000 0.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.9050 0.000 1.000 0.000
#> round_ERR2585258 1 0.0000 0.9501 1.000 0.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.9050 0.000 1.000 0.000
#> round_ERR2585249 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585268 1 0.0000 0.9501 1.000 0.000 0.000
#> aberrant_ERR2585356 2 0.0000 0.9050 0.000 1.000 0.000
#> round_ERR2585266 1 0.5968 0.4470 0.636 0.000 0.364
#> round_ERR2585231 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585230 1 0.0000 0.9501 1.000 0.000 0.000
#> round_ERR2585267 1 0.0000 0.9501 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 4 0.5203 0.3759 0.000 0.416 0.008 0.576
#> aberrant_ERR2585338 2 0.0921 0.8845 0.000 0.972 0.028 0.000
#> aberrant_ERR2585325 4 0.5229 0.3427 0.000 0.428 0.008 0.564
#> aberrant_ERR2585283 4 0.0188 0.8712 0.000 0.000 0.004 0.996
#> aberrant_ERR2585343 4 0.0376 0.8716 0.000 0.004 0.004 0.992
#> aberrant_ERR2585329 2 0.0707 0.8837 0.000 0.980 0.020 0.000
#> aberrant_ERR2585317 2 0.0707 0.8836 0.000 0.980 0.020 0.000
#> aberrant_ERR2585339 2 0.0921 0.8845 0.000 0.972 0.028 0.000
#> aberrant_ERR2585335 2 0.3668 0.7222 0.000 0.808 0.004 0.188
#> aberrant_ERR2585287 4 0.1305 0.8665 0.000 0.036 0.004 0.960
#> aberrant_ERR2585321 4 0.0927 0.8726 0.000 0.016 0.008 0.976
#> aberrant_ERR2585297 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> aberrant_ERR2585337 2 0.0817 0.8842 0.000 0.976 0.024 0.000
#> aberrant_ERR2585319 2 0.1042 0.8702 0.000 0.972 0.008 0.020
#> aberrant_ERR2585315 2 0.0336 0.8786 0.000 0.992 0.008 0.000
#> aberrant_ERR2585336 2 0.0817 0.8843 0.000 0.976 0.024 0.000
#> aberrant_ERR2585307 2 0.1211 0.8831 0.000 0.960 0.040 0.000
#> aberrant_ERR2585301 2 0.4891 0.5199 0.000 0.680 0.012 0.308
#> aberrant_ERR2585326 2 0.1118 0.8840 0.000 0.964 0.036 0.000
#> aberrant_ERR2585331 2 0.1940 0.8688 0.000 0.924 0.076 0.000
#> aberrant_ERR2585346 4 0.0188 0.8712 0.000 0.000 0.004 0.996
#> aberrant_ERR2585314 2 0.1022 0.8839 0.000 0.968 0.032 0.000
#> aberrant_ERR2585298 3 0.0469 0.9055 0.000 0.012 0.988 0.000
#> aberrant_ERR2585345 2 0.0817 0.8843 0.000 0.976 0.024 0.000
#> aberrant_ERR2585299 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> aberrant_ERR2585303 2 0.3307 0.8223 0.000 0.868 0.028 0.104
#> aberrant_ERR2585313 2 0.0592 0.8830 0.000 0.984 0.016 0.000
#> aberrant_ERR2585318 2 0.5132 0.1084 0.000 0.548 0.004 0.448
#> aberrant_ERR2585328 4 0.2805 0.8303 0.000 0.100 0.012 0.888
#> aberrant_ERR2585330 2 0.4713 0.4160 0.000 0.640 0.000 0.360
#> aberrant_ERR2585293 4 0.0188 0.8712 0.000 0.000 0.004 0.996
#> aberrant_ERR2585342 4 0.4836 0.5695 0.000 0.320 0.008 0.672
#> aberrant_ERR2585348 4 0.3300 0.7864 0.000 0.144 0.008 0.848
#> aberrant_ERR2585352 2 0.1284 0.8750 0.000 0.964 0.012 0.024
#> aberrant_ERR2585308 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> aberrant_ERR2585349 2 0.3942 0.7136 0.000 0.764 0.236 0.000
#> aberrant_ERR2585316 4 0.0188 0.8713 0.000 0.000 0.004 0.996
#> aberrant_ERR2585306 4 0.1042 0.8651 0.020 0.000 0.008 0.972
#> aberrant_ERR2585324 2 0.1042 0.8702 0.000 0.972 0.008 0.020
#> aberrant_ERR2585310 2 0.3943 0.8098 0.036 0.844 0.112 0.008
#> aberrant_ERR2585296 3 0.4610 0.6964 0.236 0.020 0.744 0.000
#> aberrant_ERR2585275 4 0.0188 0.8712 0.000 0.000 0.004 0.996
#> aberrant_ERR2585311 4 0.1489 0.8662 0.000 0.044 0.004 0.952
#> aberrant_ERR2585292 4 0.0188 0.8712 0.000 0.000 0.004 0.996
#> aberrant_ERR2585282 4 0.0524 0.8722 0.000 0.008 0.004 0.988
#> aberrant_ERR2585305 4 0.7202 0.2666 0.092 0.388 0.016 0.504
#> aberrant_ERR2585278 2 0.0524 0.8812 0.000 0.988 0.008 0.004
#> aberrant_ERR2585347 4 0.0000 0.8712 0.000 0.000 0.000 1.000
#> aberrant_ERR2585332 4 0.0657 0.8722 0.000 0.012 0.004 0.984
#> aberrant_ERR2585280 2 0.4194 0.6670 0.000 0.764 0.008 0.228
#> aberrant_ERR2585304 2 0.2530 0.8490 0.000 0.888 0.112 0.000
#> aberrant_ERR2585322 2 0.0592 0.8830 0.000 0.984 0.016 0.000
#> aberrant_ERR2585279 2 0.3444 0.7777 0.000 0.816 0.184 0.000
#> aberrant_ERR2585277 2 0.1302 0.8822 0.000 0.956 0.044 0.000
#> aberrant_ERR2585295 4 0.5296 0.0615 0.000 0.496 0.008 0.496
#> aberrant_ERR2585333 4 0.1389 0.8667 0.000 0.048 0.000 0.952
#> aberrant_ERR2585285 2 0.4008 0.6425 0.000 0.756 0.000 0.244
#> aberrant_ERR2585286 2 0.1118 0.8837 0.000 0.964 0.036 0.000
#> aberrant_ERR2585294 2 0.4814 0.5032 0.000 0.676 0.008 0.316
#> aberrant_ERR2585300 4 0.1151 0.8721 0.000 0.024 0.008 0.968
#> aberrant_ERR2585334 2 0.3444 0.7814 0.000 0.816 0.184 0.000
#> aberrant_ERR2585361 4 0.5268 0.2579 0.000 0.452 0.008 0.540
#> aberrant_ERR2585372 4 0.2976 0.8272 0.000 0.120 0.008 0.872
#> round_ERR2585217 3 0.0592 0.9052 0.000 0.016 0.984 0.000
#> round_ERR2585205 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> round_ERR2585214 3 0.0707 0.9042 0.000 0.020 0.980 0.000
#> round_ERR2585202 3 0.1716 0.8781 0.000 0.064 0.936 0.000
#> aberrant_ERR2585367 2 0.4857 0.5603 0.000 0.700 0.016 0.284
#> round_ERR2585220 1 0.2345 0.8747 0.900 0.000 0.100 0.000
#> round_ERR2585238 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> aberrant_ERR2585276 4 0.4086 0.7223 0.000 0.216 0.008 0.776
#> round_ERR2585218 1 0.0188 0.9388 0.996 0.000 0.004 0.000
#> aberrant_ERR2585363 2 0.0524 0.8754 0.000 0.988 0.004 0.008
#> round_ERR2585201 3 0.0524 0.9063 0.004 0.008 0.988 0.000
#> round_ERR2585210 1 0.0188 0.9388 0.996 0.000 0.004 0.000
#> aberrant_ERR2585362 4 0.2271 0.8521 0.000 0.076 0.008 0.916
#> aberrant_ERR2585360 4 0.4428 0.6496 0.000 0.276 0.004 0.720
#> round_ERR2585209 3 0.1867 0.8844 0.072 0.000 0.928 0.000
#> round_ERR2585242 3 0.0672 0.9065 0.008 0.008 0.984 0.000
#> round_ERR2585216 1 0.1211 0.9217 0.960 0.000 0.040 0.000
#> round_ERR2585219 1 0.0188 0.9388 0.996 0.000 0.004 0.000
#> round_ERR2585237 3 0.0592 0.9052 0.000 0.016 0.984 0.000
#> round_ERR2585198 3 0.0707 0.9042 0.000 0.020 0.980 0.000
#> round_ERR2585211 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> aberrant_ERR2585281 2 0.2408 0.8716 0.000 0.920 0.044 0.036
#> round_ERR2585212 1 0.2530 0.8650 0.888 0.000 0.112 0.000
#> round_ERR2585221 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> round_ERR2585243 1 0.0188 0.9388 0.996 0.000 0.004 0.000
#> round_ERR2585204 3 0.0592 0.9052 0.000 0.016 0.984 0.000
#> round_ERR2585213 3 0.4277 0.5732 0.000 0.280 0.720 0.000
#> aberrant_ERR2585373 4 0.2918 0.8324 0.000 0.116 0.008 0.876
#> aberrant_ERR2585358 4 0.0657 0.8725 0.000 0.012 0.004 0.984
#> aberrant_ERR2585365 2 0.3117 0.8371 0.000 0.880 0.028 0.092
#> aberrant_ERR2585359 4 0.0524 0.8717 0.000 0.004 0.008 0.988
#> aberrant_ERR2585370 2 0.1118 0.8835 0.000 0.964 0.036 0.000
#> round_ERR2585215 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> round_ERR2585262 3 0.2197 0.8919 0.028 0.012 0.936 0.024
#> round_ERR2585199 3 0.2408 0.8408 0.000 0.104 0.896 0.000
#> aberrant_ERR2585369 4 0.4053 0.7210 0.000 0.228 0.004 0.768
#> round_ERR2585208 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> round_ERR2585236 1 0.1913 0.9041 0.940 0.000 0.020 0.040
#> aberrant_ERR2585284 4 0.0376 0.8706 0.004 0.000 0.004 0.992
#> round_ERR2585224 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> round_ERR2585260 1 0.0469 0.9354 0.988 0.000 0.012 0.000
#> round_ERR2585229 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> aberrant_ERR2585364 4 0.0336 0.8715 0.000 0.000 0.008 0.992
#> round_ERR2585253 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> aberrant_ERR2585368 2 0.1389 0.8809 0.000 0.952 0.048 0.000
#> aberrant_ERR2585371 2 0.1389 0.8809 0.000 0.952 0.048 0.000
#> round_ERR2585239 1 0.0336 0.9371 0.992 0.000 0.008 0.000
#> round_ERR2585273 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> round_ERR2585256 3 0.4543 0.5312 0.324 0.000 0.676 0.000
#> round_ERR2585272 1 0.3356 0.7859 0.824 0.000 0.176 0.000
#> round_ERR2585246 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> round_ERR2585261 3 0.0804 0.9073 0.008 0.012 0.980 0.000
#> round_ERR2585254 3 0.1975 0.8975 0.048 0.016 0.936 0.000
#> round_ERR2585225 3 0.2149 0.8795 0.088 0.000 0.912 0.000
#> round_ERR2585235 1 0.2530 0.8608 0.888 0.000 0.112 0.000
#> round_ERR2585271 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> round_ERR2585251 1 0.4406 0.5871 0.700 0.000 0.300 0.000
#> round_ERR2585255 3 0.0779 0.9058 0.016 0.004 0.980 0.000
#> round_ERR2585257 3 0.2999 0.8368 0.132 0.000 0.864 0.004
#> round_ERR2585226 1 0.2216 0.8812 0.908 0.000 0.092 0.000
#> round_ERR2585265 1 0.1302 0.9189 0.956 0.000 0.044 0.000
#> round_ERR2585259 1 0.3975 0.6917 0.760 0.000 0.240 0.000
#> round_ERR2585247 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> round_ERR2585241 1 0.0188 0.9388 0.996 0.000 0.004 0.000
#> round_ERR2585263 1 0.4989 0.0981 0.528 0.000 0.472 0.000
#> round_ERR2585264 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> round_ERR2585233 3 0.4477 0.5704 0.312 0.000 0.688 0.000
#> round_ERR2585223 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> round_ERR2585234 3 0.0469 0.9055 0.000 0.012 0.988 0.000
#> round_ERR2585222 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> round_ERR2585228 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> round_ERR2585248 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> round_ERR2585240 3 0.1474 0.8951 0.052 0.000 0.948 0.000
#> round_ERR2585270 1 0.2011 0.8932 0.920 0.000 0.080 0.000
#> round_ERR2585232 1 0.4996 0.0478 0.516 0.000 0.484 0.000
#> aberrant_ERR2585341 2 0.3266 0.8199 0.000 0.868 0.024 0.108
#> aberrant_ERR2585355 2 0.0817 0.8843 0.000 0.976 0.024 0.000
#> round_ERR2585227 1 0.3123 0.8149 0.844 0.000 0.156 0.000
#> aberrant_ERR2585351 2 0.5323 0.2809 0.004 0.592 0.008 0.396
#> round_ERR2585269 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> aberrant_ERR2585357 2 0.1022 0.8840 0.000 0.968 0.032 0.000
#> aberrant_ERR2585350 2 0.1118 0.8838 0.000 0.964 0.036 0.000
#> round_ERR2585250 1 0.1792 0.9012 0.932 0.000 0.068 0.000
#> round_ERR2585245 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> aberrant_ERR2585353 4 0.1151 0.8719 0.000 0.024 0.008 0.968
#> round_ERR2585258 1 0.1389 0.9169 0.952 0.000 0.048 0.000
#> aberrant_ERR2585354 4 0.2944 0.8193 0.000 0.128 0.004 0.868
#> round_ERR2585249 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> round_ERR2585268 1 0.4730 0.4372 0.636 0.000 0.364 0.000
#> aberrant_ERR2585356 4 0.0336 0.8718 0.000 0.008 0.000 0.992
#> round_ERR2585266 3 0.1824 0.8938 0.060 0.004 0.936 0.000
#> round_ERR2585231 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> round_ERR2585230 1 0.0000 0.9402 1.000 0.000 0.000 0.000
#> round_ERR2585267 1 0.0000 0.9402 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 4 0.6598 0.3609 0.000 0.216 0.000 0.428 0.356
#> aberrant_ERR2585338 2 0.0880 0.7467 0.000 0.968 0.000 0.032 0.000
#> aberrant_ERR2585325 4 0.6632 0.3885 0.000 0.228 0.000 0.428 0.344
#> aberrant_ERR2585283 5 0.0609 0.6945 0.000 0.000 0.000 0.020 0.980
#> aberrant_ERR2585343 5 0.3274 0.6930 0.000 0.000 0.000 0.220 0.780
#> aberrant_ERR2585329 2 0.1544 0.7440 0.000 0.932 0.000 0.068 0.000
#> aberrant_ERR2585317 2 0.1671 0.7406 0.000 0.924 0.000 0.076 0.000
#> aberrant_ERR2585339 2 0.0290 0.7458 0.000 0.992 0.000 0.008 0.000
#> aberrant_ERR2585335 2 0.5472 0.3010 0.000 0.632 0.000 0.260 0.108
#> aberrant_ERR2585287 5 0.2669 0.6885 0.000 0.020 0.000 0.104 0.876
#> aberrant_ERR2585321 5 0.3934 0.6729 0.000 0.016 0.000 0.244 0.740
#> aberrant_ERR2585297 1 0.1357 0.9045 0.948 0.000 0.004 0.048 0.000
#> aberrant_ERR2585337 2 0.1121 0.7504 0.000 0.956 0.000 0.044 0.000
#> aberrant_ERR2585319 2 0.4658 0.2421 0.000 0.576 0.000 0.408 0.016
#> aberrant_ERR2585315 2 0.2074 0.7315 0.000 0.896 0.000 0.104 0.000
#> aberrant_ERR2585336 2 0.0880 0.7477 0.000 0.968 0.000 0.032 0.000
#> aberrant_ERR2585307 2 0.1282 0.7504 0.000 0.952 0.004 0.044 0.000
#> aberrant_ERR2585301 2 0.6244 -0.3788 0.000 0.444 0.000 0.412 0.144
#> aberrant_ERR2585326 2 0.0963 0.7477 0.000 0.964 0.000 0.036 0.000
#> aberrant_ERR2585331 2 0.0566 0.7442 0.000 0.984 0.012 0.004 0.000
#> aberrant_ERR2585346 5 0.0992 0.6927 0.008 0.000 0.000 0.024 0.968
#> aberrant_ERR2585314 2 0.3266 0.6529 0.000 0.796 0.004 0.200 0.000
#> aberrant_ERR2585298 3 0.1012 0.8292 0.000 0.012 0.968 0.020 0.000
#> aberrant_ERR2585345 2 0.1410 0.7480 0.000 0.940 0.000 0.060 0.000
#> aberrant_ERR2585299 1 0.2037 0.9010 0.920 0.000 0.012 0.064 0.004
#> aberrant_ERR2585309 1 0.0703 0.9022 0.976 0.000 0.000 0.024 0.000
#> aberrant_ERR2585303 2 0.3641 0.6465 0.000 0.820 0.000 0.120 0.060
#> aberrant_ERR2585313 2 0.1121 0.7494 0.000 0.956 0.000 0.044 0.000
#> aberrant_ERR2585318 4 0.6621 0.5633 0.000 0.312 0.000 0.448 0.240
#> aberrant_ERR2585328 5 0.4223 0.6256 0.000 0.060 0.016 0.128 0.796
#> aberrant_ERR2585330 2 0.6609 -0.5051 0.000 0.416 0.000 0.368 0.216
#> aberrant_ERR2585293 5 0.1168 0.6916 0.008 0.000 0.000 0.032 0.960
#> aberrant_ERR2585342 5 0.6210 0.1118 0.000 0.148 0.000 0.360 0.492
#> aberrant_ERR2585348 5 0.4617 0.4639 0.000 0.148 0.000 0.108 0.744
#> aberrant_ERR2585352 2 0.4087 0.5930 0.000 0.756 0.000 0.208 0.036
#> aberrant_ERR2585308 1 0.0963 0.9037 0.964 0.000 0.000 0.036 0.000
#> aberrant_ERR2585349 2 0.4465 0.5047 0.000 0.736 0.204 0.060 0.000
#> aberrant_ERR2585316 5 0.2230 0.7096 0.000 0.000 0.000 0.116 0.884
#> aberrant_ERR2585306 5 0.4777 0.6092 0.052 0.000 0.000 0.268 0.680
#> aberrant_ERR2585324 2 0.4658 0.2421 0.000 0.576 0.000 0.408 0.016
#> aberrant_ERR2585310 2 0.6747 0.2265 0.012 0.564 0.120 0.276 0.028
#> aberrant_ERR2585296 3 0.5881 0.5976 0.212 0.008 0.640 0.136 0.004
#> aberrant_ERR2585275 5 0.2074 0.7071 0.000 0.000 0.000 0.104 0.896
#> aberrant_ERR2585311 5 0.4707 0.5118 0.000 0.020 0.000 0.392 0.588
#> aberrant_ERR2585292 5 0.1168 0.6916 0.008 0.000 0.000 0.032 0.960
#> aberrant_ERR2585282 5 0.3305 0.6715 0.000 0.000 0.000 0.224 0.776
#> aberrant_ERR2585305 4 0.8070 0.3550 0.076 0.168 0.028 0.472 0.256
#> aberrant_ERR2585278 2 0.3305 0.6242 0.000 0.776 0.000 0.224 0.000
#> aberrant_ERR2585347 5 0.1851 0.7081 0.000 0.000 0.000 0.088 0.912
#> aberrant_ERR2585332 5 0.3707 0.6692 0.000 0.000 0.000 0.284 0.716
#> aberrant_ERR2585280 2 0.5923 0.1168 0.000 0.576 0.000 0.280 0.144
#> aberrant_ERR2585304 2 0.1915 0.7370 0.000 0.928 0.040 0.032 0.000
#> aberrant_ERR2585322 2 0.1732 0.7438 0.000 0.920 0.000 0.080 0.000
#> aberrant_ERR2585279 2 0.1831 0.7068 0.000 0.920 0.076 0.004 0.000
#> aberrant_ERR2585277 2 0.0404 0.7442 0.000 0.988 0.000 0.012 0.000
#> aberrant_ERR2585295 2 0.6390 -0.3796 0.000 0.436 0.000 0.168 0.396
#> aberrant_ERR2585333 5 0.4805 0.5796 0.000 0.040 0.000 0.312 0.648
#> aberrant_ERR2585285 2 0.6121 -0.1345 0.000 0.528 0.000 0.324 0.148
#> aberrant_ERR2585286 2 0.0703 0.7455 0.000 0.976 0.000 0.024 0.000
#> aberrant_ERR2585294 4 0.6510 0.5178 0.000 0.360 0.000 0.444 0.196
#> aberrant_ERR2585300 5 0.4251 0.5867 0.000 0.004 0.000 0.372 0.624
#> aberrant_ERR2585334 2 0.2625 0.6658 0.000 0.876 0.108 0.016 0.000
#> aberrant_ERR2585361 2 0.6602 -0.4253 0.000 0.424 0.000 0.216 0.360
#> aberrant_ERR2585372 5 0.5369 0.4128 0.000 0.060 0.000 0.388 0.552
#> round_ERR2585217 3 0.0955 0.8286 0.000 0.004 0.968 0.028 0.000
#> round_ERR2585205 1 0.1571 0.9022 0.936 0.000 0.004 0.060 0.000
#> round_ERR2585214 3 0.1168 0.8255 0.000 0.032 0.960 0.008 0.000
#> round_ERR2585202 3 0.3991 0.6957 0.000 0.172 0.780 0.048 0.000
#> aberrant_ERR2585367 2 0.5672 0.2093 0.000 0.632 0.000 0.188 0.180
#> round_ERR2585220 1 0.4808 0.7756 0.728 0.000 0.136 0.136 0.000
#> round_ERR2585238 1 0.1571 0.9048 0.936 0.000 0.004 0.060 0.000
#> aberrant_ERR2585276 5 0.6318 0.0219 0.000 0.156 0.000 0.400 0.444
#> round_ERR2585218 1 0.1270 0.9040 0.948 0.000 0.000 0.052 0.000
#> aberrant_ERR2585363 2 0.3081 0.6854 0.000 0.832 0.000 0.156 0.012
#> round_ERR2585201 3 0.0807 0.8278 0.000 0.012 0.976 0.012 0.000
#> round_ERR2585210 1 0.1197 0.9033 0.952 0.000 0.000 0.048 0.000
#> aberrant_ERR2585362 5 0.5111 0.5735 0.000 0.064 0.008 0.248 0.680
#> aberrant_ERR2585360 5 0.6482 -0.1552 0.000 0.188 0.000 0.372 0.440
#> round_ERR2585209 3 0.2797 0.8034 0.060 0.000 0.880 0.060 0.000
#> round_ERR2585242 3 0.0992 0.8289 0.000 0.008 0.968 0.024 0.000
#> round_ERR2585216 1 0.4002 0.8438 0.796 0.000 0.084 0.120 0.000
#> round_ERR2585219 1 0.1792 0.8991 0.916 0.000 0.000 0.084 0.000
#> round_ERR2585237 3 0.1082 0.8286 0.000 0.008 0.964 0.028 0.000
#> round_ERR2585198 3 0.1670 0.8165 0.000 0.052 0.936 0.012 0.000
#> round_ERR2585211 1 0.1121 0.9026 0.956 0.000 0.000 0.044 0.000
#> round_ERR2585206 1 0.0880 0.9039 0.968 0.000 0.000 0.032 0.000
#> aberrant_ERR2585281 2 0.3274 0.6849 0.000 0.856 0.004 0.076 0.064
#> round_ERR2585212 1 0.4617 0.7765 0.744 0.000 0.148 0.108 0.000
#> round_ERR2585221 1 0.0703 0.9036 0.976 0.000 0.000 0.024 0.000
#> round_ERR2585243 1 0.1952 0.8988 0.912 0.000 0.004 0.084 0.000
#> round_ERR2585204 3 0.2408 0.7897 0.000 0.092 0.892 0.016 0.000
#> round_ERR2585213 3 0.4735 0.1430 0.000 0.460 0.524 0.016 0.000
#> aberrant_ERR2585373 5 0.5483 0.3456 0.000 0.064 0.000 0.424 0.512
#> aberrant_ERR2585358 5 0.3143 0.7014 0.000 0.000 0.000 0.204 0.796
#> aberrant_ERR2585365 2 0.5262 0.4497 0.000 0.692 0.004 0.172 0.132
#> aberrant_ERR2585359 5 0.2424 0.7081 0.000 0.000 0.000 0.132 0.868
#> aberrant_ERR2585370 2 0.0794 0.7476 0.000 0.972 0.000 0.028 0.000
#> round_ERR2585215 1 0.0794 0.9030 0.972 0.000 0.000 0.028 0.000
#> round_ERR2585262 3 0.5070 0.7495 0.040 0.024 0.776 0.096 0.064
#> round_ERR2585199 3 0.4000 0.6266 0.000 0.228 0.748 0.024 0.000
#> aberrant_ERR2585369 5 0.6183 -0.0250 0.000 0.136 0.000 0.408 0.456
#> round_ERR2585208 1 0.0794 0.9037 0.972 0.000 0.000 0.028 0.000
#> round_ERR2585252 1 0.0609 0.9034 0.980 0.000 0.000 0.020 0.000
#> round_ERR2585236 1 0.3553 0.8440 0.852 0.000 0.028 0.048 0.072
#> aberrant_ERR2585284 5 0.1310 0.6831 0.020 0.000 0.000 0.024 0.956
#> round_ERR2585224 1 0.0510 0.9003 0.984 0.000 0.000 0.016 0.000
#> round_ERR2585260 1 0.2006 0.8959 0.916 0.000 0.012 0.072 0.000
#> round_ERR2585229 1 0.1270 0.9023 0.948 0.000 0.000 0.052 0.000
#> aberrant_ERR2585364 5 0.1197 0.7018 0.000 0.000 0.000 0.048 0.952
#> round_ERR2585253 1 0.0609 0.9028 0.980 0.000 0.000 0.020 0.000
#> aberrant_ERR2585368 2 0.0609 0.7460 0.000 0.980 0.000 0.020 0.000
#> aberrant_ERR2585371 2 0.0609 0.7460 0.000 0.980 0.000 0.020 0.000
#> round_ERR2585239 1 0.2130 0.9003 0.908 0.000 0.012 0.080 0.000
#> round_ERR2585273 1 0.1571 0.9000 0.936 0.000 0.004 0.060 0.000
#> round_ERR2585256 3 0.5759 0.4998 0.276 0.000 0.596 0.128 0.000
#> round_ERR2585272 1 0.4725 0.7065 0.720 0.000 0.200 0.080 0.000
#> round_ERR2585246 1 0.1502 0.9029 0.940 0.000 0.004 0.056 0.000
#> round_ERR2585261 3 0.1043 0.8280 0.000 0.000 0.960 0.040 0.000
#> round_ERR2585254 3 0.2871 0.8007 0.040 0.000 0.872 0.088 0.000
#> round_ERR2585225 3 0.2709 0.8174 0.052 0.008 0.900 0.032 0.008
#> round_ERR2585235 1 0.4449 0.8113 0.800 0.000 0.076 0.076 0.048
#> round_ERR2585271 1 0.0880 0.9049 0.968 0.000 0.000 0.032 0.000
#> round_ERR2585251 1 0.6000 0.4083 0.540 0.000 0.328 0.132 0.000
#> round_ERR2585255 3 0.1281 0.8291 0.000 0.012 0.956 0.032 0.000
#> round_ERR2585257 3 0.3694 0.7724 0.084 0.000 0.828 0.084 0.004
#> round_ERR2585226 1 0.4545 0.7845 0.752 0.000 0.132 0.116 0.000
#> round_ERR2585265 1 0.4796 0.7882 0.728 0.000 0.120 0.152 0.000
#> round_ERR2585259 1 0.5540 0.6690 0.664 0.000 0.208 0.120 0.008
#> round_ERR2585247 1 0.2193 0.8939 0.900 0.000 0.008 0.092 0.000
#> round_ERR2585241 1 0.1952 0.8986 0.912 0.000 0.004 0.084 0.000
#> round_ERR2585263 1 0.6299 0.0568 0.432 0.000 0.416 0.152 0.000
#> round_ERR2585264 1 0.0609 0.9009 0.980 0.000 0.000 0.020 0.000
#> round_ERR2585233 3 0.5246 0.5915 0.272 0.000 0.664 0.040 0.024
#> round_ERR2585223 1 0.1831 0.9004 0.920 0.000 0.004 0.076 0.000
#> round_ERR2585234 3 0.0693 0.8269 0.000 0.012 0.980 0.008 0.000
#> round_ERR2585222 1 0.1251 0.9053 0.956 0.000 0.008 0.036 0.000
#> round_ERR2585228 1 0.1478 0.9024 0.936 0.000 0.000 0.064 0.000
#> round_ERR2585248 1 0.0609 0.9007 0.980 0.000 0.000 0.020 0.000
#> round_ERR2585240 3 0.1518 0.8286 0.004 0.004 0.944 0.048 0.000
#> round_ERR2585270 1 0.4169 0.8315 0.784 0.000 0.100 0.116 0.000
#> round_ERR2585232 3 0.5790 0.1521 0.408 0.000 0.500 0.092 0.000
#> aberrant_ERR2585341 2 0.3390 0.6705 0.000 0.840 0.000 0.100 0.060
#> aberrant_ERR2585355 2 0.0794 0.7469 0.000 0.972 0.000 0.028 0.000
#> round_ERR2585227 1 0.5091 0.6482 0.676 0.000 0.236 0.088 0.000
#> aberrant_ERR2585351 4 0.6670 0.5249 0.004 0.348 0.008 0.480 0.160
#> round_ERR2585269 1 0.0510 0.9012 0.984 0.000 0.000 0.016 0.000
#> aberrant_ERR2585357 2 0.0510 0.7469 0.000 0.984 0.000 0.016 0.000
#> aberrant_ERR2585350 2 0.0703 0.7480 0.000 0.976 0.000 0.024 0.000
#> round_ERR2585250 1 0.4364 0.8096 0.768 0.000 0.112 0.120 0.000
#> round_ERR2585245 1 0.0510 0.9011 0.984 0.000 0.000 0.016 0.000
#> aberrant_ERR2585353 5 0.4229 0.6418 0.000 0.020 0.000 0.276 0.704
#> round_ERR2585258 1 0.3912 0.8355 0.804 0.000 0.088 0.108 0.000
#> aberrant_ERR2585354 5 0.5616 0.4066 0.000 0.084 0.000 0.364 0.552
#> round_ERR2585249 1 0.0510 0.9023 0.984 0.000 0.000 0.016 0.000
#> round_ERR2585268 1 0.6186 0.4090 0.548 0.000 0.300 0.148 0.004
#> aberrant_ERR2585356 5 0.3336 0.6830 0.000 0.000 0.000 0.228 0.772
#> round_ERR2585266 3 0.2312 0.8285 0.012 0.016 0.912 0.060 0.000
#> round_ERR2585231 1 0.0510 0.9013 0.984 0.000 0.000 0.016 0.000
#> round_ERR2585230 1 0.2189 0.8994 0.904 0.000 0.012 0.084 0.000
#> round_ERR2585267 1 0.0703 0.9029 0.976 0.000 0.000 0.024 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 5 0.7011 0.1695 0.000 0.120 0.000 0.288 0.444 0.148
#> aberrant_ERR2585338 2 0.1549 0.7705 0.000 0.936 0.000 0.000 0.044 0.020
#> aberrant_ERR2585325 5 0.7011 0.1695 0.000 0.120 0.000 0.288 0.444 0.148
#> aberrant_ERR2585283 4 0.1257 0.6606 0.000 0.000 0.000 0.952 0.020 0.028
#> aberrant_ERR2585343 4 0.4312 0.5682 0.000 0.000 0.000 0.676 0.272 0.052
#> aberrant_ERR2585329 2 0.2331 0.7557 0.000 0.888 0.000 0.000 0.080 0.032
#> aberrant_ERR2585317 2 0.1434 0.7665 0.000 0.940 0.000 0.000 0.048 0.012
#> aberrant_ERR2585339 2 0.1908 0.7680 0.000 0.916 0.000 0.000 0.056 0.028
#> aberrant_ERR2585335 2 0.6414 -0.0199 0.000 0.488 0.000 0.096 0.332 0.084
#> aberrant_ERR2585287 4 0.3757 0.6307 0.000 0.024 0.000 0.804 0.120 0.052
#> aberrant_ERR2585321 4 0.5461 0.4803 0.000 0.020 0.000 0.568 0.324 0.088
#> aberrant_ERR2585297 1 0.2070 0.7488 0.892 0.000 0.000 0.000 0.008 0.100
#> aberrant_ERR2585337 2 0.2070 0.7688 0.000 0.908 0.000 0.000 0.048 0.044
#> aberrant_ERR2585319 5 0.5537 0.0441 0.000 0.448 0.000 0.012 0.448 0.092
#> aberrant_ERR2585315 2 0.3473 0.7059 0.000 0.804 0.000 0.004 0.144 0.048
#> aberrant_ERR2585336 2 0.1616 0.7703 0.000 0.932 0.000 0.000 0.048 0.020
#> aberrant_ERR2585307 2 0.1693 0.7702 0.000 0.932 0.004 0.000 0.044 0.020
#> aberrant_ERR2585301 5 0.6661 0.4767 0.000 0.332 0.000 0.132 0.456 0.080
#> aberrant_ERR2585326 2 0.1003 0.7693 0.000 0.964 0.000 0.000 0.020 0.016
#> aberrant_ERR2585331 2 0.1167 0.7648 0.000 0.960 0.020 0.000 0.008 0.012
#> aberrant_ERR2585346 4 0.1511 0.6584 0.012 0.000 0.000 0.944 0.032 0.012
#> aberrant_ERR2585314 2 0.3974 0.6555 0.000 0.764 0.012 0.004 0.184 0.036
#> aberrant_ERR2585298 3 0.1578 0.7134 0.000 0.004 0.936 0.000 0.012 0.048
#> aberrant_ERR2585345 2 0.1196 0.7699 0.000 0.952 0.000 0.000 0.040 0.008
#> aberrant_ERR2585299 1 0.3600 0.6833 0.776 0.000 0.020 0.000 0.012 0.192
#> aberrant_ERR2585309 1 0.2053 0.7366 0.888 0.000 0.000 0.000 0.004 0.108
#> aberrant_ERR2585303 2 0.4960 0.5960 0.000 0.724 0.016 0.036 0.160 0.064
#> aberrant_ERR2585313 2 0.2088 0.7685 0.000 0.904 0.000 0.000 0.068 0.028
#> aberrant_ERR2585318 5 0.6404 0.5032 0.000 0.280 0.000 0.160 0.508 0.052
#> aberrant_ERR2585328 4 0.5413 0.5500 0.004 0.032 0.012 0.680 0.188 0.084
#> aberrant_ERR2585330 5 0.6857 0.4859 0.000 0.320 0.000 0.192 0.420 0.068
#> aberrant_ERR2585293 4 0.1405 0.6563 0.004 0.000 0.000 0.948 0.024 0.024
#> aberrant_ERR2585342 5 0.6630 0.1661 0.000 0.168 0.000 0.388 0.392 0.052
#> aberrant_ERR2585348 4 0.5977 0.4585 0.000 0.096 0.000 0.620 0.168 0.116
#> aberrant_ERR2585352 2 0.4890 0.5664 0.000 0.684 0.000 0.024 0.216 0.076
#> aberrant_ERR2585308 1 0.2431 0.7454 0.860 0.000 0.000 0.000 0.008 0.132
#> aberrant_ERR2585349 2 0.5114 0.4853 0.000 0.676 0.212 0.000 0.056 0.056
#> aberrant_ERR2585316 4 0.3062 0.6638 0.000 0.000 0.000 0.836 0.112 0.052
#> aberrant_ERR2585306 4 0.6146 0.4237 0.080 0.000 0.004 0.560 0.280 0.076
#> aberrant_ERR2585324 5 0.5537 0.0441 0.000 0.448 0.000 0.012 0.448 0.092
#> aberrant_ERR2585310 2 0.7614 0.0718 0.024 0.480 0.088 0.020 0.228 0.160
#> aberrant_ERR2585296 3 0.7036 -0.0705 0.172 0.028 0.500 0.000 0.060 0.240
#> aberrant_ERR2585275 4 0.2265 0.6628 0.004 0.000 0.000 0.896 0.076 0.024
#> aberrant_ERR2585311 5 0.5849 -0.1085 0.000 0.040 0.000 0.392 0.488 0.080
#> aberrant_ERR2585292 4 0.1405 0.6563 0.004 0.000 0.000 0.948 0.024 0.024
#> aberrant_ERR2585282 4 0.4670 0.6152 0.000 0.008 0.000 0.704 0.176 0.112
#> aberrant_ERR2585305 5 0.8231 0.3668 0.044 0.172 0.024 0.180 0.440 0.140
#> aberrant_ERR2585278 2 0.4768 0.4220 0.000 0.628 0.000 0.012 0.312 0.048
#> aberrant_ERR2585347 4 0.2301 0.6672 0.000 0.000 0.000 0.884 0.096 0.020
#> aberrant_ERR2585332 4 0.4889 0.5281 0.000 0.000 0.000 0.604 0.312 0.084
#> aberrant_ERR2585280 2 0.6579 0.0695 0.000 0.504 0.000 0.128 0.280 0.088
#> aberrant_ERR2585304 2 0.2766 0.7224 0.000 0.868 0.092 0.000 0.028 0.012
#> aberrant_ERR2585322 2 0.3141 0.7397 0.000 0.836 0.004 0.000 0.112 0.048
#> aberrant_ERR2585279 2 0.1753 0.7363 0.000 0.912 0.084 0.000 0.000 0.004
#> aberrant_ERR2585277 2 0.0551 0.7655 0.000 0.984 0.004 0.000 0.008 0.004
#> aberrant_ERR2585295 4 0.7160 -0.2145 0.000 0.340 0.008 0.404 0.152 0.096
#> aberrant_ERR2585333 4 0.5092 0.3792 0.000 0.028 0.000 0.560 0.376 0.036
#> aberrant_ERR2585285 2 0.6517 -0.2706 0.000 0.428 0.000 0.140 0.376 0.056
#> aberrant_ERR2585286 2 0.1390 0.7679 0.000 0.948 0.004 0.000 0.032 0.016
#> aberrant_ERR2585294 5 0.6547 0.4945 0.000 0.272 0.000 0.164 0.500 0.064
#> aberrant_ERR2585300 4 0.4600 0.3223 0.000 0.004 0.000 0.500 0.468 0.028
#> aberrant_ERR2585334 2 0.2624 0.7334 0.000 0.880 0.080 0.000 0.016 0.024
#> aberrant_ERR2585361 5 0.7596 0.3390 0.000 0.288 0.004 0.276 0.304 0.128
#> aberrant_ERR2585372 4 0.6098 0.1761 0.000 0.052 0.000 0.448 0.412 0.088
#> round_ERR2585217 3 0.1914 0.7119 0.000 0.016 0.920 0.000 0.008 0.056
#> round_ERR2585205 1 0.2871 0.7197 0.804 0.000 0.000 0.000 0.004 0.192
#> round_ERR2585214 3 0.1942 0.7060 0.000 0.064 0.916 0.000 0.008 0.012
#> round_ERR2585202 3 0.5057 0.5085 0.000 0.220 0.668 0.000 0.024 0.088
#> aberrant_ERR2585367 2 0.7229 -0.0744 0.000 0.464 0.008 0.196 0.216 0.116
#> round_ERR2585220 1 0.5231 0.3408 0.612 0.000 0.112 0.000 0.008 0.268
#> round_ERR2585238 1 0.2389 0.7450 0.864 0.000 0.000 0.000 0.008 0.128
#> aberrant_ERR2585276 5 0.6374 0.2577 0.000 0.140 0.000 0.316 0.492 0.052
#> round_ERR2585218 1 0.2135 0.7457 0.872 0.000 0.000 0.000 0.000 0.128
#> aberrant_ERR2585363 2 0.4107 0.6042 0.000 0.700 0.000 0.000 0.256 0.044
#> round_ERR2585201 3 0.1606 0.7132 0.000 0.004 0.932 0.000 0.008 0.056
#> round_ERR2585210 1 0.2558 0.7277 0.840 0.000 0.000 0.000 0.004 0.156
#> aberrant_ERR2585362 4 0.6159 0.4472 0.000 0.044 0.008 0.576 0.240 0.132
#> aberrant_ERR2585360 5 0.6666 0.2121 0.000 0.124 0.004 0.340 0.460 0.072
#> round_ERR2585209 3 0.3610 0.6374 0.052 0.004 0.792 0.000 0.000 0.152
#> round_ERR2585242 3 0.2154 0.7102 0.004 0.004 0.908 0.000 0.020 0.064
#> round_ERR2585216 1 0.5049 0.3330 0.616 0.000 0.084 0.000 0.008 0.292
#> round_ERR2585219 1 0.2553 0.7355 0.848 0.000 0.000 0.000 0.008 0.144
#> round_ERR2585237 3 0.1974 0.7108 0.000 0.020 0.920 0.000 0.012 0.048
#> round_ERR2585198 3 0.2039 0.6982 0.000 0.072 0.908 0.000 0.004 0.016
#> round_ERR2585211 1 0.2446 0.7382 0.864 0.000 0.000 0.000 0.012 0.124
#> round_ERR2585206 1 0.2624 0.7415 0.844 0.000 0.004 0.000 0.004 0.148
#> aberrant_ERR2585281 2 0.4289 0.6877 0.000 0.788 0.008 0.060 0.092 0.052
#> round_ERR2585212 1 0.5735 -0.0825 0.548 0.000 0.152 0.000 0.012 0.288
#> round_ERR2585221 1 0.2302 0.7443 0.872 0.000 0.000 0.000 0.008 0.120
#> round_ERR2585243 1 0.3121 0.7087 0.796 0.000 0.000 0.004 0.008 0.192
#> round_ERR2585204 3 0.3091 0.6358 0.000 0.148 0.824 0.000 0.004 0.024
#> round_ERR2585213 2 0.4389 0.0505 0.000 0.512 0.468 0.000 0.004 0.016
#> aberrant_ERR2585373 5 0.6174 -0.0594 0.000 0.060 0.000 0.360 0.488 0.092
#> aberrant_ERR2585358 4 0.4223 0.6189 0.000 0.004 0.000 0.712 0.232 0.052
#> aberrant_ERR2585365 2 0.5602 0.4866 0.000 0.648 0.004 0.052 0.200 0.096
#> aberrant_ERR2585359 4 0.4228 0.6241 0.000 0.000 0.000 0.708 0.228 0.064
#> aberrant_ERR2585370 2 0.0603 0.7666 0.000 0.980 0.000 0.000 0.016 0.004
#> round_ERR2585215 1 0.1901 0.7442 0.912 0.000 0.000 0.004 0.008 0.076
#> round_ERR2585262 3 0.6594 0.5021 0.028 0.036 0.624 0.068 0.064 0.180
#> round_ERR2585199 3 0.4249 0.4174 0.000 0.328 0.640 0.000 0.000 0.032
#> aberrant_ERR2585369 5 0.6137 0.2324 0.000 0.092 0.000 0.292 0.544 0.072
#> round_ERR2585208 1 0.2070 0.7467 0.892 0.000 0.000 0.000 0.008 0.100
#> round_ERR2585252 1 0.1387 0.7450 0.932 0.000 0.000 0.000 0.000 0.068
#> round_ERR2585236 1 0.6201 0.3140 0.612 0.000 0.048 0.108 0.028 0.204
#> aberrant_ERR2585284 4 0.2421 0.6434 0.032 0.000 0.000 0.900 0.028 0.040
#> round_ERR2585224 1 0.2213 0.7444 0.888 0.000 0.000 0.004 0.008 0.100
#> round_ERR2585260 1 0.3858 0.6857 0.760 0.000 0.032 0.000 0.012 0.196
#> round_ERR2585229 1 0.2489 0.7442 0.860 0.000 0.000 0.000 0.012 0.128
#> aberrant_ERR2585364 4 0.2762 0.6636 0.000 0.000 0.000 0.860 0.092 0.048
#> round_ERR2585253 1 0.1082 0.7362 0.956 0.000 0.000 0.000 0.004 0.040
#> aberrant_ERR2585368 2 0.0551 0.7666 0.000 0.984 0.004 0.000 0.004 0.008
#> aberrant_ERR2585371 2 0.0551 0.7666 0.000 0.984 0.004 0.000 0.004 0.008
#> round_ERR2585239 1 0.3138 0.7326 0.828 0.000 0.008 0.004 0.016 0.144
#> round_ERR2585273 1 0.3323 0.6804 0.752 0.000 0.000 0.000 0.008 0.240
#> round_ERR2585256 3 0.6192 -0.2074 0.196 0.004 0.504 0.000 0.016 0.280
#> round_ERR2585272 1 0.5525 0.1016 0.588 0.000 0.164 0.000 0.008 0.240
#> round_ERR2585246 1 0.2772 0.7233 0.816 0.000 0.000 0.000 0.004 0.180
#> round_ERR2585261 3 0.2600 0.6950 0.000 0.008 0.860 0.000 0.008 0.124
#> round_ERR2585254 3 0.3514 0.6436 0.008 0.012 0.788 0.000 0.008 0.184
#> round_ERR2585225 3 0.3979 0.6538 0.048 0.004 0.796 0.004 0.020 0.128
#> round_ERR2585235 1 0.6155 0.1478 0.588 0.000 0.140 0.048 0.008 0.216
#> round_ERR2585271 1 0.2665 0.7426 0.868 0.000 0.012 0.000 0.016 0.104
#> round_ERR2585251 1 0.6317 -0.6132 0.376 0.000 0.312 0.000 0.008 0.304
#> round_ERR2585255 3 0.1982 0.7099 0.000 0.004 0.912 0.000 0.016 0.068
#> round_ERR2585257 3 0.5739 0.3218 0.116 0.000 0.604 0.008 0.024 0.248
#> round_ERR2585226 1 0.5460 0.2236 0.600 0.000 0.136 0.000 0.012 0.252
#> round_ERR2585265 1 0.5145 0.4245 0.628 0.000 0.096 0.000 0.012 0.264
#> round_ERR2585259 1 0.5935 -0.0631 0.568 0.000 0.196 0.008 0.012 0.216
#> round_ERR2585247 1 0.3152 0.7111 0.792 0.000 0.008 0.000 0.004 0.196
#> round_ERR2585241 1 0.2631 0.7289 0.840 0.000 0.000 0.000 0.008 0.152
#> round_ERR2585263 6 0.6753 0.0000 0.324 0.000 0.284 0.000 0.036 0.356
#> round_ERR2585264 1 0.1075 0.7343 0.952 0.000 0.000 0.000 0.000 0.048
#> round_ERR2585233 3 0.6095 0.1324 0.196 0.000 0.580 0.012 0.024 0.188
#> round_ERR2585223 1 0.2768 0.7377 0.832 0.000 0.000 0.000 0.012 0.156
#> round_ERR2585234 3 0.1257 0.7122 0.000 0.020 0.952 0.000 0.000 0.028
#> round_ERR2585222 1 0.3122 0.7307 0.816 0.000 0.004 0.000 0.020 0.160
#> round_ERR2585228 1 0.2135 0.7477 0.872 0.000 0.000 0.000 0.000 0.128
#> round_ERR2585248 1 0.1285 0.7395 0.944 0.000 0.000 0.000 0.004 0.052
#> round_ERR2585240 3 0.3648 0.6732 0.044 0.004 0.812 0.000 0.016 0.124
#> round_ERR2585270 1 0.4529 0.5107 0.676 0.000 0.064 0.000 0.004 0.256
#> round_ERR2585232 3 0.6117 -0.6059 0.372 0.000 0.392 0.000 0.004 0.232
#> aberrant_ERR2585341 2 0.5441 0.5269 0.000 0.696 0.008 0.096 0.112 0.088
#> aberrant_ERR2585355 2 0.2474 0.7569 0.000 0.884 0.004 0.000 0.080 0.032
#> round_ERR2585227 1 0.5597 0.1813 0.576 0.000 0.144 0.000 0.012 0.268
#> aberrant_ERR2585351 5 0.6802 0.5088 0.008 0.272 0.000 0.104 0.504 0.112
#> round_ERR2585269 1 0.1524 0.7393 0.932 0.000 0.000 0.000 0.008 0.060
#> aberrant_ERR2585357 2 0.0806 0.7660 0.000 0.972 0.000 0.000 0.020 0.008
#> aberrant_ERR2585350 2 0.1575 0.7714 0.000 0.936 0.000 0.000 0.032 0.032
#> round_ERR2585250 1 0.5815 0.3972 0.636 0.000 0.088 0.024 0.036 0.216
#> round_ERR2585245 1 0.1075 0.7355 0.952 0.000 0.000 0.000 0.000 0.048
#> aberrant_ERR2585353 4 0.5431 0.4902 0.000 0.008 0.000 0.552 0.332 0.108
#> round_ERR2585258 1 0.4746 0.5224 0.676 0.000 0.084 0.000 0.008 0.232
#> aberrant_ERR2585354 4 0.6571 0.1630 0.000 0.068 0.000 0.424 0.380 0.128
#> round_ERR2585249 1 0.1333 0.7408 0.944 0.000 0.000 0.000 0.008 0.048
#> round_ERR2585268 1 0.7234 -0.7092 0.328 0.004 0.292 0.008 0.048 0.320
#> aberrant_ERR2585356 4 0.4301 0.5850 0.000 0.000 0.000 0.696 0.240 0.064
#> round_ERR2585266 3 0.3214 0.6901 0.036 0.004 0.852 0.000 0.024 0.084
#> round_ERR2585231 1 0.2056 0.7387 0.904 0.000 0.000 0.004 0.012 0.080
#> round_ERR2585230 1 0.3754 0.6938 0.756 0.000 0.016 0.000 0.016 0.212
#> round_ERR2585267 1 0.1970 0.7476 0.900 0.000 0.000 0.000 0.008 0.092
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> CV:skmeans 156 2.86e-21 2
#> CV:skmeans 148 1.81e-22 3
#> CV:skmeans 149 2.65e-27 4
#> CV:skmeans 131 1.19e-22 5
#> CV:skmeans 106 3.76e-18 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "pam"]
# you can also extract it by
# res = res_list["CV:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'CV' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.314 0.721 0.863 0.2910 0.771 0.771
#> 3 3 0.192 0.462 0.705 0.8226 0.585 0.487
#> 4 4 0.219 0.218 0.626 0.1104 0.653 0.442
#> 5 5 0.268 0.427 0.640 0.0431 0.737 0.476
#> 6 6 0.382 0.303 0.695 0.0676 0.731 0.414
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 1 0.6973 0.7366 0.812 0.188
#> aberrant_ERR2585338 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585325 1 0.4161 0.8154 0.916 0.084
#> aberrant_ERR2585283 2 0.0000 0.7243 0.000 1.000
#> aberrant_ERR2585343 2 0.9881 0.3067 0.436 0.564
#> aberrant_ERR2585329 1 0.0376 0.8336 0.996 0.004
#> aberrant_ERR2585317 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585339 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585335 1 0.6343 0.7791 0.840 0.160
#> aberrant_ERR2585287 2 0.5946 0.6663 0.144 0.856
#> aberrant_ERR2585321 1 0.9970 0.0692 0.532 0.468
#> aberrant_ERR2585297 1 0.8499 0.6789 0.724 0.276
#> aberrant_ERR2585337 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585319 1 0.4939 0.8031 0.892 0.108
#> aberrant_ERR2585315 1 0.0938 0.8336 0.988 0.012
#> aberrant_ERR2585336 1 0.0672 0.8342 0.992 0.008
#> aberrant_ERR2585307 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585301 1 0.4161 0.8202 0.916 0.084
#> aberrant_ERR2585326 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585331 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585346 2 0.0000 0.7243 0.000 1.000
#> aberrant_ERR2585314 1 0.2423 0.8316 0.960 0.040
#> aberrant_ERR2585298 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585345 1 0.0376 0.8336 0.996 0.004
#> aberrant_ERR2585299 1 0.7139 0.7566 0.804 0.196
#> aberrant_ERR2585309 1 0.9427 0.5330 0.640 0.360
#> aberrant_ERR2585303 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585313 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585318 1 0.8661 0.6540 0.712 0.288
#> aberrant_ERR2585328 1 0.8207 0.6781 0.744 0.256
#> aberrant_ERR2585330 1 0.9000 0.5784 0.684 0.316
#> aberrant_ERR2585293 2 0.0000 0.7243 0.000 1.000
#> aberrant_ERR2585342 1 0.8499 0.6282 0.724 0.276
#> aberrant_ERR2585348 1 0.8763 0.5542 0.704 0.296
#> aberrant_ERR2585352 1 0.2603 0.8322 0.956 0.044
#> aberrant_ERR2585308 2 0.9580 0.4207 0.380 0.620
#> aberrant_ERR2585349 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585316 2 0.9491 0.4613 0.368 0.632
#> aberrant_ERR2585306 1 0.9909 0.2568 0.556 0.444
#> aberrant_ERR2585324 1 0.3274 0.8243 0.940 0.060
#> aberrant_ERR2585310 1 0.0672 0.8341 0.992 0.008
#> aberrant_ERR2585296 1 0.3879 0.8220 0.924 0.076
#> aberrant_ERR2585275 2 0.7139 0.6973 0.196 0.804
#> aberrant_ERR2585311 1 0.8955 0.6186 0.688 0.312
#> aberrant_ERR2585292 2 0.0000 0.7243 0.000 1.000
#> aberrant_ERR2585282 2 0.9970 0.1122 0.468 0.532
#> aberrant_ERR2585305 1 0.7139 0.7552 0.804 0.196
#> aberrant_ERR2585278 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585347 2 0.7376 0.6945 0.208 0.792
#> aberrant_ERR2585332 1 0.9491 0.2904 0.632 0.368
#> aberrant_ERR2585280 1 0.5629 0.7876 0.868 0.132
#> aberrant_ERR2585304 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585322 1 0.0376 0.8336 0.996 0.004
#> aberrant_ERR2585279 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585277 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585295 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585333 1 0.8661 0.6248 0.712 0.288
#> aberrant_ERR2585285 1 0.5059 0.8043 0.888 0.112
#> aberrant_ERR2585286 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585294 1 0.5059 0.8027 0.888 0.112
#> aberrant_ERR2585300 1 0.9963 0.1225 0.536 0.464
#> aberrant_ERR2585334 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585361 1 0.6148 0.7698 0.848 0.152
#> aberrant_ERR2585372 1 0.9000 0.6012 0.684 0.316
#> round_ERR2585217 1 0.0672 0.8340 0.992 0.008
#> round_ERR2585205 1 0.8144 0.7070 0.748 0.252
#> round_ERR2585214 1 0.0000 0.8328 1.000 0.000
#> round_ERR2585202 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585367 1 0.3114 0.8257 0.944 0.056
#> round_ERR2585220 1 0.5519 0.8090 0.872 0.128
#> round_ERR2585238 1 0.8555 0.6736 0.720 0.280
#> aberrant_ERR2585276 1 0.6438 0.7599 0.836 0.164
#> round_ERR2585218 1 0.7453 0.7431 0.788 0.212
#> aberrant_ERR2585363 1 0.0672 0.8342 0.992 0.008
#> round_ERR2585201 1 0.0376 0.8330 0.996 0.004
#> round_ERR2585210 1 0.8661 0.6626 0.712 0.288
#> aberrant_ERR2585362 1 0.7674 0.7325 0.776 0.224
#> aberrant_ERR2585360 1 0.7219 0.7477 0.800 0.200
#> round_ERR2585209 1 0.0938 0.8327 0.988 0.012
#> round_ERR2585242 1 0.0376 0.8330 0.996 0.004
#> round_ERR2585216 1 0.5519 0.7962 0.872 0.128
#> round_ERR2585219 1 0.6887 0.7615 0.816 0.184
#> round_ERR2585237 1 0.0000 0.8328 1.000 0.000
#> round_ERR2585198 1 0.0000 0.8328 1.000 0.000
#> round_ERR2585211 1 0.8499 0.6811 0.724 0.276
#> round_ERR2585206 1 0.8144 0.7064 0.748 0.252
#> aberrant_ERR2585281 1 0.0376 0.8333 0.996 0.004
#> round_ERR2585212 1 0.6048 0.7899 0.852 0.148
#> round_ERR2585221 1 0.9209 0.5841 0.664 0.336
#> round_ERR2585243 1 0.7299 0.7571 0.796 0.204
#> round_ERR2585204 1 0.0000 0.8328 1.000 0.000
#> round_ERR2585213 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585373 1 0.9732 0.3990 0.596 0.404
#> aberrant_ERR2585358 2 0.7219 0.6962 0.200 0.800
#> aberrant_ERR2585365 1 0.0938 0.8344 0.988 0.012
#> aberrant_ERR2585359 2 0.0000 0.7243 0.000 1.000
#> aberrant_ERR2585370 1 0.0000 0.8328 1.000 0.000
#> round_ERR2585215 1 0.9881 0.3129 0.564 0.436
#> round_ERR2585262 1 0.0376 0.8330 0.996 0.004
#> round_ERR2585199 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585369 1 0.7219 0.7295 0.800 0.200
#> round_ERR2585208 1 0.9393 0.5392 0.644 0.356
#> round_ERR2585252 1 0.8861 0.6394 0.696 0.304
#> round_ERR2585236 1 0.8144 0.7101 0.748 0.252
#> aberrant_ERR2585284 2 0.0000 0.7243 0.000 1.000
#> round_ERR2585224 1 0.9933 0.2508 0.548 0.452
#> round_ERR2585260 1 0.7815 0.7292 0.768 0.232
#> round_ERR2585229 1 0.9087 0.6062 0.676 0.324
#> aberrant_ERR2585364 2 0.0672 0.7229 0.008 0.992
#> round_ERR2585253 1 0.9710 0.4295 0.600 0.400
#> aberrant_ERR2585368 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585371 1 0.0000 0.8328 1.000 0.000
#> round_ERR2585239 1 0.8386 0.6898 0.732 0.268
#> round_ERR2585273 1 0.5519 0.7988 0.872 0.128
#> round_ERR2585256 1 0.1414 0.8335 0.980 0.020
#> round_ERR2585272 1 0.1843 0.8324 0.972 0.028
#> round_ERR2585246 1 0.6048 0.7946 0.852 0.148
#> round_ERR2585261 1 0.0000 0.8328 1.000 0.000
#> round_ERR2585254 1 0.0376 0.8330 0.996 0.004
#> round_ERR2585225 1 0.0672 0.8330 0.992 0.008
#> round_ERR2585235 1 0.9661 0.4387 0.608 0.392
#> round_ERR2585271 1 0.9323 0.5591 0.652 0.348
#> round_ERR2585251 1 0.2948 0.8281 0.948 0.052
#> round_ERR2585255 1 0.0938 0.8325 0.988 0.012
#> round_ERR2585257 1 0.0376 0.8330 0.996 0.004
#> round_ERR2585226 1 0.5842 0.7985 0.860 0.140
#> round_ERR2585265 1 0.0672 0.8341 0.992 0.008
#> round_ERR2585259 1 0.3431 0.8265 0.936 0.064
#> round_ERR2585247 1 0.5842 0.7904 0.860 0.140
#> round_ERR2585241 1 0.6247 0.7761 0.844 0.156
#> round_ERR2585263 1 0.3431 0.8270 0.936 0.064
#> round_ERR2585264 2 0.5178 0.7261 0.116 0.884
#> round_ERR2585233 1 0.1843 0.8328 0.972 0.028
#> round_ERR2585223 1 0.6623 0.7697 0.828 0.172
#> round_ERR2585234 1 0.0376 0.8330 0.996 0.004
#> round_ERR2585222 1 0.7745 0.7304 0.772 0.228
#> round_ERR2585228 1 0.6531 0.7690 0.832 0.168
#> round_ERR2585248 2 0.4562 0.7293 0.096 0.904
#> round_ERR2585240 1 0.0376 0.8330 0.996 0.004
#> round_ERR2585270 1 0.4815 0.8100 0.896 0.104
#> round_ERR2585232 1 0.0938 0.8337 0.988 0.012
#> aberrant_ERR2585341 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585355 1 0.0000 0.8328 1.000 0.000
#> round_ERR2585227 1 0.2423 0.8308 0.960 0.040
#> aberrant_ERR2585351 1 0.5519 0.8027 0.872 0.128
#> round_ERR2585269 1 0.9460 0.5241 0.636 0.364
#> aberrant_ERR2585357 1 0.0000 0.8328 1.000 0.000
#> aberrant_ERR2585350 1 0.0000 0.8328 1.000 0.000
#> round_ERR2585250 1 0.6712 0.7798 0.824 0.176
#> round_ERR2585245 2 0.8499 0.6061 0.276 0.724
#> aberrant_ERR2585353 2 0.9993 0.0688 0.484 0.516
#> round_ERR2585258 1 0.7219 0.7527 0.800 0.200
#> aberrant_ERR2585354 1 0.1414 0.8327 0.980 0.020
#> round_ERR2585249 1 0.9170 0.5903 0.668 0.332
#> round_ERR2585268 1 0.1184 0.8347 0.984 0.016
#> aberrant_ERR2585356 2 0.9775 0.3443 0.412 0.588
#> round_ERR2585266 1 0.0000 0.8328 1.000 0.000
#> round_ERR2585231 1 0.9580 0.4873 0.620 0.380
#> round_ERR2585230 1 0.5519 0.8002 0.872 0.128
#> round_ERR2585267 2 0.9815 0.3018 0.420 0.580
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.7576 0.28282 0.076 0.648 0.276
#> aberrant_ERR2585338 2 0.6280 -0.32192 0.460 0.540 0.000
#> aberrant_ERR2585325 2 0.4865 0.52638 0.032 0.832 0.136
#> aberrant_ERR2585283 3 0.0237 0.78071 0.004 0.000 0.996
#> aberrant_ERR2585343 2 0.8739 0.14538 0.112 0.496 0.392
#> aberrant_ERR2585329 2 0.4047 0.61741 0.148 0.848 0.004
#> aberrant_ERR2585317 2 0.3918 0.61509 0.140 0.856 0.004
#> aberrant_ERR2585339 1 0.6244 0.52122 0.560 0.440 0.000
#> aberrant_ERR2585335 2 0.7902 0.63736 0.132 0.660 0.208
#> aberrant_ERR2585287 3 0.2173 0.77594 0.008 0.048 0.944
#> aberrant_ERR2585321 2 0.8920 0.28310 0.124 0.468 0.408
#> aberrant_ERR2585297 1 0.2796 0.46137 0.908 0.092 0.000
#> aberrant_ERR2585337 2 0.3941 0.59651 0.156 0.844 0.000
#> aberrant_ERR2585319 2 0.6644 0.65365 0.140 0.752 0.108
#> aberrant_ERR2585315 2 0.4326 0.62183 0.144 0.844 0.012
#> aberrant_ERR2585336 2 0.4261 0.62058 0.140 0.848 0.012
#> aberrant_ERR2585307 2 0.3752 0.61393 0.144 0.856 0.000
#> aberrant_ERR2585301 2 0.5944 0.64366 0.152 0.784 0.064
#> aberrant_ERR2585326 2 0.3686 0.61281 0.140 0.860 0.000
#> aberrant_ERR2585331 1 0.6215 0.53676 0.572 0.428 0.000
#> aberrant_ERR2585346 3 0.0424 0.78095 0.008 0.000 0.992
#> aberrant_ERR2585314 2 0.6057 0.44475 0.340 0.656 0.004
#> aberrant_ERR2585298 1 0.6215 0.53676 0.572 0.428 0.000
#> aberrant_ERR2585345 2 0.3752 0.60956 0.144 0.856 0.000
#> aberrant_ERR2585299 1 0.5115 0.30326 0.768 0.228 0.004
#> aberrant_ERR2585309 1 0.8236 -0.33222 0.508 0.416 0.076
#> aberrant_ERR2585303 2 0.4575 0.56403 0.184 0.812 0.004
#> aberrant_ERR2585313 2 0.5754 0.32302 0.296 0.700 0.004
#> aberrant_ERR2585318 2 0.9007 0.51223 0.268 0.552 0.180
#> aberrant_ERR2585328 2 0.8886 0.58466 0.188 0.572 0.240
#> aberrant_ERR2585330 2 0.9029 0.41578 0.144 0.504 0.352
#> aberrant_ERR2585293 3 0.0237 0.78071 0.004 0.000 0.996
#> aberrant_ERR2585342 2 0.8898 0.39231 0.128 0.500 0.372
#> aberrant_ERR2585348 2 0.6473 0.23644 0.020 0.668 0.312
#> aberrant_ERR2585352 2 0.6867 0.45068 0.288 0.672 0.040
#> aberrant_ERR2585308 1 0.8569 -0.28040 0.508 0.392 0.100
#> aberrant_ERR2585349 2 0.5926 0.07403 0.356 0.644 0.000
#> aberrant_ERR2585316 3 0.7222 0.29487 0.388 0.032 0.580
#> aberrant_ERR2585306 2 0.9709 0.36275 0.244 0.448 0.308
#> aberrant_ERR2585324 2 0.5571 0.64475 0.140 0.804 0.056
#> aberrant_ERR2585310 2 0.3879 0.61244 0.152 0.848 0.000
#> aberrant_ERR2585296 1 0.5363 0.55740 0.724 0.276 0.000
#> aberrant_ERR2585275 3 0.4342 0.70765 0.120 0.024 0.856
#> aberrant_ERR2585311 2 0.8521 0.36992 0.440 0.468 0.092
#> aberrant_ERR2585292 3 0.0237 0.78071 0.004 0.000 0.996
#> aberrant_ERR2585282 1 0.9220 -0.33345 0.468 0.376 0.156
#> aberrant_ERR2585305 2 0.7741 0.62710 0.216 0.668 0.116
#> aberrant_ERR2585278 2 0.3816 0.60625 0.148 0.852 0.000
#> aberrant_ERR2585347 3 0.7238 0.35685 0.044 0.328 0.628
#> aberrant_ERR2585332 2 0.8168 0.45145 0.108 0.612 0.280
#> aberrant_ERR2585280 2 0.6979 0.65378 0.140 0.732 0.128
#> aberrant_ERR2585304 2 0.3686 0.61281 0.140 0.860 0.000
#> aberrant_ERR2585322 2 0.3983 0.61691 0.144 0.852 0.004
#> aberrant_ERR2585279 1 0.6225 0.53197 0.568 0.432 0.000
#> aberrant_ERR2585277 1 0.6215 0.53676 0.572 0.428 0.000
#> aberrant_ERR2585295 2 0.5365 0.43751 0.252 0.744 0.004
#> aberrant_ERR2585333 2 0.9020 0.42350 0.140 0.496 0.364
#> aberrant_ERR2585285 2 0.7572 0.64130 0.184 0.688 0.128
#> aberrant_ERR2585286 1 0.6280 0.48682 0.540 0.460 0.000
#> aberrant_ERR2585294 2 0.7245 0.65054 0.168 0.712 0.120
#> aberrant_ERR2585300 2 0.9681 0.39597 0.256 0.460 0.284
#> aberrant_ERR2585334 1 0.6215 0.53676 0.572 0.428 0.000
#> aberrant_ERR2585361 2 0.7451 0.65227 0.144 0.700 0.156
#> aberrant_ERR2585372 2 0.9268 0.36994 0.268 0.524 0.208
#> round_ERR2585217 1 0.6192 0.54078 0.580 0.420 0.000
#> round_ERR2585205 1 0.0000 0.52676 1.000 0.000 0.000
#> round_ERR2585214 1 0.6215 0.53676 0.572 0.428 0.000
#> round_ERR2585202 1 0.6215 0.53676 0.572 0.428 0.000
#> aberrant_ERR2585367 1 0.8201 0.40890 0.524 0.400 0.076
#> round_ERR2585220 1 0.5541 0.55870 0.740 0.252 0.008
#> round_ERR2585238 1 0.2527 0.50644 0.936 0.044 0.020
#> aberrant_ERR2585276 2 0.7766 0.64729 0.148 0.676 0.176
#> round_ERR2585218 1 0.0000 0.52676 1.000 0.000 0.000
#> aberrant_ERR2585363 2 0.4033 0.61970 0.136 0.856 0.008
#> round_ERR2585201 1 0.6215 0.53676 0.572 0.428 0.000
#> round_ERR2585210 1 0.2096 0.49973 0.944 0.052 0.004
#> aberrant_ERR2585362 2 0.8478 0.61415 0.180 0.616 0.204
#> aberrant_ERR2585360 2 0.8112 0.63760 0.160 0.648 0.192
#> round_ERR2585209 1 0.6192 0.54154 0.580 0.420 0.000
#> round_ERR2585242 1 0.6215 0.53676 0.572 0.428 0.000
#> round_ERR2585216 1 0.3816 0.55953 0.852 0.148 0.000
#> round_ERR2585219 1 0.0661 0.52905 0.988 0.004 0.008
#> round_ERR2585237 1 0.6192 0.53965 0.580 0.420 0.000
#> round_ERR2585198 1 0.6215 0.53676 0.572 0.428 0.000
#> round_ERR2585211 1 0.0424 0.52527 0.992 0.000 0.008
#> round_ERR2585206 1 0.0000 0.52676 1.000 0.000 0.000
#> aberrant_ERR2585281 1 0.6505 0.46506 0.528 0.468 0.004
#> round_ERR2585212 1 0.3193 0.55678 0.896 0.100 0.004
#> round_ERR2585221 1 0.2486 0.49742 0.932 0.060 0.008
#> round_ERR2585243 1 0.5253 0.55543 0.792 0.188 0.020
#> round_ERR2585204 1 0.6215 0.53676 0.572 0.428 0.000
#> round_ERR2585213 1 0.6215 0.53676 0.572 0.428 0.000
#> aberrant_ERR2585373 2 0.9479 0.33174 0.348 0.460 0.192
#> aberrant_ERR2585358 3 0.6553 0.52135 0.020 0.324 0.656
#> aberrant_ERR2585365 2 0.6905 -0.20594 0.440 0.544 0.016
#> aberrant_ERR2585359 3 0.5849 0.71703 0.028 0.216 0.756
#> aberrant_ERR2585370 1 0.6225 0.53175 0.568 0.432 0.000
#> round_ERR2585215 1 0.3554 0.48501 0.900 0.036 0.064
#> round_ERR2585262 1 0.6204 0.53840 0.576 0.424 0.000
#> round_ERR2585199 1 0.6215 0.53676 0.572 0.428 0.000
#> aberrant_ERR2585369 2 0.8112 0.64031 0.160 0.648 0.192
#> round_ERR2585208 1 0.0237 0.52689 0.996 0.000 0.004
#> round_ERR2585252 1 0.2711 0.46663 0.912 0.088 0.000
#> round_ERR2585236 1 0.4519 0.55161 0.852 0.116 0.032
#> aberrant_ERR2585284 3 0.0592 0.78002 0.012 0.000 0.988
#> round_ERR2585224 1 0.7337 -0.29352 0.540 0.428 0.032
#> round_ERR2585260 1 0.1964 0.54112 0.944 0.056 0.000
#> round_ERR2585229 1 0.5988 -0.14396 0.632 0.368 0.000
#> aberrant_ERR2585364 3 0.2590 0.75683 0.004 0.072 0.924
#> round_ERR2585253 1 0.1765 0.50574 0.956 0.040 0.004
#> aberrant_ERR2585368 2 0.6111 -0.09434 0.396 0.604 0.000
#> aberrant_ERR2585371 2 0.5926 0.07881 0.356 0.644 0.000
#> round_ERR2585239 1 0.0848 0.52393 0.984 0.008 0.008
#> round_ERR2585273 1 0.3412 0.55927 0.876 0.124 0.000
#> round_ERR2585256 1 0.6095 0.54879 0.608 0.392 0.000
#> round_ERR2585272 1 0.6154 0.54663 0.592 0.408 0.000
#> round_ERR2585246 2 0.6302 0.32466 0.480 0.520 0.000
#> round_ERR2585261 1 0.6215 0.53676 0.572 0.428 0.000
#> round_ERR2585254 1 0.6180 0.54077 0.584 0.416 0.000
#> round_ERR2585225 1 0.6204 0.53962 0.576 0.424 0.000
#> round_ERR2585235 1 0.5304 0.41162 0.824 0.068 0.108
#> round_ERR2585271 1 0.0237 0.52715 0.996 0.000 0.004
#> round_ERR2585251 1 0.5650 0.54789 0.688 0.312 0.000
#> round_ERR2585255 1 0.6192 0.54163 0.580 0.420 0.000
#> round_ERR2585257 1 0.6260 0.50181 0.552 0.448 0.000
#> round_ERR2585226 1 0.6045 0.10510 0.620 0.380 0.000
#> round_ERR2585265 1 0.6192 0.53482 0.580 0.420 0.000
#> round_ERR2585259 1 0.5560 0.56034 0.700 0.300 0.000
#> round_ERR2585247 1 0.4931 0.48076 0.768 0.232 0.000
#> round_ERR2585241 1 0.1643 0.54391 0.956 0.044 0.000
#> round_ERR2585263 1 0.5650 0.55459 0.688 0.312 0.000
#> round_ERR2585264 3 0.6274 0.47493 0.456 0.000 0.544
#> round_ERR2585233 1 0.5968 0.55401 0.636 0.364 0.000
#> round_ERR2585223 1 0.1031 0.53784 0.976 0.024 0.000
#> round_ERR2585234 1 0.6204 0.53842 0.576 0.424 0.000
#> round_ERR2585222 1 0.4887 0.26422 0.772 0.228 0.000
#> round_ERR2585228 1 0.0237 0.52891 0.996 0.004 0.000
#> round_ERR2585248 3 0.6518 0.44436 0.484 0.004 0.512
#> round_ERR2585240 1 0.6215 0.53676 0.572 0.428 0.000
#> round_ERR2585270 2 0.6309 0.26184 0.496 0.504 0.000
#> round_ERR2585232 1 0.6126 0.54665 0.600 0.400 0.000
#> aberrant_ERR2585341 1 0.6587 0.53579 0.568 0.424 0.008
#> aberrant_ERR2585355 2 0.6305 -0.36658 0.484 0.516 0.000
#> round_ERR2585227 1 0.5621 0.55930 0.692 0.308 0.000
#> aberrant_ERR2585351 2 0.6678 0.63516 0.208 0.728 0.064
#> round_ERR2585269 1 0.3213 0.45991 0.900 0.092 0.008
#> aberrant_ERR2585357 2 0.3686 0.61281 0.140 0.860 0.000
#> aberrant_ERR2585350 1 0.6215 0.53676 0.572 0.428 0.000
#> round_ERR2585250 1 0.7674 -0.17425 0.484 0.472 0.044
#> round_ERR2585245 1 0.5621 -0.00975 0.692 0.000 0.308
#> aberrant_ERR2585353 2 0.9252 0.21786 0.164 0.480 0.356
#> round_ERR2585258 1 0.4654 0.27567 0.792 0.208 0.000
#> aberrant_ERR2585354 1 0.7056 0.52652 0.572 0.404 0.024
#> round_ERR2585249 1 0.3038 0.44741 0.896 0.104 0.000
#> round_ERR2585268 2 0.5465 0.35590 0.288 0.712 0.000
#> aberrant_ERR2585356 2 0.9405 0.25472 0.176 0.448 0.376
#> round_ERR2585266 1 0.6215 0.53676 0.572 0.428 0.000
#> round_ERR2585231 1 0.3910 0.43243 0.876 0.104 0.020
#> round_ERR2585230 1 0.4654 0.49900 0.792 0.208 0.000
#> round_ERR2585267 1 0.5393 0.37180 0.808 0.044 0.148
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 3 0.5868 0.367646 0.004 0.104 0.708 0.184
#> aberrant_ERR2585338 2 0.6007 0.102460 0.340 0.604 0.056 0.000
#> aberrant_ERR2585325 3 0.5949 0.512077 0.004 0.260 0.668 0.068
#> aberrant_ERR2585283 4 0.0000 0.589609 0.000 0.000 0.000 1.000
#> aberrant_ERR2585343 2 0.8877 -0.363610 0.084 0.472 0.220 0.224
#> aberrant_ERR2585329 2 0.2654 0.266844 0.004 0.888 0.108 0.000
#> aberrant_ERR2585317 2 0.3219 0.231226 0.000 0.836 0.164 0.000
#> aberrant_ERR2585339 2 0.5478 0.044370 0.444 0.540 0.016 0.000
#> aberrant_ERR2585335 2 0.7234 -0.205721 0.012 0.576 0.268 0.144
#> aberrant_ERR2585287 4 0.3400 0.504104 0.000 0.000 0.180 0.820
#> aberrant_ERR2585321 2 0.7176 -0.246057 0.000 0.552 0.196 0.252
#> aberrant_ERR2585297 1 0.4511 0.683234 0.784 0.176 0.040 0.000
#> aberrant_ERR2585337 2 0.2443 0.316294 0.024 0.916 0.060 0.000
#> aberrant_ERR2585319 2 0.7505 -0.244481 0.080 0.552 0.320 0.048
#> aberrant_ERR2585315 2 0.2384 0.287266 0.004 0.916 0.072 0.008
#> aberrant_ERR2585336 2 0.3486 0.205871 0.000 0.812 0.188 0.000
#> aberrant_ERR2585307 2 0.1305 0.311055 0.004 0.960 0.036 0.000
#> aberrant_ERR2585301 2 0.4733 0.129339 0.004 0.780 0.172 0.044
#> aberrant_ERR2585326 2 0.0592 0.317454 0.000 0.984 0.016 0.000
#> aberrant_ERR2585331 2 0.5493 0.033143 0.456 0.528 0.016 0.000
#> aberrant_ERR2585346 4 0.0188 0.589492 0.004 0.000 0.000 0.996
#> aberrant_ERR2585314 2 0.5050 0.296932 0.176 0.756 0.068 0.000
#> aberrant_ERR2585298 2 0.5388 0.035940 0.456 0.532 0.012 0.000
#> aberrant_ERR2585345 2 0.0895 0.318683 0.004 0.976 0.020 0.000
#> aberrant_ERR2585299 1 0.4876 0.542285 0.672 0.320 0.004 0.004
#> aberrant_ERR2585309 2 0.7490 -0.082808 0.408 0.480 0.044 0.068
#> aberrant_ERR2585303 2 0.3176 0.306400 0.036 0.880 0.084 0.000
#> aberrant_ERR2585313 2 0.5208 0.328762 0.172 0.748 0.080 0.000
#> aberrant_ERR2585318 2 0.7522 -0.162988 0.040 0.588 0.252 0.120
#> aberrant_ERR2585328 2 0.6283 0.044420 0.048 0.700 0.052 0.200
#> aberrant_ERR2585330 2 0.8040 -0.221412 0.048 0.552 0.224 0.176
#> aberrant_ERR2585293 4 0.0000 0.589609 0.000 0.000 0.000 1.000
#> aberrant_ERR2585342 2 0.8484 -0.291018 0.072 0.516 0.240 0.172
#> aberrant_ERR2585348 2 0.7970 -0.600874 0.004 0.396 0.344 0.256
#> aberrant_ERR2585352 2 0.6063 0.317527 0.144 0.728 0.100 0.028
#> aberrant_ERR2585308 2 0.7704 -0.149175 0.412 0.460 0.044 0.084
#> aberrant_ERR2585349 2 0.4642 0.228814 0.240 0.740 0.020 0.000
#> aberrant_ERR2585316 4 0.8053 -0.041144 0.284 0.100 0.076 0.540
#> aberrant_ERR2585306 2 0.7861 -0.181369 0.104 0.568 0.068 0.260
#> aberrant_ERR2585324 2 0.7043 -0.219125 0.080 0.588 0.304 0.028
#> aberrant_ERR2585310 2 0.0657 0.323956 0.012 0.984 0.004 0.000
#> aberrant_ERR2585296 1 0.5112 0.333611 0.608 0.384 0.008 0.000
#> aberrant_ERR2585275 4 0.4521 0.420900 0.092 0.056 0.024 0.828
#> aberrant_ERR2585311 2 0.8174 -0.145437 0.196 0.556 0.184 0.064
#> aberrant_ERR2585292 4 0.0000 0.589609 0.000 0.000 0.000 1.000
#> aberrant_ERR2585282 2 0.8945 -0.178864 0.256 0.476 0.144 0.124
#> aberrant_ERR2585305 2 0.5068 0.216019 0.064 0.804 0.044 0.088
#> aberrant_ERR2585278 2 0.0336 0.324474 0.008 0.992 0.000 0.000
#> aberrant_ERR2585347 4 0.5204 0.009043 0.000 0.376 0.012 0.612
#> aberrant_ERR2585332 2 0.8145 -0.536335 0.024 0.452 0.336 0.188
#> aberrant_ERR2585280 2 0.5030 0.156998 0.020 0.796 0.104 0.080
#> aberrant_ERR2585304 2 0.0000 0.320805 0.000 1.000 0.000 0.000
#> aberrant_ERR2585322 2 0.3172 0.234717 0.000 0.840 0.160 0.000
#> aberrant_ERR2585279 2 0.5383 0.039891 0.452 0.536 0.012 0.000
#> aberrant_ERR2585277 2 0.5493 0.034108 0.456 0.528 0.016 0.000
#> aberrant_ERR2585295 2 0.3840 0.354477 0.104 0.844 0.052 0.000
#> aberrant_ERR2585333 2 0.6783 -0.131783 0.008 0.616 0.120 0.256
#> aberrant_ERR2585285 2 0.6822 0.034169 0.040 0.660 0.212 0.088
#> aberrant_ERR2585286 2 0.5543 0.055613 0.424 0.556 0.020 0.000
#> aberrant_ERR2585294 2 0.4442 0.241632 0.024 0.832 0.052 0.092
#> aberrant_ERR2585300 2 0.7230 -0.147546 0.124 0.588 0.020 0.268
#> aberrant_ERR2585334 2 0.5682 0.029150 0.456 0.520 0.024 0.000
#> aberrant_ERR2585361 2 0.4254 0.210893 0.004 0.828 0.064 0.104
#> aberrant_ERR2585372 3 0.9462 0.380843 0.136 0.300 0.380 0.184
#> round_ERR2585217 2 0.5597 0.019602 0.464 0.516 0.020 0.000
#> round_ERR2585205 1 0.3612 0.716106 0.856 0.100 0.044 0.000
#> round_ERR2585214 2 0.5388 0.035940 0.456 0.532 0.012 0.000
#> round_ERR2585202 2 0.5388 0.035940 0.456 0.532 0.012 0.000
#> aberrant_ERR2585367 2 0.8086 -0.014139 0.400 0.432 0.128 0.040
#> round_ERR2585220 1 0.5577 0.331781 0.612 0.364 0.016 0.008
#> round_ERR2585238 1 0.4749 0.713760 0.804 0.132 0.044 0.020
#> aberrant_ERR2585276 2 0.4866 0.142116 0.004 0.780 0.060 0.156
#> round_ERR2585218 1 0.3404 0.716378 0.864 0.104 0.032 0.000
#> aberrant_ERR2585363 2 0.4088 0.144691 0.004 0.764 0.232 0.000
#> round_ERR2585201 2 0.5388 0.035940 0.456 0.532 0.012 0.000
#> round_ERR2585210 1 0.4253 0.715153 0.820 0.132 0.044 0.004
#> aberrant_ERR2585362 2 0.6432 0.009621 0.044 0.700 0.076 0.180
#> aberrant_ERR2585360 2 0.7062 -0.110392 0.020 0.612 0.248 0.120
#> round_ERR2585209 2 0.5392 0.028372 0.460 0.528 0.012 0.000
#> round_ERR2585242 2 0.5388 0.035940 0.456 0.532 0.012 0.000
#> round_ERR2585216 1 0.5055 0.615593 0.712 0.256 0.032 0.000
#> round_ERR2585219 1 0.3884 0.717492 0.848 0.108 0.036 0.008
#> round_ERR2585237 2 0.5392 0.028372 0.460 0.528 0.012 0.000
#> round_ERR2585198 2 0.5388 0.035940 0.456 0.532 0.012 0.000
#> round_ERR2585211 1 0.3940 0.716641 0.848 0.100 0.044 0.008
#> round_ERR2585206 1 0.3612 0.716106 0.856 0.100 0.044 0.000
#> aberrant_ERR2585281 2 0.6775 0.020419 0.412 0.492 0.096 0.000
#> round_ERR2585212 1 0.4754 0.628250 0.752 0.220 0.024 0.004
#> round_ERR2585221 1 0.4130 0.716017 0.824 0.136 0.036 0.004
#> round_ERR2585243 1 0.5788 0.462709 0.660 0.296 0.024 0.020
#> round_ERR2585204 2 0.5388 0.035940 0.456 0.532 0.012 0.000
#> round_ERR2585213 2 0.5493 0.034140 0.456 0.528 0.016 0.000
#> aberrant_ERR2585373 2 0.8234 -0.212115 0.264 0.524 0.056 0.156
#> aberrant_ERR2585358 4 0.6281 0.131753 0.028 0.288 0.040 0.644
#> aberrant_ERR2585365 2 0.6791 0.168257 0.316 0.564 0.120 0.000
#> aberrant_ERR2585359 4 0.6818 0.129355 0.028 0.044 0.416 0.512
#> aberrant_ERR2585370 2 0.5488 0.039064 0.452 0.532 0.016 0.000
#> round_ERR2585215 1 0.5204 0.708161 0.788 0.124 0.044 0.044
#> round_ERR2585262 2 0.5277 0.029675 0.460 0.532 0.008 0.000
#> round_ERR2585199 2 0.5388 0.035940 0.456 0.532 0.012 0.000
#> aberrant_ERR2585369 2 0.7816 -0.263952 0.036 0.500 0.348 0.116
#> round_ERR2585208 1 0.3796 0.717186 0.852 0.100 0.044 0.004
#> round_ERR2585252 1 0.4417 0.698543 0.796 0.160 0.044 0.000
#> round_ERR2585236 1 0.5423 0.586716 0.720 0.232 0.016 0.032
#> aberrant_ERR2585284 4 0.0592 0.584362 0.016 0.000 0.000 0.984
#> round_ERR2585224 2 0.6515 -0.068691 0.420 0.524 0.028 0.028
#> round_ERR2585260 1 0.4050 0.683810 0.808 0.168 0.024 0.000
#> round_ERR2585229 1 0.6055 0.174672 0.520 0.436 0.044 0.000
#> aberrant_ERR2585364 4 0.2452 0.528197 0.004 0.084 0.004 0.908
#> round_ERR2585253 1 0.4090 0.715347 0.832 0.120 0.044 0.004
#> aberrant_ERR2585368 2 0.6558 0.178108 0.296 0.596 0.108 0.000
#> aberrant_ERR2585371 2 0.6300 0.242134 0.252 0.640 0.108 0.000
#> round_ERR2585239 1 0.3824 0.718988 0.852 0.104 0.036 0.008
#> round_ERR2585273 1 0.4468 0.590019 0.752 0.232 0.016 0.000
#> round_ERR2585256 2 0.5409 -0.050997 0.492 0.496 0.012 0.000
#> round_ERR2585272 2 0.5399 0.011020 0.468 0.520 0.012 0.000
#> round_ERR2585246 2 0.5587 0.019879 0.372 0.600 0.028 0.000
#> round_ERR2585261 2 0.5388 0.035940 0.456 0.532 0.012 0.000
#> round_ERR2585254 2 0.5396 0.020898 0.464 0.524 0.012 0.000
#> round_ERR2585225 2 0.5388 0.035940 0.456 0.532 0.012 0.000
#> round_ERR2585235 1 0.6077 0.663861 0.736 0.140 0.048 0.076
#> round_ERR2585271 1 0.3940 0.717858 0.848 0.100 0.044 0.008
#> round_ERR2585251 1 0.5220 0.260270 0.568 0.424 0.008 0.000
#> round_ERR2585255 2 0.5388 0.035940 0.456 0.532 0.012 0.000
#> round_ERR2585257 2 0.5105 0.029380 0.432 0.564 0.004 0.000
#> round_ERR2585226 1 0.5000 0.247053 0.500 0.500 0.000 0.000
#> round_ERR2585265 2 0.5500 -0.000436 0.464 0.520 0.016 0.000
#> round_ERR2585259 1 0.5310 0.242532 0.576 0.412 0.012 0.000
#> round_ERR2585247 1 0.5300 0.609516 0.664 0.308 0.028 0.000
#> round_ERR2585241 1 0.4050 0.705472 0.820 0.144 0.036 0.000
#> round_ERR2585263 1 0.5337 0.232638 0.564 0.424 0.012 0.000
#> round_ERR2585264 4 0.6851 0.093969 0.436 0.032 0.040 0.492
#> round_ERR2585233 1 0.5402 0.087511 0.516 0.472 0.012 0.000
#> round_ERR2585223 1 0.3196 0.695476 0.856 0.136 0.008 0.000
#> round_ERR2585234 2 0.5392 0.028873 0.460 0.528 0.012 0.000
#> round_ERR2585222 1 0.4761 0.511497 0.664 0.332 0.004 0.000
#> round_ERR2585228 1 0.3497 0.716823 0.860 0.104 0.036 0.000
#> round_ERR2585248 1 0.6930 -0.220044 0.476 0.032 0.044 0.448
#> round_ERR2585240 2 0.5388 0.035940 0.456 0.532 0.012 0.000
#> round_ERR2585270 2 0.4713 0.090239 0.360 0.640 0.000 0.000
#> round_ERR2585232 2 0.5407 -0.028380 0.484 0.504 0.012 0.000
#> aberrant_ERR2585341 1 0.7281 0.032268 0.440 0.412 0.148 0.000
#> aberrant_ERR2585355 2 0.5024 0.124366 0.360 0.632 0.008 0.000
#> round_ERR2585227 1 0.5310 0.243860 0.576 0.412 0.012 0.000
#> aberrant_ERR2585351 2 0.4439 0.245030 0.056 0.840 0.052 0.052
#> round_ERR2585269 1 0.4646 0.703903 0.796 0.152 0.044 0.008
#> aberrant_ERR2585357 2 0.2011 0.284010 0.000 0.920 0.080 0.000
#> aberrant_ERR2585350 2 0.5388 0.035940 0.456 0.532 0.012 0.000
#> round_ERR2585250 2 0.5901 0.042689 0.364 0.596 0.004 0.036
#> round_ERR2585245 1 0.6768 0.352725 0.636 0.056 0.044 0.264
#> aberrant_ERR2585353 2 0.8266 -0.319204 0.048 0.520 0.196 0.236
#> round_ERR2585258 1 0.5169 0.559144 0.696 0.272 0.032 0.000
#> aberrant_ERR2585354 2 0.6536 0.005070 0.456 0.488 0.036 0.020
#> round_ERR2585249 1 0.4417 0.698416 0.796 0.160 0.044 0.000
#> round_ERR2585268 2 0.3925 0.305769 0.176 0.808 0.016 0.000
#> aberrant_ERR2585356 2 0.8868 -0.343432 0.096 0.484 0.188 0.232
#> round_ERR2585266 2 0.5388 0.035940 0.456 0.532 0.012 0.000
#> round_ERR2585231 1 0.4785 0.692625 0.784 0.164 0.044 0.008
#> round_ERR2585230 1 0.4655 0.582478 0.684 0.312 0.004 0.000
#> round_ERR2585267 1 0.6199 0.634475 0.732 0.116 0.048 0.104
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 3 0.6772 -0.57965 0.000 0.048 0.452 0.092 0.408
#> aberrant_ERR2585338 3 0.7290 0.52890 0.388 0.124 0.420 0.000 0.068
#> aberrant_ERR2585325 3 0.6798 -0.57923 0.000 0.084 0.452 0.056 0.408
#> aberrant_ERR2585283 4 0.1121 0.70702 0.000 0.044 0.000 0.956 0.000
#> aberrant_ERR2585343 2 0.4014 0.32748 0.024 0.800 0.004 0.156 0.016
#> aberrant_ERR2585329 2 0.6228 0.55298 0.088 0.564 0.324 0.004 0.020
#> aberrant_ERR2585317 2 0.7208 0.53199 0.084 0.536 0.240 0.000 0.140
#> aberrant_ERR2585339 3 0.4622 0.77891 0.440 0.012 0.548 0.000 0.000
#> aberrant_ERR2585335 2 0.5951 0.45728 0.080 0.716 0.020 0.100 0.084
#> aberrant_ERR2585287 4 0.4572 0.64872 0.000 0.056 0.168 0.760 0.016
#> aberrant_ERR2585321 2 0.6071 0.44436 0.076 0.668 0.008 0.196 0.052
#> aberrant_ERR2585297 1 0.2006 0.58975 0.916 0.072 0.012 0.000 0.000
#> aberrant_ERR2585337 2 0.6921 0.48541 0.104 0.460 0.384 0.000 0.052
#> aberrant_ERR2585319 2 0.1095 0.30589 0.000 0.968 0.008 0.012 0.012
#> aberrant_ERR2585315 2 0.6131 0.53736 0.084 0.540 0.356 0.000 0.020
#> aberrant_ERR2585336 2 0.7391 0.54177 0.080 0.532 0.248 0.008 0.132
#> aberrant_ERR2585307 2 0.6104 0.50226 0.088 0.488 0.412 0.000 0.012
#> aberrant_ERR2585301 2 0.5301 0.57246 0.088 0.688 0.212 0.012 0.000
#> aberrant_ERR2585326 2 0.5872 0.48976 0.084 0.480 0.432 0.000 0.004
#> aberrant_ERR2585331 3 0.4684 0.78080 0.452 0.004 0.536 0.000 0.008
#> aberrant_ERR2585346 4 0.1282 0.70668 0.004 0.044 0.000 0.952 0.000
#> aberrant_ERR2585314 2 0.6554 0.44205 0.192 0.484 0.320 0.004 0.000
#> aberrant_ERR2585298 3 0.4278 0.78908 0.452 0.000 0.548 0.000 0.000
#> aberrant_ERR2585345 2 0.6024 0.48423 0.088 0.472 0.432 0.000 0.008
#> aberrant_ERR2585299 1 0.4564 0.50104 0.748 0.176 0.072 0.004 0.000
#> aberrant_ERR2585309 1 0.4800 -0.05154 0.604 0.368 0.000 0.028 0.000
#> aberrant_ERR2585303 2 0.6990 0.48577 0.096 0.464 0.376 0.000 0.064
#> aberrant_ERR2585313 3 0.7590 -0.02233 0.220 0.316 0.416 0.004 0.044
#> aberrant_ERR2585318 2 0.6569 0.46442 0.100 0.664 0.028 0.064 0.144
#> aberrant_ERR2585328 2 0.7254 0.55895 0.128 0.560 0.156 0.156 0.000
#> aberrant_ERR2585330 2 0.4202 0.41479 0.052 0.808 0.004 0.116 0.020
#> aberrant_ERR2585293 4 0.1121 0.70702 0.000 0.044 0.000 0.956 0.000
#> aberrant_ERR2585342 2 0.2574 0.35317 0.012 0.876 0.000 0.112 0.000
#> aberrant_ERR2585348 2 0.8533 -0.16894 0.008 0.348 0.248 0.136 0.260
#> aberrant_ERR2585352 2 0.8250 0.35518 0.184 0.412 0.284 0.012 0.108
#> aberrant_ERR2585308 1 0.4824 -0.08840 0.596 0.376 0.000 0.028 0.000
#> aberrant_ERR2585349 3 0.6558 0.42783 0.300 0.232 0.468 0.000 0.000
#> aberrant_ERR2585316 4 0.7021 0.02375 0.360 0.100 0.040 0.488 0.012
#> aberrant_ERR2585306 2 0.6526 0.45475 0.200 0.568 0.020 0.212 0.000
#> aberrant_ERR2585324 2 0.1393 0.31255 0.000 0.956 0.024 0.012 0.008
#> aberrant_ERR2585310 2 0.5854 0.48182 0.096 0.468 0.436 0.000 0.000
#> aberrant_ERR2585296 1 0.4576 -0.33116 0.608 0.016 0.376 0.000 0.000
#> aberrant_ERR2585275 4 0.4057 0.55308 0.120 0.088 0.000 0.792 0.000
#> aberrant_ERR2585311 2 0.4064 0.44820 0.216 0.756 0.000 0.024 0.004
#> aberrant_ERR2585292 4 0.1121 0.70702 0.000 0.044 0.000 0.956 0.000
#> aberrant_ERR2585282 2 0.6110 0.33411 0.348 0.540 0.012 0.100 0.000
#> aberrant_ERR2585305 2 0.6547 0.57448 0.140 0.572 0.256 0.032 0.000
#> aberrant_ERR2585278 2 0.5779 0.46228 0.088 0.456 0.456 0.000 0.000
#> aberrant_ERR2585347 4 0.5312 0.22022 0.028 0.388 0.016 0.568 0.000
#> aberrant_ERR2585332 2 0.8969 -0.09399 0.056 0.396 0.228 0.124 0.196
#> aberrant_ERR2585280 2 0.6535 0.56373 0.060 0.632 0.228 0.048 0.032
#> aberrant_ERR2585304 2 0.5737 0.47200 0.084 0.464 0.452 0.000 0.000
#> aberrant_ERR2585322 2 0.7111 0.54532 0.084 0.544 0.268 0.004 0.100
#> aberrant_ERR2585279 3 0.4567 0.78543 0.448 0.004 0.544 0.000 0.004
#> aberrant_ERR2585277 3 0.4425 0.78732 0.452 0.000 0.544 0.000 0.004
#> aberrant_ERR2585295 3 0.6248 -0.25290 0.148 0.384 0.468 0.000 0.000
#> aberrant_ERR2585333 2 0.7071 0.51300 0.088 0.616 0.068 0.188 0.040
#> aberrant_ERR2585285 2 0.5728 0.54490 0.120 0.700 0.144 0.028 0.008
#> aberrant_ERR2585286 3 0.5389 0.74480 0.424 0.024 0.532 0.000 0.020
#> aberrant_ERR2585294 2 0.6738 0.56611 0.096 0.548 0.304 0.048 0.004
#> aberrant_ERR2585300 2 0.7123 0.46979 0.208 0.528 0.052 0.212 0.000
#> aberrant_ERR2585334 3 0.4811 0.77530 0.452 0.000 0.528 0.000 0.020
#> aberrant_ERR2585361 2 0.7256 0.57871 0.088 0.560 0.260 0.060 0.032
#> aberrant_ERR2585372 5 0.9298 -0.10470 0.100 0.224 0.220 0.100 0.356
#> round_ERR2585217 3 0.4434 0.78001 0.460 0.004 0.536 0.000 0.000
#> round_ERR2585205 1 0.0162 0.59156 0.996 0.000 0.004 0.000 0.000
#> round_ERR2585214 3 0.4278 0.78908 0.452 0.000 0.548 0.000 0.000
#> round_ERR2585202 3 0.4278 0.78908 0.452 0.000 0.548 0.000 0.000
#> aberrant_ERR2585367 1 0.8062 -0.47445 0.416 0.104 0.348 0.024 0.108
#> round_ERR2585220 1 0.4553 -0.37403 0.604 0.008 0.384 0.004 0.000
#> round_ERR2585238 1 0.1412 0.59984 0.952 0.036 0.004 0.008 0.000
#> aberrant_ERR2585276 2 0.6715 0.58381 0.088 0.588 0.236 0.088 0.000
#> round_ERR2585218 1 0.1341 0.56657 0.944 0.000 0.056 0.000 0.000
#> aberrant_ERR2585363 2 0.7487 0.51470 0.080 0.532 0.168 0.008 0.212
#> round_ERR2585201 3 0.4278 0.78908 0.452 0.000 0.548 0.000 0.000
#> round_ERR2585210 1 0.0671 0.59938 0.980 0.016 0.000 0.004 0.000
#> aberrant_ERR2585362 2 0.8595 0.52764 0.120 0.476 0.192 0.148 0.064
#> aberrant_ERR2585360 2 0.3339 0.48495 0.084 0.860 0.024 0.032 0.000
#> round_ERR2585209 3 0.4283 0.78504 0.456 0.000 0.544 0.000 0.000
#> round_ERR2585242 3 0.4278 0.78908 0.452 0.000 0.548 0.000 0.000
#> round_ERR2585216 1 0.3563 0.33325 0.780 0.012 0.208 0.000 0.000
#> round_ERR2585219 1 0.1329 0.58344 0.956 0.004 0.032 0.008 0.000
#> round_ERR2585237 3 0.4283 0.78504 0.456 0.000 0.544 0.000 0.000
#> round_ERR2585198 3 0.4278 0.78908 0.452 0.000 0.548 0.000 0.000
#> round_ERR2585211 1 0.0162 0.59248 0.996 0.000 0.000 0.004 0.000
#> round_ERR2585206 1 0.0000 0.59129 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585281 1 0.7038 -0.60051 0.428 0.080 0.412 0.000 0.080
#> round_ERR2585212 1 0.3562 0.35667 0.788 0.016 0.196 0.000 0.000
#> round_ERR2585221 1 0.1386 0.59930 0.952 0.032 0.016 0.000 0.000
#> round_ERR2585243 1 0.4630 -0.06193 0.672 0.008 0.300 0.020 0.000
#> round_ERR2585204 3 0.4278 0.78908 0.452 0.000 0.548 0.000 0.000
#> round_ERR2585213 3 0.4425 0.78716 0.452 0.000 0.544 0.000 0.004
#> aberrant_ERR2585373 2 0.5956 0.37856 0.264 0.616 0.000 0.100 0.020
#> aberrant_ERR2585358 4 0.4835 0.38222 0.000 0.380 0.028 0.592 0.000
#> aberrant_ERR2585365 3 0.7778 0.43094 0.320 0.172 0.416 0.000 0.092
#> aberrant_ERR2585359 4 0.8030 0.41444 0.028 0.108 0.328 0.440 0.096
#> aberrant_ERR2585370 3 0.4567 0.78563 0.448 0.004 0.544 0.000 0.004
#> round_ERR2585215 1 0.1310 0.59751 0.956 0.024 0.000 0.020 0.000
#> round_ERR2585262 3 0.4430 0.78418 0.456 0.004 0.540 0.000 0.000
#> round_ERR2585199 3 0.4278 0.78908 0.452 0.000 0.548 0.000 0.000
#> aberrant_ERR2585369 2 0.5380 0.41449 0.108 0.752 0.024 0.036 0.080
#> round_ERR2585208 1 0.0324 0.59279 0.992 0.004 0.004 0.000 0.000
#> round_ERR2585252 1 0.0880 0.59973 0.968 0.032 0.000 0.000 0.000
#> round_ERR2585236 1 0.4204 0.28045 0.752 0.012 0.216 0.020 0.000
#> aberrant_ERR2585284 4 0.1626 0.70050 0.016 0.044 0.000 0.940 0.000
#> round_ERR2585224 1 0.4666 -0.15603 0.572 0.412 0.000 0.016 0.000
#> round_ERR2585260 1 0.2848 0.45343 0.840 0.004 0.156 0.000 0.000
#> round_ERR2585229 1 0.3774 0.19229 0.704 0.296 0.000 0.000 0.000
#> aberrant_ERR2585364 4 0.2536 0.66730 0.000 0.128 0.004 0.868 0.000
#> round_ERR2585253 1 0.0404 0.59722 0.988 0.012 0.000 0.000 0.000
#> aberrant_ERR2585368 5 0.5420 0.60271 0.000 0.076 0.332 0.000 0.592
#> aberrant_ERR2585371 5 0.5420 0.60312 0.000 0.076 0.332 0.000 0.592
#> round_ERR2585239 1 0.1442 0.58677 0.952 0.012 0.032 0.004 0.000
#> round_ERR2585273 1 0.3671 0.24767 0.756 0.008 0.236 0.000 0.000
#> round_ERR2585256 3 0.4305 0.72869 0.488 0.000 0.512 0.000 0.000
#> round_ERR2585272 3 0.4291 0.77491 0.464 0.000 0.536 0.000 0.000
#> round_ERR2585246 1 0.6144 -0.10309 0.512 0.344 0.144 0.000 0.000
#> round_ERR2585261 3 0.4278 0.78908 0.452 0.000 0.548 0.000 0.000
#> round_ERR2585254 3 0.4283 0.78504 0.456 0.000 0.544 0.000 0.000
#> round_ERR2585225 3 0.4278 0.78908 0.452 0.000 0.548 0.000 0.000
#> round_ERR2585235 1 0.2589 0.59007 0.900 0.044 0.008 0.048 0.000
#> round_ERR2585271 1 0.0579 0.59207 0.984 0.000 0.008 0.008 0.000
#> round_ERR2585251 1 0.4744 -0.45571 0.572 0.020 0.408 0.000 0.000
#> round_ERR2585255 3 0.4278 0.78908 0.452 0.000 0.548 0.000 0.000
#> round_ERR2585257 3 0.5173 0.73618 0.460 0.040 0.500 0.000 0.000
#> round_ERR2585226 1 0.6004 0.26945 0.576 0.256 0.168 0.000 0.000
#> round_ERR2585265 3 0.4735 0.76086 0.460 0.016 0.524 0.000 0.000
#> round_ERR2585259 1 0.4242 -0.50550 0.572 0.000 0.428 0.000 0.000
#> round_ERR2585247 1 0.4355 0.42130 0.760 0.076 0.164 0.000 0.000
#> round_ERR2585241 1 0.1892 0.54837 0.916 0.004 0.080 0.000 0.000
#> round_ERR2585263 1 0.4590 -0.50176 0.568 0.012 0.420 0.000 0.000
#> round_ERR2585264 1 0.4825 -0.09610 0.568 0.024 0.000 0.408 0.000
#> round_ERR2585233 1 0.4306 -0.69255 0.508 0.000 0.492 0.000 0.000
#> round_ERR2585223 1 0.2439 0.49853 0.876 0.004 0.120 0.000 0.000
#> round_ERR2585234 3 0.4278 0.78908 0.452 0.000 0.548 0.000 0.000
#> round_ERR2585222 1 0.3977 0.50433 0.764 0.204 0.032 0.000 0.000
#> round_ERR2585228 1 0.0794 0.58387 0.972 0.000 0.028 0.000 0.000
#> round_ERR2585248 1 0.4505 -0.03466 0.604 0.012 0.000 0.384 0.000
#> round_ERR2585240 3 0.4278 0.78908 0.452 0.000 0.548 0.000 0.000
#> round_ERR2585270 1 0.6572 -0.20579 0.428 0.364 0.208 0.000 0.000
#> round_ERR2585232 3 0.4300 0.75354 0.476 0.000 0.524 0.000 0.000
#> aberrant_ERR2585341 1 0.6997 -0.51233 0.436 0.032 0.380 0.000 0.152
#> aberrant_ERR2585355 3 0.5793 0.63783 0.364 0.100 0.536 0.000 0.000
#> round_ERR2585227 1 0.4242 -0.50824 0.572 0.000 0.428 0.000 0.000
#> aberrant_ERR2585351 2 0.6988 0.55860 0.144 0.520 0.300 0.016 0.020
#> round_ERR2585269 1 0.0880 0.60043 0.968 0.032 0.000 0.000 0.000
#> aberrant_ERR2585357 2 0.7085 0.51444 0.084 0.484 0.344 0.000 0.088
#> aberrant_ERR2585350 3 0.4278 0.78908 0.452 0.000 0.548 0.000 0.000
#> round_ERR2585250 1 0.7013 -0.00457 0.428 0.292 0.268 0.012 0.000
#> round_ERR2585245 1 0.3242 0.38950 0.784 0.000 0.000 0.216 0.000
#> aberrant_ERR2585353 2 0.7700 0.37095 0.104 0.544 0.024 0.204 0.124
#> round_ERR2585258 1 0.3053 0.54687 0.828 0.164 0.008 0.000 0.000
#> aberrant_ERR2585354 3 0.5521 0.72845 0.452 0.040 0.496 0.012 0.000
#> round_ERR2585249 1 0.0880 0.59967 0.968 0.032 0.000 0.000 0.000
#> round_ERR2585268 3 0.6569 0.15921 0.240 0.292 0.468 0.000 0.000
#> aberrant_ERR2585356 2 0.2763 0.31982 0.004 0.848 0.000 0.148 0.000
#> round_ERR2585266 3 0.4278 0.78908 0.452 0.000 0.548 0.000 0.000
#> round_ERR2585231 1 0.1282 0.59792 0.952 0.044 0.000 0.004 0.000
#> round_ERR2585230 1 0.4681 0.32907 0.728 0.084 0.188 0.000 0.000
#> round_ERR2585267 1 0.2054 0.58724 0.920 0.028 0.000 0.052 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 5 0.0000 0.1843 0.000 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585338 3 0.3492 0.6172 0.120 0.076 0.804 0.000 0.000 0.000
#> aberrant_ERR2585325 5 0.0000 0.1843 0.000 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585283 4 0.0000 0.5891 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585343 6 0.6275 0.0860 0.376 0.000 0.000 0.204 0.016 0.404
#> aberrant_ERR2585329 1 0.5784 0.1934 0.544 0.028 0.320 0.000 0.000 0.108
#> aberrant_ERR2585317 1 0.5449 -0.0775 0.536 0.364 0.084 0.000 0.000 0.016
#> aberrant_ERR2585339 3 0.0508 0.7574 0.012 0.004 0.984 0.000 0.000 0.000
#> aberrant_ERR2585335 1 0.6929 -0.2733 0.536 0.020 0.024 0.072 0.076 0.272
#> aberrant_ERR2585287 4 0.2664 0.5002 0.000 0.000 0.000 0.816 0.184 0.000
#> aberrant_ERR2585321 1 0.6683 -0.2437 0.540 0.020 0.004 0.244 0.044 0.148
#> aberrant_ERR2585297 1 0.5182 -0.0586 0.532 0.000 0.372 0.000 0.000 0.096
#> aberrant_ERR2585337 1 0.5625 0.2078 0.500 0.136 0.360 0.000 0.000 0.004
#> aberrant_ERR2585319 6 0.1910 0.5266 0.108 0.000 0.000 0.000 0.000 0.892
#> aberrant_ERR2585315 1 0.5616 0.2184 0.536 0.036 0.372 0.008 0.000 0.048
#> aberrant_ERR2585336 1 0.5565 -0.1974 0.540 0.376 0.024 0.000 0.040 0.020
#> aberrant_ERR2585307 1 0.4439 0.2353 0.540 0.028 0.432 0.000 0.000 0.000
#> aberrant_ERR2585301 1 0.6012 0.0692 0.540 0.000 0.216 0.020 0.000 0.224
#> aberrant_ERR2585326 1 0.4841 0.2216 0.536 0.048 0.412 0.000 0.000 0.004
#> aberrant_ERR2585331 3 0.0363 0.7621 0.000 0.012 0.988 0.000 0.000 0.000
#> aberrant_ERR2585346 4 0.0146 0.5891 0.004 0.000 0.000 0.996 0.000 0.000
#> aberrant_ERR2585314 1 0.5412 0.2189 0.496 0.000 0.384 0.000 0.000 0.120
#> aberrant_ERR2585298 3 0.0000 0.7627 0.000 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585345 1 0.4208 0.2350 0.536 0.008 0.452 0.000 0.000 0.004
#> aberrant_ERR2585299 1 0.4402 -0.1044 0.564 0.000 0.412 0.004 0.000 0.020
#> aberrant_ERR2585309 1 0.4453 0.1231 0.764 0.000 0.064 0.064 0.000 0.108
#> aberrant_ERR2585303 1 0.5592 0.2111 0.492 0.128 0.376 0.000 0.000 0.004
#> aberrant_ERR2585313 3 0.5098 0.1488 0.308 0.044 0.620 0.000 0.020 0.008
#> aberrant_ERR2585318 1 0.6445 -0.2571 0.552 0.004 0.020 0.028 0.252 0.144
#> aberrant_ERR2585328 1 0.6006 0.1023 0.584 0.000 0.172 0.200 0.000 0.044
#> aberrant_ERR2585330 1 0.6002 -0.2542 0.544 0.016 0.004 0.176 0.000 0.260
#> aberrant_ERR2585293 4 0.0000 0.5891 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585342 1 0.5805 -0.2869 0.528 0.008 0.000 0.176 0.000 0.288
#> aberrant_ERR2585348 5 0.6794 0.0693 0.320 0.012 0.028 0.192 0.444 0.004
#> aberrant_ERR2585352 3 0.7137 -0.2685 0.380 0.028 0.412 0.020 0.132 0.028
#> aberrant_ERR2585308 1 0.2822 0.0842 0.852 0.000 0.000 0.040 0.000 0.108
#> aberrant_ERR2585349 3 0.3136 0.4584 0.228 0.000 0.768 0.000 0.000 0.004
#> aberrant_ERR2585316 4 0.6086 0.1484 0.084 0.000 0.304 0.552 0.052 0.008
#> aberrant_ERR2585306 1 0.5012 -0.0970 0.652 0.000 0.028 0.260 0.000 0.060
#> aberrant_ERR2585324 6 0.1910 0.5266 0.108 0.000 0.000 0.000 0.000 0.892
#> aberrant_ERR2585310 1 0.3986 0.2421 0.532 0.000 0.464 0.000 0.000 0.004
#> aberrant_ERR2585296 3 0.2778 0.6749 0.168 0.000 0.824 0.000 0.000 0.008
#> aberrant_ERR2585275 4 0.3241 0.4681 0.044 0.000 0.108 0.836 0.000 0.012
#> aberrant_ERR2585311 1 0.4431 -0.1587 0.688 0.000 0.000 0.060 0.004 0.248
#> aberrant_ERR2585292 4 0.0000 0.5891 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585282 1 0.5714 -0.0365 0.644 0.000 0.072 0.120 0.000 0.164
#> aberrant_ERR2585305 1 0.5759 0.2124 0.596 0.000 0.264 0.076 0.000 0.064
#> aberrant_ERR2585278 1 0.3864 0.2353 0.520 0.000 0.480 0.000 0.000 0.000
#> aberrant_ERR2585347 4 0.4032 -0.0651 0.420 0.000 0.000 0.572 0.008 0.000
#> aberrant_ERR2585332 5 0.7889 0.0463 0.308 0.000 0.088 0.112 0.396 0.096
#> aberrant_ERR2585280 1 0.6919 0.1222 0.540 0.000 0.228 0.036 0.076 0.120
#> aberrant_ERR2585304 1 0.3854 0.2324 0.536 0.000 0.464 0.000 0.000 0.000
#> aberrant_ERR2585322 1 0.6215 0.1084 0.536 0.220 0.208 0.000 0.000 0.036
#> aberrant_ERR2585279 3 0.0291 0.7614 0.004 0.004 0.992 0.000 0.000 0.000
#> aberrant_ERR2585277 3 0.0146 0.7628 0.000 0.004 0.996 0.000 0.000 0.000
#> aberrant_ERR2585295 3 0.4167 0.0227 0.368 0.000 0.612 0.000 0.000 0.020
#> aberrant_ERR2585333 1 0.6894 -0.0973 0.536 0.000 0.084 0.252 0.044 0.084
#> aberrant_ERR2585285 1 0.7239 0.0103 0.484 0.000 0.200 0.068 0.032 0.216
#> aberrant_ERR2585286 3 0.1334 0.7411 0.020 0.032 0.948 0.000 0.000 0.000
#> aberrant_ERR2585294 1 0.5876 0.2161 0.536 0.004 0.332 0.100 0.000 0.028
#> aberrant_ERR2585300 1 0.4854 -0.0227 0.660 0.004 0.060 0.264 0.000 0.012
#> aberrant_ERR2585334 3 0.0632 0.7594 0.000 0.024 0.976 0.000 0.000 0.000
#> aberrant_ERR2585361 1 0.7111 0.0906 0.540 0.132 0.192 0.108 0.008 0.020
#> aberrant_ERR2585372 5 0.6175 0.2954 0.172 0.000 0.088 0.108 0.620 0.012
#> round_ERR2585217 3 0.0291 0.7629 0.004 0.000 0.992 0.000 0.000 0.004
#> round_ERR2585205 1 0.5329 -0.1723 0.452 0.000 0.444 0.000 0.000 0.104
#> round_ERR2585214 3 0.0000 0.7627 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585202 3 0.0000 0.7627 0.000 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585367 3 0.5182 0.5818 0.052 0.152 0.728 0.024 0.024 0.020
#> round_ERR2585220 3 0.2669 0.6809 0.156 0.000 0.836 0.008 0.000 0.000
#> round_ERR2585238 1 0.5784 -0.1355 0.452 0.000 0.424 0.020 0.000 0.104
#> aberrant_ERR2585276 1 0.6335 0.1555 0.544 0.000 0.248 0.140 0.000 0.068
#> round_ERR2585218 3 0.4981 0.2345 0.436 0.000 0.496 0.000 0.000 0.068
#> aberrant_ERR2585363 1 0.5551 -0.1916 0.536 0.376 0.028 0.000 0.052 0.008
#> round_ERR2585201 3 0.0000 0.7627 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585210 1 0.5453 -0.1459 0.464 0.000 0.428 0.004 0.000 0.104
#> aberrant_ERR2585362 1 0.6866 0.0820 0.524 0.000 0.192 0.176 0.100 0.008
#> aberrant_ERR2585360 1 0.5082 -0.2751 0.540 0.004 0.024 0.028 0.000 0.404
#> round_ERR2585209 3 0.0146 0.7628 0.004 0.000 0.996 0.000 0.000 0.000
#> round_ERR2585242 3 0.0000 0.7627 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585216 3 0.4499 0.4835 0.288 0.000 0.652 0.000 0.000 0.060
#> round_ERR2585219 3 0.5373 0.2011 0.432 0.000 0.476 0.008 0.000 0.084
#> round_ERR2585237 3 0.0146 0.7628 0.004 0.000 0.996 0.000 0.000 0.000
#> round_ERR2585198 3 0.0000 0.7627 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585211 1 0.5486 -0.1485 0.460 0.000 0.428 0.004 0.000 0.108
#> round_ERR2585206 1 0.5359 -0.1536 0.460 0.000 0.432 0.000 0.000 0.108
#> aberrant_ERR2585281 3 0.3229 0.6757 0.048 0.120 0.828 0.000 0.000 0.004
#> round_ERR2585212 3 0.4435 0.4716 0.308 0.000 0.648 0.004 0.000 0.040
#> round_ERR2585221 1 0.5315 -0.1591 0.472 0.000 0.436 0.004 0.000 0.088
#> round_ERR2585243 3 0.3708 0.6066 0.220 0.000 0.752 0.020 0.000 0.008
#> round_ERR2585204 3 0.0000 0.7627 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585213 3 0.0146 0.7626 0.000 0.004 0.996 0.000 0.000 0.000
#> aberrant_ERR2585373 1 0.4164 -0.0631 0.764 0.000 0.000 0.152 0.020 0.064
#> aberrant_ERR2585358 4 0.5144 0.1547 0.264 0.000 0.000 0.632 0.016 0.088
#> aberrant_ERR2585365 3 0.4478 0.4915 0.148 0.116 0.728 0.000 0.000 0.008
#> aberrant_ERR2585359 4 0.5237 0.1016 0.072 0.000 0.000 0.504 0.416 0.008
#> aberrant_ERR2585370 3 0.0291 0.7612 0.004 0.004 0.992 0.000 0.000 0.000
#> round_ERR2585215 1 0.6006 -0.1293 0.440 0.000 0.420 0.032 0.000 0.108
#> round_ERR2585262 3 0.0260 0.7626 0.008 0.000 0.992 0.000 0.000 0.000
#> round_ERR2585199 3 0.0000 0.7627 0.000 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585369 1 0.7361 -0.3486 0.448 0.004 0.048 0.076 0.100 0.324
#> round_ERR2585208 1 0.5457 -0.1710 0.448 0.000 0.444 0.004 0.000 0.104
#> round_ERR2585252 1 0.5343 -0.1160 0.484 0.000 0.408 0.000 0.000 0.108
#> round_ERR2585236 3 0.4703 0.5064 0.268 0.000 0.668 0.032 0.000 0.032
#> aberrant_ERR2585284 4 0.0458 0.5843 0.016 0.000 0.000 0.984 0.000 0.000
#> round_ERR2585224 1 0.2279 0.1056 0.904 0.000 0.016 0.024 0.000 0.056
#> round_ERR2585260 3 0.4252 0.4055 0.372 0.000 0.604 0.000 0.000 0.024
#> round_ERR2585229 1 0.3701 0.1755 0.788 0.000 0.112 0.000 0.000 0.100
#> aberrant_ERR2585364 4 0.1918 0.5219 0.088 0.000 0.000 0.904 0.000 0.008
#> round_ERR2585253 1 0.5353 -0.1340 0.472 0.000 0.420 0.000 0.000 0.108
#> aberrant_ERR2585368 2 0.0000 1.0000 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585371 2 0.0000 1.0000 0.000 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585239 3 0.5297 0.1990 0.440 0.000 0.476 0.008 0.000 0.076
#> round_ERR2585273 3 0.3835 0.5208 0.300 0.000 0.684 0.000 0.000 0.016
#> round_ERR2585256 3 0.0935 0.7560 0.032 0.000 0.964 0.000 0.000 0.004
#> round_ERR2585272 3 0.0405 0.7627 0.008 0.000 0.988 0.000 0.000 0.004
#> round_ERR2585246 1 0.4110 0.2202 0.712 0.000 0.236 0.000 0.000 0.052
#> round_ERR2585261 3 0.0000 0.7627 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585254 3 0.0146 0.7628 0.004 0.000 0.996 0.000 0.000 0.000
#> round_ERR2585225 3 0.0000 0.7627 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585235 1 0.6008 -0.1030 0.464 0.000 0.400 0.040 0.000 0.096
#> round_ERR2585271 3 0.5520 0.1541 0.444 0.000 0.448 0.008 0.000 0.100
#> round_ERR2585251 3 0.2442 0.6976 0.144 0.000 0.852 0.000 0.000 0.004
#> round_ERR2585255 3 0.0000 0.7627 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585257 3 0.1075 0.7433 0.048 0.000 0.952 0.000 0.000 0.000
#> round_ERR2585226 1 0.3979 -0.1308 0.540 0.000 0.456 0.000 0.000 0.004
#> round_ERR2585265 3 0.0790 0.7586 0.032 0.000 0.968 0.000 0.000 0.000
#> round_ERR2585259 3 0.2146 0.7115 0.116 0.000 0.880 0.000 0.000 0.004
#> round_ERR2585247 3 0.4630 0.3799 0.372 0.000 0.580 0.000 0.000 0.048
#> round_ERR2585241 3 0.5071 0.2713 0.400 0.000 0.520 0.000 0.000 0.080
#> round_ERR2585263 3 0.2212 0.7137 0.112 0.000 0.880 0.000 0.000 0.008
#> round_ERR2585264 4 0.6740 0.0963 0.372 0.000 0.116 0.416 0.000 0.096
#> round_ERR2585233 3 0.1204 0.7488 0.056 0.000 0.944 0.000 0.000 0.000
#> round_ERR2585223 3 0.4508 0.3509 0.396 0.000 0.568 0.000 0.000 0.036
#> round_ERR2585234 3 0.0000 0.7627 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585222 1 0.3954 0.0150 0.636 0.000 0.352 0.000 0.000 0.012
#> round_ERR2585228 3 0.5189 0.1877 0.444 0.000 0.468 0.000 0.000 0.088
#> round_ERR2585248 1 0.6620 -0.1961 0.428 0.000 0.096 0.376 0.000 0.100
#> round_ERR2585240 3 0.0000 0.7627 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585270 1 0.3742 0.2374 0.648 0.000 0.348 0.000 0.000 0.004
#> round_ERR2585232 3 0.0632 0.7601 0.024 0.000 0.976 0.000 0.000 0.000
#> aberrant_ERR2585341 3 0.3871 0.5193 0.000 0.308 0.676 0.000 0.016 0.000
#> aberrant_ERR2585355 3 0.1958 0.6606 0.100 0.004 0.896 0.000 0.000 0.000
#> round_ERR2585227 3 0.2146 0.7136 0.116 0.000 0.880 0.000 0.000 0.004
#> aberrant_ERR2585351 1 0.5406 0.2418 0.596 0.028 0.324 0.036 0.004 0.012
#> round_ERR2585269 1 0.5472 -0.1151 0.480 0.000 0.408 0.004 0.000 0.108
#> aberrant_ERR2585357 1 0.5421 0.2043 0.536 0.136 0.328 0.000 0.000 0.000
#> aberrant_ERR2585350 3 0.0000 0.7627 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585250 3 0.4767 0.1268 0.444 0.000 0.512 0.040 0.000 0.004
#> round_ERR2585245 1 0.7042 0.0590 0.456 0.000 0.232 0.204 0.000 0.108
#> aberrant_ERR2585353 1 0.6810 -0.2293 0.568 0.108 0.000 0.196 0.092 0.036
#> round_ERR2585258 1 0.4506 -0.0069 0.608 0.000 0.348 0.000 0.000 0.044
#> aberrant_ERR2585354 3 0.1065 0.7549 0.008 0.000 0.964 0.020 0.000 0.008
#> round_ERR2585249 1 0.5347 -0.1215 0.480 0.000 0.412 0.000 0.000 0.108
#> round_ERR2585268 3 0.3482 0.2436 0.316 0.000 0.684 0.000 0.000 0.000
#> aberrant_ERR2585356 1 0.5680 -0.3114 0.516 0.000 0.000 0.192 0.000 0.292
#> round_ERR2585266 3 0.0000 0.7627 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585231 1 0.5330 -0.0985 0.496 0.000 0.396 0.000 0.000 0.108
#> round_ERR2585230 3 0.3695 0.4587 0.376 0.000 0.624 0.000 0.000 0.000
#> round_ERR2585267 1 0.6057 -0.0801 0.476 0.000 0.380 0.040 0.000 0.104
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> CV:pam 143 9.46e-02 2
#> CV:pam 102 4.63e-15 3
#> CV:pam 41 2.04e-07 4
#> CV:pam 90 2.76e-10 5
#> CV:pam 60 9.11e-04 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "mclust"]
# you can also extract it by
# res = res_list["CV:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.988 0.995 0.5034 0.497 0.497
#> 3 3 0.810 0.878 0.922 0.2310 0.894 0.786
#> 4 4 0.725 0.789 0.886 0.1598 0.870 0.670
#> 5 5 0.734 0.605 0.837 0.0480 0.940 0.797
#> 6 6 0.665 0.624 0.774 0.0366 0.937 0.779
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585338 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585325 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585283 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585343 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585329 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585317 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585339 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585335 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585287 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585321 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585297 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585319 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585315 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585336 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585307 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585301 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585326 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585331 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585346 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585314 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585298 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585345 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585299 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585309 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585303 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585313 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585318 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585328 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585330 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585293 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585342 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585348 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585352 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585308 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585349 2 0.8207 0.666 0.256 0.744
#> aberrant_ERR2585316 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585306 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585324 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585310 2 0.6343 0.813 0.160 0.840
#> aberrant_ERR2585296 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585275 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585311 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585292 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585282 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585305 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585278 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585347 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585332 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585280 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585304 2 0.8813 0.585 0.300 0.700
#> aberrant_ERR2585322 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585279 2 0.4431 0.897 0.092 0.908
#> aberrant_ERR2585277 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585295 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585333 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585285 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585286 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585294 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585300 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585334 2 0.2603 0.948 0.044 0.956
#> aberrant_ERR2585361 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585372 2 0.0000 0.989 0.000 1.000
#> round_ERR2585217 1 0.0000 1.000 1.000 0.000
#> round_ERR2585205 1 0.0000 1.000 1.000 0.000
#> round_ERR2585214 1 0.0000 1.000 1.000 0.000
#> round_ERR2585202 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585367 2 0.0000 0.989 0.000 1.000
#> round_ERR2585220 1 0.0000 1.000 1.000 0.000
#> round_ERR2585238 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585276 2 0.0000 0.989 0.000 1.000
#> round_ERR2585218 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.989 0.000 1.000
#> round_ERR2585201 1 0.0000 1.000 1.000 0.000
#> round_ERR2585210 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585362 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585360 2 0.0000 0.989 0.000 1.000
#> round_ERR2585209 1 0.0000 1.000 1.000 0.000
#> round_ERR2585242 1 0.0000 1.000 1.000 0.000
#> round_ERR2585216 1 0.0000 1.000 1.000 0.000
#> round_ERR2585219 1 0.0000 1.000 1.000 0.000
#> round_ERR2585237 1 0.0000 1.000 1.000 0.000
#> round_ERR2585198 1 0.0000 1.000 1.000 0.000
#> round_ERR2585211 1 0.0000 1.000 1.000 0.000
#> round_ERR2585206 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585281 2 0.0000 0.989 0.000 1.000
#> round_ERR2585212 1 0.0000 1.000 1.000 0.000
#> round_ERR2585221 1 0.0000 1.000 1.000 0.000
#> round_ERR2585243 1 0.0000 1.000 1.000 0.000
#> round_ERR2585204 1 0.0000 1.000 1.000 0.000
#> round_ERR2585213 1 0.1414 0.979 0.980 0.020
#> aberrant_ERR2585373 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585358 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585365 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585359 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585370 2 0.0000 0.989 0.000 1.000
#> round_ERR2585215 1 0.0000 1.000 1.000 0.000
#> round_ERR2585262 1 0.0000 1.000 1.000 0.000
#> round_ERR2585199 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585369 2 0.0000 0.989 0.000 1.000
#> round_ERR2585208 1 0.0000 1.000 1.000 0.000
#> round_ERR2585252 1 0.0000 1.000 1.000 0.000
#> round_ERR2585236 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585284 2 0.0376 0.986 0.004 0.996
#> round_ERR2585224 1 0.0000 1.000 1.000 0.000
#> round_ERR2585260 1 0.0000 1.000 1.000 0.000
#> round_ERR2585229 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585364 2 0.0000 0.989 0.000 1.000
#> round_ERR2585253 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585371 2 0.0000 0.989 0.000 1.000
#> round_ERR2585239 1 0.0000 1.000 1.000 0.000
#> round_ERR2585273 1 0.0000 1.000 1.000 0.000
#> round_ERR2585256 1 0.0000 1.000 1.000 0.000
#> round_ERR2585272 1 0.0000 1.000 1.000 0.000
#> round_ERR2585246 1 0.0000 1.000 1.000 0.000
#> round_ERR2585261 1 0.0000 1.000 1.000 0.000
#> round_ERR2585254 1 0.0000 1.000 1.000 0.000
#> round_ERR2585225 1 0.0000 1.000 1.000 0.000
#> round_ERR2585235 1 0.0000 1.000 1.000 0.000
#> round_ERR2585271 1 0.0000 1.000 1.000 0.000
#> round_ERR2585251 1 0.0000 1.000 1.000 0.000
#> round_ERR2585255 1 0.0000 1.000 1.000 0.000
#> round_ERR2585257 1 0.0000 1.000 1.000 0.000
#> round_ERR2585226 1 0.0000 1.000 1.000 0.000
#> round_ERR2585265 1 0.0000 1.000 1.000 0.000
#> round_ERR2585259 1 0.0000 1.000 1.000 0.000
#> round_ERR2585247 1 0.0000 1.000 1.000 0.000
#> round_ERR2585241 1 0.0000 1.000 1.000 0.000
#> round_ERR2585263 1 0.0000 1.000 1.000 0.000
#> round_ERR2585264 1 0.0000 1.000 1.000 0.000
#> round_ERR2585233 1 0.0000 1.000 1.000 0.000
#> round_ERR2585223 1 0.0000 1.000 1.000 0.000
#> round_ERR2585234 1 0.0000 1.000 1.000 0.000
#> round_ERR2585222 1 0.0000 1.000 1.000 0.000
#> round_ERR2585228 1 0.0000 1.000 1.000 0.000
#> round_ERR2585248 1 0.0000 1.000 1.000 0.000
#> round_ERR2585240 1 0.0000 1.000 1.000 0.000
#> round_ERR2585270 1 0.0000 1.000 1.000 0.000
#> round_ERR2585232 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585341 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585355 2 0.0000 0.989 0.000 1.000
#> round_ERR2585227 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585351 2 0.0000 0.989 0.000 1.000
#> round_ERR2585269 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585350 2 0.0000 0.989 0.000 1.000
#> round_ERR2585250 1 0.0000 1.000 1.000 0.000
#> round_ERR2585245 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.989 0.000 1.000
#> round_ERR2585258 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.989 0.000 1.000
#> round_ERR2585249 1 0.0000 1.000 1.000 0.000
#> round_ERR2585268 1 0.0000 1.000 1.000 0.000
#> aberrant_ERR2585356 2 0.0000 0.989 0.000 1.000
#> round_ERR2585266 1 0.0000 1.000 1.000 0.000
#> round_ERR2585231 1 0.0000 1.000 1.000 0.000
#> round_ERR2585230 1 0.0000 1.000 1.000 0.000
#> round_ERR2585267 1 0.0000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.1163 0.931 0.000 0.972 0.028
#> aberrant_ERR2585338 2 0.3116 0.906 0.000 0.892 0.108
#> aberrant_ERR2585325 2 0.1163 0.931 0.000 0.972 0.028
#> aberrant_ERR2585283 2 0.1643 0.925 0.000 0.956 0.044
#> aberrant_ERR2585343 2 0.0237 0.933 0.000 0.996 0.004
#> aberrant_ERR2585329 2 0.1031 0.933 0.000 0.976 0.024
#> aberrant_ERR2585317 2 0.0892 0.934 0.000 0.980 0.020
#> aberrant_ERR2585339 2 0.2796 0.914 0.000 0.908 0.092
#> aberrant_ERR2585335 2 0.0892 0.932 0.000 0.980 0.020
#> aberrant_ERR2585287 2 0.2165 0.928 0.000 0.936 0.064
#> aberrant_ERR2585321 2 0.0592 0.932 0.000 0.988 0.012
#> aberrant_ERR2585297 1 0.0000 0.954 1.000 0.000 0.000
#> aberrant_ERR2585337 2 0.2625 0.923 0.000 0.916 0.084
#> aberrant_ERR2585319 2 0.0424 0.934 0.000 0.992 0.008
#> aberrant_ERR2585315 2 0.1031 0.934 0.000 0.976 0.024
#> aberrant_ERR2585336 2 0.2448 0.924 0.000 0.924 0.076
#> aberrant_ERR2585307 2 0.3412 0.910 0.000 0.876 0.124
#> aberrant_ERR2585301 2 0.1289 0.931 0.000 0.968 0.032
#> aberrant_ERR2585326 2 0.2878 0.915 0.000 0.904 0.096
#> aberrant_ERR2585331 2 0.4235 0.853 0.000 0.824 0.176
#> aberrant_ERR2585346 2 0.1753 0.924 0.000 0.952 0.048
#> aberrant_ERR2585314 2 0.2537 0.919 0.000 0.920 0.080
#> aberrant_ERR2585298 3 0.2625 0.906 0.084 0.000 0.916
#> aberrant_ERR2585345 2 0.1860 0.931 0.000 0.948 0.052
#> aberrant_ERR2585299 1 0.0237 0.951 0.996 0.000 0.004
#> aberrant_ERR2585309 1 0.0000 0.954 1.000 0.000 0.000
#> aberrant_ERR2585303 2 0.2959 0.908 0.000 0.900 0.100
#> aberrant_ERR2585313 2 0.2261 0.927 0.000 0.932 0.068
#> aberrant_ERR2585318 2 0.0892 0.932 0.000 0.980 0.020
#> aberrant_ERR2585328 2 0.2711 0.924 0.000 0.912 0.088
#> aberrant_ERR2585330 2 0.0747 0.932 0.000 0.984 0.016
#> aberrant_ERR2585293 2 0.1964 0.921 0.000 0.944 0.056
#> aberrant_ERR2585342 2 0.0592 0.933 0.000 0.988 0.012
#> aberrant_ERR2585348 2 0.3340 0.909 0.000 0.880 0.120
#> aberrant_ERR2585352 2 0.0747 0.933 0.000 0.984 0.016
#> aberrant_ERR2585308 1 0.0000 0.954 1.000 0.000 0.000
#> aberrant_ERR2585349 2 0.7043 0.328 0.020 0.532 0.448
#> aberrant_ERR2585316 2 0.1289 0.929 0.000 0.968 0.032
#> aberrant_ERR2585306 2 0.1163 0.932 0.000 0.972 0.028
#> aberrant_ERR2585324 2 0.0424 0.934 0.000 0.992 0.008
#> aberrant_ERR2585310 2 0.7410 0.449 0.040 0.576 0.384
#> aberrant_ERR2585296 3 0.6295 0.333 0.472 0.000 0.528
#> aberrant_ERR2585275 2 0.1289 0.929 0.000 0.968 0.032
#> aberrant_ERR2585311 2 0.0892 0.932 0.000 0.980 0.020
#> aberrant_ERR2585292 2 0.1964 0.921 0.000 0.944 0.056
#> aberrant_ERR2585282 2 0.1031 0.931 0.000 0.976 0.024
#> aberrant_ERR2585305 2 0.3272 0.895 0.004 0.892 0.104
#> aberrant_ERR2585278 2 0.1031 0.932 0.000 0.976 0.024
#> aberrant_ERR2585347 2 0.1163 0.929 0.000 0.972 0.028
#> aberrant_ERR2585332 2 0.0892 0.930 0.000 0.980 0.020
#> aberrant_ERR2585280 2 0.0237 0.934 0.000 0.996 0.004
#> aberrant_ERR2585304 2 0.7188 0.249 0.024 0.492 0.484
#> aberrant_ERR2585322 2 0.2066 0.927 0.000 0.940 0.060
#> aberrant_ERR2585279 2 0.6260 0.384 0.000 0.552 0.448
#> aberrant_ERR2585277 2 0.2796 0.914 0.000 0.908 0.092
#> aberrant_ERR2585295 2 0.3116 0.914 0.000 0.892 0.108
#> aberrant_ERR2585333 2 0.0892 0.932 0.000 0.980 0.020
#> aberrant_ERR2585285 2 0.0424 0.933 0.000 0.992 0.008
#> aberrant_ERR2585286 2 0.3192 0.906 0.000 0.888 0.112
#> aberrant_ERR2585294 2 0.1289 0.931 0.000 0.968 0.032
#> aberrant_ERR2585300 2 0.0892 0.932 0.000 0.980 0.020
#> aberrant_ERR2585334 2 0.5785 0.647 0.000 0.668 0.332
#> aberrant_ERR2585361 2 0.2959 0.914 0.000 0.900 0.100
#> aberrant_ERR2585372 2 0.0000 0.933 0.000 1.000 0.000
#> round_ERR2585217 3 0.3116 0.903 0.108 0.000 0.892
#> round_ERR2585205 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585214 3 0.2625 0.906 0.084 0.000 0.916
#> round_ERR2585202 3 0.3272 0.904 0.104 0.004 0.892
#> aberrant_ERR2585367 2 0.3116 0.909 0.000 0.892 0.108
#> round_ERR2585220 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585238 1 0.0000 0.954 1.000 0.000 0.000
#> aberrant_ERR2585276 2 0.0892 0.932 0.000 0.980 0.020
#> round_ERR2585218 1 0.0000 0.954 1.000 0.000 0.000
#> aberrant_ERR2585363 2 0.2625 0.921 0.000 0.916 0.084
#> round_ERR2585201 3 0.2625 0.906 0.084 0.000 0.916
#> round_ERR2585210 1 0.0000 0.954 1.000 0.000 0.000
#> aberrant_ERR2585362 2 0.1753 0.931 0.000 0.952 0.048
#> aberrant_ERR2585360 2 0.0892 0.933 0.000 0.980 0.020
#> round_ERR2585209 3 0.5968 0.612 0.364 0.000 0.636
#> round_ERR2585242 3 0.2625 0.906 0.084 0.000 0.916
#> round_ERR2585216 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585219 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585237 3 0.3192 0.901 0.112 0.000 0.888
#> round_ERR2585198 3 0.2711 0.906 0.088 0.000 0.912
#> round_ERR2585211 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.954 1.000 0.000 0.000
#> aberrant_ERR2585281 2 0.3192 0.908 0.000 0.888 0.112
#> round_ERR2585212 1 0.2878 0.850 0.904 0.000 0.096
#> round_ERR2585221 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585243 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585204 3 0.2625 0.906 0.084 0.000 0.916
#> round_ERR2585213 3 0.2772 0.902 0.080 0.004 0.916
#> aberrant_ERR2585373 2 0.0892 0.932 0.000 0.980 0.020
#> aberrant_ERR2585358 2 0.0747 0.931 0.000 0.984 0.016
#> aberrant_ERR2585365 2 0.2711 0.914 0.000 0.912 0.088
#> aberrant_ERR2585359 2 0.1031 0.930 0.000 0.976 0.024
#> aberrant_ERR2585370 2 0.2959 0.913 0.000 0.900 0.100
#> round_ERR2585215 1 0.3752 0.782 0.856 0.000 0.144
#> round_ERR2585262 3 0.2537 0.903 0.080 0.000 0.920
#> round_ERR2585199 3 0.3272 0.903 0.104 0.004 0.892
#> aberrant_ERR2585369 2 0.0237 0.933 0.000 0.996 0.004
#> round_ERR2585208 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585236 3 0.6168 0.502 0.412 0.000 0.588
#> aberrant_ERR2585284 2 0.3116 0.915 0.000 0.892 0.108
#> round_ERR2585224 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585260 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585229 1 0.0000 0.954 1.000 0.000 0.000
#> aberrant_ERR2585364 2 0.1031 0.930 0.000 0.976 0.024
#> round_ERR2585253 1 0.0000 0.954 1.000 0.000 0.000
#> aberrant_ERR2585368 2 0.3267 0.906 0.000 0.884 0.116
#> aberrant_ERR2585371 2 0.3267 0.906 0.000 0.884 0.116
#> round_ERR2585239 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585273 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585256 1 0.6302 -0.210 0.520 0.000 0.480
#> round_ERR2585272 1 0.4887 0.633 0.772 0.000 0.228
#> round_ERR2585246 1 0.0237 0.951 0.996 0.000 0.004
#> round_ERR2585261 3 0.3619 0.887 0.136 0.000 0.864
#> round_ERR2585254 1 0.5678 0.415 0.684 0.000 0.316
#> round_ERR2585225 3 0.2625 0.906 0.084 0.000 0.916
#> round_ERR2585235 3 0.5835 0.648 0.340 0.000 0.660
#> round_ERR2585271 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585251 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585255 3 0.2625 0.906 0.084 0.000 0.916
#> round_ERR2585257 3 0.3412 0.895 0.124 0.000 0.876
#> round_ERR2585226 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585265 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585259 3 0.5363 0.737 0.276 0.000 0.724
#> round_ERR2585247 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585241 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585263 1 0.2959 0.846 0.900 0.000 0.100
#> round_ERR2585264 1 0.0747 0.941 0.984 0.000 0.016
#> round_ERR2585233 3 0.2796 0.907 0.092 0.000 0.908
#> round_ERR2585223 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585234 3 0.2796 0.906 0.092 0.000 0.908
#> round_ERR2585222 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585228 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585248 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585240 3 0.3192 0.901 0.112 0.000 0.888
#> round_ERR2585270 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585232 3 0.6180 0.496 0.416 0.000 0.584
#> aberrant_ERR2585341 2 0.3412 0.904 0.000 0.876 0.124
#> aberrant_ERR2585355 2 0.3116 0.906 0.000 0.892 0.108
#> round_ERR2585227 1 0.0592 0.943 0.988 0.000 0.012
#> aberrant_ERR2585351 2 0.1031 0.934 0.000 0.976 0.024
#> round_ERR2585269 1 0.0000 0.954 1.000 0.000 0.000
#> aberrant_ERR2585357 2 0.2959 0.913 0.000 0.900 0.100
#> aberrant_ERR2585350 2 0.2878 0.912 0.000 0.904 0.096
#> round_ERR2585250 1 0.0592 0.944 0.988 0.000 0.012
#> round_ERR2585245 1 0.0000 0.954 1.000 0.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.933 0.000 1.000 0.000
#> round_ERR2585258 1 0.0000 0.954 1.000 0.000 0.000
#> aberrant_ERR2585354 2 0.0747 0.935 0.000 0.984 0.016
#> round_ERR2585249 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585268 1 0.6299 -0.191 0.524 0.000 0.476
#> aberrant_ERR2585356 2 0.0892 0.932 0.000 0.980 0.020
#> round_ERR2585266 3 0.2625 0.906 0.084 0.000 0.916
#> round_ERR2585231 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585230 1 0.0000 0.954 1.000 0.000 0.000
#> round_ERR2585267 1 0.1031 0.933 0.976 0.000 0.024
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 4 0.4996 0.3163 0.000 0.484 0.000 0.516
#> aberrant_ERR2585338 4 0.3249 0.7815 0.000 0.140 0.008 0.852
#> aberrant_ERR2585325 4 0.4996 0.3220 0.000 0.484 0.000 0.516
#> aberrant_ERR2585283 4 0.4989 0.2585 0.000 0.472 0.000 0.528
#> aberrant_ERR2585343 2 0.1211 0.8334 0.000 0.960 0.000 0.040
#> aberrant_ERR2585329 2 0.2011 0.8240 0.000 0.920 0.000 0.080
#> aberrant_ERR2585317 2 0.1867 0.8301 0.000 0.928 0.000 0.072
#> aberrant_ERR2585339 4 0.5050 0.4277 0.000 0.408 0.004 0.588
#> aberrant_ERR2585335 2 0.0336 0.8369 0.000 0.992 0.000 0.008
#> aberrant_ERR2585287 4 0.4040 0.7216 0.000 0.248 0.000 0.752
#> aberrant_ERR2585321 2 0.0336 0.8373 0.000 0.992 0.000 0.008
#> aberrant_ERR2585297 1 0.0000 0.9621 1.000 0.000 0.000 0.000
#> aberrant_ERR2585337 2 0.3945 0.6932 0.000 0.780 0.004 0.216
#> aberrant_ERR2585319 2 0.1302 0.8401 0.000 0.956 0.000 0.044
#> aberrant_ERR2585315 2 0.1211 0.8392 0.000 0.960 0.000 0.040
#> aberrant_ERR2585336 2 0.4401 0.6111 0.000 0.724 0.004 0.272
#> aberrant_ERR2585307 2 0.4576 0.6288 0.000 0.728 0.012 0.260
#> aberrant_ERR2585301 2 0.1022 0.8397 0.000 0.968 0.000 0.032
#> aberrant_ERR2585326 2 0.4188 0.6461 0.000 0.752 0.004 0.244
#> aberrant_ERR2585331 4 0.3899 0.7630 0.000 0.108 0.052 0.840
#> aberrant_ERR2585346 4 0.4898 0.3707 0.000 0.416 0.000 0.584
#> aberrant_ERR2585314 2 0.2647 0.8035 0.000 0.880 0.000 0.120
#> aberrant_ERR2585298 3 0.0336 0.8904 0.000 0.000 0.992 0.008
#> aberrant_ERR2585345 2 0.3257 0.7679 0.000 0.844 0.004 0.152
#> aberrant_ERR2585299 1 0.0188 0.9607 0.996 0.000 0.004 0.000
#> aberrant_ERR2585309 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> aberrant_ERR2585303 4 0.3196 0.7821 0.000 0.136 0.008 0.856
#> aberrant_ERR2585313 2 0.4155 0.6636 0.000 0.756 0.004 0.240
#> aberrant_ERR2585318 2 0.0000 0.8342 0.000 1.000 0.000 0.000
#> aberrant_ERR2585328 4 0.3791 0.7494 0.000 0.200 0.004 0.796
#> aberrant_ERR2585330 2 0.0592 0.8386 0.000 0.984 0.000 0.016
#> aberrant_ERR2585293 4 0.3172 0.6985 0.000 0.160 0.000 0.840
#> aberrant_ERR2585342 2 0.0592 0.8386 0.000 0.984 0.000 0.016
#> aberrant_ERR2585348 4 0.2737 0.7779 0.000 0.104 0.008 0.888
#> aberrant_ERR2585352 2 0.1022 0.8401 0.000 0.968 0.000 0.032
#> aberrant_ERR2585308 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> aberrant_ERR2585349 4 0.7636 0.5530 0.012 0.188 0.272 0.528
#> aberrant_ERR2585316 2 0.4981 -0.1305 0.000 0.536 0.000 0.464
#> aberrant_ERR2585306 2 0.2216 0.8106 0.000 0.908 0.000 0.092
#> aberrant_ERR2585324 2 0.1389 0.8393 0.000 0.952 0.000 0.048
#> aberrant_ERR2585310 2 0.6634 0.5755 0.096 0.708 0.076 0.120
#> aberrant_ERR2585296 1 0.4817 0.3076 0.612 0.000 0.388 0.000
#> aberrant_ERR2585275 2 0.3219 0.7465 0.000 0.836 0.000 0.164
#> aberrant_ERR2585311 2 0.0188 0.8358 0.000 0.996 0.000 0.004
#> aberrant_ERR2585292 4 0.3172 0.6985 0.000 0.160 0.000 0.840
#> aberrant_ERR2585282 2 0.3610 0.7056 0.000 0.800 0.000 0.200
#> aberrant_ERR2585305 2 0.2530 0.8042 0.000 0.888 0.000 0.112
#> aberrant_ERR2585278 2 0.0469 0.8378 0.000 0.988 0.000 0.012
#> aberrant_ERR2585347 4 0.4992 0.3323 0.000 0.476 0.000 0.524
#> aberrant_ERR2585332 2 0.3801 0.6697 0.000 0.780 0.000 0.220
#> aberrant_ERR2585280 2 0.4008 0.6358 0.000 0.756 0.000 0.244
#> aberrant_ERR2585304 2 0.8141 0.1414 0.028 0.480 0.304 0.188
#> aberrant_ERR2585322 2 0.4741 0.4851 0.000 0.668 0.004 0.328
#> aberrant_ERR2585279 4 0.5783 0.6873 0.000 0.120 0.172 0.708
#> aberrant_ERR2585277 4 0.3539 0.7662 0.000 0.176 0.004 0.820
#> aberrant_ERR2585295 4 0.3142 0.7823 0.000 0.132 0.008 0.860
#> aberrant_ERR2585333 2 0.0469 0.8382 0.000 0.988 0.000 0.012
#> aberrant_ERR2585285 2 0.0469 0.8379 0.000 0.988 0.000 0.012
#> aberrant_ERR2585286 4 0.2714 0.7807 0.000 0.112 0.004 0.884
#> aberrant_ERR2585294 2 0.0921 0.8309 0.000 0.972 0.000 0.028
#> aberrant_ERR2585300 2 0.0336 0.8369 0.000 0.992 0.000 0.008
#> aberrant_ERR2585334 4 0.4359 0.7402 0.000 0.084 0.100 0.816
#> aberrant_ERR2585361 4 0.3052 0.7834 0.000 0.136 0.004 0.860
#> aberrant_ERR2585372 2 0.1940 0.8191 0.000 0.924 0.000 0.076
#> round_ERR2585217 3 0.1022 0.8870 0.032 0.000 0.968 0.000
#> round_ERR2585205 1 0.0000 0.9621 1.000 0.000 0.000 0.000
#> round_ERR2585214 3 0.0336 0.8904 0.000 0.000 0.992 0.008
#> round_ERR2585202 3 0.1639 0.8829 0.036 0.004 0.952 0.008
#> aberrant_ERR2585367 4 0.3088 0.7823 0.000 0.128 0.008 0.864
#> round_ERR2585220 1 0.0188 0.9612 0.996 0.000 0.000 0.004
#> round_ERR2585238 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> aberrant_ERR2585276 2 0.0469 0.8357 0.000 0.988 0.000 0.012
#> round_ERR2585218 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> aberrant_ERR2585363 2 0.2334 0.8221 0.000 0.908 0.004 0.088
#> round_ERR2585201 3 0.0336 0.8904 0.000 0.000 0.992 0.008
#> round_ERR2585210 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> aberrant_ERR2585362 4 0.4776 0.5537 0.000 0.376 0.000 0.624
#> aberrant_ERR2585360 2 0.0000 0.8342 0.000 1.000 0.000 0.000
#> round_ERR2585209 3 0.4804 0.4465 0.384 0.000 0.616 0.000
#> round_ERR2585242 3 0.0336 0.8904 0.000 0.000 0.992 0.008
#> round_ERR2585216 1 0.0188 0.9612 0.996 0.000 0.000 0.004
#> round_ERR2585219 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> round_ERR2585237 3 0.1389 0.8808 0.048 0.000 0.952 0.000
#> round_ERR2585198 3 0.0188 0.8898 0.000 0.000 0.996 0.004
#> round_ERR2585211 1 0.0376 0.9610 0.992 0.000 0.004 0.004
#> round_ERR2585206 1 0.0000 0.9621 1.000 0.000 0.000 0.000
#> aberrant_ERR2585281 4 0.2859 0.7806 0.000 0.112 0.008 0.880
#> round_ERR2585212 1 0.1302 0.9266 0.956 0.000 0.044 0.000
#> round_ERR2585221 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> round_ERR2585243 1 0.0000 0.9621 1.000 0.000 0.000 0.000
#> round_ERR2585204 3 0.0336 0.8904 0.000 0.000 0.992 0.008
#> round_ERR2585213 3 0.0707 0.8837 0.000 0.000 0.980 0.020
#> aberrant_ERR2585373 2 0.0188 0.8361 0.000 0.996 0.000 0.004
#> aberrant_ERR2585358 2 0.3356 0.7239 0.000 0.824 0.000 0.176
#> aberrant_ERR2585365 4 0.3893 0.7588 0.000 0.196 0.008 0.796
#> aberrant_ERR2585359 2 0.2647 0.7793 0.000 0.880 0.000 0.120
#> aberrant_ERR2585370 2 0.5285 -0.0396 0.000 0.524 0.008 0.468
#> round_ERR2585215 1 0.3569 0.7365 0.804 0.000 0.196 0.000
#> round_ERR2585262 3 0.0592 0.8874 0.000 0.000 0.984 0.016
#> round_ERR2585199 3 0.1109 0.8870 0.028 0.000 0.968 0.004
#> aberrant_ERR2585369 2 0.0592 0.8385 0.000 0.984 0.000 0.016
#> round_ERR2585208 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> round_ERR2585252 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> round_ERR2585236 3 0.5237 0.5049 0.356 0.000 0.628 0.016
#> aberrant_ERR2585284 4 0.2742 0.7203 0.000 0.076 0.024 0.900
#> round_ERR2585224 1 0.0000 0.9621 1.000 0.000 0.000 0.000
#> round_ERR2585260 1 0.0000 0.9621 1.000 0.000 0.000 0.000
#> round_ERR2585229 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> aberrant_ERR2585364 2 0.3266 0.7565 0.000 0.832 0.000 0.168
#> round_ERR2585253 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> aberrant_ERR2585368 4 0.4608 0.6318 0.000 0.304 0.004 0.692
#> aberrant_ERR2585371 4 0.4608 0.6318 0.000 0.304 0.004 0.692
#> round_ERR2585239 1 0.0000 0.9621 1.000 0.000 0.000 0.000
#> round_ERR2585273 1 0.0000 0.9621 1.000 0.000 0.000 0.000
#> round_ERR2585256 1 0.4277 0.5907 0.720 0.000 0.280 0.000
#> round_ERR2585272 1 0.4072 0.6375 0.748 0.000 0.252 0.000
#> round_ERR2585246 1 0.0000 0.9621 1.000 0.000 0.000 0.000
#> round_ERR2585261 3 0.2868 0.8235 0.136 0.000 0.864 0.000
#> round_ERR2585254 1 0.2266 0.8832 0.912 0.000 0.084 0.004
#> round_ERR2585225 3 0.0336 0.8904 0.000 0.000 0.992 0.008
#> round_ERR2585235 3 0.3975 0.7190 0.240 0.000 0.760 0.000
#> round_ERR2585271 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> round_ERR2585251 1 0.0188 0.9612 0.996 0.000 0.000 0.004
#> round_ERR2585255 3 0.0336 0.8904 0.000 0.000 0.992 0.008
#> round_ERR2585257 3 0.1557 0.8762 0.056 0.000 0.944 0.000
#> round_ERR2585226 1 0.0188 0.9612 0.996 0.000 0.000 0.004
#> round_ERR2585265 1 0.0188 0.9612 0.996 0.000 0.000 0.004
#> round_ERR2585259 3 0.3688 0.7595 0.208 0.000 0.792 0.000
#> round_ERR2585247 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> round_ERR2585241 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> round_ERR2585263 1 0.1557 0.9157 0.944 0.000 0.056 0.000
#> round_ERR2585264 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> round_ERR2585233 3 0.0524 0.8908 0.004 0.000 0.988 0.008
#> round_ERR2585223 1 0.0188 0.9612 0.996 0.000 0.000 0.004
#> round_ERR2585234 3 0.0188 0.8898 0.000 0.000 0.996 0.004
#> round_ERR2585222 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> round_ERR2585228 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> round_ERR2585248 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> round_ERR2585240 3 0.2593 0.8379 0.104 0.000 0.892 0.004
#> round_ERR2585270 1 0.0376 0.9593 0.992 0.000 0.004 0.004
#> round_ERR2585232 3 0.4830 0.4305 0.392 0.000 0.608 0.000
#> aberrant_ERR2585341 4 0.3032 0.7818 0.000 0.124 0.008 0.868
#> aberrant_ERR2585355 4 0.2999 0.7821 0.000 0.132 0.004 0.864
#> round_ERR2585227 1 0.0524 0.9568 0.988 0.000 0.008 0.004
#> aberrant_ERR2585351 2 0.0817 0.8381 0.000 0.976 0.000 0.024
#> round_ERR2585269 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> aberrant_ERR2585357 2 0.5050 0.2355 0.000 0.588 0.004 0.408
#> aberrant_ERR2585350 4 0.4837 0.5572 0.000 0.348 0.004 0.648
#> round_ERR2585250 1 0.0707 0.9486 0.980 0.000 0.020 0.000
#> round_ERR2585245 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> aberrant_ERR2585353 2 0.3400 0.7304 0.000 0.820 0.000 0.180
#> round_ERR2585258 1 0.0188 0.9612 0.996 0.000 0.000 0.004
#> aberrant_ERR2585354 2 0.1792 0.8324 0.000 0.932 0.000 0.068
#> round_ERR2585249 1 0.0000 0.9621 1.000 0.000 0.000 0.000
#> round_ERR2585268 1 0.4522 0.5010 0.680 0.000 0.320 0.000
#> aberrant_ERR2585356 2 0.0469 0.8378 0.000 0.988 0.000 0.012
#> round_ERR2585266 3 0.0336 0.8904 0.000 0.000 0.992 0.008
#> round_ERR2585231 1 0.0188 0.9625 0.996 0.000 0.000 0.004
#> round_ERR2585230 1 0.0000 0.9621 1.000 0.000 0.000 0.000
#> round_ERR2585267 1 0.0376 0.9610 0.992 0.000 0.004 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 2 0.5153 -0.42933 0.000 0.524 0.000 0.040 0.436
#> aberrant_ERR2585338 2 0.0771 0.55039 0.000 0.976 0.000 0.004 0.020
#> aberrant_ERR2585325 2 0.5148 -0.42396 0.000 0.528 0.000 0.040 0.432
#> aberrant_ERR2585283 4 0.6823 0.00000 0.000 0.328 0.000 0.348 0.324
#> aberrant_ERR2585343 5 0.2069 0.65598 0.000 0.076 0.000 0.012 0.912
#> aberrant_ERR2585329 5 0.6003 0.31447 0.000 0.192 0.000 0.224 0.584
#> aberrant_ERR2585317 5 0.4832 0.50182 0.000 0.088 0.000 0.200 0.712
#> aberrant_ERR2585339 2 0.5430 0.45504 0.000 0.660 0.000 0.192 0.148
#> aberrant_ERR2585335 5 0.0898 0.69212 0.000 0.020 0.000 0.008 0.972
#> aberrant_ERR2585287 2 0.6245 -0.47435 0.000 0.516 0.000 0.316 0.168
#> aberrant_ERR2585321 5 0.0162 0.68929 0.000 0.000 0.000 0.004 0.996
#> aberrant_ERR2585297 1 0.0290 0.93339 0.992 0.000 0.000 0.008 0.000
#> aberrant_ERR2585337 5 0.6431 -0.03749 0.000 0.388 0.000 0.176 0.436
#> aberrant_ERR2585319 5 0.2193 0.68876 0.000 0.028 0.000 0.060 0.912
#> aberrant_ERR2585315 5 0.2036 0.68629 0.000 0.056 0.000 0.024 0.920
#> aberrant_ERR2585336 2 0.6631 0.24116 0.000 0.452 0.000 0.256 0.292
#> aberrant_ERR2585307 5 0.6001 -0.00374 0.000 0.396 0.004 0.100 0.500
#> aberrant_ERR2585301 5 0.2046 0.68917 0.000 0.068 0.000 0.016 0.916
#> aberrant_ERR2585326 2 0.6661 0.22844 0.000 0.440 0.000 0.256 0.304
#> aberrant_ERR2585331 2 0.2476 0.52647 0.000 0.904 0.012 0.064 0.020
#> aberrant_ERR2585346 5 0.6823 -0.93152 0.000 0.336 0.000 0.320 0.344
#> aberrant_ERR2585314 5 0.5120 0.45534 0.000 0.212 0.000 0.104 0.684
#> aberrant_ERR2585298 3 0.0162 0.89192 0.000 0.000 0.996 0.004 0.000
#> aberrant_ERR2585345 5 0.6584 0.08998 0.000 0.272 0.000 0.260 0.468
#> aberrant_ERR2585299 1 0.1121 0.92592 0.956 0.000 0.000 0.044 0.000
#> aberrant_ERR2585309 1 0.0963 0.92747 0.964 0.000 0.000 0.036 0.000
#> aberrant_ERR2585303 2 0.1168 0.54394 0.000 0.960 0.000 0.008 0.032
#> aberrant_ERR2585313 2 0.6472 0.07780 0.000 0.432 0.000 0.184 0.384
#> aberrant_ERR2585318 5 0.0609 0.68639 0.000 0.000 0.000 0.020 0.980
#> aberrant_ERR2585328 2 0.5392 -0.13507 0.000 0.664 0.000 0.144 0.192
#> aberrant_ERR2585330 5 0.0162 0.68818 0.000 0.000 0.000 0.004 0.996
#> aberrant_ERR2585293 2 0.6003 -0.41170 0.000 0.448 0.000 0.440 0.112
#> aberrant_ERR2585342 5 0.0451 0.68999 0.000 0.008 0.000 0.004 0.988
#> aberrant_ERR2585348 2 0.3454 0.39112 0.000 0.816 0.000 0.156 0.028
#> aberrant_ERR2585352 5 0.3359 0.63249 0.000 0.072 0.000 0.084 0.844
#> aberrant_ERR2585308 1 0.1043 0.92829 0.960 0.000 0.000 0.040 0.000
#> aberrant_ERR2585349 2 0.6419 0.25520 0.000 0.604 0.240 0.108 0.048
#> aberrant_ERR2585316 5 0.6311 -0.49456 0.000 0.320 0.000 0.176 0.504
#> aberrant_ERR2585306 5 0.3779 0.59415 0.000 0.144 0.000 0.052 0.804
#> aberrant_ERR2585324 5 0.2193 0.68876 0.000 0.028 0.000 0.060 0.912
#> aberrant_ERR2585310 5 0.7599 0.19962 0.116 0.184 0.032 0.100 0.568
#> aberrant_ERR2585296 1 0.4758 0.56808 0.676 0.000 0.276 0.048 0.000
#> aberrant_ERR2585275 5 0.4693 0.39507 0.000 0.196 0.000 0.080 0.724
#> aberrant_ERR2585311 5 0.0794 0.68508 0.000 0.000 0.000 0.028 0.972
#> aberrant_ERR2585292 2 0.6003 -0.41170 0.000 0.448 0.000 0.440 0.112
#> aberrant_ERR2585282 5 0.4665 0.33907 0.000 0.260 0.000 0.048 0.692
#> aberrant_ERR2585305 5 0.4747 0.49825 0.000 0.196 0.000 0.084 0.720
#> aberrant_ERR2585278 5 0.1836 0.68944 0.000 0.036 0.000 0.032 0.932
#> aberrant_ERR2585347 5 0.6538 -0.66900 0.000 0.352 0.000 0.204 0.444
#> aberrant_ERR2585332 5 0.4263 0.46990 0.000 0.180 0.000 0.060 0.760
#> aberrant_ERR2585280 5 0.4130 0.32237 0.000 0.292 0.000 0.012 0.696
#> aberrant_ERR2585304 5 0.9001 -0.23625 0.024 0.240 0.280 0.172 0.284
#> aberrant_ERR2585322 2 0.6301 0.23609 0.000 0.512 0.000 0.180 0.308
#> aberrant_ERR2585279 2 0.4555 0.43261 0.000 0.768 0.140 0.080 0.012
#> aberrant_ERR2585277 2 0.2124 0.55196 0.000 0.916 0.000 0.056 0.028
#> aberrant_ERR2585295 2 0.2376 0.49635 0.000 0.904 0.000 0.044 0.052
#> aberrant_ERR2585333 5 0.0162 0.68818 0.000 0.000 0.000 0.004 0.996
#> aberrant_ERR2585285 5 0.0794 0.69158 0.000 0.028 0.000 0.000 0.972
#> aberrant_ERR2585286 2 0.0671 0.54694 0.000 0.980 0.000 0.004 0.016
#> aberrant_ERR2585294 5 0.2830 0.67252 0.000 0.080 0.000 0.044 0.876
#> aberrant_ERR2585300 5 0.0290 0.69032 0.000 0.008 0.000 0.000 0.992
#> aberrant_ERR2585334 2 0.2992 0.50040 0.000 0.876 0.044 0.072 0.008
#> aberrant_ERR2585361 2 0.1997 0.52095 0.000 0.924 0.000 0.040 0.036
#> aberrant_ERR2585372 5 0.2707 0.60841 0.000 0.132 0.000 0.008 0.860
#> round_ERR2585217 3 0.1582 0.88102 0.028 0.000 0.944 0.028 0.000
#> round_ERR2585205 1 0.0609 0.93350 0.980 0.000 0.000 0.020 0.000
#> round_ERR2585214 3 0.0290 0.89137 0.000 0.000 0.992 0.008 0.000
#> round_ERR2585202 3 0.1682 0.88304 0.012 0.004 0.940 0.044 0.000
#> aberrant_ERR2585367 2 0.1661 0.53423 0.000 0.940 0.000 0.024 0.036
#> round_ERR2585220 1 0.0404 0.93255 0.988 0.000 0.000 0.012 0.000
#> round_ERR2585238 1 0.0290 0.93294 0.992 0.000 0.000 0.008 0.000
#> aberrant_ERR2585276 5 0.1965 0.69167 0.000 0.052 0.000 0.024 0.924
#> round_ERR2585218 1 0.0794 0.93128 0.972 0.000 0.000 0.028 0.000
#> aberrant_ERR2585363 5 0.3795 0.54551 0.000 0.192 0.000 0.028 0.780
#> round_ERR2585201 3 0.0162 0.89192 0.000 0.000 0.996 0.004 0.000
#> round_ERR2585210 1 0.0510 0.93251 0.984 0.000 0.000 0.016 0.000
#> aberrant_ERR2585362 2 0.5204 -0.35451 0.000 0.580 0.000 0.052 0.368
#> aberrant_ERR2585360 5 0.1725 0.69557 0.000 0.044 0.000 0.020 0.936
#> round_ERR2585209 3 0.5154 0.38013 0.372 0.000 0.580 0.048 0.000
#> round_ERR2585242 3 0.0162 0.89192 0.000 0.000 0.996 0.004 0.000
#> round_ERR2585216 1 0.1041 0.93174 0.964 0.000 0.004 0.032 0.000
#> round_ERR2585219 1 0.0609 0.93198 0.980 0.000 0.000 0.020 0.000
#> round_ERR2585237 3 0.1981 0.86924 0.048 0.000 0.924 0.028 0.000
#> round_ERR2585198 3 0.0771 0.88958 0.004 0.000 0.976 0.020 0.000
#> round_ERR2585211 1 0.0794 0.93128 0.972 0.000 0.000 0.028 0.000
#> round_ERR2585206 1 0.1043 0.92849 0.960 0.000 0.000 0.040 0.000
#> aberrant_ERR2585281 2 0.0703 0.54670 0.000 0.976 0.000 0.000 0.024
#> round_ERR2585212 1 0.1981 0.90413 0.924 0.000 0.048 0.028 0.000
#> round_ERR2585221 1 0.1043 0.92675 0.960 0.000 0.000 0.040 0.000
#> round_ERR2585243 1 0.0963 0.92747 0.964 0.000 0.000 0.036 0.000
#> round_ERR2585204 3 0.0794 0.88543 0.000 0.000 0.972 0.028 0.000
#> round_ERR2585213 3 0.1764 0.86400 0.000 0.008 0.928 0.064 0.000
#> aberrant_ERR2585373 5 0.0290 0.68813 0.000 0.000 0.000 0.008 0.992
#> aberrant_ERR2585358 5 0.3531 0.54793 0.000 0.148 0.000 0.036 0.816
#> aberrant_ERR2585365 2 0.1410 0.54438 0.000 0.940 0.000 0.000 0.060
#> aberrant_ERR2585359 5 0.3115 0.59430 0.000 0.112 0.000 0.036 0.852
#> aberrant_ERR2585370 2 0.5993 0.41167 0.000 0.576 0.000 0.260 0.164
#> round_ERR2585215 1 0.3339 0.82649 0.836 0.000 0.124 0.040 0.000
#> round_ERR2585262 3 0.0162 0.89088 0.000 0.004 0.996 0.000 0.000
#> round_ERR2585199 3 0.1877 0.87841 0.012 0.000 0.924 0.064 0.000
#> aberrant_ERR2585369 5 0.0451 0.68999 0.000 0.008 0.000 0.004 0.988
#> round_ERR2585208 1 0.0880 0.93040 0.968 0.000 0.000 0.032 0.000
#> round_ERR2585252 1 0.0963 0.92994 0.964 0.000 0.000 0.036 0.000
#> round_ERR2585236 1 0.4981 0.24990 0.560 0.004 0.412 0.024 0.000
#> aberrant_ERR2585284 2 0.5170 -0.18499 0.004 0.524 0.000 0.440 0.032
#> round_ERR2585224 1 0.1043 0.92675 0.960 0.000 0.000 0.040 0.000
#> round_ERR2585260 1 0.0510 0.93180 0.984 0.000 0.000 0.016 0.000
#> round_ERR2585229 1 0.1121 0.93060 0.956 0.000 0.000 0.044 0.000
#> aberrant_ERR2585364 5 0.5372 0.23036 0.000 0.180 0.000 0.152 0.668
#> round_ERR2585253 1 0.0963 0.92962 0.964 0.000 0.000 0.036 0.000
#> aberrant_ERR2585368 2 0.4555 0.49871 0.000 0.732 0.000 0.200 0.068
#> aberrant_ERR2585371 2 0.4555 0.49871 0.000 0.732 0.000 0.200 0.068
#> round_ERR2585239 1 0.1043 0.92712 0.960 0.000 0.000 0.040 0.000
#> round_ERR2585273 1 0.0000 0.93228 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585256 1 0.4780 0.55703 0.672 0.000 0.280 0.048 0.000
#> round_ERR2585272 1 0.4100 0.73038 0.764 0.000 0.192 0.044 0.000
#> round_ERR2585246 1 0.1124 0.92737 0.960 0.000 0.004 0.036 0.000
#> round_ERR2585261 3 0.3994 0.72971 0.188 0.000 0.772 0.040 0.000
#> round_ERR2585254 1 0.3602 0.75420 0.796 0.000 0.180 0.024 0.000
#> round_ERR2585225 3 0.0162 0.89192 0.000 0.000 0.996 0.004 0.000
#> round_ERR2585235 3 0.5003 0.22314 0.424 0.000 0.544 0.032 0.000
#> round_ERR2585271 1 0.0609 0.93351 0.980 0.000 0.000 0.020 0.000
#> round_ERR2585251 1 0.0771 0.93096 0.976 0.000 0.004 0.020 0.000
#> round_ERR2585255 3 0.0162 0.89192 0.000 0.000 0.996 0.004 0.000
#> round_ERR2585257 3 0.2067 0.86652 0.048 0.000 0.920 0.032 0.000
#> round_ERR2585226 1 0.0404 0.93222 0.988 0.000 0.000 0.012 0.000
#> round_ERR2585265 1 0.0566 0.93235 0.984 0.000 0.004 0.012 0.000
#> round_ERR2585259 3 0.4276 0.64803 0.256 0.000 0.716 0.028 0.000
#> round_ERR2585247 1 0.0880 0.92877 0.968 0.000 0.000 0.032 0.000
#> round_ERR2585241 1 0.0794 0.93147 0.972 0.000 0.000 0.028 0.000
#> round_ERR2585263 1 0.2450 0.89163 0.900 0.000 0.048 0.052 0.000
#> round_ERR2585264 1 0.1251 0.92955 0.956 0.000 0.008 0.036 0.000
#> round_ERR2585233 3 0.0324 0.89224 0.004 0.000 0.992 0.004 0.000
#> round_ERR2585223 1 0.0290 0.93294 0.992 0.000 0.000 0.008 0.000
#> round_ERR2585234 3 0.0290 0.89168 0.000 0.000 0.992 0.008 0.000
#> round_ERR2585222 1 0.1043 0.92829 0.960 0.000 0.000 0.040 0.000
#> round_ERR2585228 1 0.0404 0.93305 0.988 0.000 0.000 0.012 0.000
#> round_ERR2585248 1 0.1121 0.93238 0.956 0.000 0.000 0.044 0.000
#> round_ERR2585240 3 0.2540 0.83372 0.088 0.000 0.888 0.024 0.000
#> round_ERR2585270 1 0.0671 0.93216 0.980 0.000 0.004 0.016 0.000
#> round_ERR2585232 1 0.5175 0.01019 0.496 0.000 0.464 0.040 0.000
#> aberrant_ERR2585341 2 0.1106 0.54143 0.000 0.964 0.000 0.012 0.024
#> aberrant_ERR2585355 2 0.1211 0.55221 0.000 0.960 0.000 0.016 0.024
#> round_ERR2585227 1 0.1310 0.92138 0.956 0.000 0.020 0.024 0.000
#> aberrant_ERR2585351 5 0.1992 0.68552 0.000 0.044 0.000 0.032 0.924
#> round_ERR2585269 1 0.1043 0.92862 0.960 0.000 0.000 0.040 0.000
#> aberrant_ERR2585357 2 0.6072 0.39728 0.000 0.568 0.000 0.252 0.180
#> aberrant_ERR2585350 2 0.4901 0.48366 0.000 0.700 0.000 0.216 0.084
#> round_ERR2585250 1 0.1331 0.92432 0.952 0.000 0.008 0.040 0.000
#> round_ERR2585245 1 0.1043 0.92849 0.960 0.000 0.000 0.040 0.000
#> aberrant_ERR2585353 5 0.4233 0.44759 0.000 0.208 0.000 0.044 0.748
#> round_ERR2585258 1 0.0865 0.93259 0.972 0.000 0.004 0.024 0.000
#> aberrant_ERR2585354 5 0.2470 0.67699 0.000 0.104 0.000 0.012 0.884
#> round_ERR2585249 1 0.1043 0.92862 0.960 0.000 0.000 0.040 0.000
#> round_ERR2585268 1 0.4337 0.70778 0.748 0.000 0.196 0.056 0.000
#> aberrant_ERR2585356 5 0.0290 0.69032 0.000 0.008 0.000 0.000 0.992
#> round_ERR2585266 3 0.0162 0.89192 0.000 0.000 0.996 0.004 0.000
#> round_ERR2585231 1 0.1043 0.93189 0.960 0.000 0.000 0.040 0.000
#> round_ERR2585230 1 0.0404 0.93312 0.988 0.000 0.000 0.012 0.000
#> round_ERR2585267 1 0.1331 0.92828 0.952 0.000 0.008 0.040 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 5 0.5982 -0.0622 0.000 0.212 0.000 0.320 0.464 NA
#> aberrant_ERR2585338 2 0.4506 0.5350 0.000 0.652 0.000 0.300 0.040 NA
#> aberrant_ERR2585325 5 0.6032 -0.0878 0.000 0.224 0.000 0.320 0.452 NA
#> aberrant_ERR2585283 4 0.4433 0.5041 0.000 0.028 0.000 0.668 0.288 NA
#> aberrant_ERR2585343 5 0.1367 0.7250 0.000 0.012 0.000 0.044 0.944 NA
#> aberrant_ERR2585329 2 0.5027 -0.1005 0.000 0.496 0.000 0.052 0.444 NA
#> aberrant_ERR2585317 5 0.4570 0.3800 0.000 0.376 0.000 0.028 0.588 NA
#> aberrant_ERR2585339 2 0.3204 0.5283 0.000 0.832 0.000 0.052 0.112 NA
#> aberrant_ERR2585335 5 0.0935 0.7373 0.000 0.032 0.000 0.004 0.964 NA
#> aberrant_ERR2585287 4 0.4935 0.4654 0.000 0.156 0.000 0.668 0.172 NA
#> aberrant_ERR2585321 5 0.0777 0.7313 0.000 0.004 0.000 0.024 0.972 NA
#> aberrant_ERR2585297 1 0.1075 0.8497 0.952 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585337 2 0.4957 0.3174 0.000 0.624 0.000 0.076 0.292 NA
#> aberrant_ERR2585319 5 0.2272 0.7299 0.000 0.040 0.000 0.056 0.900 NA
#> aberrant_ERR2585315 5 0.2882 0.6922 0.000 0.120 0.000 0.028 0.848 NA
#> aberrant_ERR2585336 2 0.3390 0.4176 0.000 0.780 0.000 0.012 0.200 NA
#> aberrant_ERR2585307 5 0.6782 -0.1707 0.000 0.300 0.000 0.284 0.376 NA
#> aberrant_ERR2585301 5 0.2983 0.6945 0.000 0.032 0.000 0.136 0.832 NA
#> aberrant_ERR2585326 2 0.3089 0.4425 0.000 0.800 0.000 0.008 0.188 NA
#> aberrant_ERR2585331 2 0.4676 0.4821 0.000 0.636 0.012 0.320 0.016 NA
#> aberrant_ERR2585346 4 0.4471 0.4669 0.000 0.020 0.000 0.648 0.312 NA
#> aberrant_ERR2585314 5 0.6302 0.2650 0.000 0.116 0.000 0.304 0.516 NA
#> aberrant_ERR2585298 3 0.0000 0.8736 0.000 0.000 1.000 0.000 0.000 NA
#> aberrant_ERR2585345 2 0.4594 0.2005 0.000 0.600 0.000 0.032 0.360 NA
#> aberrant_ERR2585299 1 0.3244 0.7860 0.732 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585309 1 0.3076 0.7952 0.760 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585303 2 0.4617 0.5126 0.000 0.624 0.000 0.324 0.048 NA
#> aberrant_ERR2585313 2 0.4718 0.2830 0.000 0.608 0.000 0.044 0.340 NA
#> aberrant_ERR2585318 5 0.0891 0.7308 0.000 0.008 0.000 0.024 0.968 NA
#> aberrant_ERR2585328 4 0.5964 0.1624 0.000 0.320 0.000 0.468 0.208 NA
#> aberrant_ERR2585330 5 0.0291 0.7326 0.000 0.004 0.000 0.004 0.992 NA
#> aberrant_ERR2585293 4 0.3063 0.5016 0.000 0.064 0.000 0.860 0.052 NA
#> aberrant_ERR2585342 5 0.0363 0.7333 0.000 0.000 0.000 0.012 0.988 NA
#> aberrant_ERR2585348 2 0.4722 0.2846 0.000 0.492 0.000 0.468 0.036 NA
#> aberrant_ERR2585352 5 0.2703 0.6714 0.000 0.172 0.000 0.004 0.824 NA
#> aberrant_ERR2585308 1 0.3101 0.7930 0.756 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585349 4 0.7983 -0.0222 0.000 0.308 0.180 0.332 0.032 NA
#> aberrant_ERR2585316 5 0.4523 0.0402 0.000 0.032 0.000 0.452 0.516 NA
#> aberrant_ERR2585306 5 0.4505 0.5294 0.000 0.032 0.000 0.272 0.676 NA
#> aberrant_ERR2585324 5 0.2272 0.7299 0.000 0.040 0.000 0.056 0.900 NA
#> aberrant_ERR2585310 5 0.7685 -0.0154 0.056 0.088 0.004 0.304 0.428 NA
#> aberrant_ERR2585296 1 0.5943 0.1136 0.404 0.000 0.380 0.000 0.000 NA
#> aberrant_ERR2585275 5 0.4098 0.4996 0.000 0.032 0.000 0.292 0.676 NA
#> aberrant_ERR2585311 5 0.0790 0.7289 0.000 0.000 0.000 0.032 0.968 NA
#> aberrant_ERR2585292 4 0.3063 0.5016 0.000 0.064 0.000 0.860 0.052 NA
#> aberrant_ERR2585282 5 0.4272 0.4697 0.000 0.044 0.000 0.288 0.668 NA
#> aberrant_ERR2585305 5 0.5642 0.3868 0.000 0.068 0.000 0.296 0.584 NA
#> aberrant_ERR2585278 5 0.1863 0.7301 0.000 0.036 0.000 0.044 0.920 NA
#> aberrant_ERR2585347 4 0.4897 0.0911 0.000 0.060 0.000 0.492 0.448 NA
#> aberrant_ERR2585332 5 0.3318 0.6513 0.000 0.032 0.000 0.172 0.796 NA
#> aberrant_ERR2585280 5 0.4243 0.5958 0.000 0.148 0.000 0.104 0.744 NA
#> aberrant_ERR2585304 4 0.8754 0.2455 0.000 0.140 0.172 0.312 0.204 NA
#> aberrant_ERR2585322 2 0.4665 0.3921 0.000 0.672 0.000 0.068 0.252 NA
#> aberrant_ERR2585279 2 0.6255 0.3344 0.000 0.524 0.080 0.328 0.012 NA
#> aberrant_ERR2585277 2 0.3759 0.5536 0.000 0.752 0.000 0.216 0.024 NA
#> aberrant_ERR2585295 2 0.4947 0.4094 0.000 0.552 0.000 0.384 0.060 NA
#> aberrant_ERR2585333 5 0.0405 0.7321 0.000 0.004 0.000 0.008 0.988 NA
#> aberrant_ERR2585285 5 0.0891 0.7365 0.000 0.024 0.000 0.008 0.968 NA
#> aberrant_ERR2585286 2 0.4260 0.5146 0.000 0.640 0.000 0.332 0.024 NA
#> aberrant_ERR2585294 5 0.3529 0.6704 0.000 0.028 0.000 0.176 0.788 NA
#> aberrant_ERR2585300 5 0.0508 0.7348 0.000 0.004 0.000 0.012 0.984 NA
#> aberrant_ERR2585334 2 0.5648 0.3969 0.000 0.556 0.064 0.344 0.012 NA
#> aberrant_ERR2585361 2 0.4968 0.4718 0.000 0.588 0.000 0.336 0.072 NA
#> aberrant_ERR2585372 5 0.1829 0.7234 0.000 0.024 0.000 0.056 0.920 NA
#> round_ERR2585217 3 0.2633 0.8488 0.032 0.000 0.864 0.000 0.000 NA
#> round_ERR2585205 1 0.1501 0.8487 0.924 0.000 0.000 0.000 0.000 NA
#> round_ERR2585214 3 0.0000 0.8736 0.000 0.000 1.000 0.000 0.000 NA
#> round_ERR2585202 3 0.3082 0.8288 0.000 0.008 0.828 0.020 0.000 NA
#> aberrant_ERR2585367 2 0.4709 0.4801 0.000 0.596 0.000 0.352 0.048 NA
#> round_ERR2585220 1 0.2631 0.8255 0.820 0.000 0.000 0.000 0.000 NA
#> round_ERR2585238 1 0.0632 0.8476 0.976 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585276 5 0.2988 0.6871 0.000 0.028 0.000 0.144 0.828 NA
#> round_ERR2585218 1 0.1267 0.8442 0.940 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585363 5 0.3732 0.5731 0.000 0.228 0.000 0.024 0.744 NA
#> round_ERR2585201 3 0.0000 0.8736 0.000 0.000 1.000 0.000 0.000 NA
#> round_ERR2585210 1 0.1267 0.8480 0.940 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585362 5 0.6082 -0.2590 0.000 0.272 0.000 0.360 0.368 NA
#> aberrant_ERR2585360 5 0.2263 0.7278 0.000 0.016 0.000 0.100 0.884 NA
#> round_ERR2585209 3 0.5208 0.5869 0.236 0.000 0.608 0.000 0.000 NA
#> round_ERR2585242 3 0.0000 0.8736 0.000 0.000 1.000 0.000 0.000 NA
#> round_ERR2585216 1 0.2135 0.8375 0.872 0.000 0.000 0.000 0.000 NA
#> round_ERR2585219 1 0.1267 0.8496 0.940 0.000 0.000 0.000 0.000 NA
#> round_ERR2585237 3 0.2487 0.8527 0.032 0.000 0.876 0.000 0.000 NA
#> round_ERR2585198 3 0.0291 0.8734 0.000 0.000 0.992 0.004 0.000 NA
#> round_ERR2585211 1 0.1610 0.8428 0.916 0.000 0.000 0.000 0.000 NA
#> round_ERR2585206 1 0.1610 0.8410 0.916 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585281 2 0.4331 0.5146 0.000 0.636 0.000 0.332 0.028 NA
#> round_ERR2585212 1 0.3946 0.7620 0.756 0.000 0.076 0.000 0.000 NA
#> round_ERR2585221 1 0.3198 0.7865 0.740 0.000 0.000 0.000 0.000 NA
#> round_ERR2585243 1 0.3151 0.7898 0.748 0.000 0.000 0.000 0.000 NA
#> round_ERR2585204 3 0.0000 0.8736 0.000 0.000 1.000 0.000 0.000 NA
#> round_ERR2585213 3 0.2174 0.8467 0.000 0.016 0.912 0.036 0.000 NA
#> aberrant_ERR2585373 5 0.0363 0.7313 0.000 0.000 0.000 0.012 0.988 NA
#> aberrant_ERR2585358 5 0.2331 0.7087 0.000 0.032 0.000 0.080 0.888 NA
#> aberrant_ERR2585365 2 0.4945 0.5193 0.000 0.620 0.000 0.292 0.084 NA
#> aberrant_ERR2585359 5 0.2445 0.7027 0.000 0.020 0.000 0.108 0.872 NA
#> aberrant_ERR2585370 2 0.2588 0.5104 0.000 0.876 0.000 0.024 0.092 NA
#> round_ERR2585215 1 0.3518 0.7930 0.804 0.000 0.104 0.000 0.000 NA
#> round_ERR2585262 3 0.1148 0.8609 0.000 0.004 0.960 0.016 0.000 NA
#> round_ERR2585199 3 0.2491 0.8459 0.000 0.000 0.868 0.020 0.000 NA
#> aberrant_ERR2585369 5 0.0622 0.7337 0.000 0.008 0.000 0.012 0.980 NA
#> round_ERR2585208 1 0.1556 0.8422 0.920 0.000 0.000 0.000 0.000 NA
#> round_ERR2585252 1 0.1714 0.8392 0.908 0.000 0.000 0.000 0.000 NA
#> round_ERR2585236 1 0.6106 0.0166 0.400 0.000 0.392 0.008 0.000 NA
#> aberrant_ERR2585284 4 0.2637 0.4481 0.000 0.096 0.000 0.872 0.008 NA
#> round_ERR2585224 1 0.3421 0.7847 0.736 0.000 0.000 0.008 0.000 NA
#> round_ERR2585260 1 0.2092 0.8436 0.876 0.000 0.000 0.000 0.000 NA
#> round_ERR2585229 1 0.1957 0.8433 0.888 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585364 5 0.4219 0.4382 0.000 0.032 0.000 0.320 0.648 NA
#> round_ERR2585253 1 0.1663 0.8394 0.912 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585368 2 0.2547 0.5360 0.000 0.880 0.000 0.080 0.036 NA
#> aberrant_ERR2585371 2 0.2474 0.5348 0.000 0.884 0.000 0.080 0.032 NA
#> round_ERR2585239 1 0.3023 0.8059 0.768 0.000 0.000 0.000 0.000 NA
#> round_ERR2585273 1 0.1075 0.8485 0.952 0.000 0.000 0.000 0.000 NA
#> round_ERR2585256 1 0.5970 0.1162 0.416 0.000 0.356 0.000 0.000 NA
#> round_ERR2585272 1 0.5008 0.6099 0.644 0.000 0.168 0.000 0.000 NA
#> round_ERR2585246 1 0.3290 0.7869 0.744 0.000 0.004 0.000 0.000 NA
#> round_ERR2585261 3 0.3985 0.7768 0.100 0.000 0.760 0.000 0.000 NA
#> round_ERR2585254 1 0.5620 0.3572 0.512 0.000 0.320 0.000 0.000 NA
#> round_ERR2585225 3 0.0000 0.8736 0.000 0.000 1.000 0.000 0.000 NA
#> round_ERR2585235 3 0.5428 0.3574 0.320 0.000 0.540 0.000 0.000 NA
#> round_ERR2585271 1 0.1327 0.8510 0.936 0.000 0.000 0.000 0.000 NA
#> round_ERR2585251 1 0.2595 0.8222 0.836 0.000 0.004 0.000 0.000 NA
#> round_ERR2585255 3 0.0000 0.8736 0.000 0.000 1.000 0.000 0.000 NA
#> round_ERR2585257 3 0.2457 0.8536 0.036 0.000 0.880 0.000 0.000 NA
#> round_ERR2585226 1 0.2996 0.8116 0.772 0.000 0.000 0.000 0.000 NA
#> round_ERR2585265 1 0.2135 0.8344 0.872 0.000 0.000 0.000 0.000 NA
#> round_ERR2585259 3 0.4801 0.6557 0.196 0.000 0.668 0.000 0.000 NA
#> round_ERR2585247 1 0.2454 0.8310 0.840 0.000 0.000 0.000 0.000 NA
#> round_ERR2585241 1 0.1141 0.8438 0.948 0.000 0.000 0.000 0.000 NA
#> round_ERR2585263 1 0.5296 0.6118 0.596 0.000 0.168 0.000 0.000 NA
#> round_ERR2585264 1 0.2146 0.8383 0.880 0.000 0.004 0.000 0.000 NA
#> round_ERR2585233 3 0.0935 0.8708 0.004 0.000 0.964 0.000 0.000 NA
#> round_ERR2585223 1 0.0937 0.8484 0.960 0.000 0.000 0.000 0.000 NA
#> round_ERR2585234 3 0.0146 0.8734 0.000 0.000 0.996 0.000 0.000 NA
#> round_ERR2585222 1 0.2969 0.8029 0.776 0.000 0.000 0.000 0.000 NA
#> round_ERR2585228 1 0.0865 0.8491 0.964 0.000 0.000 0.000 0.000 NA
#> round_ERR2585248 1 0.2219 0.8367 0.864 0.000 0.000 0.000 0.000 NA
#> round_ERR2585240 3 0.2384 0.8526 0.032 0.000 0.884 0.000 0.000 NA
#> round_ERR2585270 1 0.2664 0.8273 0.816 0.000 0.000 0.000 0.000 NA
#> round_ERR2585232 3 0.5583 0.3248 0.348 0.000 0.500 0.000 0.000 NA
#> aberrant_ERR2585341 2 0.4506 0.5002 0.000 0.616 0.000 0.344 0.036 NA
#> aberrant_ERR2585355 2 0.4322 0.5404 0.000 0.672 0.000 0.288 0.032 NA
#> round_ERR2585227 1 0.2948 0.8096 0.804 0.000 0.008 0.000 0.000 NA
#> aberrant_ERR2585351 5 0.2065 0.7291 0.000 0.032 0.000 0.052 0.912 NA
#> round_ERR2585269 1 0.1714 0.8390 0.908 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585357 2 0.2053 0.4927 0.000 0.888 0.000 0.000 0.108 NA
#> aberrant_ERR2585350 2 0.2070 0.5289 0.000 0.908 0.000 0.044 0.048 NA
#> round_ERR2585250 1 0.3998 0.7690 0.712 0.000 0.040 0.000 0.000 NA
#> round_ERR2585245 1 0.1610 0.8397 0.916 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585353 5 0.3620 0.6290 0.000 0.044 0.000 0.184 0.772 NA
#> round_ERR2585258 1 0.1806 0.8398 0.908 0.000 0.004 0.000 0.000 NA
#> aberrant_ERR2585354 5 0.3078 0.7065 0.000 0.056 0.000 0.108 0.836 NA
#> round_ERR2585249 1 0.1610 0.8407 0.916 0.000 0.000 0.000 0.000 NA
#> round_ERR2585268 1 0.5922 0.3484 0.464 0.000 0.284 0.000 0.000 NA
#> aberrant_ERR2585356 5 0.0777 0.7344 0.000 0.004 0.000 0.024 0.972 NA
#> round_ERR2585266 3 0.0000 0.8736 0.000 0.000 1.000 0.000 0.000 NA
#> round_ERR2585231 1 0.2135 0.8400 0.872 0.000 0.000 0.000 0.000 NA
#> round_ERR2585230 1 0.1387 0.8486 0.932 0.000 0.000 0.000 0.000 NA
#> round_ERR2585267 1 0.1970 0.8420 0.900 0.000 0.008 0.000 0.000 NA
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> CV:mclust 160 5.69e-31 2
#> CV:mclust 151 1.88e-29 3
#> CV:mclust 146 1.99e-27 4
#> CV:mclust 116 4.06e-20 5
#> CV:mclust 119 8.64e-21 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "NMF"]
# you can also extract it by
# res = res_list["CV:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'CV' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.961 0.950 0.979 0.5013 0.498 0.498
#> 3 3 0.835 0.871 0.940 0.2905 0.794 0.609
#> 4 4 0.613 0.713 0.842 0.0794 0.959 0.887
#> 5 5 0.577 0.461 0.736 0.0647 0.929 0.805
#> 6 6 0.594 0.574 0.729 0.0474 0.910 0.730
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585338 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585325 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585283 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585343 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585329 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585317 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585339 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585335 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585287 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585321 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585297 1 0.0000 0.966 1.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585319 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585315 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585336 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585307 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585301 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585326 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585331 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585346 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585314 2 0.0938 0.979 0.012 0.988
#> aberrant_ERR2585298 1 0.3274 0.915 0.940 0.060
#> aberrant_ERR2585345 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585299 1 0.0000 0.966 1.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.966 1.000 0.000
#> aberrant_ERR2585303 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585313 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585318 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585328 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585330 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585293 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585342 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585348 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585352 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585308 1 0.0000 0.966 1.000 0.000
#> aberrant_ERR2585349 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585316 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585306 2 0.2423 0.951 0.040 0.960
#> aberrant_ERR2585324 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585310 1 0.9661 0.380 0.608 0.392
#> aberrant_ERR2585296 1 0.0000 0.966 1.000 0.000
#> aberrant_ERR2585275 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585311 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585292 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585282 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585305 2 0.6801 0.773 0.180 0.820
#> aberrant_ERR2585278 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585347 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585332 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585280 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585304 2 0.9323 0.447 0.348 0.652
#> aberrant_ERR2585322 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585279 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585277 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585295 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585333 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585285 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585286 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585294 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585300 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585334 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585361 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585372 2 0.0000 0.989 0.000 1.000
#> round_ERR2585217 1 0.0376 0.963 0.996 0.004
#> round_ERR2585205 1 0.0000 0.966 1.000 0.000
#> round_ERR2585214 1 0.9881 0.274 0.564 0.436
#> round_ERR2585202 1 0.8081 0.684 0.752 0.248
#> aberrant_ERR2585367 2 0.0000 0.989 0.000 1.000
#> round_ERR2585220 1 0.0000 0.966 1.000 0.000
#> round_ERR2585238 1 0.0000 0.966 1.000 0.000
#> aberrant_ERR2585276 2 0.0000 0.989 0.000 1.000
#> round_ERR2585218 1 0.0000 0.966 1.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.989 0.000 1.000
#> round_ERR2585201 1 0.5946 0.827 0.856 0.144
#> round_ERR2585210 1 0.0000 0.966 1.000 0.000
#> aberrant_ERR2585362 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585360 2 0.0000 0.989 0.000 1.000
#> round_ERR2585209 1 0.0000 0.966 1.000 0.000
#> round_ERR2585242 1 0.0000 0.966 1.000 0.000
#> round_ERR2585216 1 0.0000 0.966 1.000 0.000
#> round_ERR2585219 1 0.0000 0.966 1.000 0.000
#> round_ERR2585237 1 0.0000 0.966 1.000 0.000
#> round_ERR2585198 1 0.0672 0.960 0.992 0.008
#> round_ERR2585211 1 0.0000 0.966 1.000 0.000
#> round_ERR2585206 1 0.0000 0.966 1.000 0.000
#> aberrant_ERR2585281 2 0.0000 0.989 0.000 1.000
#> round_ERR2585212 1 0.0000 0.966 1.000 0.000
#> round_ERR2585221 1 0.0000 0.966 1.000 0.000
#> round_ERR2585243 1 0.0000 0.966 1.000 0.000
#> round_ERR2585204 1 0.9661 0.396 0.608 0.392
#> round_ERR2585213 2 0.2236 0.954 0.036 0.964
#> aberrant_ERR2585373 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585358 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585365 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585359 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585370 2 0.0000 0.989 0.000 1.000
#> round_ERR2585215 1 0.0000 0.966 1.000 0.000
#> round_ERR2585262 2 0.7376 0.731 0.208 0.792
#> round_ERR2585199 1 0.1633 0.947 0.976 0.024
#> aberrant_ERR2585369 2 0.0000 0.989 0.000 1.000
#> round_ERR2585208 1 0.0000 0.966 1.000 0.000
#> round_ERR2585252 1 0.0000 0.966 1.000 0.000
#> round_ERR2585236 1 0.0000 0.966 1.000 0.000
#> aberrant_ERR2585284 2 0.0000 0.989 0.000 1.000
#> round_ERR2585224 1 0.0000 0.966 1.000 0.000
#> round_ERR2585260 1 0.0000 0.966 1.000 0.000
#> round_ERR2585229 1 0.0000 0.966 1.000 0.000
#> aberrant_ERR2585364 2 0.0000 0.989 0.000 1.000
#> round_ERR2585253 1 0.0000 0.966 1.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585371 2 0.0000 0.989 0.000 1.000
#> round_ERR2585239 1 0.0000 0.966 1.000 0.000
#> round_ERR2585273 1 0.0000 0.966 1.000 0.000
#> round_ERR2585256 1 0.0000 0.966 1.000 0.000
#> round_ERR2585272 1 0.0000 0.966 1.000 0.000
#> round_ERR2585246 1 0.0000 0.966 1.000 0.000
#> round_ERR2585261 1 0.0000 0.966 1.000 0.000
#> round_ERR2585254 1 0.0000 0.966 1.000 0.000
#> round_ERR2585225 1 0.8713 0.611 0.708 0.292
#> round_ERR2585235 1 0.0000 0.966 1.000 0.000
#> round_ERR2585271 1 0.0000 0.966 1.000 0.000
#> round_ERR2585251 1 0.0000 0.966 1.000 0.000
#> round_ERR2585255 1 0.9248 0.517 0.660 0.340
#> round_ERR2585257 1 0.0000 0.966 1.000 0.000
#> round_ERR2585226 1 0.0000 0.966 1.000 0.000
#> round_ERR2585265 1 0.0000 0.966 1.000 0.000
#> round_ERR2585259 1 0.0000 0.966 1.000 0.000
#> round_ERR2585247 1 0.0000 0.966 1.000 0.000
#> round_ERR2585241 1 0.0000 0.966 1.000 0.000
#> round_ERR2585263 1 0.0000 0.966 1.000 0.000
#> round_ERR2585264 1 0.0000 0.966 1.000 0.000
#> round_ERR2585233 1 0.0000 0.966 1.000 0.000
#> round_ERR2585223 1 0.0000 0.966 1.000 0.000
#> round_ERR2585234 1 0.0000 0.966 1.000 0.000
#> round_ERR2585222 1 0.0000 0.966 1.000 0.000
#> round_ERR2585228 1 0.0000 0.966 1.000 0.000
#> round_ERR2585248 1 0.0000 0.966 1.000 0.000
#> round_ERR2585240 1 0.0376 0.963 0.996 0.004
#> round_ERR2585270 1 0.0000 0.966 1.000 0.000
#> round_ERR2585232 1 0.0000 0.966 1.000 0.000
#> aberrant_ERR2585341 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585355 2 0.0000 0.989 0.000 1.000
#> round_ERR2585227 1 0.0000 0.966 1.000 0.000
#> aberrant_ERR2585351 2 0.0672 0.982 0.008 0.992
#> round_ERR2585269 1 0.0000 0.966 1.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.989 0.000 1.000
#> aberrant_ERR2585350 2 0.0000 0.989 0.000 1.000
#> round_ERR2585250 1 0.0000 0.966 1.000 0.000
#> round_ERR2585245 1 0.0000 0.966 1.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.989 0.000 1.000
#> round_ERR2585258 1 0.0000 0.966 1.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.989 0.000 1.000
#> round_ERR2585249 1 0.0000 0.966 1.000 0.000
#> round_ERR2585268 1 0.0000 0.966 1.000 0.000
#> aberrant_ERR2585356 2 0.0000 0.989 0.000 1.000
#> round_ERR2585266 1 0.6887 0.778 0.816 0.184
#> round_ERR2585231 1 0.0000 0.966 1.000 0.000
#> round_ERR2585230 1 0.0000 0.966 1.000 0.000
#> round_ERR2585267 1 0.0000 0.966 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.2066 0.90857 0.000 0.940 0.060
#> aberrant_ERR2585338 3 0.1289 0.83659 0.000 0.032 0.968
#> aberrant_ERR2585325 2 0.2261 0.90282 0.000 0.932 0.068
#> aberrant_ERR2585283 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585343 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585329 2 0.2796 0.88201 0.000 0.908 0.092
#> aberrant_ERR2585317 2 0.0892 0.92846 0.000 0.980 0.020
#> aberrant_ERR2585339 2 0.4121 0.80433 0.000 0.832 0.168
#> aberrant_ERR2585335 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585287 2 0.2066 0.90221 0.000 0.940 0.060
#> aberrant_ERR2585321 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585297 1 0.0000 0.97831 1.000 0.000 0.000
#> aberrant_ERR2585337 2 0.0892 0.92882 0.000 0.980 0.020
#> aberrant_ERR2585319 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585315 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585336 2 0.4654 0.75026 0.000 0.792 0.208
#> aberrant_ERR2585307 2 0.3752 0.83062 0.000 0.856 0.144
#> aberrant_ERR2585301 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585326 2 0.1289 0.92372 0.000 0.968 0.032
#> aberrant_ERR2585331 3 0.0000 0.84402 0.000 0.000 1.000
#> aberrant_ERR2585346 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585314 2 0.1860 0.91198 0.000 0.948 0.052
#> aberrant_ERR2585298 3 0.1860 0.83540 0.052 0.000 0.948
#> aberrant_ERR2585345 2 0.2796 0.88098 0.000 0.908 0.092
#> aberrant_ERR2585299 1 0.0000 0.97831 1.000 0.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.97831 1.000 0.000 0.000
#> aberrant_ERR2585303 2 0.5706 0.55877 0.000 0.680 0.320
#> aberrant_ERR2585313 2 0.1964 0.90957 0.000 0.944 0.056
#> aberrant_ERR2585318 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585328 3 0.6307 0.00625 0.000 0.488 0.512
#> aberrant_ERR2585330 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585293 2 0.2878 0.86899 0.000 0.904 0.096
#> aberrant_ERR2585342 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585348 3 0.5138 0.62553 0.000 0.252 0.748
#> aberrant_ERR2585352 2 0.0592 0.93169 0.000 0.988 0.012
#> aberrant_ERR2585308 1 0.0000 0.97831 1.000 0.000 0.000
#> aberrant_ERR2585349 3 0.0000 0.84402 0.000 0.000 1.000
#> aberrant_ERR2585316 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585306 2 0.0237 0.93300 0.004 0.996 0.000
#> aberrant_ERR2585324 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585310 1 0.6302 0.03021 0.520 0.480 0.000
#> aberrant_ERR2585296 1 0.0424 0.97427 0.992 0.000 0.008
#> aberrant_ERR2585275 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585311 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585292 2 0.2878 0.86899 0.000 0.904 0.096
#> aberrant_ERR2585282 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585305 2 0.1643 0.90146 0.044 0.956 0.000
#> aberrant_ERR2585278 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585347 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585332 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585280 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585304 1 0.3337 0.88639 0.908 0.032 0.060
#> aberrant_ERR2585322 2 0.3551 0.84413 0.000 0.868 0.132
#> aberrant_ERR2585279 3 0.0000 0.84402 0.000 0.000 1.000
#> aberrant_ERR2585277 3 0.2878 0.80386 0.000 0.096 0.904
#> aberrant_ERR2585295 3 0.6260 0.12545 0.000 0.448 0.552
#> aberrant_ERR2585333 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585285 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585286 3 0.0892 0.84088 0.000 0.020 0.980
#> aberrant_ERR2585294 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585300 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585334 3 0.0000 0.84402 0.000 0.000 1.000
#> aberrant_ERR2585361 2 0.6215 0.28149 0.000 0.572 0.428
#> aberrant_ERR2585372 2 0.0000 0.93546 0.000 1.000 0.000
#> round_ERR2585217 3 0.5138 0.68121 0.252 0.000 0.748
#> round_ERR2585205 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585214 3 0.0000 0.84402 0.000 0.000 1.000
#> round_ERR2585202 3 0.3941 0.78090 0.156 0.000 0.844
#> aberrant_ERR2585367 2 0.6008 0.45497 0.000 0.628 0.372
#> round_ERR2585220 1 0.0237 0.97692 0.996 0.000 0.004
#> round_ERR2585238 1 0.0000 0.97831 1.000 0.000 0.000
#> aberrant_ERR2585276 2 0.0000 0.93546 0.000 1.000 0.000
#> round_ERR2585218 1 0.0000 0.97831 1.000 0.000 0.000
#> aberrant_ERR2585363 2 0.1411 0.92111 0.000 0.964 0.036
#> round_ERR2585201 3 0.0237 0.84397 0.004 0.000 0.996
#> round_ERR2585210 1 0.0237 0.97692 0.996 0.000 0.004
#> aberrant_ERR2585362 2 0.6026 0.41885 0.000 0.624 0.376
#> aberrant_ERR2585360 2 0.0000 0.93546 0.000 1.000 0.000
#> round_ERR2585209 1 0.2959 0.87666 0.900 0.000 0.100
#> round_ERR2585242 3 0.4750 0.71701 0.216 0.000 0.784
#> round_ERR2585216 1 0.0237 0.97692 0.996 0.000 0.004
#> round_ERR2585219 1 0.0237 0.97692 0.996 0.000 0.004
#> round_ERR2585237 3 0.6267 0.27691 0.452 0.000 0.548
#> round_ERR2585198 3 0.4555 0.73953 0.200 0.000 0.800
#> round_ERR2585211 1 0.0237 0.97692 0.996 0.000 0.004
#> round_ERR2585206 1 0.0000 0.97831 1.000 0.000 0.000
#> aberrant_ERR2585281 3 0.4062 0.74473 0.000 0.164 0.836
#> round_ERR2585212 1 0.0237 0.97692 0.996 0.000 0.004
#> round_ERR2585221 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585243 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585204 3 0.0000 0.84402 0.000 0.000 1.000
#> round_ERR2585213 3 0.0000 0.84402 0.000 0.000 1.000
#> aberrant_ERR2585373 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585358 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585365 2 0.5926 0.48172 0.000 0.644 0.356
#> aberrant_ERR2585359 2 0.0000 0.93546 0.000 1.000 0.000
#> aberrant_ERR2585370 3 0.5363 0.59573 0.000 0.276 0.724
#> round_ERR2585215 1 0.0237 0.97692 0.996 0.000 0.004
#> round_ERR2585262 3 0.0000 0.84402 0.000 0.000 1.000
#> round_ERR2585199 3 0.6095 0.43538 0.392 0.000 0.608
#> aberrant_ERR2585369 2 0.0000 0.93546 0.000 1.000 0.000
#> round_ERR2585208 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585236 1 0.0424 0.97427 0.992 0.000 0.008
#> aberrant_ERR2585284 3 0.4399 0.72253 0.000 0.188 0.812
#> round_ERR2585224 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585260 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585229 1 0.0000 0.97831 1.000 0.000 0.000
#> aberrant_ERR2585364 2 0.0000 0.93546 0.000 1.000 0.000
#> round_ERR2585253 1 0.0000 0.97831 1.000 0.000 0.000
#> aberrant_ERR2585368 3 0.1031 0.84036 0.000 0.024 0.976
#> aberrant_ERR2585371 3 0.0892 0.84101 0.000 0.020 0.980
#> round_ERR2585239 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585273 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585256 1 0.0237 0.97692 0.996 0.000 0.004
#> round_ERR2585272 1 0.0237 0.97692 0.996 0.000 0.004
#> round_ERR2585246 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585261 1 0.1964 0.92957 0.944 0.000 0.056
#> round_ERR2585254 1 0.0237 0.97692 0.996 0.000 0.004
#> round_ERR2585225 3 0.0000 0.84402 0.000 0.000 1.000
#> round_ERR2585235 1 0.1529 0.94498 0.960 0.000 0.040
#> round_ERR2585271 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585251 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585255 3 0.0000 0.84402 0.000 0.000 1.000
#> round_ERR2585257 3 0.5882 0.52511 0.348 0.000 0.652
#> round_ERR2585226 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585265 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585259 1 0.3412 0.84328 0.876 0.000 0.124
#> round_ERR2585247 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585241 1 0.0237 0.97692 0.996 0.000 0.004
#> round_ERR2585263 1 0.0747 0.96779 0.984 0.000 0.016
#> round_ERR2585264 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585233 3 0.4654 0.73452 0.208 0.000 0.792
#> round_ERR2585223 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585234 3 0.2537 0.82797 0.080 0.000 0.920
#> round_ERR2585222 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585228 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585248 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585240 1 0.3816 0.81038 0.852 0.000 0.148
#> round_ERR2585270 1 0.0237 0.97692 0.996 0.000 0.004
#> round_ERR2585232 1 0.0237 0.97692 0.996 0.000 0.004
#> aberrant_ERR2585341 3 0.3340 0.78387 0.000 0.120 0.880
#> aberrant_ERR2585355 3 0.2165 0.82216 0.000 0.064 0.936
#> round_ERR2585227 1 0.0000 0.97831 1.000 0.000 0.000
#> aberrant_ERR2585351 2 0.0829 0.93083 0.004 0.984 0.012
#> round_ERR2585269 1 0.0000 0.97831 1.000 0.000 0.000
#> aberrant_ERR2585357 2 0.2066 0.90737 0.000 0.940 0.060
#> aberrant_ERR2585350 2 0.5465 0.62913 0.000 0.712 0.288
#> round_ERR2585250 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585245 1 0.0000 0.97831 1.000 0.000 0.000
#> aberrant_ERR2585353 2 0.0237 0.93421 0.000 0.996 0.004
#> round_ERR2585258 1 0.0000 0.97831 1.000 0.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.93546 0.000 1.000 0.000
#> round_ERR2585249 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585268 1 0.0237 0.97692 0.996 0.000 0.004
#> aberrant_ERR2585356 2 0.0000 0.93546 0.000 1.000 0.000
#> round_ERR2585266 3 0.5178 0.66253 0.256 0.000 0.744
#> round_ERR2585231 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585230 1 0.0000 0.97831 1.000 0.000 0.000
#> round_ERR2585267 1 0.0000 0.97831 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 2 0.5393 0.2448 0.000 0.688 0.044 0.268
#> aberrant_ERR2585338 3 0.2021 0.7929 0.000 0.056 0.932 0.012
#> aberrant_ERR2585325 2 0.5312 0.2693 0.000 0.712 0.052 0.236
#> aberrant_ERR2585283 2 0.3610 0.5162 0.000 0.800 0.000 0.200
#> aberrant_ERR2585343 2 0.2469 0.7220 0.000 0.892 0.000 0.108
#> aberrant_ERR2585329 2 0.2623 0.7267 0.000 0.908 0.028 0.064
#> aberrant_ERR2585317 2 0.2730 0.7168 0.000 0.896 0.016 0.088
#> aberrant_ERR2585339 2 0.5343 0.3467 0.000 0.656 0.316 0.028
#> aberrant_ERR2585335 2 0.2530 0.6986 0.000 0.888 0.000 0.112
#> aberrant_ERR2585287 2 0.6848 -0.1794 0.000 0.592 0.160 0.248
#> aberrant_ERR2585321 2 0.2281 0.7236 0.000 0.904 0.000 0.096
#> aberrant_ERR2585297 1 0.2345 0.8964 0.900 0.000 0.000 0.100
#> aberrant_ERR2585337 2 0.2413 0.6995 0.000 0.916 0.020 0.064
#> aberrant_ERR2585319 2 0.2081 0.7083 0.000 0.916 0.000 0.084
#> aberrant_ERR2585315 2 0.2216 0.6769 0.000 0.908 0.000 0.092
#> aberrant_ERR2585336 2 0.4039 0.6694 0.000 0.836 0.080 0.084
#> aberrant_ERR2585307 2 0.4599 0.5704 0.004 0.780 0.184 0.032
#> aberrant_ERR2585301 2 0.1474 0.7170 0.000 0.948 0.000 0.052
#> aberrant_ERR2585326 2 0.3501 0.6563 0.000 0.848 0.132 0.020
#> aberrant_ERR2585331 3 0.0376 0.8061 0.000 0.004 0.992 0.004
#> aberrant_ERR2585346 2 0.3024 0.6420 0.000 0.852 0.000 0.148
#> aberrant_ERR2585314 2 0.4614 0.6311 0.004 0.752 0.016 0.228
#> aberrant_ERR2585298 3 0.0707 0.8095 0.020 0.000 0.980 0.000
#> aberrant_ERR2585345 2 0.2739 0.7266 0.000 0.904 0.036 0.060
#> aberrant_ERR2585299 1 0.1902 0.8929 0.932 0.004 0.000 0.064
#> aberrant_ERR2585309 1 0.3400 0.8616 0.820 0.000 0.000 0.180
#> aberrant_ERR2585303 3 0.5137 0.0511 0.000 0.452 0.544 0.004
#> aberrant_ERR2585313 2 0.3245 0.6889 0.000 0.872 0.028 0.100
#> aberrant_ERR2585318 2 0.2647 0.7166 0.000 0.880 0.000 0.120
#> aberrant_ERR2585328 2 0.6031 0.0758 0.000 0.536 0.420 0.044
#> aberrant_ERR2585330 2 0.2216 0.7092 0.000 0.908 0.000 0.092
#> aberrant_ERR2585293 4 0.5906 1.0000 0.000 0.436 0.036 0.528
#> aberrant_ERR2585342 2 0.1716 0.7157 0.000 0.936 0.000 0.064
#> aberrant_ERR2585348 3 0.5971 0.0852 0.000 0.428 0.532 0.040
#> aberrant_ERR2585352 2 0.1902 0.7165 0.000 0.932 0.004 0.064
#> aberrant_ERR2585308 1 0.2704 0.8908 0.876 0.000 0.000 0.124
#> aberrant_ERR2585349 3 0.4475 0.7387 0.016 0.076 0.828 0.080
#> aberrant_ERR2585316 2 0.3688 0.6549 0.000 0.792 0.000 0.208
#> aberrant_ERR2585306 2 0.5436 0.2004 0.024 0.620 0.000 0.356
#> aberrant_ERR2585324 2 0.2530 0.6927 0.000 0.888 0.000 0.112
#> aberrant_ERR2585310 2 0.7587 -0.0813 0.292 0.476 0.000 0.232
#> aberrant_ERR2585296 1 0.4426 0.8301 0.816 0.016 0.032 0.136
#> aberrant_ERR2585275 2 0.4008 0.5455 0.000 0.756 0.000 0.244
#> aberrant_ERR2585311 2 0.3837 0.6513 0.000 0.776 0.000 0.224
#> aberrant_ERR2585292 4 0.5906 1.0000 0.000 0.436 0.036 0.528
#> aberrant_ERR2585282 2 0.2011 0.7197 0.000 0.920 0.000 0.080
#> aberrant_ERR2585305 2 0.5312 0.4103 0.040 0.692 0.000 0.268
#> aberrant_ERR2585278 2 0.3311 0.6907 0.000 0.828 0.000 0.172
#> aberrant_ERR2585347 2 0.2011 0.7014 0.000 0.920 0.000 0.080
#> aberrant_ERR2585332 2 0.3444 0.6566 0.000 0.816 0.000 0.184
#> aberrant_ERR2585280 2 0.2469 0.6636 0.000 0.892 0.000 0.108
#> aberrant_ERR2585304 1 0.7492 0.6376 0.644 0.124 0.096 0.136
#> aberrant_ERR2585322 2 0.4224 0.6907 0.000 0.824 0.076 0.100
#> aberrant_ERR2585279 3 0.0336 0.8043 0.000 0.000 0.992 0.008
#> aberrant_ERR2585277 3 0.1743 0.7987 0.000 0.056 0.940 0.004
#> aberrant_ERR2585295 3 0.4882 0.4911 0.000 0.272 0.708 0.020
#> aberrant_ERR2585333 2 0.2281 0.6771 0.000 0.904 0.000 0.096
#> aberrant_ERR2585285 2 0.2647 0.7171 0.000 0.880 0.000 0.120
#> aberrant_ERR2585286 3 0.1452 0.8051 0.000 0.036 0.956 0.008
#> aberrant_ERR2585294 2 0.3726 0.6361 0.000 0.788 0.000 0.212
#> aberrant_ERR2585300 2 0.3610 0.6322 0.000 0.800 0.000 0.200
#> aberrant_ERR2585334 3 0.0524 0.8069 0.000 0.008 0.988 0.004
#> aberrant_ERR2585361 2 0.5698 0.3443 0.000 0.636 0.320 0.044
#> aberrant_ERR2585372 2 0.2704 0.7162 0.000 0.876 0.000 0.124
#> round_ERR2585217 3 0.7711 0.4273 0.260 0.024 0.548 0.168
#> round_ERR2585205 1 0.3088 0.8617 0.864 0.000 0.008 0.128
#> round_ERR2585214 3 0.1706 0.8075 0.036 0.000 0.948 0.016
#> round_ERR2585202 3 0.3652 0.7601 0.092 0.000 0.856 0.052
#> aberrant_ERR2585367 2 0.5793 0.2183 0.000 0.580 0.384 0.036
#> round_ERR2585220 1 0.0921 0.8935 0.972 0.000 0.000 0.028
#> round_ERR2585238 1 0.1118 0.8989 0.964 0.000 0.000 0.036
#> aberrant_ERR2585276 2 0.3444 0.6619 0.000 0.816 0.000 0.184
#> round_ERR2585218 1 0.0707 0.8937 0.980 0.000 0.000 0.020
#> aberrant_ERR2585363 2 0.3853 0.6833 0.000 0.820 0.020 0.160
#> round_ERR2585201 3 0.2124 0.7975 0.068 0.000 0.924 0.008
#> round_ERR2585210 1 0.4327 0.8236 0.812 0.020 0.016 0.152
#> aberrant_ERR2585362 2 0.6214 0.4186 0.004 0.684 0.160 0.152
#> aberrant_ERR2585360 2 0.2973 0.7015 0.000 0.856 0.000 0.144
#> round_ERR2585209 1 0.4036 0.8315 0.836 0.000 0.088 0.076
#> round_ERR2585242 3 0.2125 0.7875 0.076 0.000 0.920 0.004
#> round_ERR2585216 1 0.3509 0.8572 0.860 0.004 0.024 0.112
#> round_ERR2585219 1 0.1970 0.8863 0.932 0.000 0.008 0.060
#> round_ERR2585237 1 0.6619 0.0199 0.492 0.008 0.440 0.060
#> round_ERR2585198 3 0.2867 0.7730 0.104 0.000 0.884 0.012
#> round_ERR2585211 1 0.2988 0.8664 0.876 0.000 0.012 0.112
#> round_ERR2585206 1 0.1004 0.8939 0.972 0.000 0.004 0.024
#> aberrant_ERR2585281 3 0.2918 0.7437 0.000 0.116 0.876 0.008
#> round_ERR2585212 1 0.3134 0.8661 0.884 0.004 0.024 0.088
#> round_ERR2585221 1 0.3726 0.8470 0.788 0.000 0.000 0.212
#> round_ERR2585243 1 0.2589 0.8967 0.884 0.000 0.000 0.116
#> round_ERR2585204 3 0.0779 0.8098 0.016 0.000 0.980 0.004
#> round_ERR2585213 3 0.0592 0.8095 0.016 0.000 0.984 0.000
#> aberrant_ERR2585373 2 0.2921 0.7144 0.000 0.860 0.000 0.140
#> aberrant_ERR2585358 2 0.2011 0.7011 0.000 0.920 0.000 0.080
#> aberrant_ERR2585365 2 0.5954 0.3008 0.000 0.604 0.344 0.052
#> aberrant_ERR2585359 2 0.3649 0.6628 0.000 0.796 0.000 0.204
#> aberrant_ERR2585370 3 0.4761 0.3872 0.000 0.332 0.664 0.004
#> round_ERR2585215 1 0.3221 0.8799 0.876 0.004 0.020 0.100
#> round_ERR2585262 3 0.1377 0.8118 0.020 0.008 0.964 0.008
#> round_ERR2585199 3 0.5355 0.4366 0.360 0.000 0.620 0.020
#> aberrant_ERR2585369 2 0.2081 0.7223 0.000 0.916 0.000 0.084
#> round_ERR2585208 1 0.1792 0.8999 0.932 0.000 0.000 0.068
#> round_ERR2585252 1 0.2216 0.8975 0.908 0.000 0.000 0.092
#> round_ERR2585236 1 0.3681 0.8613 0.848 0.004 0.024 0.124
#> aberrant_ERR2585284 3 0.5614 0.3423 0.000 0.336 0.628 0.036
#> round_ERR2585224 1 0.3852 0.8466 0.800 0.008 0.000 0.192
#> round_ERR2585260 1 0.2589 0.8863 0.884 0.000 0.000 0.116
#> round_ERR2585229 1 0.1474 0.8914 0.948 0.000 0.000 0.052
#> aberrant_ERR2585364 2 0.1716 0.7044 0.000 0.936 0.000 0.064
#> round_ERR2585253 1 0.1211 0.8989 0.960 0.000 0.000 0.040
#> aberrant_ERR2585368 3 0.1716 0.7945 0.000 0.064 0.936 0.000
#> aberrant_ERR2585371 3 0.1557 0.7987 0.000 0.056 0.944 0.000
#> round_ERR2585239 1 0.1557 0.9000 0.944 0.000 0.000 0.056
#> round_ERR2585273 1 0.3074 0.8768 0.848 0.000 0.000 0.152
#> round_ERR2585256 1 0.1042 0.8969 0.972 0.000 0.008 0.020
#> round_ERR2585272 1 0.1807 0.9005 0.940 0.000 0.008 0.052
#> round_ERR2585246 1 0.3444 0.8595 0.816 0.000 0.000 0.184
#> round_ERR2585261 1 0.3501 0.8342 0.848 0.000 0.132 0.020
#> round_ERR2585254 1 0.0657 0.8942 0.984 0.000 0.004 0.012
#> round_ERR2585225 3 0.0779 0.8095 0.016 0.000 0.980 0.004
#> round_ERR2585235 1 0.4300 0.8524 0.820 0.000 0.092 0.088
#> round_ERR2585271 1 0.1305 0.8953 0.960 0.000 0.004 0.036
#> round_ERR2585251 1 0.1940 0.8995 0.924 0.000 0.000 0.076
#> round_ERR2585255 3 0.1151 0.8095 0.024 0.000 0.968 0.008
#> round_ERR2585257 3 0.4914 0.5167 0.312 0.000 0.676 0.012
#> round_ERR2585226 1 0.3172 0.8723 0.840 0.000 0.000 0.160
#> round_ERR2585265 1 0.0817 0.8982 0.976 0.000 0.000 0.024
#> round_ERR2585259 1 0.4514 0.8054 0.812 0.004 0.112 0.072
#> round_ERR2585247 1 0.2647 0.8860 0.880 0.000 0.000 0.120
#> round_ERR2585241 1 0.2662 0.8798 0.900 0.000 0.016 0.084
#> round_ERR2585263 1 0.6768 0.6164 0.644 0.052 0.052 0.252
#> round_ERR2585264 1 0.1389 0.8994 0.952 0.000 0.000 0.048
#> round_ERR2585233 3 0.3048 0.7574 0.108 0.000 0.876 0.016
#> round_ERR2585223 1 0.2530 0.8880 0.888 0.000 0.000 0.112
#> round_ERR2585234 3 0.2179 0.7973 0.064 0.000 0.924 0.012
#> round_ERR2585222 1 0.2814 0.8868 0.868 0.000 0.000 0.132
#> round_ERR2585228 1 0.1209 0.8923 0.964 0.000 0.004 0.032
#> round_ERR2585248 1 0.2011 0.8976 0.920 0.000 0.000 0.080
#> round_ERR2585240 1 0.5839 0.4565 0.604 0.000 0.352 0.044
#> round_ERR2585270 1 0.1807 0.8871 0.940 0.000 0.008 0.052
#> round_ERR2585232 1 0.2021 0.9003 0.936 0.000 0.024 0.040
#> aberrant_ERR2585341 3 0.3636 0.6698 0.000 0.172 0.820 0.008
#> aberrant_ERR2585355 3 0.1824 0.7951 0.000 0.060 0.936 0.004
#> round_ERR2585227 1 0.2408 0.8909 0.896 0.000 0.000 0.104
#> aberrant_ERR2585351 2 0.4220 0.6186 0.000 0.748 0.004 0.248
#> round_ERR2585269 1 0.2469 0.8905 0.892 0.000 0.000 0.108
#> aberrant_ERR2585357 2 0.3958 0.6479 0.000 0.836 0.112 0.052
#> aberrant_ERR2585350 2 0.5364 0.2376 0.000 0.592 0.392 0.016
#> round_ERR2585250 1 0.1489 0.9013 0.952 0.000 0.004 0.044
#> round_ERR2585245 1 0.3074 0.8725 0.848 0.000 0.000 0.152
#> aberrant_ERR2585353 2 0.2149 0.7106 0.000 0.912 0.000 0.088
#> round_ERR2585258 1 0.2868 0.8809 0.864 0.000 0.000 0.136
#> aberrant_ERR2585354 2 0.2760 0.7163 0.000 0.872 0.000 0.128
#> round_ERR2585249 1 0.3311 0.8623 0.828 0.000 0.000 0.172
#> round_ERR2585268 1 0.4233 0.8665 0.820 0.008 0.032 0.140
#> aberrant_ERR2585356 2 0.2704 0.7061 0.000 0.876 0.000 0.124
#> round_ERR2585266 3 0.2988 0.7588 0.112 0.000 0.876 0.012
#> round_ERR2585231 1 0.3528 0.8519 0.808 0.000 0.000 0.192
#> round_ERR2585230 1 0.1305 0.8938 0.960 0.000 0.004 0.036
#> round_ERR2585267 1 0.2345 0.8942 0.900 0.000 0.000 0.100
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 2 0.7013 -0.4088 0.000 0.412 0.028 0.396 0.164
#> aberrant_ERR2585338 3 0.1168 0.8423 0.000 0.032 0.960 0.008 0.000
#> aberrant_ERR2585325 4 0.7079 0.2465 0.000 0.400 0.032 0.404 0.164
#> aberrant_ERR2585283 2 0.5068 0.2707 0.000 0.592 0.000 0.364 0.044
#> aberrant_ERR2585343 2 0.2592 0.7185 0.000 0.892 0.000 0.052 0.056
#> aberrant_ERR2585329 2 0.2653 0.7174 0.000 0.900 0.020 0.028 0.052
#> aberrant_ERR2585317 2 0.3577 0.6961 0.000 0.836 0.004 0.076 0.084
#> aberrant_ERR2585339 2 0.4749 0.3951 0.000 0.628 0.348 0.008 0.016
#> aberrant_ERR2585335 2 0.4219 0.6587 0.000 0.772 0.000 0.156 0.072
#> aberrant_ERR2585287 4 0.7475 0.3855 0.000 0.376 0.152 0.404 0.068
#> aberrant_ERR2585321 2 0.2661 0.7140 0.000 0.888 0.000 0.056 0.056
#> aberrant_ERR2585297 1 0.4045 -0.1943 0.644 0.000 0.000 0.000 0.356
#> aberrant_ERR2585337 2 0.3556 0.6933 0.000 0.836 0.036 0.116 0.012
#> aberrant_ERR2585319 2 0.4171 0.6698 0.000 0.784 0.000 0.112 0.104
#> aberrant_ERR2585315 2 0.3409 0.6700 0.000 0.824 0.000 0.144 0.032
#> aberrant_ERR2585336 2 0.5003 0.6541 0.000 0.764 0.080 0.072 0.084
#> aberrant_ERR2585307 2 0.5612 0.4515 0.000 0.660 0.248 0.044 0.048
#> aberrant_ERR2585301 2 0.3110 0.7122 0.000 0.860 0.000 0.060 0.080
#> aberrant_ERR2585326 2 0.4287 0.6435 0.000 0.780 0.164 0.032 0.024
#> aberrant_ERR2585331 3 0.0290 0.8550 0.000 0.000 0.992 0.008 0.000
#> aberrant_ERR2585346 2 0.4351 0.6339 0.000 0.768 0.000 0.132 0.100
#> aberrant_ERR2585314 2 0.5157 0.5866 0.004 0.692 0.004 0.076 0.224
#> aberrant_ERR2585298 3 0.0566 0.8571 0.012 0.000 0.984 0.000 0.004
#> aberrant_ERR2585345 2 0.2447 0.7171 0.000 0.912 0.024 0.032 0.032
#> aberrant_ERR2585299 1 0.2403 0.5122 0.904 0.012 0.000 0.012 0.072
#> aberrant_ERR2585309 5 0.4306 0.7047 0.492 0.000 0.000 0.000 0.508
#> aberrant_ERR2585303 3 0.4641 -0.1042 0.000 0.456 0.532 0.000 0.012
#> aberrant_ERR2585313 2 0.4234 0.6571 0.000 0.776 0.012 0.172 0.040
#> aberrant_ERR2585318 2 0.3303 0.7065 0.000 0.848 0.000 0.076 0.076
#> aberrant_ERR2585328 2 0.6547 0.3735 0.000 0.588 0.252 0.052 0.108
#> aberrant_ERR2585330 2 0.2863 0.7046 0.000 0.876 0.000 0.064 0.060
#> aberrant_ERR2585293 4 0.2777 0.6626 0.000 0.120 0.016 0.864 0.000
#> aberrant_ERR2585342 2 0.2592 0.7077 0.000 0.892 0.000 0.052 0.056
#> aberrant_ERR2585348 2 0.6531 0.2058 0.000 0.508 0.364 0.036 0.092
#> aberrant_ERR2585352 2 0.2381 0.7130 0.000 0.908 0.004 0.052 0.036
#> aberrant_ERR2585308 1 0.4182 -0.3867 0.600 0.000 0.000 0.000 0.400
#> aberrant_ERR2585349 3 0.6051 0.5486 0.020 0.136 0.692 0.036 0.116
#> aberrant_ERR2585316 2 0.4779 0.6037 0.000 0.716 0.000 0.084 0.200
#> aberrant_ERR2585306 2 0.5491 0.0287 0.008 0.476 0.000 0.044 0.472
#> aberrant_ERR2585324 2 0.4501 0.6450 0.000 0.756 0.000 0.128 0.116
#> aberrant_ERR2585310 2 0.6343 0.1821 0.096 0.524 0.000 0.024 0.356
#> aberrant_ERR2585296 1 0.4879 0.4550 0.744 0.012 0.020 0.036 0.188
#> aberrant_ERR2585275 2 0.5491 0.4927 0.004 0.668 0.000 0.176 0.152
#> aberrant_ERR2585311 2 0.3365 0.7006 0.000 0.836 0.000 0.044 0.120
#> aberrant_ERR2585292 4 0.2777 0.6626 0.000 0.120 0.016 0.864 0.000
#> aberrant_ERR2585282 2 0.3110 0.7162 0.000 0.860 0.000 0.060 0.080
#> aberrant_ERR2585305 2 0.4879 0.3943 0.012 0.636 0.000 0.020 0.332
#> aberrant_ERR2585278 2 0.3146 0.7065 0.000 0.856 0.000 0.052 0.092
#> aberrant_ERR2585347 2 0.4138 0.6762 0.000 0.780 0.000 0.148 0.072
#> aberrant_ERR2585332 2 0.4887 0.6200 0.000 0.720 0.000 0.132 0.148
#> aberrant_ERR2585280 2 0.3847 0.6410 0.000 0.784 0.000 0.180 0.036
#> aberrant_ERR2585304 5 0.7950 0.3646 0.368 0.152 0.104 0.004 0.372
#> aberrant_ERR2585322 2 0.4295 0.6903 0.000 0.808 0.092 0.052 0.048
#> aberrant_ERR2585279 3 0.0162 0.8561 0.000 0.000 0.996 0.004 0.000
#> aberrant_ERR2585277 3 0.1444 0.8407 0.000 0.040 0.948 0.012 0.000
#> aberrant_ERR2585295 3 0.4737 0.6412 0.000 0.112 0.768 0.096 0.024
#> aberrant_ERR2585333 2 0.3565 0.6725 0.000 0.816 0.000 0.144 0.040
#> aberrant_ERR2585285 2 0.1918 0.7123 0.000 0.928 0.000 0.036 0.036
#> aberrant_ERR2585286 3 0.0854 0.8553 0.000 0.012 0.976 0.008 0.004
#> aberrant_ERR2585294 2 0.3962 0.6682 0.000 0.800 0.000 0.088 0.112
#> aberrant_ERR2585300 2 0.4624 0.6316 0.000 0.744 0.000 0.112 0.144
#> aberrant_ERR2585334 3 0.0162 0.8561 0.000 0.000 0.996 0.004 0.000
#> aberrant_ERR2585361 2 0.5386 0.5219 0.000 0.680 0.236 0.032 0.052
#> aberrant_ERR2585372 2 0.3912 0.6927 0.000 0.804 0.000 0.108 0.088
#> round_ERR2585217 1 0.6928 0.2324 0.568 0.016 0.208 0.024 0.184
#> round_ERR2585205 1 0.3759 0.4810 0.820 0.008 0.004 0.032 0.136
#> round_ERR2585214 3 0.1331 0.8454 0.040 0.000 0.952 0.000 0.008
#> round_ERR2585202 3 0.3453 0.7873 0.048 0.004 0.864 0.028 0.056
#> aberrant_ERR2585367 2 0.5911 0.2338 0.000 0.532 0.392 0.040 0.036
#> round_ERR2585220 1 0.1357 0.5131 0.948 0.000 0.004 0.000 0.048
#> round_ERR2585238 1 0.3039 0.3301 0.808 0.000 0.000 0.000 0.192
#> aberrant_ERR2585276 2 0.3906 0.6883 0.000 0.804 0.000 0.084 0.112
#> round_ERR2585218 1 0.1357 0.5166 0.948 0.000 0.004 0.000 0.048
#> aberrant_ERR2585363 2 0.3477 0.6901 0.000 0.832 0.000 0.056 0.112
#> round_ERR2585201 3 0.1502 0.8348 0.056 0.000 0.940 0.004 0.000
#> round_ERR2585210 1 0.3530 0.4729 0.812 0.004 0.008 0.008 0.168
#> aberrant_ERR2585362 2 0.6212 0.4685 0.004 0.652 0.040 0.132 0.172
#> aberrant_ERR2585360 2 0.2900 0.7074 0.000 0.864 0.000 0.028 0.108
#> round_ERR2585209 1 0.3690 0.4910 0.828 0.000 0.052 0.008 0.112
#> round_ERR2585242 3 0.0566 0.8572 0.012 0.000 0.984 0.000 0.004
#> round_ERR2585216 1 0.3708 0.4680 0.808 0.000 0.012 0.020 0.160
#> round_ERR2585219 1 0.2411 0.5111 0.884 0.000 0.000 0.008 0.108
#> round_ERR2585237 1 0.5315 0.3591 0.712 0.004 0.160 0.012 0.112
#> round_ERR2585198 3 0.0865 0.8542 0.024 0.000 0.972 0.000 0.004
#> round_ERR2585211 1 0.3022 0.4959 0.848 0.000 0.004 0.012 0.136
#> round_ERR2585206 1 0.1197 0.5076 0.952 0.000 0.000 0.000 0.048
#> aberrant_ERR2585281 3 0.2340 0.8061 0.000 0.068 0.908 0.012 0.012
#> round_ERR2585212 1 0.3320 0.4904 0.828 0.000 0.012 0.008 0.152
#> round_ERR2585221 5 0.5156 0.7299 0.440 0.012 0.000 0.020 0.528
#> round_ERR2585243 1 0.4252 -0.0433 0.652 0.000 0.000 0.008 0.340
#> round_ERR2585204 3 0.0162 0.8574 0.004 0.000 0.996 0.000 0.000
#> round_ERR2585213 3 0.0451 0.8580 0.004 0.000 0.988 0.008 0.000
#> aberrant_ERR2585373 2 0.2570 0.7164 0.000 0.888 0.000 0.028 0.084
#> aberrant_ERR2585358 2 0.3621 0.6669 0.000 0.788 0.000 0.192 0.020
#> aberrant_ERR2585365 2 0.5422 0.4738 0.000 0.656 0.268 0.024 0.052
#> aberrant_ERR2585359 2 0.4297 0.6553 0.000 0.764 0.000 0.072 0.164
#> aberrant_ERR2585370 3 0.4810 0.1118 0.000 0.400 0.580 0.012 0.008
#> round_ERR2585215 1 0.3158 0.5014 0.840 0.008 0.004 0.004 0.144
#> round_ERR2585262 3 0.0613 0.8580 0.008 0.000 0.984 0.004 0.004
#> round_ERR2585199 1 0.4827 0.0211 0.504 0.000 0.476 0.000 0.020
#> aberrant_ERR2585369 2 0.1907 0.7155 0.000 0.928 0.000 0.044 0.028
#> round_ERR2585208 1 0.4084 -0.0285 0.668 0.000 0.000 0.004 0.328
#> round_ERR2585252 1 0.3661 0.1085 0.724 0.000 0.000 0.000 0.276
#> round_ERR2585236 1 0.4837 0.4276 0.752 0.028 0.008 0.036 0.176
#> aberrant_ERR2585284 2 0.6559 0.0458 0.000 0.448 0.432 0.044 0.076
#> round_ERR2585224 5 0.4533 0.7426 0.448 0.008 0.000 0.000 0.544
#> round_ERR2585260 1 0.4150 -0.3579 0.612 0.000 0.000 0.000 0.388
#> round_ERR2585229 1 0.2237 0.5141 0.904 0.004 0.000 0.008 0.084
#> aberrant_ERR2585364 2 0.3413 0.6967 0.000 0.832 0.000 0.124 0.044
#> round_ERR2585253 1 0.2929 0.3777 0.820 0.000 0.000 0.000 0.180
#> aberrant_ERR2585368 3 0.0992 0.8544 0.000 0.008 0.968 0.024 0.000
#> aberrant_ERR2585371 3 0.0992 0.8544 0.000 0.008 0.968 0.024 0.000
#> round_ERR2585239 1 0.2929 0.4690 0.860 0.004 0.004 0.008 0.124
#> round_ERR2585273 1 0.4410 -0.5464 0.556 0.000 0.000 0.004 0.440
#> round_ERR2585256 1 0.2597 0.4629 0.872 0.000 0.004 0.004 0.120
#> round_ERR2585272 1 0.4396 0.3484 0.744 0.004 0.016 0.016 0.220
#> round_ERR2585246 5 0.4307 0.7033 0.496 0.000 0.000 0.000 0.504
#> round_ERR2585261 1 0.4238 0.3352 0.740 0.000 0.228 0.004 0.028
#> round_ERR2585254 1 0.1764 0.4941 0.928 0.000 0.008 0.000 0.064
#> round_ERR2585225 3 0.0324 0.8573 0.004 0.000 0.992 0.000 0.004
#> round_ERR2585235 1 0.6792 -0.3741 0.432 0.000 0.204 0.008 0.356
#> round_ERR2585271 1 0.1408 0.5120 0.948 0.000 0.000 0.008 0.044
#> round_ERR2585251 1 0.3636 0.1674 0.728 0.000 0.000 0.000 0.272
#> round_ERR2585255 3 0.0579 0.8582 0.008 0.000 0.984 0.000 0.008
#> round_ERR2585257 3 0.4387 0.5681 0.232 0.000 0.732 0.008 0.028
#> round_ERR2585226 1 0.4283 -0.5877 0.544 0.000 0.000 0.000 0.456
#> round_ERR2585265 1 0.2233 0.4571 0.892 0.000 0.004 0.000 0.104
#> round_ERR2585259 1 0.3831 0.4857 0.828 0.004 0.032 0.020 0.116
#> round_ERR2585247 1 0.4182 -0.3904 0.600 0.000 0.000 0.000 0.400
#> round_ERR2585241 1 0.3512 0.4870 0.828 0.004 0.004 0.024 0.140
#> round_ERR2585263 1 0.6620 0.2896 0.608 0.056 0.020 0.064 0.252
#> round_ERR2585264 1 0.3480 0.2049 0.752 0.000 0.000 0.000 0.248
#> round_ERR2585233 3 0.2308 0.8145 0.048 0.000 0.912 0.004 0.036
#> round_ERR2585223 1 0.4182 -0.4032 0.600 0.000 0.000 0.000 0.400
#> round_ERR2585234 3 0.1124 0.8493 0.036 0.000 0.960 0.000 0.004
#> round_ERR2585222 1 0.4331 -0.4154 0.596 0.000 0.000 0.004 0.400
#> round_ERR2585228 1 0.1864 0.5175 0.924 0.004 0.004 0.000 0.068
#> round_ERR2585248 1 0.3579 0.2166 0.756 0.000 0.000 0.004 0.240
#> round_ERR2585240 3 0.6562 -0.2307 0.308 0.000 0.464 0.000 0.228
#> round_ERR2585270 1 0.2177 0.5159 0.908 0.000 0.008 0.004 0.080
#> round_ERR2585232 1 0.5502 0.0307 0.652 0.000 0.156 0.000 0.192
#> aberrant_ERR2585341 3 0.3478 0.7194 0.000 0.108 0.844 0.016 0.032
#> aberrant_ERR2585355 3 0.1026 0.8496 0.000 0.024 0.968 0.004 0.004
#> round_ERR2585227 1 0.4150 -0.3464 0.612 0.000 0.000 0.000 0.388
#> aberrant_ERR2585351 2 0.4527 0.6372 0.000 0.732 0.000 0.064 0.204
#> round_ERR2585269 1 0.4101 -0.3007 0.628 0.000 0.000 0.000 0.372
#> aberrant_ERR2585357 2 0.5160 0.6081 0.000 0.724 0.132 0.128 0.016
#> aberrant_ERR2585350 2 0.4666 0.2958 0.000 0.572 0.412 0.016 0.000
#> round_ERR2585250 1 0.3560 0.4463 0.816 0.004 0.008 0.012 0.160
#> round_ERR2585245 1 0.4294 -0.6209 0.532 0.000 0.000 0.000 0.468
#> aberrant_ERR2585353 2 0.3800 0.6882 0.000 0.812 0.000 0.108 0.080
#> round_ERR2585258 1 0.4367 -0.5055 0.580 0.000 0.000 0.004 0.416
#> aberrant_ERR2585354 2 0.3918 0.6967 0.000 0.804 0.000 0.096 0.100
#> round_ERR2585249 1 0.4283 -0.6067 0.544 0.000 0.000 0.000 0.456
#> round_ERR2585268 1 0.5959 0.2252 0.636 0.032 0.020 0.040 0.272
#> aberrant_ERR2585356 2 0.3477 0.6910 0.000 0.824 0.000 0.040 0.136
#> round_ERR2585266 3 0.1682 0.8397 0.032 0.000 0.944 0.012 0.012
#> round_ERR2585231 5 0.4434 0.7419 0.460 0.000 0.000 0.004 0.536
#> round_ERR2585230 1 0.1857 0.5180 0.928 0.000 0.004 0.008 0.060
#> round_ERR2585267 1 0.4225 -0.2492 0.632 0.000 0.004 0.000 0.364
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 6 0.6898 0.98291 0.000 0.016 0.040 0.268 0.216 0.460
#> aberrant_ERR2585338 2 0.0837 0.84467 0.000 0.972 0.000 0.004 0.020 0.004
#> aberrant_ERR2585325 6 0.6954 0.98279 0.000 0.020 0.040 0.268 0.212 0.460
#> aberrant_ERR2585283 5 0.5630 0.31777 0.004 0.000 0.000 0.268 0.552 0.176
#> aberrant_ERR2585343 5 0.3078 0.65536 0.000 0.000 0.012 0.032 0.844 0.112
#> aberrant_ERR2585329 5 0.2382 0.66049 0.000 0.008 0.004 0.020 0.896 0.072
#> aberrant_ERR2585317 5 0.4177 0.61368 0.000 0.000 0.052 0.036 0.772 0.140
#> aberrant_ERR2585339 5 0.4489 0.30144 0.000 0.404 0.000 0.008 0.568 0.020
#> aberrant_ERR2585335 5 0.4326 0.60790 0.000 0.000 0.024 0.128 0.760 0.088
#> aberrant_ERR2585287 5 0.8152 -0.51398 0.000 0.208 0.024 0.236 0.280 0.252
#> aberrant_ERR2585321 5 0.3752 0.65211 0.012 0.000 0.028 0.048 0.824 0.088
#> aberrant_ERR2585297 1 0.3349 0.66750 0.804 0.000 0.164 0.008 0.000 0.024
#> aberrant_ERR2585337 5 0.3977 0.64598 0.000 0.056 0.004 0.108 0.800 0.032
#> aberrant_ERR2585319 5 0.5581 0.48575 0.016 0.000 0.020 0.088 0.624 0.252
#> aberrant_ERR2585315 5 0.3699 0.63702 0.000 0.000 0.004 0.112 0.796 0.088
#> aberrant_ERR2585336 5 0.4544 0.62928 0.000 0.056 0.016 0.064 0.776 0.088
#> aberrant_ERR2585307 5 0.6282 0.16442 0.064 0.348 0.000 0.020 0.512 0.056
#> aberrant_ERR2585301 5 0.3538 0.65944 0.004 0.000 0.016 0.048 0.824 0.108
#> aberrant_ERR2585326 5 0.4164 0.62621 0.000 0.128 0.004 0.012 0.772 0.084
#> aberrant_ERR2585331 2 0.0000 0.84919 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585346 5 0.5206 0.51196 0.016 0.000 0.008 0.076 0.644 0.256
#> aberrant_ERR2585314 5 0.5555 0.40899 0.008 0.000 0.148 0.012 0.624 0.208
#> aberrant_ERR2585298 2 0.0146 0.84975 0.000 0.996 0.000 0.000 0.000 0.004
#> aberrant_ERR2585345 5 0.2794 0.66560 0.004 0.028 0.012 0.016 0.888 0.052
#> aberrant_ERR2585299 3 0.4882 0.67501 0.232 0.000 0.676 0.004 0.012 0.076
#> aberrant_ERR2585309 1 0.2408 0.69774 0.896 0.000 0.068 0.008 0.004 0.024
#> aberrant_ERR2585303 2 0.4393 -0.11236 0.000 0.524 0.000 0.000 0.452 0.024
#> aberrant_ERR2585313 5 0.4847 0.58455 0.000 0.016 0.004 0.128 0.712 0.140
#> aberrant_ERR2585318 5 0.4086 0.64954 0.000 0.000 0.040 0.052 0.784 0.124
#> aberrant_ERR2585328 5 0.7211 0.27226 0.004 0.192 0.060 0.040 0.516 0.188
#> aberrant_ERR2585330 5 0.2316 0.65704 0.004 0.000 0.004 0.028 0.900 0.064
#> aberrant_ERR2585293 4 0.1411 1.00000 0.000 0.004 0.000 0.936 0.060 0.000
#> aberrant_ERR2585342 5 0.3139 0.65287 0.000 0.000 0.008 0.036 0.836 0.120
#> aberrant_ERR2585348 5 0.6454 0.13275 0.000 0.340 0.016 0.020 0.468 0.156
#> aberrant_ERR2585352 5 0.3192 0.64730 0.000 0.004 0.004 0.028 0.828 0.136
#> aberrant_ERR2585308 1 0.3829 0.66834 0.768 0.000 0.192 0.016 0.004 0.020
#> aberrant_ERR2585349 2 0.6417 0.38384 0.000 0.592 0.116 0.008 0.120 0.164
#> aberrant_ERR2585316 5 0.4666 0.53799 0.000 0.000 0.088 0.008 0.692 0.212
#> aberrant_ERR2585306 5 0.6335 0.05316 0.344 0.000 0.004 0.020 0.452 0.180
#> aberrant_ERR2585324 5 0.5579 0.47356 0.016 0.000 0.016 0.088 0.612 0.268
#> aberrant_ERR2585310 5 0.6632 0.11940 0.276 0.000 0.028 0.008 0.460 0.228
#> aberrant_ERR2585296 3 0.6032 0.55945 0.260 0.004 0.564 0.000 0.032 0.140
#> aberrant_ERR2585275 5 0.6016 0.39707 0.032 0.000 0.020 0.080 0.560 0.308
#> aberrant_ERR2585311 5 0.2426 0.66137 0.000 0.000 0.012 0.012 0.884 0.092
#> aberrant_ERR2585292 4 0.1411 1.00000 0.000 0.004 0.000 0.936 0.060 0.000
#> aberrant_ERR2585282 5 0.3303 0.65728 0.004 0.000 0.020 0.040 0.844 0.092
#> aberrant_ERR2585305 5 0.6143 0.24856 0.236 0.000 0.008 0.012 0.528 0.216
#> aberrant_ERR2585278 5 0.3801 0.65416 0.008 0.000 0.040 0.012 0.796 0.144
#> aberrant_ERR2585347 5 0.5438 0.53439 0.000 0.000 0.040 0.116 0.652 0.192
#> aberrant_ERR2585332 5 0.5524 0.55069 0.000 0.000 0.076 0.088 0.660 0.176
#> aberrant_ERR2585280 5 0.4785 0.56280 0.000 0.012 0.008 0.072 0.700 0.208
#> aberrant_ERR2585304 1 0.7486 0.13948 0.492 0.184 0.016 0.012 0.172 0.124
#> aberrant_ERR2585322 5 0.3862 0.65240 0.000 0.084 0.012 0.004 0.800 0.100
#> aberrant_ERR2585279 2 0.0000 0.84919 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585277 2 0.0993 0.84302 0.000 0.964 0.000 0.000 0.024 0.012
#> aberrant_ERR2585295 2 0.5473 0.56593 0.000 0.696 0.012 0.076 0.096 0.120
#> aberrant_ERR2585333 5 0.3239 0.64200 0.004 0.000 0.004 0.064 0.840 0.088
#> aberrant_ERR2585285 5 0.2362 0.65944 0.000 0.000 0.012 0.016 0.892 0.080
#> aberrant_ERR2585286 2 0.0665 0.84927 0.000 0.980 0.008 0.000 0.004 0.008
#> aberrant_ERR2585294 5 0.4148 0.59226 0.020 0.000 0.020 0.012 0.752 0.196
#> aberrant_ERR2585300 5 0.4798 0.55687 0.060 0.000 0.004 0.032 0.712 0.192
#> aberrant_ERR2585334 2 0.0000 0.84919 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585361 5 0.5144 0.48708 0.000 0.240 0.028 0.004 0.660 0.068
#> aberrant_ERR2585372 5 0.4594 0.53774 0.000 0.000 0.020 0.052 0.696 0.232
#> round_ERR2585217 3 0.4812 0.54328 0.016 0.132 0.736 0.000 0.020 0.096
#> round_ERR2585205 3 0.3355 0.71316 0.132 0.000 0.816 0.000 0.004 0.048
#> round_ERR2585214 2 0.1984 0.82079 0.000 0.912 0.056 0.000 0.000 0.032
#> round_ERR2585202 2 0.4731 0.68110 0.028 0.760 0.112 0.012 0.008 0.080
#> aberrant_ERR2585367 5 0.5892 0.20944 0.000 0.356 0.004 0.016 0.504 0.120
#> round_ERR2585220 3 0.4029 0.64406 0.292 0.000 0.680 0.000 0.000 0.028
#> round_ERR2585238 1 0.4494 0.17522 0.544 0.000 0.424 0.000 0.000 0.032
#> aberrant_ERR2585276 5 0.4332 0.60451 0.012 0.000 0.028 0.020 0.740 0.200
#> round_ERR2585218 3 0.4414 0.67001 0.260 0.000 0.676 0.000 0.000 0.064
#> aberrant_ERR2585363 5 0.4217 0.60142 0.000 0.004 0.072 0.016 0.768 0.140
#> round_ERR2585201 2 0.1750 0.82490 0.004 0.928 0.056 0.004 0.000 0.008
#> round_ERR2585210 3 0.4063 0.69529 0.128 0.004 0.780 0.000 0.012 0.076
#> aberrant_ERR2585362 5 0.6687 0.10791 0.000 0.020 0.056 0.128 0.520 0.276
#> aberrant_ERR2585360 5 0.3596 0.66171 0.024 0.000 0.012 0.032 0.828 0.104
#> round_ERR2585209 3 0.4931 0.70922 0.184 0.032 0.712 0.004 0.004 0.064
#> round_ERR2585242 2 0.0436 0.85083 0.000 0.988 0.004 0.004 0.000 0.004
#> round_ERR2585216 3 0.4537 0.67486 0.128 0.012 0.748 0.004 0.004 0.104
#> round_ERR2585219 3 0.3351 0.71328 0.160 0.000 0.800 0.000 0.000 0.040
#> round_ERR2585237 3 0.5225 0.59595 0.104 0.156 0.688 0.000 0.000 0.052
#> round_ERR2585198 2 0.0725 0.84939 0.000 0.976 0.012 0.000 0.000 0.012
#> round_ERR2585211 3 0.2923 0.69787 0.100 0.000 0.848 0.000 0.000 0.052
#> round_ERR2585206 3 0.4098 0.63952 0.292 0.000 0.676 0.000 0.000 0.032
#> aberrant_ERR2585281 2 0.2511 0.78487 0.000 0.880 0.000 0.000 0.056 0.064
#> round_ERR2585212 3 0.3990 0.70003 0.120 0.004 0.800 0.012 0.012 0.052
#> round_ERR2585221 1 0.3773 0.64082 0.820 0.000 0.056 0.016 0.016 0.092
#> round_ERR2585243 1 0.4507 0.54138 0.660 0.000 0.284 0.004 0.000 0.052
#> round_ERR2585204 2 0.0260 0.84979 0.000 0.992 0.000 0.000 0.000 0.008
#> round_ERR2585213 2 0.0260 0.85071 0.000 0.992 0.000 0.000 0.000 0.008
#> aberrant_ERR2585373 5 0.3528 0.66055 0.012 0.000 0.028 0.012 0.820 0.128
#> aberrant_ERR2585358 5 0.4107 0.61769 0.000 0.000 0.016 0.168 0.760 0.056
#> aberrant_ERR2585365 5 0.5243 0.46900 0.000 0.240 0.028 0.004 0.652 0.076
#> aberrant_ERR2585359 5 0.4720 0.55571 0.000 0.000 0.084 0.016 0.700 0.200
#> aberrant_ERR2585370 2 0.3956 0.40535 0.000 0.684 0.000 0.000 0.292 0.024
#> round_ERR2585215 3 0.5773 0.61003 0.268 0.008 0.600 0.012 0.012 0.100
#> round_ERR2585262 2 0.0951 0.84923 0.000 0.968 0.008 0.000 0.004 0.020
#> round_ERR2585199 2 0.5027 0.13322 0.032 0.532 0.412 0.000 0.000 0.024
#> aberrant_ERR2585369 5 0.2647 0.65620 0.000 0.000 0.020 0.016 0.876 0.088
#> round_ERR2585208 1 0.4947 0.19288 0.552 0.000 0.384 0.004 0.000 0.060
#> round_ERR2585252 1 0.3808 0.51640 0.700 0.000 0.284 0.004 0.000 0.012
#> round_ERR2585236 3 0.6057 0.56329 0.228 0.004 0.564 0.008 0.012 0.184
#> aberrant_ERR2585284 5 0.7051 -0.00415 0.000 0.376 0.024 0.048 0.392 0.160
#> round_ERR2585224 1 0.2688 0.62701 0.884 0.000 0.020 0.004 0.024 0.068
#> round_ERR2585260 1 0.2053 0.70304 0.888 0.000 0.108 0.000 0.000 0.004
#> round_ERR2585229 3 0.3883 0.71427 0.200 0.000 0.752 0.000 0.004 0.044
#> aberrant_ERR2585364 5 0.3633 0.65505 0.004 0.000 0.008 0.112 0.812 0.064
#> round_ERR2585253 3 0.4922 0.27982 0.444 0.000 0.504 0.008 0.000 0.044
#> aberrant_ERR2585368 2 0.0551 0.85023 0.000 0.984 0.004 0.004 0.000 0.008
#> aberrant_ERR2585371 2 0.0551 0.85023 0.000 0.984 0.004 0.004 0.000 0.008
#> round_ERR2585239 3 0.4697 0.57470 0.324 0.000 0.612 0.000 0.000 0.064
#> round_ERR2585273 1 0.2505 0.70214 0.880 0.000 0.092 0.008 0.000 0.020
#> round_ERR2585256 1 0.4466 -0.16963 0.500 0.000 0.476 0.004 0.000 0.020
#> round_ERR2585272 1 0.5827 -0.07127 0.464 0.036 0.428 0.004 0.000 0.068
#> round_ERR2585246 1 0.2189 0.67778 0.912 0.000 0.044 0.008 0.004 0.032
#> round_ERR2585261 3 0.6626 0.26604 0.204 0.356 0.400 0.000 0.000 0.040
#> round_ERR2585254 3 0.4420 0.59449 0.320 0.000 0.640 0.004 0.000 0.036
#> round_ERR2585225 2 0.0976 0.84820 0.000 0.968 0.008 0.008 0.000 0.016
#> round_ERR2585235 1 0.6477 0.45772 0.612 0.176 0.104 0.020 0.008 0.080
#> round_ERR2585271 3 0.4775 0.63746 0.296 0.000 0.636 0.008 0.000 0.060
#> round_ERR2585251 1 0.4435 0.29697 0.604 0.000 0.364 0.004 0.000 0.028
#> round_ERR2585255 2 0.0405 0.85034 0.000 0.988 0.008 0.000 0.000 0.004
#> round_ERR2585257 2 0.5523 0.52368 0.112 0.672 0.152 0.004 0.000 0.060
#> round_ERR2585226 1 0.1802 0.69640 0.916 0.000 0.072 0.000 0.000 0.012
#> round_ERR2585265 3 0.4437 0.36801 0.436 0.000 0.540 0.004 0.000 0.020
#> round_ERR2585259 3 0.4556 0.70583 0.176 0.032 0.744 0.004 0.008 0.036
#> round_ERR2585247 1 0.3130 0.69292 0.824 0.000 0.144 0.004 0.000 0.028
#> round_ERR2585241 3 0.4154 0.70420 0.164 0.000 0.740 0.000 0.000 0.096
#> round_ERR2585263 3 0.4539 0.55932 0.040 0.008 0.740 0.004 0.024 0.184
#> round_ERR2585264 1 0.5005 0.06104 0.520 0.000 0.420 0.008 0.000 0.052
#> round_ERR2585233 2 0.2483 0.79811 0.060 0.896 0.024 0.004 0.000 0.016
#> round_ERR2585223 1 0.2358 0.70144 0.876 0.000 0.108 0.000 0.000 0.016
#> round_ERR2585234 2 0.1461 0.83412 0.000 0.940 0.044 0.000 0.000 0.016
#> round_ERR2585222 1 0.4108 0.64713 0.748 0.000 0.184 0.008 0.000 0.060
#> round_ERR2585228 3 0.3876 0.67115 0.276 0.000 0.700 0.000 0.000 0.024
#> round_ERR2585248 1 0.5188 0.15066 0.540 0.000 0.388 0.008 0.004 0.060
#> round_ERR2585240 2 0.4052 0.49009 0.304 0.676 0.008 0.004 0.000 0.008
#> round_ERR2585270 3 0.4149 0.70740 0.212 0.004 0.728 0.000 0.000 0.056
#> round_ERR2585232 1 0.6692 0.24298 0.472 0.252 0.232 0.008 0.000 0.036
#> aberrant_ERR2585341 2 0.3668 0.70652 0.000 0.816 0.012 0.004 0.084 0.084
#> aberrant_ERR2585355 2 0.0951 0.84476 0.000 0.968 0.008 0.000 0.020 0.004
#> round_ERR2585227 1 0.2791 0.69558 0.852 0.000 0.124 0.008 0.000 0.016
#> aberrant_ERR2585351 5 0.5482 0.53111 0.028 0.004 0.108 0.008 0.668 0.184
#> round_ERR2585269 1 0.2982 0.68320 0.828 0.000 0.152 0.008 0.000 0.012
#> aberrant_ERR2585357 5 0.5612 0.51832 0.000 0.176 0.000 0.096 0.652 0.076
#> aberrant_ERR2585350 5 0.4192 0.32038 0.000 0.412 0.000 0.000 0.572 0.016
#> round_ERR2585250 3 0.5821 0.41560 0.332 0.000 0.524 0.004 0.012 0.128
#> round_ERR2585245 1 0.2424 0.69819 0.892 0.000 0.080 0.008 0.008 0.012
#> aberrant_ERR2585353 5 0.3969 0.62325 0.000 0.000 0.040 0.076 0.800 0.084
#> round_ERR2585258 1 0.2804 0.70084 0.852 0.000 0.120 0.004 0.000 0.024
#> aberrant_ERR2585354 5 0.5129 0.58911 0.016 0.000 0.040 0.080 0.716 0.148
#> round_ERR2585249 1 0.2182 0.69976 0.900 0.000 0.076 0.004 0.000 0.020
#> round_ERR2585268 3 0.6427 0.07358 0.408 0.032 0.424 0.000 0.012 0.124
#> aberrant_ERR2585356 5 0.3772 0.63422 0.012 0.000 0.004 0.036 0.788 0.160
#> round_ERR2585266 2 0.0547 0.84715 0.020 0.980 0.000 0.000 0.000 0.000
#> round_ERR2585231 1 0.1860 0.67422 0.928 0.000 0.028 0.004 0.004 0.036
#> round_ERR2585230 3 0.4549 0.67738 0.232 0.000 0.680 0.000 0.000 0.088
#> round_ERR2585267 1 0.3730 0.67034 0.784 0.000 0.160 0.008 0.000 0.048
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> CV:NMF 156 2.05e-28 2
#> CV:NMF 151 4.96e-22 3
#> CV:NMF 138 2.91e-19 4
#> CV:NMF 93 1.66e-10 5
#> CV:NMF 121 5.43e-15 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "hclust"]
# you can also extract it by
# res = res_list["MAD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.204 0.459 0.770 0.2982 0.718 0.718
#> 3 3 0.300 0.693 0.825 0.6865 0.596 0.488
#> 4 4 0.446 0.621 0.761 0.2579 0.809 0.614
#> 5 5 0.506 0.486 0.761 0.0832 0.982 0.949
#> 6 6 0.528 0.418 0.707 0.0557 0.855 0.636
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.3431 0.664046 0.064 0.936
#> aberrant_ERR2585338 2 0.0376 0.657948 0.004 0.996
#> aberrant_ERR2585325 2 0.3431 0.664046 0.064 0.936
#> aberrant_ERR2585283 1 0.5059 0.491431 0.888 0.112
#> aberrant_ERR2585343 2 0.6801 0.606066 0.180 0.820
#> aberrant_ERR2585329 2 0.0000 0.656225 0.000 1.000
#> aberrant_ERR2585317 2 0.0000 0.656225 0.000 1.000
#> aberrant_ERR2585339 2 0.0000 0.656225 0.000 1.000
#> aberrant_ERR2585335 2 0.2043 0.665498 0.032 0.968
#> aberrant_ERR2585287 1 0.9815 0.342845 0.580 0.420
#> aberrant_ERR2585321 2 0.5946 0.634175 0.144 0.856
#> aberrant_ERR2585297 1 0.9998 0.332090 0.508 0.492
#> aberrant_ERR2585337 2 0.0000 0.656225 0.000 1.000
#> aberrant_ERR2585319 2 0.1843 0.659916 0.028 0.972
#> aberrant_ERR2585315 2 0.0672 0.658869 0.008 0.992
#> aberrant_ERR2585336 2 0.0000 0.656225 0.000 1.000
#> aberrant_ERR2585307 2 0.2423 0.662089 0.040 0.960
#> aberrant_ERR2585301 2 0.2778 0.665951 0.048 0.952
#> aberrant_ERR2585326 2 0.0000 0.656225 0.000 1.000
#> aberrant_ERR2585331 2 0.0000 0.656225 0.000 1.000
#> aberrant_ERR2585346 1 0.4939 0.490890 0.892 0.108
#> aberrant_ERR2585314 2 0.0000 0.656225 0.000 1.000
#> aberrant_ERR2585298 2 0.8144 0.511042 0.252 0.748
#> aberrant_ERR2585345 2 0.0000 0.656225 0.000 1.000
#> aberrant_ERR2585299 2 0.9977 -0.195690 0.472 0.528
#> aberrant_ERR2585309 1 0.9552 0.619568 0.624 0.376
#> aberrant_ERR2585303 2 0.1184 0.662112 0.016 0.984
#> aberrant_ERR2585313 2 0.0000 0.656225 0.000 1.000
#> aberrant_ERR2585318 2 0.3114 0.667506 0.056 0.944
#> aberrant_ERR2585328 2 0.5629 0.643511 0.132 0.868
#> aberrant_ERR2585330 2 0.1414 0.660923 0.020 0.980
#> aberrant_ERR2585293 1 0.5059 0.491431 0.888 0.112
#> aberrant_ERR2585342 2 0.4161 0.660970 0.084 0.916
#> aberrant_ERR2585348 2 0.5178 0.648263 0.116 0.884
#> aberrant_ERR2585352 2 0.0672 0.656091 0.008 0.992
#> aberrant_ERR2585308 1 0.9933 0.464763 0.548 0.452
#> aberrant_ERR2585349 2 0.2423 0.667634 0.040 0.960
#> aberrant_ERR2585316 2 0.9087 0.392892 0.324 0.676
#> aberrant_ERR2585306 2 0.8955 0.405127 0.312 0.688
#> aberrant_ERR2585324 2 0.1843 0.659916 0.028 0.972
#> aberrant_ERR2585310 2 0.6973 0.595879 0.188 0.812
#> aberrant_ERR2585296 2 0.9000 0.395242 0.316 0.684
#> aberrant_ERR2585275 1 0.7056 0.490238 0.808 0.192
#> aberrant_ERR2585311 2 0.5059 0.646433 0.112 0.888
#> aberrant_ERR2585292 1 0.5059 0.491431 0.888 0.112
#> aberrant_ERR2585282 2 0.5178 0.648194 0.116 0.884
#> aberrant_ERR2585305 2 0.3584 0.667658 0.068 0.932
#> aberrant_ERR2585278 2 0.1184 0.660523 0.016 0.984
#> aberrant_ERR2585347 2 0.7376 0.578770 0.208 0.792
#> aberrant_ERR2585332 2 0.6048 0.631709 0.148 0.852
#> aberrant_ERR2585280 2 0.3274 0.666588 0.060 0.940
#> aberrant_ERR2585304 2 0.6531 0.602847 0.168 0.832
#> aberrant_ERR2585322 2 0.0672 0.659908 0.008 0.992
#> aberrant_ERR2585279 2 0.1184 0.662456 0.016 0.984
#> aberrant_ERR2585277 2 0.0000 0.656225 0.000 1.000
#> aberrant_ERR2585295 2 0.4161 0.657377 0.084 0.916
#> aberrant_ERR2585333 2 0.4690 0.656922 0.100 0.900
#> aberrant_ERR2585285 2 0.2423 0.666974 0.040 0.960
#> aberrant_ERR2585286 2 0.0000 0.656225 0.000 1.000
#> aberrant_ERR2585294 2 0.3431 0.664317 0.064 0.936
#> aberrant_ERR2585300 2 0.6148 0.627856 0.152 0.848
#> aberrant_ERR2585334 2 0.0000 0.656225 0.000 1.000
#> aberrant_ERR2585361 2 0.1414 0.660083 0.020 0.980
#> aberrant_ERR2585372 2 0.2948 0.659486 0.052 0.948
#> round_ERR2585217 2 0.7602 0.556996 0.220 0.780
#> round_ERR2585205 2 0.9977 -0.181198 0.472 0.528
#> round_ERR2585214 2 0.8081 0.517560 0.248 0.752
#> round_ERR2585202 2 0.7602 0.552672 0.220 0.780
#> aberrant_ERR2585367 2 0.3584 0.665287 0.068 0.932
#> round_ERR2585220 2 0.9775 0.123666 0.412 0.588
#> round_ERR2585238 2 0.9993 -0.241252 0.484 0.516
#> aberrant_ERR2585276 2 0.3274 0.669100 0.060 0.940
#> round_ERR2585218 2 0.9970 -0.152277 0.468 0.532
#> aberrant_ERR2585363 2 0.1414 0.661574 0.020 0.980
#> round_ERR2585201 2 0.8016 0.518448 0.244 0.756
#> round_ERR2585210 1 0.9998 0.331587 0.508 0.492
#> aberrant_ERR2585362 2 0.2236 0.663380 0.036 0.964
#> aberrant_ERR2585360 2 0.4298 0.655867 0.088 0.912
#> round_ERR2585209 2 0.8861 0.417218 0.304 0.696
#> round_ERR2585242 2 0.8386 0.483149 0.268 0.732
#> round_ERR2585216 2 0.9815 0.093196 0.420 0.580
#> round_ERR2585219 2 0.9775 0.132396 0.412 0.588
#> round_ERR2585237 2 0.7883 0.531934 0.236 0.764
#> round_ERR2585198 2 0.6973 0.587445 0.188 0.812
#> round_ERR2585211 2 1.0000 -0.305139 0.496 0.504
#> round_ERR2585206 1 1.0000 0.312318 0.504 0.496
#> aberrant_ERR2585281 2 0.1184 0.659395 0.016 0.984
#> round_ERR2585212 2 0.9732 0.145027 0.404 0.596
#> round_ERR2585221 1 0.9954 0.445724 0.540 0.460
#> round_ERR2585243 2 0.9881 0.019043 0.436 0.564
#> round_ERR2585204 2 0.7528 0.556743 0.216 0.784
#> round_ERR2585213 2 0.6887 0.589556 0.184 0.816
#> aberrant_ERR2585373 2 0.5408 0.650149 0.124 0.876
#> aberrant_ERR2585358 2 0.6623 0.614715 0.172 0.828
#> aberrant_ERR2585365 2 0.1184 0.660616 0.016 0.984
#> aberrant_ERR2585359 2 0.7453 0.581398 0.212 0.788
#> aberrant_ERR2585370 2 0.0000 0.656225 0.000 1.000
#> round_ERR2585215 1 0.9963 0.429385 0.536 0.464
#> round_ERR2585262 2 0.8144 0.518777 0.252 0.748
#> round_ERR2585199 2 0.6973 0.587445 0.188 0.812
#> aberrant_ERR2585369 2 0.2603 0.666101 0.044 0.956
#> round_ERR2585208 1 0.9922 0.494340 0.552 0.448
#> round_ERR2585252 1 0.9522 0.623826 0.628 0.372
#> round_ERR2585236 2 0.9286 0.319889 0.344 0.656
#> aberrant_ERR2585284 1 0.1843 0.441703 0.972 0.028
#> round_ERR2585224 1 0.9427 0.632403 0.640 0.360
#> round_ERR2585260 2 0.9881 0.020037 0.436 0.564
#> round_ERR2585229 2 0.9993 -0.257022 0.484 0.516
#> aberrant_ERR2585364 1 0.9977 0.257512 0.528 0.472
#> round_ERR2585253 1 0.9323 0.635164 0.652 0.348
#> aberrant_ERR2585368 2 0.0000 0.656225 0.000 1.000
#> aberrant_ERR2585371 2 0.0000 0.656225 0.000 1.000
#> round_ERR2585239 2 0.9881 -0.000569 0.436 0.564
#> round_ERR2585273 2 0.9988 -0.243367 0.480 0.520
#> round_ERR2585256 2 0.8955 0.410366 0.312 0.688
#> round_ERR2585272 2 0.9922 -0.057461 0.448 0.552
#> round_ERR2585246 1 0.9963 0.426314 0.536 0.464
#> round_ERR2585261 2 0.9087 0.375818 0.324 0.676
#> round_ERR2585254 2 0.7745 0.547295 0.228 0.772
#> round_ERR2585225 2 0.8267 0.503718 0.260 0.740
#> round_ERR2585235 2 0.9896 -0.042433 0.440 0.560
#> round_ERR2585271 2 0.9922 -0.039254 0.448 0.552
#> round_ERR2585251 2 0.9754 0.140028 0.408 0.592
#> round_ERR2585255 2 0.8267 0.505951 0.260 0.740
#> round_ERR2585257 2 0.8207 0.508144 0.256 0.744
#> round_ERR2585226 2 0.9815 0.086061 0.420 0.580
#> round_ERR2585265 2 0.9686 0.173690 0.396 0.604
#> round_ERR2585259 2 0.9491 0.241741 0.368 0.632
#> round_ERR2585247 2 0.9996 -0.265591 0.488 0.512
#> round_ERR2585241 2 0.9988 -0.209465 0.480 0.520
#> round_ERR2585263 2 0.9795 0.117545 0.416 0.584
#> round_ERR2585264 1 0.9323 0.635352 0.652 0.348
#> round_ERR2585233 2 0.8499 0.484896 0.276 0.724
#> round_ERR2585223 2 0.9896 -0.006914 0.440 0.560
#> round_ERR2585234 2 0.7883 0.530441 0.236 0.764
#> round_ERR2585222 2 0.9881 0.014445 0.436 0.564
#> round_ERR2585228 2 0.9896 -0.001454 0.440 0.560
#> round_ERR2585248 1 0.9286 0.633811 0.656 0.344
#> round_ERR2585240 2 0.9732 0.129915 0.404 0.596
#> round_ERR2585270 2 0.9866 0.038967 0.432 0.568
#> round_ERR2585232 2 0.8955 0.413433 0.312 0.688
#> aberrant_ERR2585341 2 0.2778 0.667288 0.048 0.952
#> aberrant_ERR2585355 2 0.0000 0.656225 0.000 1.000
#> round_ERR2585227 2 0.9815 0.075824 0.420 0.580
#> aberrant_ERR2585351 2 0.2778 0.665964 0.048 0.952
#> round_ERR2585269 1 0.9686 0.591977 0.604 0.396
#> aberrant_ERR2585357 2 0.0000 0.656225 0.000 1.000
#> aberrant_ERR2585350 2 0.0000 0.656225 0.000 1.000
#> round_ERR2585250 2 0.9795 0.120306 0.416 0.584
#> round_ERR2585245 1 0.9323 0.635352 0.652 0.348
#> aberrant_ERR2585353 2 0.4690 0.661042 0.100 0.900
#> round_ERR2585258 2 0.9732 0.154336 0.404 0.596
#> aberrant_ERR2585354 2 0.3879 0.659647 0.076 0.924
#> round_ERR2585249 1 0.9661 0.597972 0.608 0.392
#> round_ERR2585268 2 0.9491 0.261800 0.368 0.632
#> aberrant_ERR2585356 2 0.8207 0.515711 0.256 0.744
#> round_ERR2585266 2 0.8386 0.483149 0.268 0.732
#> round_ERR2585231 1 0.9522 0.624354 0.628 0.372
#> round_ERR2585230 2 0.9909 -0.045862 0.444 0.556
#> round_ERR2585267 1 0.9460 0.630205 0.636 0.364
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.2384 0.8347 0.008 0.936 0.056
#> aberrant_ERR2585338 2 0.0983 0.8402 0.016 0.980 0.004
#> aberrant_ERR2585325 2 0.2384 0.8347 0.008 0.936 0.056
#> aberrant_ERR2585283 3 0.3484 0.8495 0.048 0.048 0.904
#> aberrant_ERR2585343 2 0.5072 0.7069 0.012 0.792 0.196
#> aberrant_ERR2585329 2 0.0747 0.8395 0.016 0.984 0.000
#> aberrant_ERR2585317 2 0.0592 0.8401 0.012 0.988 0.000
#> aberrant_ERR2585339 2 0.0592 0.8400 0.012 0.988 0.000
#> aberrant_ERR2585335 2 0.1267 0.8420 0.004 0.972 0.024
#> aberrant_ERR2585287 3 0.7104 0.4959 0.032 0.360 0.608
#> aberrant_ERR2585321 2 0.4228 0.7646 0.008 0.844 0.148
#> aberrant_ERR2585297 1 0.3918 0.7656 0.868 0.120 0.012
#> aberrant_ERR2585337 2 0.0592 0.8401 0.012 0.988 0.000
#> aberrant_ERR2585319 2 0.1399 0.8383 0.004 0.968 0.028
#> aberrant_ERR2585315 2 0.0829 0.8417 0.012 0.984 0.004
#> aberrant_ERR2585336 2 0.0592 0.8401 0.012 0.988 0.000
#> aberrant_ERR2585307 2 0.1860 0.8186 0.052 0.948 0.000
#> aberrant_ERR2585301 2 0.2414 0.8376 0.020 0.940 0.040
#> aberrant_ERR2585326 2 0.0592 0.8401 0.012 0.988 0.000
#> aberrant_ERR2585331 2 0.0747 0.8390 0.016 0.984 0.000
#> aberrant_ERR2585346 3 0.3155 0.8448 0.044 0.040 0.916
#> aberrant_ERR2585314 2 0.0747 0.8406 0.016 0.984 0.000
#> aberrant_ERR2585298 1 0.6518 0.4314 0.512 0.484 0.004
#> aberrant_ERR2585345 2 0.0592 0.8401 0.012 0.988 0.000
#> aberrant_ERR2585299 1 0.4589 0.7957 0.820 0.172 0.008
#> aberrant_ERR2585309 1 0.2743 0.6431 0.928 0.020 0.052
#> aberrant_ERR2585303 2 0.1182 0.8426 0.012 0.976 0.012
#> aberrant_ERR2585313 2 0.0592 0.8401 0.012 0.988 0.000
#> aberrant_ERR2585318 2 0.2229 0.8381 0.012 0.944 0.044
#> aberrant_ERR2585328 2 0.4063 0.7949 0.020 0.868 0.112
#> aberrant_ERR2585330 2 0.1129 0.8418 0.004 0.976 0.020
#> aberrant_ERR2585293 3 0.3484 0.8495 0.048 0.048 0.904
#> aberrant_ERR2585342 2 0.3213 0.8138 0.008 0.900 0.092
#> aberrant_ERR2585348 2 0.3532 0.8020 0.008 0.884 0.108
#> aberrant_ERR2585352 2 0.0475 0.8409 0.004 0.992 0.004
#> aberrant_ERR2585308 1 0.3678 0.7274 0.892 0.080 0.028
#> aberrant_ERR2585349 2 0.2261 0.8084 0.068 0.932 0.000
#> aberrant_ERR2585316 2 0.6521 0.4461 0.016 0.644 0.340
#> aberrant_ERR2585306 2 0.8977 0.3461 0.252 0.560 0.188
#> aberrant_ERR2585324 2 0.1399 0.8383 0.004 0.968 0.028
#> aberrant_ERR2585310 2 0.5864 0.4526 0.288 0.704 0.008
#> aberrant_ERR2585296 1 0.6398 0.6045 0.580 0.416 0.004
#> aberrant_ERR2585275 3 0.4821 0.8166 0.040 0.120 0.840
#> aberrant_ERR2585311 2 0.3644 0.7900 0.004 0.872 0.124
#> aberrant_ERR2585292 3 0.3484 0.8495 0.048 0.048 0.904
#> aberrant_ERR2585282 2 0.3618 0.8066 0.012 0.884 0.104
#> aberrant_ERR2585305 2 0.2982 0.8320 0.024 0.920 0.056
#> aberrant_ERR2585278 2 0.0747 0.8411 0.000 0.984 0.016
#> aberrant_ERR2585347 2 0.5202 0.6780 0.008 0.772 0.220
#> aberrant_ERR2585332 2 0.4413 0.7504 0.008 0.832 0.160
#> aberrant_ERR2585280 2 0.2496 0.8305 0.004 0.928 0.068
#> aberrant_ERR2585304 2 0.5754 0.3869 0.296 0.700 0.004
#> aberrant_ERR2585322 2 0.1015 0.8421 0.008 0.980 0.012
#> aberrant_ERR2585279 2 0.1411 0.8322 0.036 0.964 0.000
#> aberrant_ERR2585277 2 0.0592 0.8401 0.012 0.988 0.000
#> aberrant_ERR2585295 2 0.3031 0.8271 0.012 0.912 0.076
#> aberrant_ERR2585333 2 0.3532 0.8036 0.008 0.884 0.108
#> aberrant_ERR2585285 2 0.1585 0.8416 0.008 0.964 0.028
#> aberrant_ERR2585286 2 0.0592 0.8400 0.012 0.988 0.000
#> aberrant_ERR2585294 2 0.2879 0.8325 0.024 0.924 0.052
#> aberrant_ERR2585300 2 0.5167 0.7204 0.024 0.804 0.172
#> aberrant_ERR2585334 2 0.0747 0.8390 0.016 0.984 0.000
#> aberrant_ERR2585361 2 0.1315 0.8424 0.008 0.972 0.020
#> aberrant_ERR2585372 2 0.2301 0.8302 0.004 0.936 0.060
#> round_ERR2585217 2 0.6008 0.2867 0.332 0.664 0.004
#> round_ERR2585205 1 0.4351 0.7959 0.828 0.168 0.004
#> round_ERR2585214 2 0.6518 -0.3575 0.484 0.512 0.004
#> round_ERR2585202 2 0.6476 -0.2285 0.448 0.548 0.004
#> aberrant_ERR2585367 2 0.2496 0.8275 0.004 0.928 0.068
#> round_ERR2585220 1 0.5443 0.7895 0.736 0.260 0.004
#> round_ERR2585238 1 0.3784 0.7777 0.864 0.132 0.004
#> aberrant_ERR2585276 2 0.2982 0.8330 0.024 0.920 0.056
#> round_ERR2585218 1 0.4521 0.8009 0.816 0.180 0.004
#> aberrant_ERR2585363 2 0.0983 0.8411 0.004 0.980 0.016
#> round_ERR2585201 1 0.6521 0.4020 0.500 0.496 0.004
#> round_ERR2585210 1 0.3989 0.7663 0.864 0.124 0.012
#> aberrant_ERR2585362 2 0.1999 0.8421 0.012 0.952 0.036
#> aberrant_ERR2585360 2 0.3207 0.8185 0.012 0.904 0.084
#> round_ERR2585209 1 0.6633 0.5386 0.548 0.444 0.008
#> round_ERR2585242 1 0.6505 0.4763 0.528 0.468 0.004
#> round_ERR2585216 1 0.5138 0.7956 0.748 0.252 0.000
#> round_ERR2585219 1 0.5327 0.7796 0.728 0.272 0.000
#> round_ERR2585237 2 0.6476 -0.2160 0.448 0.548 0.004
#> round_ERR2585198 2 0.6247 0.1048 0.376 0.620 0.004
#> round_ERR2585211 1 0.4602 0.7869 0.832 0.152 0.016
#> round_ERR2585206 1 0.4475 0.7820 0.840 0.144 0.016
#> aberrant_ERR2585281 2 0.1482 0.8406 0.012 0.968 0.020
#> round_ERR2585212 1 0.5465 0.7680 0.712 0.288 0.000
#> round_ERR2585221 1 0.3722 0.7355 0.888 0.088 0.024
#> round_ERR2585243 1 0.5201 0.8043 0.760 0.236 0.004
#> round_ERR2585204 2 0.6398 -0.0894 0.416 0.580 0.004
#> round_ERR2585213 2 0.6033 0.2590 0.336 0.660 0.004
#> aberrant_ERR2585373 2 0.3755 0.7984 0.008 0.872 0.120
#> aberrant_ERR2585358 2 0.4968 0.7178 0.012 0.800 0.188
#> aberrant_ERR2585365 2 0.1337 0.8426 0.012 0.972 0.016
#> aberrant_ERR2585359 2 0.5450 0.6656 0.012 0.760 0.228
#> aberrant_ERR2585370 2 0.0592 0.8401 0.012 0.988 0.000
#> round_ERR2585215 1 0.4015 0.7356 0.876 0.096 0.028
#> round_ERR2585262 2 0.6298 0.0556 0.388 0.608 0.004
#> round_ERR2585199 2 0.6247 0.1048 0.376 0.620 0.004
#> aberrant_ERR2585369 2 0.1878 0.8364 0.004 0.952 0.044
#> round_ERR2585208 1 0.3325 0.7245 0.904 0.076 0.020
#> round_ERR2585252 1 0.2446 0.6335 0.936 0.012 0.052
#> round_ERR2585236 1 0.6026 0.6612 0.624 0.376 0.000
#> aberrant_ERR2585284 3 0.1525 0.7964 0.032 0.004 0.964
#> round_ERR2585224 1 0.2066 0.6093 0.940 0.000 0.060
#> round_ERR2585260 1 0.5115 0.8064 0.768 0.228 0.004
#> round_ERR2585229 1 0.4413 0.7900 0.832 0.160 0.008
#> aberrant_ERR2585364 3 0.7464 0.3615 0.040 0.400 0.560
#> round_ERR2585253 1 0.2356 0.5986 0.928 0.000 0.072
#> aberrant_ERR2585368 2 0.0592 0.8401 0.012 0.988 0.000
#> aberrant_ERR2585371 2 0.0592 0.8401 0.012 0.988 0.000
#> round_ERR2585239 1 0.5058 0.8013 0.756 0.244 0.000
#> round_ERR2585273 1 0.4110 0.7870 0.844 0.152 0.004
#> round_ERR2585256 1 0.6330 0.6247 0.600 0.396 0.004
#> round_ERR2585272 1 0.4883 0.8061 0.788 0.208 0.004
#> round_ERR2585246 1 0.3722 0.7369 0.888 0.088 0.024
#> round_ERR2585261 1 0.6398 0.5881 0.580 0.416 0.004
#> round_ERR2585254 2 0.6451 -0.1538 0.436 0.560 0.004
#> round_ERR2585225 1 0.6521 0.3988 0.500 0.496 0.004
#> round_ERR2585235 1 0.5899 0.7837 0.736 0.244 0.020
#> round_ERR2585271 1 0.5070 0.8074 0.772 0.224 0.004
#> round_ERR2585251 1 0.5461 0.8003 0.748 0.244 0.008
#> round_ERR2585255 1 0.6521 0.3966 0.500 0.496 0.004
#> round_ERR2585257 1 0.6521 0.4128 0.504 0.492 0.004
#> round_ERR2585226 1 0.5024 0.8056 0.776 0.220 0.004
#> round_ERR2585265 1 0.5244 0.7996 0.756 0.240 0.004
#> round_ERR2585259 1 0.5859 0.7110 0.656 0.344 0.000
#> round_ERR2585247 1 0.4723 0.7914 0.824 0.160 0.016
#> round_ERR2585241 1 0.4473 0.7955 0.828 0.164 0.008
#> round_ERR2585263 1 0.5363 0.7766 0.724 0.276 0.000
#> round_ERR2585264 1 0.2261 0.6016 0.932 0.000 0.068
#> round_ERR2585233 1 0.6505 0.4665 0.528 0.468 0.004
#> round_ERR2585223 1 0.4654 0.8057 0.792 0.208 0.000
#> round_ERR2585234 2 0.6495 -0.2653 0.460 0.536 0.004
#> round_ERR2585222 1 0.5158 0.8071 0.764 0.232 0.004
#> round_ERR2585228 1 0.5115 0.8061 0.768 0.228 0.004
#> round_ERR2585248 1 0.2625 0.5864 0.916 0.000 0.084
#> round_ERR2585240 1 0.5098 0.8007 0.752 0.248 0.000
#> round_ERR2585270 1 0.5404 0.7923 0.740 0.256 0.004
#> round_ERR2585232 1 0.6587 0.5632 0.568 0.424 0.008
#> aberrant_ERR2585341 2 0.2229 0.8405 0.012 0.944 0.044
#> aberrant_ERR2585355 2 0.0592 0.8400 0.012 0.988 0.000
#> round_ERR2585227 1 0.4842 0.8066 0.776 0.224 0.000
#> aberrant_ERR2585351 2 0.2063 0.8374 0.008 0.948 0.044
#> round_ERR2585269 1 0.3263 0.6718 0.912 0.040 0.048
#> aberrant_ERR2585357 2 0.0592 0.8401 0.012 0.988 0.000
#> aberrant_ERR2585350 2 0.0592 0.8401 0.012 0.988 0.000
#> round_ERR2585250 1 0.5656 0.7715 0.712 0.284 0.004
#> round_ERR2585245 1 0.2261 0.6016 0.932 0.000 0.068
#> aberrant_ERR2585353 2 0.3213 0.8205 0.008 0.900 0.092
#> round_ERR2585258 1 0.5158 0.8026 0.764 0.232 0.004
#> aberrant_ERR2585354 2 0.2860 0.8188 0.004 0.912 0.084
#> round_ERR2585249 1 0.2527 0.6503 0.936 0.020 0.044
#> round_ERR2585268 1 0.5760 0.7251 0.672 0.328 0.000
#> aberrant_ERR2585356 2 0.6388 0.5401 0.024 0.692 0.284
#> round_ERR2585266 1 0.6505 0.4763 0.528 0.468 0.004
#> round_ERR2585231 1 0.2703 0.6357 0.928 0.016 0.056
#> round_ERR2585230 1 0.5115 0.8072 0.768 0.228 0.004
#> round_ERR2585267 1 0.2651 0.6198 0.928 0.012 0.060
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 2 0.251 0.882715 0.008 0.920 0.020 0.052
#> aberrant_ERR2585338 2 0.220 0.880849 0.000 0.920 0.072 0.008
#> aberrant_ERR2585325 2 0.251 0.882715 0.008 0.920 0.020 0.052
#> aberrant_ERR2585283 4 0.157 0.838132 0.028 0.012 0.004 0.956
#> aberrant_ERR2585343 2 0.536 0.689862 0.020 0.736 0.032 0.212
#> aberrant_ERR2585329 2 0.213 0.877892 0.000 0.920 0.076 0.004
#> aberrant_ERR2585317 2 0.164 0.880931 0.000 0.940 0.060 0.000
#> aberrant_ERR2585339 2 0.164 0.882046 0.000 0.940 0.060 0.000
#> aberrant_ERR2585335 2 0.184 0.889294 0.000 0.944 0.028 0.028
#> aberrant_ERR2585287 4 0.553 0.525148 0.016 0.304 0.016 0.664
#> aberrant_ERR2585321 2 0.443 0.788356 0.016 0.808 0.024 0.152
#> aberrant_ERR2585297 1 0.552 0.514411 0.568 0.020 0.412 0.000
#> aberrant_ERR2585337 2 0.182 0.882259 0.000 0.936 0.060 0.004
#> aberrant_ERR2585319 2 0.161 0.885692 0.000 0.952 0.016 0.032
#> aberrant_ERR2585315 2 0.164 0.887243 0.000 0.948 0.044 0.008
#> aberrant_ERR2585336 2 0.172 0.879576 0.000 0.936 0.064 0.000
#> aberrant_ERR2585307 2 0.294 0.836426 0.000 0.868 0.128 0.004
#> aberrant_ERR2585301 2 0.239 0.879777 0.008 0.924 0.016 0.052
#> aberrant_ERR2585326 2 0.164 0.880931 0.000 0.940 0.060 0.000
#> aberrant_ERR2585331 2 0.187 0.877255 0.000 0.928 0.072 0.000
#> aberrant_ERR2585346 4 0.154 0.831322 0.032 0.008 0.004 0.956
#> aberrant_ERR2585314 2 0.205 0.880545 0.000 0.924 0.072 0.004
#> aberrant_ERR2585298 3 0.292 0.550192 0.008 0.116 0.876 0.000
#> aberrant_ERR2585345 2 0.182 0.883186 0.000 0.936 0.060 0.004
#> aberrant_ERR2585299 3 0.551 -0.337206 0.488 0.016 0.496 0.000
#> aberrant_ERR2585309 1 0.340 0.696010 0.820 0.000 0.180 0.000
#> aberrant_ERR2585303 2 0.185 0.889695 0.000 0.940 0.048 0.012
#> aberrant_ERR2585313 2 0.174 0.883167 0.000 0.940 0.056 0.004
#> aberrant_ERR2585318 2 0.250 0.885680 0.004 0.920 0.032 0.044
#> aberrant_ERR2585328 2 0.459 0.833153 0.008 0.812 0.068 0.112
#> aberrant_ERR2585330 2 0.157 0.888761 0.004 0.956 0.028 0.012
#> aberrant_ERR2585293 4 0.157 0.838132 0.028 0.012 0.004 0.956
#> aberrant_ERR2585342 2 0.304 0.871391 0.008 0.892 0.020 0.080
#> aberrant_ERR2585348 2 0.460 0.820285 0.020 0.820 0.056 0.104
#> aberrant_ERR2585352 2 0.139 0.885894 0.000 0.952 0.048 0.000
#> aberrant_ERR2585308 1 0.463 0.662977 0.688 0.004 0.308 0.000
#> aberrant_ERR2585349 2 0.506 0.488747 0.012 0.648 0.340 0.000
#> aberrant_ERR2585316 2 0.632 0.403354 0.028 0.596 0.028 0.348
#> aberrant_ERR2585306 2 0.855 0.237165 0.212 0.520 0.080 0.188
#> aberrant_ERR2585324 2 0.161 0.885692 0.000 0.952 0.016 0.032
#> aberrant_ERR2585310 2 0.653 0.266014 0.060 0.580 0.348 0.012
#> aberrant_ERR2585296 3 0.599 0.515109 0.168 0.140 0.692 0.000
#> aberrant_ERR2585275 4 0.334 0.813628 0.032 0.080 0.008 0.880
#> aberrant_ERR2585311 2 0.400 0.837074 0.016 0.844 0.028 0.112
#> aberrant_ERR2585292 4 0.157 0.838132 0.028 0.012 0.004 0.956
#> aberrant_ERR2585282 2 0.393 0.841018 0.020 0.852 0.028 0.100
#> aberrant_ERR2585305 2 0.295 0.874287 0.008 0.900 0.028 0.064
#> aberrant_ERR2585278 2 0.121 0.888370 0.000 0.964 0.032 0.004
#> aberrant_ERR2585347 2 0.521 0.696431 0.008 0.732 0.036 0.224
#> aberrant_ERR2585332 2 0.452 0.759958 0.008 0.796 0.032 0.164
#> aberrant_ERR2585280 2 0.263 0.883036 0.004 0.912 0.024 0.060
#> aberrant_ERR2585304 3 0.551 0.116063 0.016 0.476 0.508 0.000
#> aberrant_ERR2585322 2 0.185 0.887250 0.004 0.940 0.052 0.004
#> aberrant_ERR2585279 2 0.253 0.855250 0.000 0.888 0.112 0.000
#> aberrant_ERR2585277 2 0.194 0.876590 0.000 0.924 0.076 0.000
#> aberrant_ERR2585295 2 0.320 0.875278 0.004 0.884 0.032 0.080
#> aberrant_ERR2585333 2 0.335 0.846501 0.004 0.864 0.016 0.116
#> aberrant_ERR2585285 2 0.204 0.889621 0.000 0.936 0.032 0.032
#> aberrant_ERR2585286 2 0.179 0.879341 0.000 0.932 0.068 0.000
#> aberrant_ERR2585294 2 0.276 0.874698 0.012 0.908 0.016 0.064
#> aberrant_ERR2585300 2 0.496 0.711495 0.020 0.760 0.020 0.200
#> aberrant_ERR2585334 2 0.208 0.872476 0.000 0.916 0.084 0.000
#> aberrant_ERR2585361 2 0.182 0.886706 0.000 0.944 0.036 0.020
#> aberrant_ERR2585372 2 0.253 0.877157 0.008 0.920 0.024 0.048
#> round_ERR2585217 3 0.588 0.395928 0.048 0.344 0.608 0.000
#> round_ERR2585205 1 0.578 0.271891 0.496 0.028 0.476 0.000
#> round_ERR2585214 3 0.335 0.543843 0.008 0.148 0.844 0.000
#> round_ERR2585202 3 0.533 0.504102 0.048 0.248 0.704 0.000
#> aberrant_ERR2585367 2 0.324 0.876422 0.004 0.884 0.040 0.072
#> round_ERR2585220 3 0.581 0.378973 0.312 0.052 0.636 0.000
#> round_ERR2585238 1 0.544 0.482792 0.564 0.016 0.420 0.000
#> aberrant_ERR2585276 2 0.320 0.880461 0.012 0.892 0.036 0.060
#> round_ERR2585218 3 0.578 -0.235694 0.476 0.028 0.496 0.000
#> aberrant_ERR2585363 2 0.144 0.886545 0.008 0.960 0.028 0.004
#> round_ERR2585201 3 0.298 0.548887 0.008 0.120 0.872 0.000
#> round_ERR2585210 1 0.522 0.485450 0.568 0.008 0.424 0.000
#> aberrant_ERR2585362 2 0.299 0.885330 0.016 0.904 0.044 0.036
#> aberrant_ERR2585360 2 0.361 0.869809 0.020 0.872 0.028 0.080
#> round_ERR2585209 3 0.472 0.557941 0.092 0.116 0.792 0.000
#> round_ERR2585242 3 0.333 0.554273 0.024 0.112 0.864 0.000
#> round_ERR2585216 3 0.594 0.320649 0.340 0.052 0.608 0.000
#> round_ERR2585219 3 0.581 0.376704 0.312 0.052 0.636 0.000
#> round_ERR2585237 3 0.402 0.517872 0.012 0.196 0.792 0.000
#> round_ERR2585198 3 0.529 0.414443 0.020 0.344 0.636 0.000
#> round_ERR2585211 1 0.564 0.428706 0.548 0.024 0.428 0.000
#> round_ERR2585206 1 0.554 0.453782 0.556 0.020 0.424 0.000
#> aberrant_ERR2585281 2 0.263 0.883682 0.008 0.916 0.048 0.028
#> round_ERR2585212 3 0.579 0.397654 0.296 0.056 0.648 0.000
#> round_ERR2585221 1 0.496 0.656895 0.684 0.016 0.300 0.000
#> round_ERR2585243 3 0.598 0.127095 0.396 0.044 0.560 0.000
#> round_ERR2585204 3 0.416 0.489308 0.004 0.240 0.756 0.000
#> round_ERR2585213 3 0.503 0.304239 0.004 0.400 0.596 0.000
#> aberrant_ERR2585373 2 0.350 0.841150 0.012 0.860 0.012 0.116
#> aberrant_ERR2585358 2 0.529 0.697528 0.020 0.744 0.032 0.204
#> aberrant_ERR2585365 2 0.207 0.888568 0.004 0.936 0.044 0.016
#> aberrant_ERR2585359 2 0.550 0.652160 0.020 0.712 0.028 0.240
#> aberrant_ERR2585370 2 0.172 0.879576 0.000 0.936 0.064 0.000
#> round_ERR2585215 1 0.475 0.649281 0.688 0.008 0.304 0.000
#> round_ERR2585262 3 0.531 0.448176 0.040 0.268 0.692 0.000
#> round_ERR2585199 3 0.523 0.421724 0.020 0.332 0.648 0.000
#> aberrant_ERR2585369 2 0.189 0.882522 0.004 0.944 0.016 0.036
#> round_ERR2585208 1 0.511 0.643509 0.672 0.020 0.308 0.000
#> round_ERR2585252 1 0.345 0.694530 0.828 0.000 0.168 0.004
#> round_ERR2585236 3 0.617 0.461426 0.248 0.100 0.652 0.000
#> aberrant_ERR2585284 4 0.390 0.771577 0.080 0.000 0.076 0.844
#> round_ERR2585224 1 0.353 0.683837 0.836 0.000 0.152 0.012
#> round_ERR2585260 3 0.606 0.140768 0.400 0.048 0.552 0.000
#> round_ERR2585229 1 0.548 0.456114 0.540 0.016 0.444 0.000
#> aberrant_ERR2585364 4 0.656 0.442539 0.036 0.344 0.032 0.588
#> round_ERR2585253 1 0.261 0.649466 0.896 0.000 0.096 0.008
#> aberrant_ERR2585368 2 0.179 0.878460 0.000 0.932 0.068 0.000
#> aberrant_ERR2585371 2 0.179 0.878460 0.000 0.932 0.068 0.000
#> round_ERR2585239 3 0.602 0.174986 0.384 0.048 0.568 0.000
#> round_ERR2585273 1 0.578 0.309919 0.488 0.028 0.484 0.000
#> round_ERR2585256 3 0.503 0.540876 0.140 0.092 0.768 0.000
#> round_ERR2585272 3 0.602 0.030916 0.416 0.044 0.540 0.000
#> round_ERR2585246 1 0.482 0.633318 0.652 0.004 0.344 0.000
#> round_ERR2585261 3 0.562 0.540562 0.148 0.128 0.724 0.000
#> round_ERR2585254 3 0.506 0.482334 0.032 0.256 0.712 0.000
#> round_ERR2585225 3 0.382 0.550368 0.040 0.120 0.840 0.000
#> round_ERR2585235 3 0.690 0.066502 0.400 0.068 0.516 0.016
#> round_ERR2585271 3 0.603 0.188089 0.388 0.048 0.564 0.000
#> round_ERR2585251 3 0.582 0.237570 0.368 0.040 0.592 0.000
#> round_ERR2585255 3 0.364 0.550995 0.032 0.120 0.848 0.000
#> round_ERR2585257 3 0.364 0.547946 0.032 0.120 0.848 0.000
#> round_ERR2585226 3 0.582 0.235175 0.368 0.040 0.592 0.000
#> round_ERR2585265 3 0.558 0.327087 0.328 0.036 0.636 0.000
#> round_ERR2585259 3 0.575 0.445009 0.248 0.072 0.680 0.000
#> round_ERR2585247 1 0.574 0.435648 0.540 0.028 0.432 0.000
#> round_ERR2585241 1 0.560 0.305954 0.508 0.020 0.472 0.000
#> round_ERR2585263 3 0.590 0.338551 0.332 0.052 0.616 0.000
#> round_ERR2585264 1 0.233 0.645285 0.908 0.000 0.088 0.004
#> round_ERR2585233 3 0.401 0.544837 0.064 0.100 0.836 0.000
#> round_ERR2585223 3 0.589 0.165232 0.392 0.040 0.568 0.000
#> round_ERR2585234 3 0.381 0.530029 0.012 0.176 0.812 0.000
#> round_ERR2585222 3 0.585 0.180050 0.376 0.040 0.584 0.000
#> round_ERR2585228 3 0.587 0.167840 0.384 0.040 0.576 0.000
#> round_ERR2585248 1 0.284 0.636134 0.892 0.000 0.088 0.020
#> round_ERR2585240 3 0.577 0.305660 0.336 0.044 0.620 0.000
#> round_ERR2585270 3 0.582 0.292354 0.348 0.044 0.608 0.000
#> round_ERR2585232 3 0.473 0.548526 0.108 0.100 0.792 0.000
#> aberrant_ERR2585341 2 0.283 0.889104 0.008 0.908 0.044 0.040
#> aberrant_ERR2585355 2 0.172 0.880612 0.000 0.936 0.064 0.000
#> round_ERR2585227 3 0.574 0.203308 0.368 0.036 0.596 0.000
#> aberrant_ERR2585351 2 0.181 0.883829 0.004 0.948 0.020 0.028
#> round_ERR2585269 1 0.385 0.697035 0.800 0.008 0.192 0.000
#> aberrant_ERR2585357 2 0.164 0.880931 0.000 0.940 0.060 0.000
#> aberrant_ERR2585350 2 0.164 0.880931 0.000 0.940 0.060 0.000
#> round_ERR2585250 3 0.586 0.402182 0.284 0.064 0.652 0.000
#> round_ERR2585245 1 0.233 0.645285 0.908 0.000 0.088 0.004
#> aberrant_ERR2585353 2 0.369 0.860277 0.016 0.864 0.028 0.092
#> round_ERR2585258 3 0.571 0.265986 0.360 0.036 0.604 0.000
#> aberrant_ERR2585354 2 0.304 0.861309 0.008 0.892 0.020 0.080
#> round_ERR2585249 1 0.354 0.695500 0.820 0.004 0.176 0.000
#> round_ERR2585268 3 0.582 0.471282 0.240 0.080 0.680 0.000
#> aberrant_ERR2585356 2 0.606 0.494222 0.020 0.636 0.032 0.312
#> round_ERR2585266 3 0.343 0.554793 0.028 0.112 0.860 0.000
#> round_ERR2585231 1 0.302 0.686343 0.852 0.000 0.148 0.000
#> round_ERR2585230 3 0.605 0.000618 0.432 0.044 0.524 0.000
#> round_ERR2585267 1 0.399 0.673329 0.808 0.004 0.176 0.012
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 2 0.3294 0.71088 0.000 0.844 0.008 0.024 0.124
#> aberrant_ERR2585338 2 0.1854 0.74731 0.000 0.936 0.020 0.008 0.036
#> aberrant_ERR2585325 2 0.3294 0.71088 0.000 0.844 0.008 0.024 0.124
#> aberrant_ERR2585283 4 0.0771 0.79961 0.004 0.000 0.000 0.976 0.020
#> aberrant_ERR2585343 2 0.5868 -0.21078 0.000 0.576 0.000 0.132 0.292
#> aberrant_ERR2585329 2 0.1728 0.74958 0.000 0.940 0.036 0.004 0.020
#> aberrant_ERR2585317 2 0.1018 0.74594 0.000 0.968 0.016 0.000 0.016
#> aberrant_ERR2585339 2 0.1399 0.74917 0.000 0.952 0.020 0.000 0.028
#> aberrant_ERR2585335 2 0.1983 0.74906 0.000 0.924 0.008 0.008 0.060
#> aberrant_ERR2585287 4 0.5167 0.13066 0.000 0.240 0.000 0.668 0.092
#> aberrant_ERR2585321 2 0.5304 0.16458 0.000 0.628 0.000 0.080 0.292
#> aberrant_ERR2585297 1 0.4505 0.49569 0.604 0.000 0.384 0.000 0.012
#> aberrant_ERR2585337 2 0.1074 0.74782 0.000 0.968 0.016 0.004 0.012
#> aberrant_ERR2585319 2 0.2006 0.73983 0.000 0.916 0.000 0.012 0.072
#> aberrant_ERR2585315 2 0.1569 0.75348 0.000 0.948 0.012 0.008 0.032
#> aberrant_ERR2585336 2 0.1117 0.74440 0.000 0.964 0.016 0.000 0.020
#> aberrant_ERR2585307 2 0.2575 0.68837 0.000 0.884 0.100 0.004 0.012
#> aberrant_ERR2585301 2 0.3499 0.69143 0.004 0.836 0.008 0.024 0.128
#> aberrant_ERR2585326 2 0.1018 0.74594 0.000 0.968 0.016 0.000 0.016
#> aberrant_ERR2585331 2 0.1661 0.73910 0.000 0.940 0.024 0.000 0.036
#> aberrant_ERR2585346 4 0.0771 0.79294 0.004 0.000 0.000 0.976 0.020
#> aberrant_ERR2585314 2 0.1808 0.74866 0.000 0.936 0.040 0.004 0.020
#> aberrant_ERR2585298 3 0.1768 0.55635 0.000 0.072 0.924 0.000 0.004
#> aberrant_ERR2585345 2 0.1471 0.74956 0.000 0.952 0.020 0.004 0.024
#> aberrant_ERR2585299 1 0.4656 0.26460 0.508 0.000 0.480 0.000 0.012
#> aberrant_ERR2585309 1 0.3146 0.63864 0.844 0.000 0.128 0.000 0.028
#> aberrant_ERR2585303 2 0.1956 0.75320 0.000 0.928 0.008 0.012 0.052
#> aberrant_ERR2585313 2 0.1179 0.74872 0.000 0.964 0.016 0.004 0.016
#> aberrant_ERR2585318 2 0.2789 0.72564 0.000 0.880 0.008 0.020 0.092
#> aberrant_ERR2585328 2 0.5685 0.31990 0.004 0.656 0.028 0.060 0.252
#> aberrant_ERR2585330 2 0.1792 0.75014 0.000 0.916 0.000 0.000 0.084
#> aberrant_ERR2585293 4 0.0771 0.79961 0.004 0.000 0.000 0.976 0.020
#> aberrant_ERR2585342 2 0.3577 0.67136 0.000 0.808 0.000 0.032 0.160
#> aberrant_ERR2585348 2 0.5325 0.39827 0.000 0.684 0.024 0.060 0.232
#> aberrant_ERR2585352 2 0.1331 0.75305 0.000 0.952 0.008 0.000 0.040
#> aberrant_ERR2585308 1 0.4157 0.61082 0.716 0.000 0.264 0.000 0.020
#> aberrant_ERR2585349 2 0.5162 0.19735 0.000 0.628 0.308 0.000 0.064
#> aberrant_ERR2585316 2 0.6992 -0.80722 0.008 0.388 0.000 0.268 0.336
#> aberrant_ERR2585306 2 0.8759 -0.53461 0.196 0.408 0.052 0.096 0.248
#> aberrant_ERR2585324 2 0.2006 0.73983 0.000 0.916 0.000 0.012 0.072
#> aberrant_ERR2585310 2 0.6591 0.00769 0.060 0.540 0.340 0.008 0.052
#> aberrant_ERR2585296 3 0.5236 0.52370 0.144 0.108 0.724 0.000 0.024
#> aberrant_ERR2585275 4 0.2407 0.76628 0.004 0.012 0.000 0.896 0.088
#> aberrant_ERR2585311 2 0.4717 0.45824 0.000 0.704 0.004 0.048 0.244
#> aberrant_ERR2585292 4 0.0771 0.79961 0.004 0.000 0.000 0.976 0.020
#> aberrant_ERR2585282 2 0.4476 0.53661 0.000 0.744 0.008 0.044 0.204
#> aberrant_ERR2585305 2 0.3998 0.65685 0.004 0.804 0.012 0.032 0.148
#> aberrant_ERR2585278 2 0.1547 0.75261 0.000 0.948 0.016 0.004 0.032
#> aberrant_ERR2585347 2 0.6152 -0.21285 0.000 0.572 0.004 0.168 0.256
#> aberrant_ERR2585332 2 0.5425 -0.06311 0.000 0.600 0.000 0.080 0.320
#> aberrant_ERR2585280 2 0.3106 0.70741 0.000 0.844 0.000 0.024 0.132
#> aberrant_ERR2585304 3 0.4807 0.08336 0.008 0.464 0.520 0.000 0.008
#> aberrant_ERR2585322 2 0.1630 0.75259 0.000 0.944 0.016 0.004 0.036
#> aberrant_ERR2585279 2 0.2359 0.71191 0.000 0.904 0.060 0.000 0.036
#> aberrant_ERR2585277 2 0.1661 0.74026 0.000 0.940 0.024 0.000 0.036
#> aberrant_ERR2585295 2 0.4057 0.66392 0.000 0.804 0.012 0.056 0.128
#> aberrant_ERR2585333 2 0.4528 0.51608 0.000 0.728 0.000 0.060 0.212
#> aberrant_ERR2585285 2 0.1628 0.75136 0.000 0.936 0.000 0.008 0.056
#> aberrant_ERR2585286 2 0.1485 0.74190 0.000 0.948 0.020 0.000 0.032
#> aberrant_ERR2585294 2 0.3849 0.66146 0.004 0.808 0.008 0.028 0.152
#> aberrant_ERR2585300 2 0.5813 -0.27352 0.000 0.560 0.000 0.112 0.328
#> aberrant_ERR2585334 2 0.1836 0.73497 0.000 0.932 0.032 0.000 0.036
#> aberrant_ERR2585361 2 0.2407 0.74033 0.000 0.896 0.004 0.012 0.088
#> aberrant_ERR2585372 2 0.3566 0.67684 0.000 0.812 0.004 0.024 0.160
#> round_ERR2585217 3 0.5874 0.37143 0.032 0.304 0.604 0.000 0.060
#> round_ERR2585205 1 0.4803 0.18327 0.496 0.004 0.488 0.000 0.012
#> round_ERR2585214 3 0.2233 0.55264 0.000 0.104 0.892 0.000 0.004
#> round_ERR2585202 3 0.4501 0.50709 0.036 0.212 0.740 0.000 0.012
#> aberrant_ERR2585367 2 0.3523 0.69330 0.000 0.832 0.004 0.044 0.120
#> round_ERR2585220 3 0.4665 0.40366 0.304 0.016 0.668 0.000 0.012
#> round_ERR2585238 1 0.4752 0.41294 0.568 0.000 0.412 0.000 0.020
#> aberrant_ERR2585276 2 0.3846 0.68180 0.004 0.816 0.016 0.024 0.140
#> round_ERR2585218 1 0.4803 0.16623 0.492 0.004 0.492 0.000 0.012
#> aberrant_ERR2585363 2 0.1768 0.74436 0.000 0.924 0.000 0.004 0.072
#> round_ERR2585201 3 0.2331 0.55674 0.000 0.080 0.900 0.000 0.020
#> round_ERR2585210 1 0.4974 0.40171 0.560 0.000 0.408 0.000 0.032
#> aberrant_ERR2585362 2 0.3387 0.71912 0.000 0.836 0.024 0.008 0.132
#> aberrant_ERR2585360 2 0.3438 0.66597 0.000 0.808 0.000 0.020 0.172
#> round_ERR2585209 3 0.3919 0.56442 0.100 0.076 0.816 0.000 0.008
#> round_ERR2585242 3 0.1981 0.56010 0.016 0.064 0.920 0.000 0.000
#> round_ERR2585216 3 0.4962 0.32913 0.332 0.012 0.632 0.000 0.024
#> round_ERR2585219 3 0.4820 0.38379 0.300 0.012 0.664 0.000 0.024
#> round_ERR2585237 3 0.3129 0.52678 0.004 0.156 0.832 0.000 0.008
#> round_ERR2585198 3 0.4464 0.41697 0.008 0.304 0.676 0.000 0.012
#> round_ERR2585211 1 0.4722 0.39268 0.572 0.004 0.412 0.000 0.012
#> round_ERR2585206 1 0.4705 0.40907 0.580 0.004 0.404 0.000 0.012
#> aberrant_ERR2585281 2 0.3047 0.72428 0.000 0.868 0.012 0.024 0.096
#> round_ERR2585212 3 0.4965 0.40507 0.292 0.020 0.664 0.000 0.024
#> round_ERR2585221 1 0.4227 0.59918 0.692 0.000 0.292 0.000 0.016
#> round_ERR2585243 3 0.4989 0.11635 0.400 0.008 0.572 0.000 0.020
#> round_ERR2585204 3 0.3266 0.50180 0.000 0.200 0.796 0.000 0.004
#> round_ERR2585213 3 0.4538 0.29068 0.000 0.364 0.620 0.000 0.016
#> aberrant_ERR2585373 2 0.4681 0.42303 0.000 0.696 0.000 0.052 0.252
#> aberrant_ERR2585358 2 0.5845 -0.37883 0.000 0.540 0.000 0.108 0.352
#> aberrant_ERR2585365 2 0.2645 0.74132 0.000 0.884 0.012 0.008 0.096
#> aberrant_ERR2585359 2 0.6203 -0.63873 0.000 0.464 0.000 0.140 0.396
#> aberrant_ERR2585370 2 0.1117 0.74440 0.000 0.964 0.016 0.000 0.020
#> round_ERR2585215 1 0.4193 0.59445 0.720 0.000 0.256 0.000 0.024
#> round_ERR2585262 3 0.4975 0.43280 0.008 0.204 0.712 0.000 0.076
#> round_ERR2585199 3 0.4318 0.42512 0.008 0.296 0.688 0.000 0.008
#> aberrant_ERR2585369 2 0.2674 0.72518 0.000 0.868 0.000 0.012 0.120
#> round_ERR2585208 1 0.3989 0.60074 0.728 0.004 0.260 0.000 0.008
#> round_ERR2585252 1 0.2813 0.63457 0.868 0.000 0.108 0.000 0.024
#> round_ERR2585236 3 0.5883 0.45364 0.224 0.072 0.656 0.000 0.048
#> aberrant_ERR2585284 4 0.4418 0.65447 0.000 0.000 0.016 0.652 0.332
#> round_ERR2585224 1 0.2830 0.61963 0.876 0.000 0.080 0.000 0.044
#> round_ERR2585260 3 0.4810 0.16872 0.400 0.008 0.580 0.000 0.012
#> round_ERR2585229 1 0.4738 0.41233 0.564 0.004 0.420 0.000 0.012
#> aberrant_ERR2585364 4 0.5928 0.09326 0.000 0.108 0.000 0.500 0.392
#> round_ERR2585253 1 0.1646 0.58267 0.944 0.000 0.020 0.004 0.032
#> aberrant_ERR2585368 2 0.1211 0.74375 0.000 0.960 0.016 0.000 0.024
#> aberrant_ERR2585371 2 0.1211 0.74375 0.000 0.960 0.016 0.000 0.024
#> round_ERR2585239 3 0.5355 0.12760 0.404 0.020 0.552 0.000 0.024
#> round_ERR2585273 1 0.4704 0.28185 0.508 0.004 0.480 0.000 0.008
#> round_ERR2585256 3 0.4140 0.55139 0.124 0.064 0.800 0.000 0.012
#> round_ERR2585272 3 0.4481 0.09176 0.416 0.008 0.576 0.000 0.000
#> round_ERR2585246 1 0.4503 0.57782 0.664 0.000 0.312 0.000 0.024
#> round_ERR2585261 3 0.4693 0.54475 0.148 0.084 0.756 0.000 0.012
#> round_ERR2585254 3 0.3972 0.49055 0.016 0.212 0.764 0.000 0.008
#> round_ERR2585225 3 0.2941 0.54862 0.020 0.064 0.884 0.000 0.032
#> round_ERR2585235 3 0.5866 0.05718 0.396 0.028 0.540 0.016 0.020
#> round_ERR2585271 3 0.4770 0.18778 0.384 0.008 0.596 0.000 0.012
#> round_ERR2585251 3 0.4791 0.26660 0.360 0.008 0.616 0.000 0.016
#> round_ERR2585255 3 0.3003 0.54850 0.016 0.064 0.880 0.000 0.040
#> round_ERR2585257 3 0.2949 0.55347 0.016 0.076 0.880 0.000 0.028
#> round_ERR2585226 3 0.4777 0.27387 0.356 0.008 0.620 0.000 0.016
#> round_ERR2585265 3 0.4739 0.35135 0.320 0.012 0.652 0.000 0.016
#> round_ERR2585259 3 0.5583 0.43875 0.244 0.052 0.664 0.000 0.040
#> round_ERR2585247 1 0.5136 0.38812 0.528 0.008 0.440 0.000 0.024
#> round_ERR2585241 1 0.4791 0.25991 0.524 0.004 0.460 0.000 0.012
#> round_ERR2585263 3 0.5009 0.34505 0.324 0.012 0.636 0.000 0.028
#> round_ERR2585264 1 0.0955 0.58020 0.968 0.000 0.004 0.000 0.028
#> round_ERR2585233 3 0.3296 0.54337 0.028 0.052 0.868 0.000 0.052
#> round_ERR2585223 3 0.4770 0.20529 0.384 0.008 0.596 0.000 0.012
#> round_ERR2585234 3 0.2833 0.53749 0.004 0.140 0.852 0.000 0.004
#> round_ERR2585222 3 0.4657 0.22181 0.380 0.008 0.604 0.000 0.008
#> round_ERR2585228 3 0.4781 0.18738 0.388 0.008 0.592 0.000 0.012
#> round_ERR2585248 1 0.1538 0.56934 0.948 0.000 0.008 0.008 0.036
#> round_ERR2585240 3 0.4790 0.32695 0.332 0.012 0.640 0.000 0.016
#> round_ERR2585270 3 0.5020 0.30731 0.344 0.016 0.620 0.000 0.020
#> round_ERR2585232 3 0.3269 0.55708 0.096 0.056 0.848 0.000 0.000
#> aberrant_ERR2585341 2 0.2856 0.73580 0.000 0.872 0.008 0.016 0.104
#> aberrant_ERR2585355 2 0.1399 0.74355 0.000 0.952 0.020 0.000 0.028
#> round_ERR2585227 3 0.4735 0.23542 0.372 0.012 0.608 0.000 0.008
#> aberrant_ERR2585351 2 0.2304 0.73417 0.000 0.892 0.000 0.008 0.100
#> round_ERR2585269 1 0.3055 0.64125 0.840 0.000 0.144 0.000 0.016
#> aberrant_ERR2585357 2 0.1018 0.74594 0.000 0.968 0.016 0.000 0.016
#> aberrant_ERR2585350 2 0.1018 0.74594 0.000 0.968 0.016 0.000 0.016
#> round_ERR2585250 3 0.4692 0.41906 0.276 0.024 0.688 0.000 0.012
#> round_ERR2585245 1 0.0794 0.58012 0.972 0.000 0.000 0.000 0.028
#> aberrant_ERR2585353 2 0.3929 0.62612 0.000 0.788 0.004 0.036 0.172
#> round_ERR2585258 3 0.4866 0.29231 0.352 0.012 0.620 0.000 0.016
#> aberrant_ERR2585354 2 0.4132 0.58345 0.000 0.760 0.004 0.032 0.204
#> round_ERR2585249 1 0.2773 0.63794 0.868 0.000 0.112 0.000 0.020
#> round_ERR2585268 3 0.5051 0.47752 0.236 0.052 0.696 0.000 0.016
#> aberrant_ERR2585356 5 0.6519 0.00000 0.000 0.340 0.000 0.204 0.456
#> round_ERR2585266 3 0.2012 0.56063 0.020 0.060 0.920 0.000 0.000
#> round_ERR2585231 1 0.2293 0.62944 0.900 0.000 0.084 0.000 0.016
#> round_ERR2585230 3 0.4787 0.01854 0.444 0.008 0.540 0.000 0.008
#> round_ERR2585267 1 0.2900 0.61315 0.864 0.000 0.108 0.000 0.028
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 2 0.2854 0.6620 0.000 0.792 0.000 0.000 0.208 0.000
#> aberrant_ERR2585338 2 0.1492 0.7324 0.000 0.940 0.024 0.000 0.036 0.000
#> aberrant_ERR2585325 2 0.2854 0.6620 0.000 0.792 0.000 0.000 0.208 0.000
#> aberrant_ERR2585283 4 0.1610 0.8490 0.000 0.000 0.000 0.916 0.084 0.000
#> aberrant_ERR2585343 5 0.4644 0.4507 0.000 0.456 0.000 0.040 0.504 0.000
#> aberrant_ERR2585329 2 0.1572 0.7347 0.000 0.936 0.036 0.000 0.028 0.000
#> aberrant_ERR2585317 2 0.0820 0.7299 0.000 0.972 0.016 0.000 0.012 0.000
#> aberrant_ERR2585339 2 0.1092 0.7326 0.000 0.960 0.020 0.000 0.020 0.000
#> aberrant_ERR2585335 2 0.2053 0.7235 0.000 0.888 0.004 0.000 0.108 0.000
#> aberrant_ERR2585287 4 0.5297 0.2780 0.000 0.212 0.004 0.616 0.168 0.000
#> aberrant_ERR2585321 2 0.4222 -0.2474 0.000 0.516 0.004 0.008 0.472 0.000
#> aberrant_ERR2585297 1 0.4524 0.0848 0.628 0.000 0.052 0.000 0.000 0.320
#> aberrant_ERR2585337 2 0.0820 0.7313 0.000 0.972 0.016 0.000 0.012 0.000
#> aberrant_ERR2585319 2 0.2482 0.6986 0.000 0.848 0.004 0.000 0.148 0.000
#> aberrant_ERR2585315 2 0.1719 0.7345 0.000 0.924 0.016 0.000 0.060 0.000
#> aberrant_ERR2585336 2 0.0717 0.7284 0.000 0.976 0.016 0.000 0.008 0.000
#> aberrant_ERR2585307 2 0.2808 0.6756 0.040 0.876 0.060 0.000 0.024 0.000
#> aberrant_ERR2585301 2 0.3411 0.6085 0.004 0.756 0.008 0.000 0.232 0.000
#> aberrant_ERR2585326 2 0.0820 0.7299 0.000 0.972 0.016 0.000 0.012 0.000
#> aberrant_ERR2585331 2 0.1405 0.7218 0.004 0.948 0.024 0.000 0.024 0.000
#> aberrant_ERR2585346 4 0.1802 0.8409 0.000 0.000 0.012 0.916 0.072 0.000
#> aberrant_ERR2585314 2 0.1977 0.7330 0.008 0.920 0.032 0.000 0.040 0.000
#> aberrant_ERR2585298 1 0.4751 -0.5693 0.500 0.048 0.452 0.000 0.000 0.000
#> aberrant_ERR2585345 2 0.1176 0.7334 0.000 0.956 0.020 0.000 0.024 0.000
#> aberrant_ERR2585299 1 0.4158 0.3775 0.704 0.000 0.052 0.000 0.000 0.244
#> aberrant_ERR2585309 6 0.4465 0.7356 0.332 0.000 0.036 0.000 0.004 0.628
#> aberrant_ERR2585303 2 0.2320 0.7204 0.000 0.864 0.004 0.000 0.132 0.000
#> aberrant_ERR2585313 2 0.0914 0.7319 0.000 0.968 0.016 0.000 0.016 0.000
#> aberrant_ERR2585318 2 0.2879 0.6792 0.004 0.816 0.004 0.000 0.176 0.000
#> aberrant_ERR2585328 2 0.6053 0.0475 0.008 0.592 0.060 0.028 0.280 0.032
#> aberrant_ERR2585330 2 0.2513 0.7189 0.000 0.852 0.008 0.000 0.140 0.000
#> aberrant_ERR2585293 4 0.1610 0.8490 0.000 0.000 0.000 0.916 0.084 0.000
#> aberrant_ERR2585342 2 0.3714 0.5836 0.000 0.720 0.008 0.008 0.264 0.000
#> aberrant_ERR2585348 2 0.4899 0.1446 0.012 0.596 0.032 0.008 0.352 0.000
#> aberrant_ERR2585352 2 0.1657 0.7367 0.000 0.928 0.016 0.000 0.056 0.000
#> aberrant_ERR2585308 1 0.4601 -0.3817 0.496 0.000 0.028 0.000 0.004 0.472
#> aberrant_ERR2585349 2 0.5987 0.1549 0.144 0.600 0.212 0.000 0.036 0.008
#> aberrant_ERR2585316 5 0.6277 0.5917 0.000 0.280 0.020 0.180 0.512 0.008
#> aberrant_ERR2585306 5 0.7940 0.5321 0.092 0.304 0.020 0.032 0.400 0.152
#> aberrant_ERR2585324 2 0.2482 0.6986 0.000 0.848 0.004 0.000 0.148 0.000
#> aberrant_ERR2585310 2 0.7106 -0.0702 0.260 0.484 0.140 0.000 0.100 0.016
#> aberrant_ERR2585296 1 0.5167 0.0143 0.664 0.096 0.212 0.000 0.000 0.028
#> aberrant_ERR2585275 4 0.3265 0.8097 0.000 0.008 0.024 0.824 0.140 0.004
#> aberrant_ERR2585311 2 0.4034 0.2724 0.000 0.624 0.000 0.008 0.364 0.004
#> aberrant_ERR2585292 4 0.1610 0.8490 0.000 0.000 0.000 0.916 0.084 0.000
#> aberrant_ERR2585282 2 0.3861 0.4195 0.004 0.672 0.008 0.000 0.316 0.000
#> aberrant_ERR2585305 2 0.3691 0.5601 0.008 0.724 0.008 0.000 0.260 0.000
#> aberrant_ERR2585278 2 0.1686 0.7345 0.000 0.924 0.012 0.000 0.064 0.000
#> aberrant_ERR2585347 2 0.5994 -0.3567 0.000 0.496 0.024 0.100 0.372 0.008
#> aberrant_ERR2585332 5 0.4788 0.3897 0.000 0.476 0.016 0.016 0.488 0.004
#> aberrant_ERR2585280 2 0.3192 0.6421 0.000 0.776 0.004 0.004 0.216 0.000
#> aberrant_ERR2585304 2 0.6257 -0.2625 0.268 0.460 0.260 0.000 0.008 0.004
#> aberrant_ERR2585322 2 0.1225 0.7360 0.000 0.952 0.012 0.000 0.036 0.000
#> aberrant_ERR2585279 2 0.2151 0.6988 0.016 0.912 0.048 0.000 0.024 0.000
#> aberrant_ERR2585277 2 0.1405 0.7229 0.004 0.948 0.024 0.000 0.024 0.000
#> aberrant_ERR2585295 2 0.3915 0.5774 0.000 0.736 0.008 0.028 0.228 0.000
#> aberrant_ERR2585333 2 0.3905 0.3397 0.000 0.636 0.004 0.004 0.356 0.000
#> aberrant_ERR2585285 2 0.2070 0.7275 0.000 0.892 0.008 0.000 0.100 0.000
#> aberrant_ERR2585286 2 0.1176 0.7230 0.000 0.956 0.020 0.000 0.024 0.000
#> aberrant_ERR2585294 2 0.3722 0.5603 0.004 0.724 0.008 0.000 0.260 0.004
#> aberrant_ERR2585300 5 0.4968 0.5287 0.000 0.416 0.008 0.040 0.532 0.004
#> aberrant_ERR2585334 2 0.1498 0.7187 0.000 0.940 0.032 0.000 0.028 0.000
#> aberrant_ERR2585361 2 0.2673 0.7069 0.000 0.852 0.012 0.004 0.132 0.000
#> aberrant_ERR2585372 2 0.3665 0.6176 0.000 0.760 0.016 0.012 0.212 0.000
#> round_ERR2585217 3 0.6736 0.3663 0.348 0.276 0.348 0.000 0.020 0.008
#> round_ERR2585205 1 0.3867 0.4096 0.748 0.000 0.052 0.000 0.000 0.200
#> round_ERR2585214 1 0.5034 -0.5913 0.472 0.072 0.456 0.000 0.000 0.000
#> round_ERR2585202 1 0.6068 -0.4289 0.516 0.188 0.280 0.000 0.004 0.012
#> aberrant_ERR2585367 2 0.3607 0.6306 0.000 0.768 0.012 0.016 0.204 0.000
#> round_ERR2585220 1 0.3645 0.4609 0.804 0.012 0.128 0.000 0.000 0.056
#> round_ERR2585238 1 0.4024 0.2635 0.700 0.000 0.036 0.000 0.000 0.264
#> aberrant_ERR2585276 2 0.3894 0.5774 0.008 0.732 0.016 0.004 0.240 0.000
#> round_ERR2585218 1 0.3582 0.4350 0.768 0.000 0.036 0.000 0.000 0.196
#> aberrant_ERR2585363 2 0.2146 0.7190 0.000 0.880 0.004 0.000 0.116 0.000
#> round_ERR2585201 1 0.4979 -0.5847 0.492 0.056 0.448 0.000 0.000 0.004
#> round_ERR2585210 1 0.5209 0.1222 0.564 0.000 0.112 0.000 0.000 0.324
#> aberrant_ERR2585362 2 0.3453 0.6697 0.000 0.788 0.028 0.000 0.180 0.004
#> aberrant_ERR2585360 2 0.3986 0.5763 0.000 0.720 0.012 0.008 0.252 0.008
#> round_ERR2585209 1 0.4853 -0.2149 0.624 0.048 0.312 0.000 0.000 0.016
#> round_ERR2585242 1 0.4632 -0.5321 0.520 0.040 0.440 0.000 0.000 0.000
#> round_ERR2585216 1 0.2978 0.5040 0.860 0.012 0.072 0.000 0.000 0.056
#> round_ERR2585219 1 0.2781 0.4835 0.868 0.008 0.084 0.000 0.000 0.040
#> round_ERR2585237 1 0.5508 -0.5661 0.444 0.128 0.428 0.000 0.000 0.000
#> round_ERR2585198 1 0.6430 -0.4502 0.372 0.284 0.332 0.000 0.008 0.004
#> round_ERR2585211 1 0.4309 0.2229 0.660 0.000 0.044 0.000 0.000 0.296
#> round_ERR2585206 1 0.4344 0.2017 0.652 0.000 0.044 0.000 0.000 0.304
#> aberrant_ERR2585281 2 0.3411 0.6837 0.000 0.828 0.028 0.020 0.120 0.004
#> round_ERR2585212 1 0.3103 0.4626 0.848 0.016 0.100 0.000 0.000 0.036
#> round_ERR2585221 1 0.4735 -0.2934 0.540 0.000 0.040 0.000 0.004 0.416
#> round_ERR2585243 1 0.3839 0.5237 0.792 0.004 0.088 0.000 0.004 0.112
#> round_ERR2585204 1 0.5724 -0.5582 0.424 0.164 0.412 0.000 0.000 0.000
#> round_ERR2585213 2 0.6333 -0.5585 0.308 0.356 0.328 0.000 0.008 0.000
#> aberrant_ERR2585373 2 0.3965 0.2229 0.000 0.604 0.000 0.008 0.388 0.000
#> aberrant_ERR2585358 5 0.4261 0.5574 0.000 0.408 0.000 0.020 0.572 0.000
#> aberrant_ERR2585365 2 0.2581 0.7128 0.000 0.860 0.020 0.000 0.120 0.000
#> aberrant_ERR2585359 5 0.5002 0.6330 0.000 0.340 0.012 0.048 0.596 0.004
#> aberrant_ERR2585370 2 0.0717 0.7284 0.000 0.976 0.016 0.000 0.008 0.000
#> round_ERR2585215 6 0.5255 0.4221 0.428 0.000 0.096 0.000 0.000 0.476
#> round_ERR2585262 3 0.6221 0.5661 0.276 0.152 0.536 0.000 0.028 0.008
#> round_ERR2585199 1 0.6329 -0.4566 0.376 0.276 0.340 0.000 0.004 0.004
#> aberrant_ERR2585369 2 0.2703 0.6945 0.000 0.824 0.004 0.000 0.172 0.000
#> round_ERR2585208 1 0.4406 -0.3893 0.500 0.000 0.024 0.000 0.000 0.476
#> round_ERR2585252 6 0.4236 0.7738 0.308 0.000 0.036 0.000 0.000 0.656
#> round_ERR2585236 1 0.5531 0.2949 0.660 0.044 0.204 0.000 0.012 0.080
#> aberrant_ERR2585284 4 0.5889 0.6337 0.000 0.000 0.196 0.592 0.176 0.036
#> round_ERR2585224 6 0.4445 0.7879 0.280 0.000 0.028 0.004 0.012 0.676
#> round_ERR2585260 1 0.2821 0.5335 0.860 0.004 0.040 0.000 0.000 0.096
#> round_ERR2585229 1 0.4326 0.2554 0.656 0.000 0.044 0.000 0.000 0.300
#> aberrant_ERR2585364 5 0.5257 -0.3782 0.000 0.036 0.016 0.408 0.528 0.012
#> round_ERR2585253 6 0.3309 0.7817 0.148 0.000 0.028 0.004 0.004 0.816
#> aberrant_ERR2585368 2 0.0806 0.7279 0.000 0.972 0.020 0.000 0.008 0.000
#> aberrant_ERR2585371 2 0.0806 0.7279 0.000 0.972 0.020 0.000 0.008 0.000
#> round_ERR2585239 1 0.3458 0.5288 0.816 0.012 0.044 0.000 0.000 0.128
#> round_ERR2585273 1 0.4914 0.3552 0.660 0.004 0.092 0.000 0.004 0.240
#> round_ERR2585256 1 0.4496 -0.0487 0.672 0.048 0.272 0.000 0.000 0.008
#> round_ERR2585272 1 0.4277 0.5023 0.732 0.000 0.124 0.000 0.000 0.144
#> round_ERR2585246 1 0.4594 -0.1918 0.560 0.000 0.032 0.000 0.004 0.404
#> round_ERR2585261 1 0.5052 -0.0360 0.644 0.060 0.268 0.000 0.000 0.028
#> round_ERR2585254 1 0.6164 -0.5249 0.416 0.184 0.388 0.000 0.004 0.008
#> round_ERR2585225 3 0.4936 0.6737 0.440 0.036 0.512 0.000 0.004 0.008
#> round_ERR2585235 1 0.5925 0.3618 0.576 0.008 0.184 0.008 0.004 0.220
#> round_ERR2585271 1 0.2968 0.5322 0.852 0.004 0.052 0.000 0.000 0.092
#> round_ERR2585251 1 0.3801 0.5036 0.796 0.004 0.100 0.000 0.004 0.096
#> round_ERR2585255 3 0.4990 0.6759 0.436 0.040 0.512 0.000 0.004 0.008
#> round_ERR2585257 3 0.5168 0.6692 0.452 0.040 0.488 0.000 0.004 0.016
#> round_ERR2585226 1 0.3352 0.5125 0.832 0.004 0.072 0.000 0.004 0.088
#> round_ERR2585265 1 0.3589 0.4842 0.812 0.004 0.108 0.000 0.004 0.072
#> round_ERR2585259 1 0.4718 0.3748 0.720 0.032 0.172 0.000 0.000 0.076
#> round_ERR2585247 1 0.4617 0.2341 0.644 0.000 0.056 0.000 0.004 0.296
#> round_ERR2585241 1 0.3858 0.3721 0.740 0.000 0.044 0.000 0.000 0.216
#> round_ERR2585263 1 0.3075 0.4883 0.856 0.012 0.088 0.000 0.004 0.040
#> round_ERR2585264 6 0.3175 0.7931 0.164 0.000 0.028 0.000 0.000 0.808
#> round_ERR2585233 3 0.4979 0.6469 0.424 0.028 0.524 0.000 0.000 0.024
#> round_ERR2585223 1 0.3144 0.5253 0.844 0.004 0.048 0.000 0.004 0.100
#> round_ERR2585234 1 0.5419 -0.5600 0.460 0.116 0.424 0.000 0.000 0.000
#> round_ERR2585222 1 0.3233 0.5277 0.832 0.004 0.060 0.000 0.000 0.104
#> round_ERR2585228 1 0.2547 0.5293 0.880 0.004 0.036 0.000 0.000 0.080
#> round_ERR2585248 6 0.3350 0.7394 0.120 0.000 0.040 0.008 0.004 0.828
#> round_ERR2585240 1 0.3666 0.5030 0.812 0.008 0.096 0.000 0.004 0.080
#> round_ERR2585270 1 0.2619 0.5130 0.884 0.012 0.056 0.000 0.000 0.048
#> round_ERR2585232 1 0.4925 -0.2942 0.600 0.036 0.340 0.000 0.000 0.024
#> aberrant_ERR2585341 2 0.2597 0.6917 0.000 0.824 0.000 0.000 0.176 0.000
#> aberrant_ERR2585355 2 0.1092 0.7261 0.000 0.960 0.020 0.000 0.020 0.000
#> round_ERR2585227 1 0.4157 0.5010 0.764 0.004 0.112 0.000 0.004 0.116
#> aberrant_ERR2585351 2 0.2482 0.7037 0.000 0.848 0.004 0.000 0.148 0.000
#> round_ERR2585269 6 0.4318 0.7306 0.340 0.000 0.020 0.000 0.008 0.632
#> aberrant_ERR2585357 2 0.0820 0.7299 0.000 0.972 0.016 0.000 0.012 0.000
#> aberrant_ERR2585350 2 0.0820 0.7299 0.000 0.972 0.016 0.000 0.012 0.000
#> round_ERR2585250 1 0.3407 0.4443 0.832 0.016 0.108 0.000 0.004 0.040
#> round_ERR2585245 6 0.3062 0.7955 0.160 0.000 0.024 0.000 0.000 0.816
#> aberrant_ERR2585353 2 0.3934 0.5346 0.000 0.716 0.020 0.008 0.256 0.000
#> round_ERR2585258 1 0.3844 0.4966 0.792 0.004 0.108 0.000 0.004 0.092
#> aberrant_ERR2585354 2 0.4044 0.4853 0.000 0.696 0.008 0.008 0.280 0.008
#> round_ERR2585249 6 0.3898 0.7826 0.296 0.000 0.020 0.000 0.000 0.684
#> round_ERR2585268 1 0.3873 0.3191 0.772 0.032 0.176 0.000 0.000 0.020
#> aberrant_ERR2585356 5 0.5047 0.5388 0.000 0.208 0.004 0.104 0.672 0.012
#> round_ERR2585266 1 0.4701 -0.5153 0.524 0.036 0.436 0.000 0.000 0.004
#> round_ERR2585231 6 0.3734 0.8073 0.264 0.000 0.020 0.000 0.000 0.716
#> round_ERR2585230 1 0.3381 0.5060 0.808 0.004 0.040 0.000 0.000 0.148
#> round_ERR2585267 6 0.4434 0.7847 0.260 0.000 0.048 0.000 0.008 0.684
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> MAD:hclust 99 7.49e-07 2
#> MAD:hclust 135 6.69e-26 3
#> MAD:hclust 109 8.28e-19 4
#> MAD:hclust 97 2.41e-17 5
#> MAD:hclust 97 1.78e-18 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "kmeans"]
# you can also extract it by
# res = res_list["MAD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'MAD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.829 0.932 0.970 0.5013 0.500 0.500
#> 3 3 0.608 0.690 0.838 0.2554 0.804 0.635
#> 4 4 0.655 0.619 0.787 0.1249 0.855 0.634
#> 5 5 0.660 0.742 0.828 0.0748 0.892 0.643
#> 6 6 0.706 0.717 0.797 0.0433 0.917 0.673
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585338 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585325 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585283 1 0.5946 0.8345 0.856 0.144
#> aberrant_ERR2585343 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585329 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585317 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585339 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585335 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585287 2 0.7139 0.7474 0.196 0.804
#> aberrant_ERR2585321 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585297 1 0.0000 0.9730 1.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585319 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585315 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585336 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585307 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585301 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585326 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585331 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585346 1 0.5737 0.8441 0.864 0.136
#> aberrant_ERR2585314 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585298 1 0.0000 0.9730 1.000 0.000
#> aberrant_ERR2585345 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585299 1 0.0000 0.9730 1.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.9730 1.000 0.000
#> aberrant_ERR2585303 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585313 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585318 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585328 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585330 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585293 1 0.5178 0.8667 0.884 0.116
#> aberrant_ERR2585342 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585348 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585352 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585308 1 0.0000 0.9730 1.000 0.000
#> aberrant_ERR2585349 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585316 2 0.9491 0.4079 0.368 0.632
#> aberrant_ERR2585306 1 0.6438 0.8113 0.836 0.164
#> aberrant_ERR2585324 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585310 2 0.4939 0.8699 0.108 0.892
#> aberrant_ERR2585296 2 0.9491 0.4618 0.368 0.632
#> aberrant_ERR2585275 1 0.6148 0.8251 0.848 0.152
#> aberrant_ERR2585311 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585292 1 0.5178 0.8667 0.884 0.116
#> aberrant_ERR2585282 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585305 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585278 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585347 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585332 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585280 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585304 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585322 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585279 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585277 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585295 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585333 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585285 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585286 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585294 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585300 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585334 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585361 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585372 2 0.0000 0.9643 0.000 1.000
#> round_ERR2585217 2 0.9044 0.5663 0.320 0.680
#> round_ERR2585205 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585214 2 0.7056 0.7715 0.192 0.808
#> round_ERR2585202 2 0.1633 0.9452 0.024 0.976
#> aberrant_ERR2585367 2 0.0000 0.9643 0.000 1.000
#> round_ERR2585220 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585238 1 0.0000 0.9730 1.000 0.000
#> aberrant_ERR2585276 2 0.0000 0.9643 0.000 1.000
#> round_ERR2585218 1 0.0000 0.9730 1.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.9643 0.000 1.000
#> round_ERR2585201 1 0.1633 0.9530 0.976 0.024
#> round_ERR2585210 1 0.0000 0.9730 1.000 0.000
#> aberrant_ERR2585362 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585360 2 0.0000 0.9643 0.000 1.000
#> round_ERR2585209 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585242 1 0.5946 0.8220 0.856 0.144
#> round_ERR2585216 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585219 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585237 2 0.6148 0.8198 0.152 0.848
#> round_ERR2585198 2 0.5842 0.8329 0.140 0.860
#> round_ERR2585211 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585206 1 0.0000 0.9730 1.000 0.000
#> aberrant_ERR2585281 2 0.0000 0.9643 0.000 1.000
#> round_ERR2585212 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585221 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585243 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585204 2 0.4939 0.8672 0.108 0.892
#> round_ERR2585213 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585373 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585358 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585365 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585359 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585370 2 0.0000 0.9643 0.000 1.000
#> round_ERR2585215 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585262 2 0.9087 0.5573 0.324 0.676
#> round_ERR2585199 2 0.0376 0.9613 0.004 0.996
#> aberrant_ERR2585369 2 0.0000 0.9643 0.000 1.000
#> round_ERR2585208 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585252 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585236 1 0.0000 0.9730 1.000 0.000
#> aberrant_ERR2585284 1 0.5408 0.8579 0.876 0.124
#> round_ERR2585224 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585260 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585229 1 0.0000 0.9730 1.000 0.000
#> aberrant_ERR2585364 1 0.8499 0.6363 0.724 0.276
#> round_ERR2585253 1 0.0000 0.9730 1.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585371 2 0.0000 0.9643 0.000 1.000
#> round_ERR2585239 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585273 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585256 1 0.0376 0.9699 0.996 0.004
#> round_ERR2585272 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585246 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585261 1 0.9954 0.0859 0.540 0.460
#> round_ERR2585254 2 0.9286 0.5163 0.344 0.656
#> round_ERR2585225 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585235 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585271 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585251 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585255 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585257 1 0.0376 0.9699 0.996 0.004
#> round_ERR2585226 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585265 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585259 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585247 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585241 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585263 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585264 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585233 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585223 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585234 2 0.8386 0.6609 0.268 0.732
#> round_ERR2585222 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585228 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585248 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585240 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585270 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585232 1 0.0000 0.9730 1.000 0.000
#> aberrant_ERR2585341 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585355 2 0.0000 0.9643 0.000 1.000
#> round_ERR2585227 1 0.0000 0.9730 1.000 0.000
#> aberrant_ERR2585351 2 0.0000 0.9643 0.000 1.000
#> round_ERR2585269 1 0.0000 0.9730 1.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.9643 0.000 1.000
#> aberrant_ERR2585350 2 0.0000 0.9643 0.000 1.000
#> round_ERR2585250 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585245 1 0.0000 0.9730 1.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.9643 0.000 1.000
#> round_ERR2585258 1 0.0000 0.9730 1.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.9643 0.000 1.000
#> round_ERR2585249 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585268 1 0.0000 0.9730 1.000 0.000
#> aberrant_ERR2585356 2 0.1184 0.9515 0.016 0.984
#> round_ERR2585266 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585231 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585230 1 0.0000 0.9730 1.000 0.000
#> round_ERR2585267 1 0.0000 0.9730 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.5621 0.3908 0.000 0.692 0.308
#> aberrant_ERR2585338 2 0.0000 0.7828 0.000 1.000 0.000
#> aberrant_ERR2585325 2 0.5621 0.3908 0.000 0.692 0.308
#> aberrant_ERR2585283 3 0.0000 0.6236 0.000 0.000 1.000
#> aberrant_ERR2585343 3 0.6045 0.6334 0.000 0.380 0.620
#> aberrant_ERR2585329 2 0.0000 0.7828 0.000 1.000 0.000
#> aberrant_ERR2585317 2 0.0000 0.7828 0.000 1.000 0.000
#> aberrant_ERR2585339 2 0.0000 0.7828 0.000 1.000 0.000
#> aberrant_ERR2585335 2 0.0747 0.7778 0.000 0.984 0.016
#> aberrant_ERR2585287 3 0.3192 0.6453 0.000 0.112 0.888
#> aberrant_ERR2585321 3 0.6215 0.5760 0.000 0.428 0.572
#> aberrant_ERR2585297 1 0.4796 0.8403 0.780 0.000 0.220
#> aberrant_ERR2585337 2 0.0000 0.7828 0.000 1.000 0.000
#> aberrant_ERR2585319 2 0.2537 0.7427 0.000 0.920 0.080
#> aberrant_ERR2585315 2 0.0000 0.7828 0.000 1.000 0.000
#> aberrant_ERR2585336 2 0.0000 0.7828 0.000 1.000 0.000
#> aberrant_ERR2585307 2 0.0000 0.7828 0.000 1.000 0.000
#> aberrant_ERR2585301 2 0.3816 0.6807 0.000 0.852 0.148
#> aberrant_ERR2585326 2 0.0000 0.7828 0.000 1.000 0.000
#> aberrant_ERR2585331 2 0.0000 0.7828 0.000 1.000 0.000
#> aberrant_ERR2585346 3 0.0000 0.6236 0.000 0.000 1.000
#> aberrant_ERR2585314 2 0.0000 0.7828 0.000 1.000 0.000
#> aberrant_ERR2585298 1 0.1163 0.8438 0.972 0.028 0.000
#> aberrant_ERR2585345 2 0.0000 0.7828 0.000 1.000 0.000
#> aberrant_ERR2585299 1 0.4555 0.8480 0.800 0.000 0.200
#> aberrant_ERR2585309 1 0.4842 0.8383 0.776 0.000 0.224
#> aberrant_ERR2585303 2 0.1643 0.7640 0.000 0.956 0.044
#> aberrant_ERR2585313 2 0.0000 0.7828 0.000 1.000 0.000
#> aberrant_ERR2585318 2 0.5138 0.5232 0.000 0.748 0.252
#> aberrant_ERR2585328 2 0.6307 -0.3494 0.000 0.512 0.488
#> aberrant_ERR2585330 2 0.4062 0.6614 0.000 0.836 0.164
#> aberrant_ERR2585293 3 0.0000 0.6236 0.000 0.000 1.000
#> aberrant_ERR2585342 2 0.5882 0.2725 0.000 0.652 0.348
#> aberrant_ERR2585348 3 0.6192 0.5909 0.000 0.420 0.580
#> aberrant_ERR2585352 2 0.0237 0.7817 0.000 0.996 0.004
#> aberrant_ERR2585308 1 0.4842 0.8383 0.776 0.000 0.224
#> aberrant_ERR2585349 2 0.0592 0.7752 0.012 0.988 0.000
#> aberrant_ERR2585316 3 0.3267 0.6461 0.000 0.116 0.884
#> aberrant_ERR2585306 3 0.5085 0.5993 0.072 0.092 0.836
#> aberrant_ERR2585324 2 0.2537 0.7427 0.000 0.920 0.080
#> aberrant_ERR2585310 2 0.5706 0.4256 0.320 0.680 0.000
#> aberrant_ERR2585296 1 0.5497 0.5218 0.708 0.292 0.000
#> aberrant_ERR2585275 3 0.0000 0.6236 0.000 0.000 1.000
#> aberrant_ERR2585311 3 0.6309 0.3697 0.000 0.496 0.504
#> aberrant_ERR2585292 3 0.0000 0.6236 0.000 0.000 1.000
#> aberrant_ERR2585282 3 0.6180 0.5970 0.000 0.416 0.584
#> aberrant_ERR2585305 2 0.4654 0.5994 0.000 0.792 0.208
#> aberrant_ERR2585278 2 0.2625 0.7398 0.000 0.916 0.084
#> aberrant_ERR2585347 3 0.6026 0.6352 0.000 0.376 0.624
#> aberrant_ERR2585332 3 0.6111 0.6203 0.000 0.396 0.604
#> aberrant_ERR2585280 2 0.5650 0.3852 0.000 0.688 0.312
#> aberrant_ERR2585304 2 0.1753 0.7444 0.048 0.952 0.000
#> aberrant_ERR2585322 2 0.0000 0.7828 0.000 1.000 0.000
#> aberrant_ERR2585279 2 0.0424 0.7779 0.008 0.992 0.000
#> aberrant_ERR2585277 2 0.0000 0.7828 0.000 1.000 0.000
#> aberrant_ERR2585295 3 0.6235 0.5584 0.000 0.436 0.564
#> aberrant_ERR2585333 3 0.6192 0.5910 0.000 0.420 0.580
#> aberrant_ERR2585285 2 0.3941 0.6727 0.000 0.844 0.156
#> aberrant_ERR2585286 2 0.0000 0.7828 0.000 1.000 0.000
#> aberrant_ERR2585294 2 0.4654 0.6008 0.000 0.792 0.208
#> aberrant_ERR2585300 3 0.6008 0.6369 0.000 0.372 0.628
#> aberrant_ERR2585334 2 0.0000 0.7828 0.000 1.000 0.000
#> aberrant_ERR2585361 2 0.6026 0.1507 0.000 0.624 0.376
#> aberrant_ERR2585372 2 0.6274 -0.2169 0.000 0.544 0.456
#> round_ERR2585217 1 0.6267 0.1020 0.548 0.452 0.000
#> round_ERR2585205 1 0.4452 0.8505 0.808 0.000 0.192
#> round_ERR2585214 2 0.4974 0.5376 0.236 0.764 0.000
#> round_ERR2585202 2 0.4842 0.5531 0.224 0.776 0.000
#> aberrant_ERR2585367 2 0.5733 0.3414 0.000 0.676 0.324
#> round_ERR2585220 1 0.0000 0.8571 1.000 0.000 0.000
#> round_ERR2585238 1 0.4504 0.8493 0.804 0.000 0.196
#> aberrant_ERR2585276 2 0.5760 0.3393 0.000 0.672 0.328
#> round_ERR2585218 1 0.4605 0.8465 0.796 0.000 0.204
#> aberrant_ERR2585363 2 0.1163 0.7733 0.000 0.972 0.028
#> round_ERR2585201 1 0.1860 0.8274 0.948 0.052 0.000
#> round_ERR2585210 1 0.4235 0.8543 0.824 0.000 0.176
#> aberrant_ERR2585362 2 0.5905 0.2485 0.000 0.648 0.352
#> aberrant_ERR2585360 2 0.6008 0.1841 0.000 0.628 0.372
#> round_ERR2585209 1 0.0000 0.8571 1.000 0.000 0.000
#> round_ERR2585242 1 0.3551 0.7501 0.868 0.132 0.000
#> round_ERR2585216 1 0.0000 0.8571 1.000 0.000 0.000
#> round_ERR2585219 1 0.1529 0.8636 0.960 0.000 0.040
#> round_ERR2585237 2 0.4931 0.5430 0.232 0.768 0.000
#> round_ERR2585198 2 0.4931 0.5430 0.232 0.768 0.000
#> round_ERR2585211 1 0.4842 0.8383 0.776 0.000 0.224
#> round_ERR2585206 1 0.4702 0.8436 0.788 0.000 0.212
#> aberrant_ERR2585281 2 0.3340 0.7012 0.000 0.880 0.120
#> round_ERR2585212 1 0.0000 0.8571 1.000 0.000 0.000
#> round_ERR2585221 1 0.4842 0.8383 0.776 0.000 0.224
#> round_ERR2585243 1 0.4346 0.8527 0.816 0.000 0.184
#> round_ERR2585204 2 0.4887 0.5482 0.228 0.772 0.000
#> round_ERR2585213 2 0.4750 0.5623 0.216 0.784 0.000
#> aberrant_ERR2585373 3 0.6235 0.5582 0.000 0.436 0.564
#> aberrant_ERR2585358 3 0.6154 0.6073 0.000 0.408 0.592
#> aberrant_ERR2585365 2 0.1163 0.7729 0.000 0.972 0.028
#> aberrant_ERR2585359 3 0.6045 0.6334 0.000 0.380 0.620
#> aberrant_ERR2585370 2 0.0000 0.7828 0.000 1.000 0.000
#> round_ERR2585215 1 0.4842 0.8383 0.776 0.000 0.224
#> round_ERR2585262 1 0.7075 -0.1509 0.492 0.488 0.020
#> round_ERR2585199 2 0.4842 0.5531 0.224 0.776 0.000
#> aberrant_ERR2585369 2 0.5785 0.3263 0.000 0.668 0.332
#> round_ERR2585208 1 0.4750 0.8420 0.784 0.000 0.216
#> round_ERR2585252 1 0.4796 0.8404 0.780 0.000 0.220
#> round_ERR2585236 1 0.3116 0.8647 0.892 0.000 0.108
#> aberrant_ERR2585284 3 0.0000 0.6236 0.000 0.000 1.000
#> round_ERR2585224 1 0.4887 0.8361 0.772 0.000 0.228
#> round_ERR2585260 1 0.1964 0.8646 0.944 0.000 0.056
#> round_ERR2585229 1 0.4702 0.8436 0.788 0.000 0.212
#> aberrant_ERR2585364 3 0.0000 0.6236 0.000 0.000 1.000
#> round_ERR2585253 1 0.4842 0.8383 0.776 0.000 0.224
#> aberrant_ERR2585368 2 0.0000 0.7828 0.000 1.000 0.000
#> aberrant_ERR2585371 2 0.0000 0.7828 0.000 1.000 0.000
#> round_ERR2585239 1 0.2537 0.8654 0.920 0.000 0.080
#> round_ERR2585273 1 0.1860 0.8647 0.948 0.000 0.052
#> round_ERR2585256 1 0.0237 0.8556 0.996 0.004 0.000
#> round_ERR2585272 1 0.1529 0.8637 0.960 0.000 0.040
#> round_ERR2585246 1 0.4702 0.8436 0.788 0.000 0.212
#> round_ERR2585261 1 0.4974 0.6185 0.764 0.236 0.000
#> round_ERR2585254 1 0.6008 0.3545 0.628 0.372 0.000
#> round_ERR2585225 1 0.0747 0.8501 0.984 0.016 0.000
#> round_ERR2585235 1 0.3879 0.8588 0.848 0.000 0.152
#> round_ERR2585271 1 0.3038 0.8647 0.896 0.000 0.104
#> round_ERR2585251 1 0.0000 0.8571 1.000 0.000 0.000
#> round_ERR2585255 1 0.0747 0.8501 0.984 0.016 0.000
#> round_ERR2585257 1 0.1163 0.8438 0.972 0.028 0.000
#> round_ERR2585226 1 0.0000 0.8571 1.000 0.000 0.000
#> round_ERR2585265 1 0.0000 0.8571 1.000 0.000 0.000
#> round_ERR2585259 1 0.0237 0.8581 0.996 0.000 0.004
#> round_ERR2585247 1 0.4235 0.8547 0.824 0.000 0.176
#> round_ERR2585241 1 0.4555 0.8481 0.800 0.000 0.200
#> round_ERR2585263 1 0.0000 0.8571 1.000 0.000 0.000
#> round_ERR2585264 1 0.4931 0.8340 0.768 0.000 0.232
#> round_ERR2585233 1 0.0000 0.8571 1.000 0.000 0.000
#> round_ERR2585223 1 0.3267 0.8635 0.884 0.000 0.116
#> round_ERR2585234 1 0.6307 0.0384 0.512 0.488 0.000
#> round_ERR2585222 1 0.1289 0.8627 0.968 0.000 0.032
#> round_ERR2585228 1 0.2878 0.8654 0.904 0.000 0.096
#> round_ERR2585248 1 0.4931 0.8340 0.768 0.000 0.232
#> round_ERR2585240 1 0.0000 0.8571 1.000 0.000 0.000
#> round_ERR2585270 1 0.0237 0.8581 0.996 0.000 0.004
#> round_ERR2585232 1 0.0000 0.8571 1.000 0.000 0.000
#> aberrant_ERR2585341 2 0.2261 0.7504 0.000 0.932 0.068
#> aberrant_ERR2585355 2 0.0000 0.7828 0.000 1.000 0.000
#> round_ERR2585227 1 0.0000 0.8571 1.000 0.000 0.000
#> aberrant_ERR2585351 2 0.3941 0.6716 0.000 0.844 0.156
#> round_ERR2585269 1 0.4842 0.8383 0.776 0.000 0.224
#> aberrant_ERR2585357 2 0.0000 0.7828 0.000 1.000 0.000
#> aberrant_ERR2585350 2 0.0000 0.7828 0.000 1.000 0.000
#> round_ERR2585250 1 0.0424 0.8588 0.992 0.000 0.008
#> round_ERR2585245 1 0.4887 0.8361 0.772 0.000 0.228
#> aberrant_ERR2585353 3 0.6244 0.5489 0.000 0.440 0.560
#> round_ERR2585258 1 0.0000 0.8571 1.000 0.000 0.000
#> aberrant_ERR2585354 2 0.6308 -0.3604 0.000 0.508 0.492
#> round_ERR2585249 1 0.4842 0.8383 0.776 0.000 0.224
#> round_ERR2585268 1 0.0000 0.8571 1.000 0.000 0.000
#> aberrant_ERR2585356 3 0.5882 0.6418 0.000 0.348 0.652
#> round_ERR2585266 1 0.0892 0.8481 0.980 0.020 0.000
#> round_ERR2585231 1 0.4887 0.8361 0.772 0.000 0.228
#> round_ERR2585230 1 0.2711 0.8659 0.912 0.000 0.088
#> round_ERR2585267 1 0.4796 0.8404 0.780 0.000 0.220
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 2 0.7301 0.2568 0.000 0.484 0.356 0.160
#> aberrant_ERR2585338 2 0.0000 0.6843 0.000 1.000 0.000 0.000
#> aberrant_ERR2585325 2 0.7301 0.2568 0.000 0.484 0.356 0.160
#> aberrant_ERR2585283 4 0.0188 0.5576 0.004 0.000 0.000 0.996
#> aberrant_ERR2585343 4 0.7375 0.6069 0.000 0.172 0.348 0.480
#> aberrant_ERR2585329 2 0.0000 0.6843 0.000 1.000 0.000 0.000
#> aberrant_ERR2585317 2 0.0000 0.6843 0.000 1.000 0.000 0.000
#> aberrant_ERR2585339 2 0.0469 0.6845 0.000 0.988 0.012 0.000
#> aberrant_ERR2585335 2 0.2081 0.6733 0.000 0.916 0.084 0.000
#> aberrant_ERR2585287 4 0.0657 0.5613 0.004 0.000 0.012 0.984
#> aberrant_ERR2585321 4 0.7901 0.4102 0.000 0.296 0.348 0.356
#> aberrant_ERR2585297 1 0.0188 0.9033 0.996 0.000 0.000 0.004
#> aberrant_ERR2585337 2 0.0000 0.6843 0.000 1.000 0.000 0.000
#> aberrant_ERR2585319 2 0.3853 0.6417 0.000 0.820 0.160 0.020
#> aberrant_ERR2585315 2 0.1637 0.6786 0.000 0.940 0.060 0.000
#> aberrant_ERR2585336 2 0.0000 0.6843 0.000 1.000 0.000 0.000
#> aberrant_ERR2585307 2 0.0000 0.6843 0.000 1.000 0.000 0.000
#> aberrant_ERR2585301 2 0.6627 0.4090 0.000 0.556 0.348 0.096
#> aberrant_ERR2585326 2 0.0000 0.6843 0.000 1.000 0.000 0.000
#> aberrant_ERR2585331 2 0.0336 0.6783 0.000 0.992 0.008 0.000
#> aberrant_ERR2585346 4 0.0188 0.5576 0.004 0.000 0.000 0.996
#> aberrant_ERR2585314 2 0.0000 0.6843 0.000 1.000 0.000 0.000
#> aberrant_ERR2585298 3 0.5639 0.6611 0.324 0.040 0.636 0.000
#> aberrant_ERR2585345 2 0.0000 0.6843 0.000 1.000 0.000 0.000
#> aberrant_ERR2585299 1 0.0188 0.9035 0.996 0.000 0.004 0.000
#> aberrant_ERR2585309 1 0.0469 0.9001 0.988 0.000 0.000 0.012
#> aberrant_ERR2585303 2 0.4477 0.5538 0.000 0.688 0.312 0.000
#> aberrant_ERR2585313 2 0.0188 0.6847 0.000 0.996 0.004 0.000
#> aberrant_ERR2585318 2 0.7314 0.2674 0.000 0.488 0.348 0.164
#> aberrant_ERR2585328 2 0.7836 -0.1137 0.000 0.388 0.348 0.264
#> aberrant_ERR2585330 2 0.5973 0.4865 0.000 0.612 0.332 0.056
#> aberrant_ERR2585293 4 0.0376 0.5561 0.004 0.000 0.004 0.992
#> aberrant_ERR2585342 2 0.7108 0.3287 0.000 0.512 0.348 0.140
#> aberrant_ERR2585348 4 0.7756 0.5392 0.000 0.240 0.348 0.412
#> aberrant_ERR2585352 2 0.3074 0.6533 0.000 0.848 0.152 0.000
#> aberrant_ERR2585308 1 0.0336 0.9020 0.992 0.000 0.000 0.008
#> aberrant_ERR2585349 2 0.2011 0.6175 0.000 0.920 0.080 0.000
#> aberrant_ERR2585316 4 0.4744 0.6058 0.000 0.012 0.284 0.704
#> aberrant_ERR2585306 4 0.8540 0.5525 0.160 0.056 0.348 0.436
#> aberrant_ERR2585324 2 0.3853 0.6417 0.000 0.820 0.160 0.020
#> aberrant_ERR2585310 3 0.5708 0.5471 0.028 0.416 0.556 0.000
#> aberrant_ERR2585296 3 0.6492 0.7021 0.144 0.220 0.636 0.000
#> aberrant_ERR2585275 4 0.0524 0.5603 0.004 0.000 0.008 0.988
#> aberrant_ERR2585311 2 0.7824 -0.0925 0.000 0.392 0.348 0.260
#> aberrant_ERR2585292 4 0.0376 0.5561 0.004 0.000 0.004 0.992
#> aberrant_ERR2585282 4 0.7785 0.5252 0.000 0.248 0.348 0.404
#> aberrant_ERR2585305 2 0.7031 0.3446 0.000 0.520 0.348 0.132
#> aberrant_ERR2585278 2 0.2676 0.6685 0.000 0.896 0.092 0.012
#> aberrant_ERR2585347 4 0.7385 0.6073 0.000 0.176 0.340 0.484
#> aberrant_ERR2585332 4 0.7628 0.5769 0.000 0.212 0.348 0.440
#> aberrant_ERR2585280 2 0.7070 0.3357 0.000 0.516 0.348 0.136
#> aberrant_ERR2585304 2 0.1637 0.6331 0.000 0.940 0.060 0.000
#> aberrant_ERR2585322 2 0.0921 0.6833 0.000 0.972 0.028 0.000
#> aberrant_ERR2585279 2 0.1302 0.6449 0.000 0.956 0.044 0.000
#> aberrant_ERR2585277 2 0.0000 0.6843 0.000 1.000 0.000 0.000
#> aberrant_ERR2585295 4 0.7889 0.4346 0.000 0.288 0.348 0.364
#> aberrant_ERR2585333 4 0.7740 0.5447 0.000 0.236 0.348 0.416
#> aberrant_ERR2585285 2 0.5823 0.4811 0.000 0.608 0.348 0.044
#> aberrant_ERR2585286 2 0.0000 0.6843 0.000 1.000 0.000 0.000
#> aberrant_ERR2585294 2 0.7031 0.3448 0.000 0.520 0.348 0.132
#> aberrant_ERR2585300 4 0.7606 0.5810 0.000 0.208 0.348 0.444
#> aberrant_ERR2585334 2 0.0336 0.6783 0.000 0.992 0.008 0.000
#> aberrant_ERR2585361 2 0.6663 0.4008 0.000 0.556 0.344 0.100
#> aberrant_ERR2585372 2 0.7740 0.0178 0.000 0.416 0.348 0.236
#> round_ERR2585217 3 0.5698 0.6807 0.044 0.320 0.636 0.000
#> round_ERR2585205 1 0.0188 0.9035 0.996 0.000 0.004 0.000
#> round_ERR2585214 3 0.5007 0.6475 0.008 0.356 0.636 0.000
#> round_ERR2585202 3 0.4889 0.6436 0.004 0.360 0.636 0.000
#> aberrant_ERR2585367 2 0.5898 0.4749 0.000 0.604 0.348 0.048
#> round_ERR2585220 1 0.4961 -0.1606 0.552 0.000 0.448 0.000
#> round_ERR2585238 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> aberrant_ERR2585276 2 0.7314 0.2695 0.000 0.488 0.348 0.164
#> round_ERR2585218 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> aberrant_ERR2585363 2 0.4008 0.6058 0.000 0.756 0.244 0.000
#> round_ERR2585201 3 0.6350 0.6873 0.252 0.112 0.636 0.000
#> round_ERR2585210 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> aberrant_ERR2585362 2 0.7180 0.3058 0.000 0.504 0.348 0.148
#> aberrant_ERR2585360 2 0.7432 0.2204 0.000 0.472 0.348 0.180
#> round_ERR2585209 3 0.4889 0.6351 0.360 0.004 0.636 0.000
#> round_ERR2585242 3 0.6523 0.6969 0.208 0.156 0.636 0.000
#> round_ERR2585216 1 0.4679 0.2433 0.648 0.000 0.352 0.000
#> round_ERR2585219 1 0.2704 0.7716 0.876 0.000 0.124 0.000
#> round_ERR2585237 3 0.4889 0.6434 0.004 0.360 0.636 0.000
#> round_ERR2585198 3 0.4730 0.6388 0.000 0.364 0.636 0.000
#> round_ERR2585211 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> aberrant_ERR2585281 2 0.4795 0.5575 0.000 0.696 0.292 0.012
#> round_ERR2585212 3 0.4888 0.5550 0.412 0.000 0.588 0.000
#> round_ERR2585221 1 0.0188 0.9033 0.996 0.000 0.000 0.004
#> round_ERR2585243 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> round_ERR2585204 3 0.4730 0.6388 0.000 0.364 0.636 0.000
#> round_ERR2585213 3 0.4776 0.6252 0.000 0.376 0.624 0.000
#> aberrant_ERR2585373 4 0.7836 0.4919 0.000 0.264 0.348 0.388
#> aberrant_ERR2585358 4 0.7628 0.5771 0.000 0.212 0.348 0.440
#> aberrant_ERR2585365 2 0.4477 0.5532 0.000 0.688 0.312 0.000
#> aberrant_ERR2585359 4 0.7345 0.6094 0.000 0.172 0.336 0.492
#> aberrant_ERR2585370 2 0.0000 0.6843 0.000 1.000 0.000 0.000
#> round_ERR2585215 1 0.0188 0.9033 0.996 0.000 0.000 0.004
#> round_ERR2585262 3 0.4417 0.5801 0.084 0.092 0.820 0.004
#> round_ERR2585199 3 0.4843 0.6020 0.000 0.396 0.604 0.000
#> aberrant_ERR2585369 2 0.6949 0.3599 0.000 0.528 0.348 0.124
#> round_ERR2585208 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> round_ERR2585252 1 0.0469 0.9001 0.988 0.000 0.000 0.012
#> round_ERR2585236 1 0.2053 0.8381 0.924 0.000 0.072 0.004
#> aberrant_ERR2585284 4 0.0188 0.5576 0.004 0.000 0.000 0.996
#> round_ERR2585224 1 0.0469 0.9001 0.988 0.000 0.000 0.012
#> round_ERR2585260 1 0.0336 0.9018 0.992 0.000 0.008 0.000
#> round_ERR2585229 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> aberrant_ERR2585364 4 0.2593 0.5795 0.004 0.000 0.104 0.892
#> round_ERR2585253 1 0.0469 0.9001 0.988 0.000 0.000 0.012
#> aberrant_ERR2585368 2 0.0336 0.6795 0.000 0.992 0.008 0.000
#> aberrant_ERR2585371 2 0.0336 0.6795 0.000 0.992 0.008 0.000
#> round_ERR2585239 1 0.0817 0.8913 0.976 0.000 0.024 0.000
#> round_ERR2585273 1 0.1302 0.8723 0.956 0.000 0.044 0.000
#> round_ERR2585256 3 0.4889 0.6354 0.360 0.004 0.636 0.000
#> round_ERR2585272 1 0.1118 0.8818 0.964 0.000 0.036 0.000
#> round_ERR2585246 1 0.0000 0.9042 1.000 0.000 0.000 0.000
#> round_ERR2585261 3 0.6148 0.6997 0.084 0.280 0.636 0.000
#> round_ERR2585254 3 0.5698 0.6813 0.044 0.320 0.636 0.000
#> round_ERR2585225 3 0.5285 0.6442 0.352 0.012 0.632 0.004
#> round_ERR2585235 1 0.0524 0.9005 0.988 0.000 0.008 0.004
#> round_ERR2585271 1 0.0188 0.9035 0.996 0.000 0.004 0.000
#> round_ERR2585251 3 0.4855 0.5767 0.400 0.000 0.600 0.000
#> round_ERR2585255 3 0.5285 0.6442 0.352 0.012 0.632 0.004
#> round_ERR2585257 3 0.5464 0.6506 0.344 0.020 0.632 0.004
#> round_ERR2585226 1 0.4543 0.3417 0.676 0.000 0.324 0.000
#> round_ERR2585265 1 0.4761 0.1693 0.628 0.000 0.372 0.000
#> round_ERR2585259 3 0.5132 0.4751 0.448 0.000 0.548 0.004
#> round_ERR2585247 1 0.0188 0.9035 0.996 0.000 0.004 0.000
#> round_ERR2585241 1 0.0188 0.9035 0.996 0.000 0.004 0.000
#> round_ERR2585263 1 0.4996 -0.3011 0.516 0.000 0.484 0.000
#> round_ERR2585264 1 0.0469 0.9001 0.988 0.000 0.000 0.012
#> round_ERR2585233 3 0.4964 0.6101 0.380 0.000 0.616 0.004
#> round_ERR2585223 1 0.0188 0.9035 0.996 0.000 0.004 0.000
#> round_ERR2585234 3 0.5203 0.6568 0.016 0.348 0.636 0.000
#> round_ERR2585222 1 0.1118 0.8816 0.964 0.000 0.036 0.000
#> round_ERR2585228 1 0.0188 0.9035 0.996 0.000 0.004 0.000
#> round_ERR2585248 1 0.0469 0.9001 0.988 0.000 0.000 0.012
#> round_ERR2585240 3 0.4889 0.6351 0.360 0.004 0.636 0.000
#> round_ERR2585270 1 0.4916 -0.0621 0.576 0.000 0.424 0.000
#> round_ERR2585232 3 0.4920 0.6255 0.368 0.004 0.628 0.000
#> aberrant_ERR2585341 2 0.4454 0.5560 0.000 0.692 0.308 0.000
#> aberrant_ERR2585355 2 0.0000 0.6843 0.000 1.000 0.000 0.000
#> round_ERR2585227 3 0.4843 0.5848 0.396 0.000 0.604 0.000
#> aberrant_ERR2585351 2 0.5970 0.4715 0.000 0.600 0.348 0.052
#> round_ERR2585269 1 0.0469 0.9001 0.988 0.000 0.000 0.012
#> aberrant_ERR2585357 2 0.0000 0.6843 0.000 1.000 0.000 0.000
#> aberrant_ERR2585350 2 0.0188 0.6847 0.000 0.996 0.004 0.000
#> round_ERR2585250 3 0.4998 0.3648 0.488 0.000 0.512 0.000
#> round_ERR2585245 1 0.0469 0.9001 0.988 0.000 0.000 0.012
#> aberrant_ERR2585353 4 0.7896 0.4203 0.000 0.292 0.348 0.360
#> round_ERR2585258 1 0.3837 0.5948 0.776 0.000 0.224 0.000
#> aberrant_ERR2585354 2 0.7866 -0.1569 0.000 0.376 0.348 0.276
#> round_ERR2585249 1 0.0469 0.9001 0.988 0.000 0.000 0.012
#> round_ERR2585268 3 0.4961 0.4763 0.448 0.000 0.552 0.000
#> aberrant_ERR2585356 4 0.7314 0.6100 0.000 0.164 0.348 0.488
#> round_ERR2585266 3 0.5464 0.6506 0.344 0.020 0.632 0.004
#> round_ERR2585231 1 0.0469 0.9001 0.988 0.000 0.000 0.012
#> round_ERR2585230 1 0.0592 0.8973 0.984 0.000 0.016 0.000
#> round_ERR2585267 1 0.0469 0.9001 0.988 0.000 0.000 0.012
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 5 0.5489 0.76481 0.000 0.276 0.072 0.012 0.640
#> aberrant_ERR2585338 2 0.0290 0.86637 0.000 0.992 0.008 0.000 0.000
#> aberrant_ERR2585325 5 0.5489 0.76481 0.000 0.276 0.072 0.012 0.640
#> aberrant_ERR2585283 4 0.3010 0.94148 0.004 0.000 0.000 0.824 0.172
#> aberrant_ERR2585343 5 0.3313 0.81068 0.000 0.088 0.028 0.024 0.860
#> aberrant_ERR2585329 2 0.0510 0.86731 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585317 2 0.0510 0.86731 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585339 2 0.0162 0.86217 0.000 0.996 0.000 0.000 0.004
#> aberrant_ERR2585335 2 0.2439 0.76808 0.000 0.876 0.004 0.000 0.120
#> aberrant_ERR2585287 4 0.3388 0.92063 0.000 0.000 0.008 0.792 0.200
#> aberrant_ERR2585321 5 0.2798 0.86261 0.000 0.140 0.008 0.000 0.852
#> aberrant_ERR2585297 1 0.3494 0.81716 0.840 0.000 0.004 0.096 0.060
#> aberrant_ERR2585337 2 0.0510 0.86731 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585319 2 0.3639 0.65269 0.000 0.792 0.024 0.000 0.184
#> aberrant_ERR2585315 2 0.1300 0.83907 0.000 0.956 0.016 0.000 0.028
#> aberrant_ERR2585336 2 0.0404 0.86699 0.000 0.988 0.012 0.000 0.000
#> aberrant_ERR2585307 2 0.0510 0.86731 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585301 5 0.4106 0.82516 0.000 0.256 0.020 0.000 0.724
#> aberrant_ERR2585326 2 0.0510 0.86731 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585331 2 0.0880 0.85499 0.000 0.968 0.032 0.000 0.000
#> aberrant_ERR2585346 4 0.3010 0.94148 0.004 0.000 0.000 0.824 0.172
#> aberrant_ERR2585314 2 0.0510 0.86731 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585298 3 0.2856 0.82215 0.104 0.016 0.872 0.008 0.000
#> aberrant_ERR2585345 2 0.0510 0.86731 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585299 1 0.0324 0.81984 0.992 0.000 0.004 0.004 0.000
#> aberrant_ERR2585309 1 0.4254 0.78787 0.772 0.000 0.000 0.148 0.080
#> aberrant_ERR2585303 2 0.4798 -0.19725 0.000 0.540 0.020 0.000 0.440
#> aberrant_ERR2585313 2 0.0162 0.86217 0.000 0.996 0.000 0.000 0.004
#> aberrant_ERR2585318 5 0.3612 0.85677 0.000 0.228 0.008 0.000 0.764
#> aberrant_ERR2585328 5 0.3574 0.86933 0.000 0.168 0.028 0.000 0.804
#> aberrant_ERR2585330 2 0.4653 -0.24697 0.000 0.516 0.012 0.000 0.472
#> aberrant_ERR2585293 4 0.3651 0.93235 0.004 0.000 0.028 0.808 0.160
#> aberrant_ERR2585342 5 0.3353 0.86663 0.000 0.196 0.008 0.000 0.796
#> aberrant_ERR2585348 5 0.3134 0.85329 0.000 0.120 0.032 0.000 0.848
#> aberrant_ERR2585352 2 0.1831 0.80867 0.000 0.920 0.004 0.000 0.076
#> aberrant_ERR2585308 1 0.4155 0.79244 0.780 0.000 0.000 0.144 0.076
#> aberrant_ERR2585349 2 0.3177 0.66022 0.000 0.792 0.208 0.000 0.000
#> aberrant_ERR2585316 5 0.3807 0.41219 0.000 0.000 0.012 0.240 0.748
#> aberrant_ERR2585306 5 0.3356 0.73879 0.028 0.048 0.020 0.028 0.876
#> aberrant_ERR2585324 2 0.3639 0.65269 0.000 0.792 0.024 0.000 0.184
#> aberrant_ERR2585310 3 0.3921 0.73057 0.044 0.172 0.784 0.000 0.000
#> aberrant_ERR2585296 3 0.2729 0.81615 0.056 0.060 0.884 0.000 0.000
#> aberrant_ERR2585275 4 0.2891 0.94007 0.000 0.000 0.000 0.824 0.176
#> aberrant_ERR2585311 5 0.3132 0.86933 0.000 0.172 0.008 0.000 0.820
#> aberrant_ERR2585292 4 0.3651 0.93235 0.004 0.000 0.028 0.808 0.160
#> aberrant_ERR2585282 5 0.3012 0.85692 0.000 0.124 0.024 0.000 0.852
#> aberrant_ERR2585305 5 0.3878 0.84574 0.000 0.236 0.016 0.000 0.748
#> aberrant_ERR2585278 2 0.2864 0.74416 0.000 0.852 0.012 0.000 0.136
#> aberrant_ERR2585347 5 0.3963 0.78869 0.000 0.084 0.036 0.052 0.828
#> aberrant_ERR2585332 5 0.2972 0.84068 0.000 0.108 0.024 0.004 0.864
#> aberrant_ERR2585280 5 0.4425 0.83922 0.000 0.244 0.040 0.000 0.716
#> aberrant_ERR2585304 2 0.1892 0.80421 0.000 0.916 0.080 0.004 0.000
#> aberrant_ERR2585322 2 0.0771 0.85215 0.000 0.976 0.004 0.000 0.020
#> aberrant_ERR2585279 2 0.2286 0.75899 0.000 0.888 0.108 0.004 0.000
#> aberrant_ERR2585277 2 0.0404 0.86699 0.000 0.988 0.012 0.000 0.000
#> aberrant_ERR2585295 5 0.3880 0.86162 0.000 0.152 0.044 0.004 0.800
#> aberrant_ERR2585333 5 0.3059 0.84546 0.000 0.108 0.028 0.004 0.860
#> aberrant_ERR2585285 5 0.4446 0.58712 0.000 0.400 0.008 0.000 0.592
#> aberrant_ERR2585286 2 0.0290 0.86639 0.000 0.992 0.008 0.000 0.000
#> aberrant_ERR2585294 5 0.3819 0.85191 0.000 0.228 0.016 0.000 0.756
#> aberrant_ERR2585300 5 0.2573 0.83975 0.000 0.104 0.016 0.000 0.880
#> aberrant_ERR2585334 2 0.0880 0.85499 0.000 0.968 0.032 0.000 0.000
#> aberrant_ERR2585361 5 0.4404 0.78857 0.000 0.292 0.024 0.000 0.684
#> aberrant_ERR2585372 5 0.3456 0.86988 0.000 0.184 0.016 0.000 0.800
#> round_ERR2585217 3 0.2505 0.79802 0.020 0.092 0.888 0.000 0.000
#> round_ERR2585205 1 0.0854 0.82380 0.976 0.000 0.004 0.008 0.012
#> round_ERR2585214 3 0.2462 0.78123 0.000 0.112 0.880 0.008 0.000
#> round_ERR2585202 3 0.2439 0.77799 0.000 0.120 0.876 0.004 0.000
#> aberrant_ERR2585367 5 0.4442 0.79508 0.000 0.284 0.028 0.000 0.688
#> round_ERR2585220 3 0.4302 0.32864 0.480 0.000 0.520 0.000 0.000
#> round_ERR2585238 1 0.0727 0.82320 0.980 0.000 0.004 0.012 0.004
#> aberrant_ERR2585276 5 0.3663 0.86273 0.000 0.208 0.016 0.000 0.776
#> round_ERR2585218 1 0.0854 0.82409 0.976 0.000 0.004 0.012 0.008
#> aberrant_ERR2585363 2 0.3496 0.64693 0.000 0.788 0.012 0.000 0.200
#> round_ERR2585201 3 0.3113 0.81873 0.068 0.048 0.872 0.012 0.000
#> round_ERR2585210 1 0.0854 0.82186 0.976 0.000 0.008 0.004 0.012
#> aberrant_ERR2585362 5 0.3690 0.86645 0.000 0.200 0.020 0.000 0.780
#> aberrant_ERR2585360 5 0.3160 0.86795 0.000 0.188 0.004 0.000 0.808
#> round_ERR2585209 3 0.2536 0.81744 0.128 0.004 0.868 0.000 0.000
#> round_ERR2585242 3 0.3071 0.82099 0.080 0.036 0.872 0.012 0.000
#> round_ERR2585216 1 0.4219 0.00936 0.584 0.000 0.416 0.000 0.000
#> round_ERR2585219 1 0.3143 0.60705 0.796 0.000 0.204 0.000 0.000
#> round_ERR2585237 3 0.2389 0.77818 0.000 0.116 0.880 0.004 0.000
#> round_ERR2585198 3 0.2389 0.77818 0.000 0.116 0.880 0.004 0.000
#> round_ERR2585211 1 0.2344 0.82532 0.904 0.000 0.000 0.064 0.032
#> round_ERR2585206 1 0.2982 0.82101 0.860 0.000 0.004 0.116 0.020
#> aberrant_ERR2585281 5 0.4977 0.40205 0.000 0.472 0.028 0.000 0.500
#> round_ERR2585212 3 0.4074 0.61486 0.364 0.000 0.636 0.000 0.000
#> round_ERR2585221 1 0.4113 0.79401 0.784 0.000 0.000 0.140 0.076
#> round_ERR2585243 1 0.0613 0.82127 0.984 0.000 0.004 0.008 0.004
#> round_ERR2585204 3 0.2488 0.77192 0.000 0.124 0.872 0.004 0.000
#> round_ERR2585213 3 0.3579 0.64278 0.000 0.240 0.756 0.004 0.000
#> aberrant_ERR2585373 5 0.2488 0.85539 0.000 0.124 0.004 0.000 0.872
#> aberrant_ERR2585358 5 0.2731 0.83596 0.000 0.104 0.016 0.004 0.876
#> aberrant_ERR2585365 2 0.4743 -0.30466 0.000 0.512 0.016 0.000 0.472
#> aberrant_ERR2585359 5 0.3251 0.79701 0.000 0.080 0.016 0.040 0.864
#> aberrant_ERR2585370 2 0.0510 0.86731 0.000 0.984 0.016 0.000 0.000
#> round_ERR2585215 1 0.3906 0.80136 0.800 0.000 0.000 0.132 0.068
#> round_ERR2585262 3 0.3292 0.79735 0.040 0.028 0.880 0.020 0.032
#> round_ERR2585199 3 0.4288 0.40645 0.000 0.384 0.612 0.004 0.000
#> aberrant_ERR2585369 5 0.3487 0.86108 0.000 0.212 0.008 0.000 0.780
#> round_ERR2585208 1 0.3474 0.81529 0.836 0.000 0.004 0.116 0.044
#> round_ERR2585252 1 0.4212 0.79010 0.776 0.000 0.000 0.144 0.080
#> round_ERR2585236 1 0.3599 0.67062 0.812 0.000 0.160 0.020 0.008
#> aberrant_ERR2585284 4 0.2970 0.93967 0.004 0.000 0.000 0.828 0.168
#> round_ERR2585224 1 0.4390 0.78164 0.760 0.000 0.000 0.156 0.084
#> round_ERR2585260 1 0.0880 0.80628 0.968 0.000 0.032 0.000 0.000
#> round_ERR2585229 1 0.1412 0.82627 0.952 0.000 0.004 0.036 0.008
#> aberrant_ERR2585364 4 0.4504 0.58091 0.000 0.000 0.008 0.564 0.428
#> round_ERR2585253 1 0.4294 0.78562 0.768 0.000 0.000 0.152 0.080
#> aberrant_ERR2585368 2 0.0510 0.86731 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585371 2 0.0510 0.86731 0.000 0.984 0.016 0.000 0.000
#> round_ERR2585239 1 0.1864 0.77669 0.924 0.000 0.068 0.004 0.004
#> round_ERR2585273 1 0.3619 0.72997 0.828 0.000 0.124 0.040 0.008
#> round_ERR2585256 3 0.2563 0.81994 0.120 0.008 0.872 0.000 0.000
#> round_ERR2585272 1 0.2179 0.75893 0.896 0.000 0.100 0.004 0.000
#> round_ERR2585246 1 0.2932 0.82219 0.864 0.000 0.004 0.112 0.020
#> round_ERR2585261 3 0.2654 0.80512 0.032 0.084 0.884 0.000 0.000
#> round_ERR2585254 3 0.2540 0.80069 0.024 0.088 0.888 0.000 0.000
#> round_ERR2585225 3 0.3285 0.81611 0.128 0.004 0.844 0.020 0.004
#> round_ERR2585235 1 0.1280 0.80946 0.960 0.000 0.024 0.008 0.008
#> round_ERR2585271 1 0.0451 0.81888 0.988 0.000 0.008 0.004 0.000
#> round_ERR2585251 3 0.3876 0.67536 0.316 0.000 0.684 0.000 0.000
#> round_ERR2585255 3 0.3325 0.82115 0.112 0.012 0.852 0.020 0.004
#> round_ERR2585257 3 0.3126 0.82149 0.112 0.012 0.860 0.012 0.004
#> round_ERR2585226 1 0.4438 0.12399 0.608 0.000 0.384 0.004 0.004
#> round_ERR2585265 1 0.4249 -0.05504 0.568 0.000 0.432 0.000 0.000
#> round_ERR2585259 3 0.4389 0.60071 0.368 0.000 0.624 0.004 0.004
#> round_ERR2585247 1 0.2102 0.82656 0.916 0.000 0.004 0.068 0.012
#> round_ERR2585241 1 0.0671 0.82335 0.980 0.000 0.004 0.016 0.000
#> round_ERR2585263 3 0.4249 0.47642 0.432 0.000 0.568 0.000 0.000
#> round_ERR2585264 1 0.4334 0.78265 0.764 0.000 0.000 0.156 0.080
#> round_ERR2585233 3 0.3538 0.79132 0.176 0.000 0.804 0.016 0.004
#> round_ERR2585223 1 0.0162 0.81832 0.996 0.000 0.004 0.000 0.000
#> round_ERR2585234 3 0.2445 0.78476 0.004 0.108 0.884 0.004 0.000
#> round_ERR2585222 1 0.1908 0.76238 0.908 0.000 0.092 0.000 0.000
#> round_ERR2585228 1 0.0290 0.81724 0.992 0.000 0.008 0.000 0.000
#> round_ERR2585248 1 0.4428 0.77793 0.756 0.000 0.000 0.160 0.084
#> round_ERR2585240 3 0.3061 0.81273 0.136 0.000 0.844 0.020 0.000
#> round_ERR2585270 1 0.4305 -0.27586 0.512 0.000 0.488 0.000 0.000
#> round_ERR2585232 3 0.3196 0.78418 0.192 0.000 0.804 0.004 0.000
#> aberrant_ERR2585341 2 0.5171 -0.32431 0.000 0.504 0.040 0.000 0.456
#> aberrant_ERR2585355 2 0.0162 0.86217 0.000 0.996 0.000 0.000 0.004
#> round_ERR2585227 3 0.3861 0.70398 0.284 0.000 0.712 0.004 0.000
#> aberrant_ERR2585351 5 0.4380 0.64515 0.000 0.376 0.008 0.000 0.616
#> round_ERR2585269 1 0.4294 0.78562 0.768 0.000 0.000 0.152 0.080
#> aberrant_ERR2585357 2 0.0510 0.86731 0.000 0.984 0.016 0.000 0.000
#> aberrant_ERR2585350 2 0.0000 0.86379 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585250 3 0.4621 0.51608 0.412 0.000 0.576 0.008 0.004
#> round_ERR2585245 1 0.4334 0.78265 0.764 0.000 0.000 0.156 0.080
#> aberrant_ERR2585353 5 0.2771 0.85817 0.000 0.128 0.012 0.000 0.860
#> round_ERR2585258 1 0.4280 0.35646 0.676 0.000 0.312 0.008 0.004
#> aberrant_ERR2585354 5 0.3053 0.86901 0.000 0.164 0.008 0.000 0.828
#> round_ERR2585249 1 0.4294 0.78562 0.768 0.000 0.000 0.152 0.080
#> round_ERR2585268 3 0.4101 0.59817 0.372 0.000 0.628 0.000 0.000
#> aberrant_ERR2585356 5 0.3018 0.80381 0.000 0.080 0.020 0.024 0.876
#> round_ERR2585266 3 0.3374 0.82011 0.116 0.012 0.848 0.020 0.004
#> round_ERR2585231 1 0.4334 0.78265 0.764 0.000 0.000 0.156 0.080
#> round_ERR2585230 1 0.0609 0.81279 0.980 0.000 0.020 0.000 0.000
#> round_ERR2585267 1 0.3946 0.79953 0.800 0.000 0.000 0.120 0.080
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 5 0.5781 0.69922 0.000 0.156 0.016 0.032 0.644 0.152
#> aberrant_ERR2585338 2 0.1606 0.92236 0.000 0.932 0.004 0.000 0.056 0.008
#> aberrant_ERR2585325 5 0.5781 0.69922 0.000 0.156 0.016 0.032 0.644 0.152
#> aberrant_ERR2585283 4 0.1644 0.92453 0.000 0.000 0.000 0.920 0.076 0.004
#> aberrant_ERR2585343 5 0.3096 0.76842 0.000 0.000 0.004 0.048 0.840 0.108
#> aberrant_ERR2585329 2 0.1349 0.92416 0.000 0.940 0.004 0.000 0.056 0.000
#> aberrant_ERR2585317 2 0.1349 0.92416 0.000 0.940 0.004 0.000 0.056 0.000
#> aberrant_ERR2585339 2 0.1349 0.92196 0.000 0.940 0.000 0.000 0.056 0.004
#> aberrant_ERR2585335 2 0.3345 0.75815 0.000 0.776 0.000 0.000 0.204 0.020
#> aberrant_ERR2585287 4 0.2509 0.91209 0.000 0.000 0.000 0.876 0.088 0.036
#> aberrant_ERR2585321 5 0.2213 0.82857 0.000 0.044 0.000 0.004 0.904 0.048
#> aberrant_ERR2585297 1 0.4145 -0.49400 0.628 0.004 0.000 0.008 0.004 0.356
#> aberrant_ERR2585337 2 0.1349 0.92416 0.000 0.940 0.004 0.000 0.056 0.000
#> aberrant_ERR2585319 2 0.4612 0.51562 0.000 0.636 0.004 0.000 0.308 0.052
#> aberrant_ERR2585315 2 0.2094 0.89584 0.000 0.900 0.000 0.000 0.080 0.020
#> aberrant_ERR2585336 2 0.1349 0.92416 0.000 0.940 0.004 0.000 0.056 0.000
#> aberrant_ERR2585307 2 0.1349 0.92416 0.000 0.940 0.004 0.000 0.056 0.000
#> aberrant_ERR2585301 5 0.3325 0.81790 0.000 0.084 0.000 0.000 0.820 0.096
#> aberrant_ERR2585326 2 0.1349 0.92416 0.000 0.940 0.004 0.000 0.056 0.000
#> aberrant_ERR2585331 2 0.1636 0.90573 0.000 0.936 0.024 0.000 0.036 0.004
#> aberrant_ERR2585346 4 0.1788 0.92476 0.000 0.004 0.000 0.916 0.076 0.004
#> aberrant_ERR2585314 2 0.1462 0.92305 0.000 0.936 0.008 0.000 0.056 0.000
#> aberrant_ERR2585298 3 0.1382 0.87294 0.036 0.008 0.948 0.000 0.000 0.008
#> aberrant_ERR2585345 2 0.1349 0.92416 0.000 0.940 0.004 0.000 0.056 0.000
#> aberrant_ERR2585299 1 0.1485 0.58875 0.944 0.004 0.000 0.024 0.000 0.028
#> aberrant_ERR2585309 6 0.3828 0.97519 0.440 0.000 0.000 0.000 0.000 0.560
#> aberrant_ERR2585303 5 0.4829 0.31823 0.000 0.424 0.000 0.000 0.520 0.056
#> aberrant_ERR2585313 2 0.1204 0.92294 0.000 0.944 0.000 0.000 0.056 0.000
#> aberrant_ERR2585318 5 0.2201 0.82786 0.000 0.076 0.000 0.000 0.896 0.028
#> aberrant_ERR2585328 5 0.2563 0.82612 0.000 0.052 0.000 0.000 0.876 0.072
#> aberrant_ERR2585330 5 0.4824 0.24356 0.000 0.420 0.000 0.000 0.524 0.056
#> aberrant_ERR2585293 4 0.3777 0.89316 0.000 0.000 0.020 0.804 0.068 0.108
#> aberrant_ERR2585342 5 0.2733 0.82427 0.000 0.056 0.000 0.000 0.864 0.080
#> aberrant_ERR2585348 5 0.2264 0.80369 0.000 0.012 0.000 0.004 0.888 0.096
#> aberrant_ERR2585352 2 0.2431 0.86040 0.000 0.860 0.000 0.000 0.132 0.008
#> aberrant_ERR2585308 6 0.4111 0.96242 0.456 0.000 0.000 0.004 0.004 0.536
#> aberrant_ERR2585349 2 0.3628 0.60991 0.000 0.720 0.268 0.000 0.008 0.004
#> aberrant_ERR2585316 5 0.5015 0.37620 0.000 0.000 0.004 0.288 0.616 0.092
#> aberrant_ERR2585306 5 0.3739 0.70428 0.020 0.004 0.000 0.012 0.772 0.192
#> aberrant_ERR2585324 2 0.4612 0.51562 0.000 0.636 0.004 0.000 0.308 0.052
#> aberrant_ERR2585310 3 0.5454 0.68207 0.112 0.112 0.708 0.004 0.032 0.032
#> aberrant_ERR2585296 3 0.1478 0.87613 0.020 0.032 0.944 0.004 0.000 0.000
#> aberrant_ERR2585275 4 0.2006 0.92143 0.000 0.000 0.000 0.904 0.080 0.016
#> aberrant_ERR2585311 5 0.2776 0.82352 0.000 0.052 0.000 0.000 0.860 0.088
#> aberrant_ERR2585292 4 0.3777 0.89316 0.000 0.000 0.020 0.804 0.068 0.108
#> aberrant_ERR2585282 5 0.1982 0.81696 0.000 0.016 0.000 0.004 0.912 0.068
#> aberrant_ERR2585305 5 0.3215 0.82012 0.000 0.072 0.000 0.000 0.828 0.100
#> aberrant_ERR2585278 2 0.3791 0.70055 0.000 0.732 0.000 0.000 0.236 0.032
#> aberrant_ERR2585347 5 0.3972 0.71711 0.000 0.000 0.004 0.104 0.772 0.120
#> aberrant_ERR2585332 5 0.2011 0.79920 0.000 0.004 0.000 0.020 0.912 0.064
#> aberrant_ERR2585280 5 0.3426 0.82274 0.000 0.068 0.000 0.000 0.808 0.124
#> aberrant_ERR2585304 2 0.1983 0.85848 0.000 0.908 0.072 0.000 0.020 0.000
#> aberrant_ERR2585322 2 0.1807 0.91201 0.000 0.920 0.000 0.000 0.060 0.020
#> aberrant_ERR2585279 2 0.1753 0.83463 0.000 0.912 0.084 0.000 0.000 0.004
#> aberrant_ERR2585277 2 0.1349 0.92416 0.000 0.940 0.004 0.000 0.056 0.000
#> aberrant_ERR2585295 5 0.3627 0.80722 0.000 0.032 0.000 0.028 0.808 0.132
#> aberrant_ERR2585333 5 0.2748 0.80688 0.000 0.016 0.000 0.008 0.856 0.120
#> aberrant_ERR2585285 5 0.3979 0.67162 0.000 0.256 0.000 0.000 0.708 0.036
#> aberrant_ERR2585286 2 0.1493 0.92333 0.000 0.936 0.004 0.000 0.056 0.004
#> aberrant_ERR2585294 5 0.3215 0.81931 0.000 0.072 0.000 0.000 0.828 0.100
#> aberrant_ERR2585300 5 0.2540 0.80755 0.000 0.020 0.000 0.004 0.872 0.104
#> aberrant_ERR2585334 2 0.1636 0.90573 0.000 0.936 0.024 0.000 0.036 0.004
#> aberrant_ERR2585361 5 0.3654 0.78921 0.000 0.144 0.000 0.004 0.792 0.060
#> aberrant_ERR2585372 5 0.2471 0.82570 0.000 0.052 0.000 0.004 0.888 0.056
#> round_ERR2585217 3 0.1382 0.87163 0.008 0.036 0.948 0.000 0.000 0.008
#> round_ERR2585205 1 0.1785 0.57019 0.928 0.008 0.000 0.016 0.000 0.048
#> round_ERR2585214 3 0.1297 0.86784 0.000 0.040 0.948 0.000 0.000 0.012
#> round_ERR2585202 3 0.1410 0.86937 0.008 0.044 0.944 0.000 0.000 0.004
#> aberrant_ERR2585367 5 0.3693 0.79902 0.000 0.116 0.000 0.004 0.796 0.084
#> round_ERR2585220 1 0.4329 0.43733 0.624 0.004 0.352 0.008 0.000 0.012
#> round_ERR2585238 1 0.1625 0.56506 0.928 0.000 0.000 0.012 0.000 0.060
#> aberrant_ERR2585276 5 0.3103 0.82100 0.000 0.064 0.000 0.000 0.836 0.100
#> round_ERR2585218 1 0.1668 0.55978 0.928 0.004 0.000 0.008 0.000 0.060
#> aberrant_ERR2585363 2 0.4045 0.58110 0.000 0.664 0.000 0.000 0.312 0.024
#> round_ERR2585201 3 0.1570 0.87588 0.016 0.028 0.944 0.004 0.000 0.008
#> round_ERR2585210 1 0.1944 0.58969 0.924 0.024 0.000 0.016 0.000 0.036
#> aberrant_ERR2585362 5 0.2631 0.82393 0.000 0.076 0.000 0.004 0.876 0.044
#> aberrant_ERR2585360 5 0.2263 0.82881 0.000 0.056 0.000 0.000 0.896 0.048
#> round_ERR2585209 3 0.1349 0.86725 0.056 0.000 0.940 0.000 0.000 0.004
#> round_ERR2585242 3 0.1895 0.87572 0.024 0.020 0.932 0.012 0.000 0.012
#> round_ERR2585216 1 0.3894 0.57572 0.732 0.004 0.240 0.008 0.000 0.016
#> round_ERR2585219 1 0.2785 0.62114 0.852 0.004 0.128 0.008 0.000 0.008
#> round_ERR2585237 3 0.1082 0.86826 0.000 0.040 0.956 0.000 0.000 0.004
#> round_ERR2585198 3 0.1152 0.86654 0.000 0.044 0.952 0.000 0.000 0.004
#> round_ERR2585211 1 0.3630 0.19429 0.772 0.012 0.000 0.020 0.000 0.196
#> round_ERR2585206 1 0.3934 0.02292 0.728 0.012 0.000 0.020 0.000 0.240
#> aberrant_ERR2585281 5 0.5422 0.51607 0.000 0.340 0.000 0.004 0.540 0.116
#> round_ERR2585212 1 0.4454 0.29937 0.576 0.004 0.400 0.012 0.000 0.008
#> round_ERR2585221 6 0.4111 0.96376 0.456 0.000 0.000 0.004 0.004 0.536
#> round_ERR2585243 1 0.1592 0.59258 0.944 0.012 0.000 0.016 0.004 0.024
#> round_ERR2585204 3 0.1219 0.86375 0.000 0.048 0.948 0.000 0.000 0.004
#> round_ERR2585213 3 0.2772 0.72050 0.000 0.180 0.816 0.000 0.000 0.004
#> aberrant_ERR2585373 5 0.2132 0.82339 0.000 0.028 0.004 0.004 0.912 0.052
#> aberrant_ERR2585358 5 0.2339 0.80320 0.000 0.016 0.004 0.020 0.904 0.056
#> aberrant_ERR2585365 5 0.4446 0.47758 0.000 0.368 0.000 0.004 0.600 0.028
#> aberrant_ERR2585359 5 0.2631 0.77880 0.000 0.000 0.004 0.044 0.876 0.076
#> aberrant_ERR2585370 2 0.1349 0.92416 0.000 0.940 0.004 0.000 0.056 0.000
#> round_ERR2585215 1 0.4885 -0.69534 0.544 0.020 0.000 0.028 0.000 0.408
#> round_ERR2585262 3 0.2928 0.85034 0.016 0.012 0.884 0.012 0.024 0.052
#> round_ERR2585199 3 0.3742 0.47485 0.000 0.348 0.648 0.000 0.000 0.004
#> aberrant_ERR2585369 5 0.1983 0.82623 0.000 0.072 0.000 0.000 0.908 0.020
#> round_ERR2585208 1 0.3875 -0.16320 0.700 0.004 0.000 0.016 0.000 0.280
#> round_ERR2585252 6 0.4062 0.97189 0.440 0.000 0.000 0.008 0.000 0.552
#> round_ERR2585236 1 0.4272 0.60569 0.788 0.024 0.088 0.016 0.000 0.084
#> aberrant_ERR2585284 4 0.1788 0.92476 0.000 0.004 0.000 0.916 0.076 0.004
#> round_ERR2585224 6 0.4195 0.97275 0.440 0.008 0.000 0.000 0.004 0.548
#> round_ERR2585260 1 0.1381 0.61538 0.952 0.004 0.020 0.004 0.000 0.020
#> round_ERR2585229 1 0.2402 0.52357 0.888 0.008 0.000 0.020 0.000 0.084
#> aberrant_ERR2585364 4 0.4756 0.58888 0.000 0.000 0.004 0.628 0.304 0.064
#> round_ERR2585253 6 0.4195 0.96153 0.440 0.004 0.000 0.008 0.000 0.548
#> aberrant_ERR2585368 2 0.1493 0.92365 0.000 0.936 0.004 0.000 0.056 0.004
#> aberrant_ERR2585371 2 0.1493 0.92365 0.000 0.936 0.004 0.000 0.056 0.004
#> round_ERR2585239 1 0.2402 0.62950 0.900 0.008 0.060 0.024 0.000 0.008
#> round_ERR2585273 1 0.4364 0.55256 0.764 0.008 0.088 0.008 0.004 0.128
#> round_ERR2585256 3 0.1542 0.86912 0.052 0.004 0.936 0.000 0.000 0.008
#> round_ERR2585272 1 0.2596 0.62368 0.892 0.008 0.056 0.012 0.000 0.032
#> round_ERR2585246 1 0.3681 -0.06561 0.716 0.004 0.000 0.004 0.004 0.272
#> round_ERR2585261 3 0.1553 0.87534 0.012 0.032 0.944 0.008 0.000 0.004
#> round_ERR2585254 3 0.1296 0.87419 0.012 0.032 0.952 0.000 0.000 0.004
#> round_ERR2585225 3 0.2639 0.85938 0.044 0.008 0.892 0.016 0.000 0.040
#> round_ERR2585235 1 0.3239 0.60185 0.860 0.024 0.020 0.028 0.000 0.068
#> round_ERR2585271 1 0.1138 0.59451 0.960 0.004 0.000 0.012 0.000 0.024
#> round_ERR2585251 3 0.4541 -0.00653 0.476 0.004 0.500 0.012 0.000 0.008
#> round_ERR2585255 3 0.2579 0.86267 0.040 0.012 0.896 0.012 0.000 0.040
#> round_ERR2585257 3 0.2838 0.85758 0.052 0.008 0.880 0.016 0.000 0.044
#> round_ERR2585226 1 0.4495 0.55630 0.692 0.008 0.256 0.012 0.000 0.032
#> round_ERR2585265 1 0.4013 0.56511 0.712 0.004 0.260 0.008 0.000 0.016
#> round_ERR2585259 1 0.5565 0.28981 0.540 0.020 0.376 0.024 0.000 0.040
#> round_ERR2585247 1 0.3538 0.17357 0.764 0.012 0.000 0.004 0.004 0.216
#> round_ERR2585241 1 0.1666 0.58226 0.936 0.008 0.000 0.020 0.000 0.036
#> round_ERR2585263 1 0.4285 0.39703 0.612 0.004 0.368 0.008 0.000 0.008
#> round_ERR2585264 6 0.4098 0.96942 0.444 0.004 0.000 0.004 0.000 0.548
#> round_ERR2585233 3 0.4069 0.78733 0.120 0.016 0.796 0.024 0.000 0.044
#> round_ERR2585223 1 0.0951 0.59913 0.968 0.004 0.000 0.008 0.000 0.020
#> round_ERR2585234 3 0.1010 0.87164 0.004 0.036 0.960 0.000 0.000 0.000
#> round_ERR2585222 1 0.1655 0.63002 0.932 0.008 0.052 0.000 0.000 0.008
#> round_ERR2585228 1 0.0767 0.60267 0.976 0.004 0.000 0.008 0.000 0.012
#> round_ERR2585248 6 0.4555 0.95312 0.440 0.016 0.000 0.012 0.000 0.532
#> round_ERR2585240 3 0.2271 0.86218 0.056 0.008 0.908 0.012 0.000 0.016
#> round_ERR2585270 1 0.3693 0.55705 0.708 0.000 0.280 0.004 0.000 0.008
#> round_ERR2585232 3 0.3269 0.74127 0.168 0.008 0.808 0.012 0.000 0.004
#> aberrant_ERR2585341 5 0.5294 0.46599 0.000 0.356 0.000 0.000 0.532 0.112
#> aberrant_ERR2585355 2 0.1349 0.92196 0.000 0.940 0.000 0.000 0.056 0.004
#> round_ERR2585227 3 0.4881 0.22164 0.396 0.008 0.560 0.016 0.000 0.020
#> aberrant_ERR2585351 5 0.3807 0.71089 0.000 0.228 0.000 0.004 0.740 0.028
#> round_ERR2585269 6 0.3961 0.97443 0.440 0.000 0.000 0.000 0.004 0.556
#> aberrant_ERR2585357 2 0.1349 0.92416 0.000 0.940 0.004 0.000 0.056 0.000
#> aberrant_ERR2585350 2 0.1349 0.92416 0.000 0.940 0.004 0.000 0.056 0.000
#> round_ERR2585250 1 0.4795 0.40611 0.612 0.012 0.340 0.008 0.000 0.028
#> round_ERR2585245 6 0.4093 0.97482 0.440 0.004 0.000 0.004 0.000 0.552
#> aberrant_ERR2585353 5 0.1938 0.82652 0.000 0.036 0.000 0.004 0.920 0.040
#> round_ERR2585258 1 0.3756 0.59661 0.776 0.004 0.184 0.012 0.000 0.024
#> aberrant_ERR2585354 5 0.1909 0.82873 0.000 0.052 0.000 0.004 0.920 0.024
#> round_ERR2585249 6 0.4062 0.97518 0.440 0.000 0.000 0.008 0.000 0.552
#> round_ERR2585268 1 0.4571 0.27849 0.564 0.008 0.408 0.012 0.000 0.008
#> aberrant_ERR2585356 5 0.2622 0.78005 0.000 0.000 0.004 0.024 0.868 0.104
#> round_ERR2585266 3 0.2547 0.86044 0.044 0.008 0.896 0.012 0.000 0.040
#> round_ERR2585231 6 0.4195 0.97275 0.440 0.008 0.000 0.000 0.004 0.548
#> round_ERR2585230 1 0.0798 0.61201 0.976 0.004 0.004 0.012 0.000 0.004
#> round_ERR2585267 6 0.4217 0.95118 0.464 0.008 0.000 0.000 0.004 0.524
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> MAD:kmeans 157 1.66e-17 2
#> MAD:kmeans 141 2.34e-21 3
#> MAD:kmeans 126 1.04e-21 4
#> MAD:kmeans 146 4.45e-25 5
#> MAD:kmeans 139 1.28e-23 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "skmeans"]
# you can also extract it by
# res = res_list["MAD:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.877 0.926 0.970 0.5029 0.498 0.498
#> 3 3 0.871 0.883 0.949 0.3124 0.770 0.569
#> 4 4 0.828 0.830 0.923 0.1201 0.845 0.592
#> 5 5 0.725 0.665 0.817 0.0533 0.969 0.885
#> 6 6 0.669 0.617 0.753 0.0361 0.975 0.896
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585338 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585325 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585283 1 0.5519 0.851 0.872 0.128
#> aberrant_ERR2585343 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585329 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585317 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585339 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585335 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585287 2 0.8861 0.549 0.304 0.696
#> aberrant_ERR2585321 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585297 1 0.0000 0.968 1.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585319 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585315 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585336 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585307 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585301 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585326 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585331 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585346 1 0.5408 0.855 0.876 0.124
#> aberrant_ERR2585314 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585298 1 0.0000 0.968 1.000 0.000
#> aberrant_ERR2585345 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585299 1 0.0000 0.968 1.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.968 1.000 0.000
#> aberrant_ERR2585303 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585313 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585318 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585328 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585330 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585293 1 0.4161 0.896 0.916 0.084
#> aberrant_ERR2585342 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585348 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585352 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585308 1 0.0000 0.968 1.000 0.000
#> aberrant_ERR2585349 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585316 2 0.9996 0.014 0.488 0.512
#> aberrant_ERR2585306 1 0.5408 0.857 0.876 0.124
#> aberrant_ERR2585324 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585310 2 0.6623 0.791 0.172 0.828
#> aberrant_ERR2585296 1 0.8763 0.574 0.704 0.296
#> aberrant_ERR2585275 1 0.5842 0.837 0.860 0.140
#> aberrant_ERR2585311 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585292 1 0.4161 0.896 0.916 0.084
#> aberrant_ERR2585282 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585305 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585278 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585347 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585332 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585280 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585304 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585322 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585279 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585277 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585295 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585333 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585285 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585286 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585294 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585300 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585334 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585361 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585372 2 0.0000 0.967 0.000 1.000
#> round_ERR2585217 2 0.9970 0.139 0.468 0.532
#> round_ERR2585205 1 0.0000 0.968 1.000 0.000
#> round_ERR2585214 2 0.7299 0.746 0.204 0.796
#> round_ERR2585202 2 0.3431 0.910 0.064 0.936
#> aberrant_ERR2585367 2 0.0000 0.967 0.000 1.000
#> round_ERR2585220 1 0.0000 0.968 1.000 0.000
#> round_ERR2585238 1 0.0000 0.968 1.000 0.000
#> aberrant_ERR2585276 2 0.0000 0.967 0.000 1.000
#> round_ERR2585218 1 0.0000 0.968 1.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.967 0.000 1.000
#> round_ERR2585201 1 0.0000 0.968 1.000 0.000
#> round_ERR2585210 1 0.0000 0.968 1.000 0.000
#> aberrant_ERR2585362 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585360 2 0.0000 0.967 0.000 1.000
#> round_ERR2585209 1 0.0000 0.968 1.000 0.000
#> round_ERR2585242 1 0.0938 0.959 0.988 0.012
#> round_ERR2585216 1 0.0000 0.968 1.000 0.000
#> round_ERR2585219 1 0.0000 0.968 1.000 0.000
#> round_ERR2585237 2 0.5842 0.830 0.140 0.860
#> round_ERR2585198 2 0.5842 0.829 0.140 0.860
#> round_ERR2585211 1 0.0000 0.968 1.000 0.000
#> round_ERR2585206 1 0.0000 0.968 1.000 0.000
#> aberrant_ERR2585281 2 0.0000 0.967 0.000 1.000
#> round_ERR2585212 1 0.0000 0.968 1.000 0.000
#> round_ERR2585221 1 0.0000 0.968 1.000 0.000
#> round_ERR2585243 1 0.0000 0.968 1.000 0.000
#> round_ERR2585204 2 0.4562 0.878 0.096 0.904
#> round_ERR2585213 2 0.1184 0.954 0.016 0.984
#> aberrant_ERR2585373 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585358 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585365 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585359 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585370 2 0.0000 0.967 0.000 1.000
#> round_ERR2585215 1 0.0000 0.968 1.000 0.000
#> round_ERR2585262 2 0.9933 0.198 0.452 0.548
#> round_ERR2585199 2 0.1633 0.947 0.024 0.976
#> aberrant_ERR2585369 2 0.0000 0.967 0.000 1.000
#> round_ERR2585208 1 0.0000 0.968 1.000 0.000
#> round_ERR2585252 1 0.0000 0.968 1.000 0.000
#> round_ERR2585236 1 0.0000 0.968 1.000 0.000
#> aberrant_ERR2585284 1 0.5059 0.869 0.888 0.112
#> round_ERR2585224 1 0.0000 0.968 1.000 0.000
#> round_ERR2585260 1 0.0000 0.968 1.000 0.000
#> round_ERR2585229 1 0.0000 0.968 1.000 0.000
#> aberrant_ERR2585364 1 0.8443 0.640 0.728 0.272
#> round_ERR2585253 1 0.0000 0.968 1.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585371 2 0.0000 0.967 0.000 1.000
#> round_ERR2585239 1 0.0000 0.968 1.000 0.000
#> round_ERR2585273 1 0.0000 0.968 1.000 0.000
#> round_ERR2585256 1 0.0000 0.968 1.000 0.000
#> round_ERR2585272 1 0.0000 0.968 1.000 0.000
#> round_ERR2585246 1 0.0000 0.968 1.000 0.000
#> round_ERR2585261 1 0.4939 0.866 0.892 0.108
#> round_ERR2585254 1 0.9815 0.256 0.580 0.420
#> round_ERR2585225 1 0.0000 0.968 1.000 0.000
#> round_ERR2585235 1 0.0000 0.968 1.000 0.000
#> round_ERR2585271 1 0.0000 0.968 1.000 0.000
#> round_ERR2585251 1 0.0000 0.968 1.000 0.000
#> round_ERR2585255 1 0.0000 0.968 1.000 0.000
#> round_ERR2585257 1 0.0000 0.968 1.000 0.000
#> round_ERR2585226 1 0.0000 0.968 1.000 0.000
#> round_ERR2585265 1 0.0000 0.968 1.000 0.000
#> round_ERR2585259 1 0.0000 0.968 1.000 0.000
#> round_ERR2585247 1 0.0000 0.968 1.000 0.000
#> round_ERR2585241 1 0.0000 0.968 1.000 0.000
#> round_ERR2585263 1 0.0000 0.968 1.000 0.000
#> round_ERR2585264 1 0.0000 0.968 1.000 0.000
#> round_ERR2585233 1 0.0000 0.968 1.000 0.000
#> round_ERR2585223 1 0.0000 0.968 1.000 0.000
#> round_ERR2585234 1 0.9580 0.374 0.620 0.380
#> round_ERR2585222 1 0.0000 0.968 1.000 0.000
#> round_ERR2585228 1 0.0000 0.968 1.000 0.000
#> round_ERR2585248 1 0.0000 0.968 1.000 0.000
#> round_ERR2585240 1 0.0000 0.968 1.000 0.000
#> round_ERR2585270 1 0.0000 0.968 1.000 0.000
#> round_ERR2585232 1 0.0000 0.968 1.000 0.000
#> aberrant_ERR2585341 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585355 2 0.0000 0.967 0.000 1.000
#> round_ERR2585227 1 0.0000 0.968 1.000 0.000
#> aberrant_ERR2585351 2 0.0000 0.967 0.000 1.000
#> round_ERR2585269 1 0.0000 0.968 1.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.967 0.000 1.000
#> aberrant_ERR2585350 2 0.0000 0.967 0.000 1.000
#> round_ERR2585250 1 0.0000 0.968 1.000 0.000
#> round_ERR2585245 1 0.0000 0.968 1.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.967 0.000 1.000
#> round_ERR2585258 1 0.0000 0.968 1.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.967 0.000 1.000
#> round_ERR2585249 1 0.0000 0.968 1.000 0.000
#> round_ERR2585268 1 0.0000 0.968 1.000 0.000
#> aberrant_ERR2585356 2 0.1414 0.951 0.020 0.980
#> round_ERR2585266 1 0.0000 0.968 1.000 0.000
#> round_ERR2585231 1 0.0000 0.968 1.000 0.000
#> round_ERR2585230 1 0.0000 0.968 1.000 0.000
#> round_ERR2585267 1 0.0000 0.968 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 3 0.0747 0.9394 0.000 0.016 0.984
#> aberrant_ERR2585338 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585325 3 0.0892 0.9373 0.000 0.020 0.980
#> aberrant_ERR2585283 3 0.0424 0.9427 0.008 0.000 0.992
#> aberrant_ERR2585343 3 0.0000 0.9450 0.000 0.000 1.000
#> aberrant_ERR2585329 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585317 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585339 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585335 2 0.1289 0.8827 0.000 0.968 0.032
#> aberrant_ERR2585287 3 0.0424 0.9427 0.008 0.000 0.992
#> aberrant_ERR2585321 3 0.0000 0.9450 0.000 0.000 1.000
#> aberrant_ERR2585297 1 0.0000 0.9838 1.000 0.000 0.000
#> aberrant_ERR2585337 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585319 2 0.3879 0.7859 0.000 0.848 0.152
#> aberrant_ERR2585315 2 0.0892 0.8900 0.000 0.980 0.020
#> aberrant_ERR2585336 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585307 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585301 2 0.5098 0.6753 0.000 0.752 0.248
#> aberrant_ERR2585326 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585331 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585346 3 0.0424 0.9427 0.008 0.000 0.992
#> aberrant_ERR2585314 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585298 1 0.2959 0.8941 0.900 0.100 0.000
#> aberrant_ERR2585345 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585299 1 0.0000 0.9838 1.000 0.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.9838 1.000 0.000 0.000
#> aberrant_ERR2585303 2 0.5760 0.5208 0.000 0.672 0.328
#> aberrant_ERR2585313 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585318 3 0.5733 0.4795 0.000 0.324 0.676
#> aberrant_ERR2585328 3 0.0592 0.9411 0.000 0.012 0.988
#> aberrant_ERR2585330 2 0.6095 0.4038 0.000 0.608 0.392
#> aberrant_ERR2585293 3 0.0424 0.9427 0.008 0.000 0.992
#> aberrant_ERR2585342 3 0.1643 0.9196 0.000 0.044 0.956
#> aberrant_ERR2585348 3 0.0000 0.9450 0.000 0.000 1.000
#> aberrant_ERR2585352 2 0.0747 0.8918 0.000 0.984 0.016
#> aberrant_ERR2585308 1 0.0000 0.9838 1.000 0.000 0.000
#> aberrant_ERR2585349 2 0.0237 0.8939 0.000 0.996 0.004
#> aberrant_ERR2585316 3 0.0424 0.9427 0.008 0.000 0.992
#> aberrant_ERR2585306 3 0.0424 0.9427 0.008 0.000 0.992
#> aberrant_ERR2585324 2 0.3879 0.7859 0.000 0.848 0.152
#> aberrant_ERR2585310 2 0.1267 0.8810 0.024 0.972 0.004
#> aberrant_ERR2585296 2 0.5560 0.5660 0.300 0.700 0.000
#> aberrant_ERR2585275 3 0.0424 0.9427 0.008 0.000 0.992
#> aberrant_ERR2585311 3 0.0000 0.9450 0.000 0.000 1.000
#> aberrant_ERR2585292 3 0.0424 0.9427 0.008 0.000 0.992
#> aberrant_ERR2585282 3 0.0000 0.9450 0.000 0.000 1.000
#> aberrant_ERR2585305 3 0.6647 0.0818 0.008 0.452 0.540
#> aberrant_ERR2585278 2 0.2959 0.8342 0.000 0.900 0.100
#> aberrant_ERR2585347 3 0.0000 0.9450 0.000 0.000 1.000
#> aberrant_ERR2585332 3 0.0000 0.9450 0.000 0.000 1.000
#> aberrant_ERR2585280 3 0.2066 0.9068 0.000 0.060 0.940
#> aberrant_ERR2585304 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585322 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585279 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585277 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585295 3 0.0424 0.9427 0.000 0.008 0.992
#> aberrant_ERR2585333 3 0.0000 0.9450 0.000 0.000 1.000
#> aberrant_ERR2585285 2 0.6252 0.2600 0.000 0.556 0.444
#> aberrant_ERR2585286 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585294 3 0.6260 0.1126 0.000 0.448 0.552
#> aberrant_ERR2585300 3 0.0237 0.9441 0.004 0.000 0.996
#> aberrant_ERR2585334 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585361 3 0.1964 0.9092 0.000 0.056 0.944
#> aberrant_ERR2585372 3 0.0000 0.9450 0.000 0.000 1.000
#> round_ERR2585217 2 0.3551 0.7843 0.132 0.868 0.000
#> round_ERR2585205 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585214 2 0.0000 0.8923 0.000 1.000 0.000
#> round_ERR2585202 2 0.0000 0.8923 0.000 1.000 0.000
#> aberrant_ERR2585367 3 0.2959 0.8679 0.000 0.100 0.900
#> round_ERR2585220 1 0.0424 0.9812 0.992 0.008 0.000
#> round_ERR2585238 1 0.0000 0.9838 1.000 0.000 0.000
#> aberrant_ERR2585276 3 0.3038 0.8601 0.000 0.104 0.896
#> round_ERR2585218 1 0.0000 0.9838 1.000 0.000 0.000
#> aberrant_ERR2585363 2 0.1860 0.8697 0.000 0.948 0.052
#> round_ERR2585201 1 0.4887 0.7130 0.772 0.228 0.000
#> round_ERR2585210 1 0.0000 0.9838 1.000 0.000 0.000
#> aberrant_ERR2585362 3 0.3340 0.8429 0.000 0.120 0.880
#> aberrant_ERR2585360 3 0.1289 0.9292 0.000 0.032 0.968
#> round_ERR2585209 1 0.0747 0.9762 0.984 0.016 0.000
#> round_ERR2585242 1 0.6026 0.3968 0.624 0.376 0.000
#> round_ERR2585216 1 0.0424 0.9812 0.992 0.008 0.000
#> round_ERR2585219 1 0.0237 0.9827 0.996 0.004 0.000
#> round_ERR2585237 2 0.0000 0.8923 0.000 1.000 0.000
#> round_ERR2585198 2 0.0000 0.8923 0.000 1.000 0.000
#> round_ERR2585211 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.9838 1.000 0.000 0.000
#> aberrant_ERR2585281 2 0.6295 0.1306 0.000 0.528 0.472
#> round_ERR2585212 1 0.0424 0.9812 0.992 0.008 0.000
#> round_ERR2585221 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585243 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585204 2 0.0000 0.8923 0.000 1.000 0.000
#> round_ERR2585213 2 0.0000 0.8923 0.000 1.000 0.000
#> aberrant_ERR2585373 3 0.0000 0.9450 0.000 0.000 1.000
#> aberrant_ERR2585358 3 0.0000 0.9450 0.000 0.000 1.000
#> aberrant_ERR2585365 2 0.5835 0.4970 0.000 0.660 0.340
#> aberrant_ERR2585359 3 0.0000 0.9450 0.000 0.000 1.000
#> aberrant_ERR2585370 2 0.0424 0.8953 0.000 0.992 0.008
#> round_ERR2585215 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585262 2 0.8698 0.4517 0.300 0.564 0.136
#> round_ERR2585199 2 0.0000 0.8923 0.000 1.000 0.000
#> aberrant_ERR2585369 3 0.2165 0.9036 0.000 0.064 0.936
#> round_ERR2585208 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585236 1 0.0000 0.9838 1.000 0.000 0.000
#> aberrant_ERR2585284 3 0.0424 0.9427 0.008 0.000 0.992
#> round_ERR2585224 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585260 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585229 1 0.0000 0.9838 1.000 0.000 0.000
#> aberrant_ERR2585364 3 0.0424 0.9427 0.008 0.000 0.992
#> round_ERR2585253 1 0.0000 0.9838 1.000 0.000 0.000
#> aberrant_ERR2585368 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585371 2 0.0424 0.8953 0.000 0.992 0.008
#> round_ERR2585239 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585273 1 0.0237 0.9827 0.996 0.004 0.000
#> round_ERR2585256 1 0.1031 0.9703 0.976 0.024 0.000
#> round_ERR2585272 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585246 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585261 2 0.5988 0.4148 0.368 0.632 0.000
#> round_ERR2585254 2 0.3412 0.7920 0.124 0.876 0.000
#> round_ERR2585225 1 0.1163 0.9666 0.972 0.028 0.000
#> round_ERR2585235 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585271 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585251 1 0.0424 0.9812 0.992 0.008 0.000
#> round_ERR2585255 1 0.1163 0.9666 0.972 0.028 0.000
#> round_ERR2585257 1 0.2711 0.9075 0.912 0.088 0.000
#> round_ERR2585226 1 0.0424 0.9812 0.992 0.008 0.000
#> round_ERR2585265 1 0.0424 0.9812 0.992 0.008 0.000
#> round_ERR2585259 1 0.0237 0.9826 0.996 0.004 0.000
#> round_ERR2585247 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585241 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585263 1 0.0424 0.9812 0.992 0.008 0.000
#> round_ERR2585264 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585233 1 0.0237 0.9827 0.996 0.004 0.000
#> round_ERR2585223 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585234 2 0.0892 0.8807 0.020 0.980 0.000
#> round_ERR2585222 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585228 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585248 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585240 1 0.0424 0.9812 0.992 0.008 0.000
#> round_ERR2585270 1 0.0424 0.9812 0.992 0.008 0.000
#> round_ERR2585232 1 0.0424 0.9812 0.992 0.008 0.000
#> aberrant_ERR2585341 2 0.6267 0.2002 0.000 0.548 0.452
#> aberrant_ERR2585355 2 0.0424 0.8953 0.000 0.992 0.008
#> round_ERR2585227 1 0.0424 0.9812 0.992 0.008 0.000
#> aberrant_ERR2585351 2 0.6192 0.3341 0.000 0.580 0.420
#> round_ERR2585269 1 0.0000 0.9838 1.000 0.000 0.000
#> aberrant_ERR2585357 2 0.0424 0.8953 0.000 0.992 0.008
#> aberrant_ERR2585350 2 0.0424 0.8953 0.000 0.992 0.008
#> round_ERR2585250 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585245 1 0.0000 0.9838 1.000 0.000 0.000
#> aberrant_ERR2585353 3 0.0000 0.9450 0.000 0.000 1.000
#> round_ERR2585258 1 0.0424 0.9812 0.992 0.008 0.000
#> aberrant_ERR2585354 3 0.0000 0.9450 0.000 0.000 1.000
#> round_ERR2585249 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585268 1 0.0237 0.9826 0.996 0.004 0.000
#> aberrant_ERR2585356 3 0.0237 0.9441 0.004 0.000 0.996
#> round_ERR2585266 1 0.1753 0.9481 0.952 0.048 0.000
#> round_ERR2585231 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585230 1 0.0000 0.9838 1.000 0.000 0.000
#> round_ERR2585267 1 0.0000 0.9838 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 4 0.4483 0.6215 0.000 0.284 0.004 0.712
#> aberrant_ERR2585338 2 0.0336 0.8921 0.000 0.992 0.008 0.000
#> aberrant_ERR2585325 4 0.4483 0.6215 0.000 0.284 0.004 0.712
#> aberrant_ERR2585283 4 0.0000 0.9065 0.000 0.000 0.000 1.000
#> aberrant_ERR2585343 4 0.0000 0.9065 0.000 0.000 0.000 1.000
#> aberrant_ERR2585329 2 0.0469 0.8921 0.000 0.988 0.012 0.000
#> aberrant_ERR2585317 2 0.0469 0.8921 0.000 0.988 0.012 0.000
#> aberrant_ERR2585339 2 0.0188 0.8916 0.000 0.996 0.004 0.000
#> aberrant_ERR2585335 2 0.0188 0.8902 0.000 0.996 0.004 0.000
#> aberrant_ERR2585287 4 0.0000 0.9065 0.000 0.000 0.000 1.000
#> aberrant_ERR2585321 4 0.1743 0.8906 0.000 0.056 0.004 0.940
#> aberrant_ERR2585297 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> aberrant_ERR2585337 2 0.0336 0.8921 0.000 0.992 0.008 0.000
#> aberrant_ERR2585319 2 0.0657 0.8881 0.000 0.984 0.004 0.012
#> aberrant_ERR2585315 2 0.0000 0.8909 0.000 1.000 0.000 0.000
#> aberrant_ERR2585336 2 0.0469 0.8921 0.000 0.988 0.012 0.000
#> aberrant_ERR2585307 2 0.0469 0.8921 0.000 0.988 0.012 0.000
#> aberrant_ERR2585301 2 0.1970 0.8640 0.000 0.932 0.008 0.060
#> aberrant_ERR2585326 2 0.0469 0.8921 0.000 0.988 0.012 0.000
#> aberrant_ERR2585331 2 0.0592 0.8909 0.000 0.984 0.016 0.000
#> aberrant_ERR2585346 4 0.0000 0.9065 0.000 0.000 0.000 1.000
#> aberrant_ERR2585314 2 0.0469 0.8921 0.000 0.988 0.012 0.000
#> aberrant_ERR2585298 3 0.0592 0.9087 0.016 0.000 0.984 0.000
#> aberrant_ERR2585345 2 0.0469 0.8921 0.000 0.988 0.012 0.000
#> aberrant_ERR2585299 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> aberrant_ERR2585303 2 0.1767 0.8712 0.000 0.944 0.012 0.044
#> aberrant_ERR2585313 2 0.0000 0.8909 0.000 1.000 0.000 0.000
#> aberrant_ERR2585318 2 0.5038 0.4812 0.000 0.652 0.012 0.336
#> aberrant_ERR2585328 4 0.2988 0.8449 0.000 0.112 0.012 0.876
#> aberrant_ERR2585330 2 0.1970 0.8636 0.000 0.932 0.008 0.060
#> aberrant_ERR2585293 4 0.0000 0.9065 0.000 0.000 0.000 1.000
#> aberrant_ERR2585342 2 0.5406 0.0407 0.000 0.508 0.012 0.480
#> aberrant_ERR2585348 4 0.0188 0.9062 0.000 0.000 0.004 0.996
#> aberrant_ERR2585352 2 0.0376 0.8894 0.000 0.992 0.004 0.004
#> aberrant_ERR2585308 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> aberrant_ERR2585349 2 0.3444 0.7562 0.000 0.816 0.184 0.000
#> aberrant_ERR2585316 4 0.0000 0.9065 0.000 0.000 0.000 1.000
#> aberrant_ERR2585306 4 0.1489 0.8779 0.044 0.000 0.004 0.952
#> aberrant_ERR2585324 2 0.0657 0.8881 0.000 0.984 0.004 0.012
#> aberrant_ERR2585310 2 0.5906 0.1519 0.036 0.528 0.436 0.000
#> aberrant_ERR2585296 3 0.1151 0.9073 0.008 0.024 0.968 0.000
#> aberrant_ERR2585275 4 0.0000 0.9065 0.000 0.000 0.000 1.000
#> aberrant_ERR2585311 4 0.3032 0.8380 0.000 0.124 0.008 0.868
#> aberrant_ERR2585292 4 0.0000 0.9065 0.000 0.000 0.000 1.000
#> aberrant_ERR2585282 4 0.0524 0.9064 0.000 0.008 0.004 0.988
#> aberrant_ERR2585305 2 0.4333 0.7122 0.008 0.776 0.008 0.208
#> aberrant_ERR2585278 2 0.0000 0.8909 0.000 1.000 0.000 0.000
#> aberrant_ERR2585347 4 0.0000 0.9065 0.000 0.000 0.000 1.000
#> aberrant_ERR2585332 4 0.0524 0.9062 0.000 0.004 0.008 0.988
#> aberrant_ERR2585280 2 0.5105 0.2388 0.000 0.564 0.004 0.432
#> aberrant_ERR2585304 2 0.2149 0.8484 0.000 0.912 0.088 0.000
#> aberrant_ERR2585322 2 0.0469 0.8902 0.000 0.988 0.012 0.000
#> aberrant_ERR2585279 2 0.2281 0.8417 0.000 0.904 0.096 0.000
#> aberrant_ERR2585277 2 0.0469 0.8921 0.000 0.988 0.012 0.000
#> aberrant_ERR2585295 4 0.2125 0.8771 0.000 0.076 0.004 0.920
#> aberrant_ERR2585333 4 0.1545 0.8974 0.000 0.040 0.008 0.952
#> aberrant_ERR2585285 2 0.2402 0.8512 0.000 0.912 0.012 0.076
#> aberrant_ERR2585286 2 0.0336 0.8920 0.000 0.992 0.008 0.000
#> aberrant_ERR2585294 2 0.4567 0.6125 0.000 0.716 0.008 0.276
#> aberrant_ERR2585300 4 0.0672 0.9053 0.000 0.008 0.008 0.984
#> aberrant_ERR2585334 2 0.0707 0.8896 0.000 0.980 0.020 0.000
#> aberrant_ERR2585361 2 0.5366 0.1716 0.000 0.548 0.012 0.440
#> aberrant_ERR2585372 4 0.3400 0.7817 0.000 0.180 0.000 0.820
#> round_ERR2585217 3 0.0469 0.9098 0.000 0.012 0.988 0.000
#> round_ERR2585205 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585214 3 0.0469 0.9098 0.000 0.012 0.988 0.000
#> round_ERR2585202 3 0.2281 0.8563 0.000 0.096 0.904 0.000
#> aberrant_ERR2585367 2 0.5300 0.2924 0.000 0.580 0.012 0.408
#> round_ERR2585220 1 0.3123 0.8081 0.844 0.000 0.156 0.000
#> round_ERR2585238 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> aberrant_ERR2585276 4 0.5229 0.2234 0.000 0.428 0.008 0.564
#> round_ERR2585218 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> aberrant_ERR2585363 2 0.0469 0.8877 0.000 0.988 0.012 0.000
#> round_ERR2585201 3 0.0524 0.9100 0.008 0.004 0.988 0.000
#> round_ERR2585210 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> aberrant_ERR2585362 4 0.5337 0.2657 0.000 0.424 0.012 0.564
#> aberrant_ERR2585360 4 0.4978 0.5332 0.000 0.324 0.012 0.664
#> round_ERR2585209 3 0.0817 0.9056 0.024 0.000 0.976 0.000
#> round_ERR2585242 3 0.0524 0.9102 0.004 0.008 0.988 0.000
#> round_ERR2585216 1 0.2345 0.8693 0.900 0.000 0.100 0.000
#> round_ERR2585219 1 0.0592 0.9376 0.984 0.000 0.016 0.000
#> round_ERR2585237 3 0.0592 0.9085 0.000 0.016 0.984 0.000
#> round_ERR2585198 3 0.0469 0.9098 0.000 0.012 0.988 0.000
#> round_ERR2585211 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> aberrant_ERR2585281 2 0.4137 0.7124 0.000 0.780 0.012 0.208
#> round_ERR2585212 1 0.4790 0.3886 0.620 0.000 0.380 0.000
#> round_ERR2585221 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585243 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585204 3 0.0469 0.9098 0.000 0.012 0.988 0.000
#> round_ERR2585213 3 0.3486 0.7423 0.000 0.188 0.812 0.000
#> aberrant_ERR2585373 4 0.2412 0.8707 0.000 0.084 0.008 0.908
#> aberrant_ERR2585358 4 0.0804 0.9058 0.000 0.012 0.008 0.980
#> aberrant_ERR2585365 2 0.2480 0.8414 0.000 0.904 0.008 0.088
#> aberrant_ERR2585359 4 0.0188 0.9062 0.000 0.000 0.004 0.996
#> aberrant_ERR2585370 2 0.0469 0.8921 0.000 0.988 0.012 0.000
#> round_ERR2585215 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585262 3 0.3091 0.8559 0.008 0.048 0.896 0.048
#> round_ERR2585199 3 0.2408 0.8492 0.000 0.104 0.896 0.000
#> aberrant_ERR2585369 2 0.4936 0.5335 0.000 0.672 0.012 0.316
#> round_ERR2585208 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585236 1 0.1182 0.9275 0.968 0.000 0.016 0.016
#> aberrant_ERR2585284 4 0.0000 0.9065 0.000 0.000 0.000 1.000
#> round_ERR2585224 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585260 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585229 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> aberrant_ERR2585364 4 0.0188 0.9062 0.000 0.000 0.004 0.996
#> round_ERR2585253 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> aberrant_ERR2585368 2 0.0469 0.8921 0.000 0.988 0.012 0.000
#> aberrant_ERR2585371 2 0.0469 0.8921 0.000 0.988 0.012 0.000
#> round_ERR2585239 1 0.0188 0.9437 0.996 0.000 0.004 0.000
#> round_ERR2585273 1 0.0469 0.9399 0.988 0.000 0.012 0.000
#> round_ERR2585256 3 0.0921 0.9044 0.028 0.000 0.972 0.000
#> round_ERR2585272 1 0.1211 0.9221 0.960 0.000 0.040 0.000
#> round_ERR2585246 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585261 3 0.0469 0.9098 0.000 0.012 0.988 0.000
#> round_ERR2585254 3 0.0469 0.9098 0.000 0.012 0.988 0.000
#> round_ERR2585225 3 0.2814 0.8271 0.132 0.000 0.868 0.000
#> round_ERR2585235 1 0.0188 0.9437 0.996 0.000 0.004 0.000
#> round_ERR2585271 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585251 3 0.4985 0.1093 0.468 0.000 0.532 0.000
#> round_ERR2585255 3 0.0707 0.9077 0.020 0.000 0.980 0.000
#> round_ERR2585257 3 0.0817 0.9079 0.024 0.000 0.976 0.000
#> round_ERR2585226 1 0.1637 0.9070 0.940 0.000 0.060 0.000
#> round_ERR2585265 1 0.1302 0.9195 0.956 0.000 0.044 0.000
#> round_ERR2585259 1 0.4164 0.6462 0.736 0.000 0.264 0.000
#> round_ERR2585247 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585241 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585263 1 0.4981 0.1320 0.536 0.000 0.464 0.000
#> round_ERR2585264 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585233 3 0.4898 0.2975 0.416 0.000 0.584 0.000
#> round_ERR2585223 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585234 3 0.0469 0.9098 0.000 0.012 0.988 0.000
#> round_ERR2585222 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585228 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585248 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585240 3 0.1792 0.8818 0.068 0.000 0.932 0.000
#> round_ERR2585270 1 0.2081 0.8853 0.916 0.000 0.084 0.000
#> round_ERR2585232 3 0.4222 0.6334 0.272 0.000 0.728 0.000
#> aberrant_ERR2585341 2 0.2401 0.8425 0.000 0.904 0.004 0.092
#> aberrant_ERR2585355 2 0.0188 0.8916 0.000 0.996 0.004 0.000
#> round_ERR2585227 1 0.4925 0.2334 0.572 0.000 0.428 0.000
#> aberrant_ERR2585351 2 0.2021 0.8650 0.000 0.932 0.012 0.056
#> round_ERR2585269 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> aberrant_ERR2585357 2 0.0469 0.8921 0.000 0.988 0.012 0.000
#> aberrant_ERR2585350 2 0.0336 0.8921 0.000 0.992 0.008 0.000
#> round_ERR2585250 1 0.3649 0.7414 0.796 0.000 0.204 0.000
#> round_ERR2585245 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> aberrant_ERR2585353 4 0.0937 0.9051 0.000 0.012 0.012 0.976
#> round_ERR2585258 1 0.1557 0.9103 0.944 0.000 0.056 0.000
#> aberrant_ERR2585354 4 0.2799 0.8524 0.000 0.108 0.008 0.884
#> round_ERR2585249 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585268 1 0.4008 0.6785 0.756 0.000 0.244 0.000
#> aberrant_ERR2585356 4 0.0336 0.9058 0.000 0.000 0.008 0.992
#> round_ERR2585266 3 0.0707 0.9084 0.020 0.000 0.980 0.000
#> round_ERR2585231 1 0.0000 0.9456 1.000 0.000 0.000 0.000
#> round_ERR2585230 1 0.0188 0.9437 0.996 0.000 0.004 0.000
#> round_ERR2585267 1 0.0000 0.9456 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 5 0.6047 0.3080 0.000 0.120 0.000 0.400 0.480
#> aberrant_ERR2585338 2 0.0703 0.7818 0.000 0.976 0.000 0.000 0.024
#> aberrant_ERR2585325 5 0.6047 0.3080 0.000 0.120 0.000 0.400 0.480
#> aberrant_ERR2585283 4 0.0000 0.7073 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585343 4 0.2516 0.6970 0.000 0.000 0.000 0.860 0.140
#> aberrant_ERR2585329 2 0.0162 0.7803 0.000 0.996 0.000 0.000 0.004
#> aberrant_ERR2585317 2 0.0290 0.7810 0.000 0.992 0.000 0.000 0.008
#> aberrant_ERR2585339 2 0.1121 0.7793 0.000 0.956 0.000 0.000 0.044
#> aberrant_ERR2585335 2 0.2732 0.7197 0.000 0.840 0.000 0.000 0.160
#> aberrant_ERR2585287 4 0.0794 0.7063 0.000 0.000 0.000 0.972 0.028
#> aberrant_ERR2585321 4 0.5137 0.2502 0.000 0.040 0.000 0.536 0.424
#> aberrant_ERR2585297 1 0.0963 0.9040 0.964 0.000 0.000 0.000 0.036
#> aberrant_ERR2585337 2 0.0000 0.7804 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585319 2 0.4047 0.5063 0.000 0.676 0.000 0.004 0.320
#> aberrant_ERR2585315 2 0.1965 0.7578 0.000 0.904 0.000 0.000 0.096
#> aberrant_ERR2585336 2 0.0404 0.7809 0.000 0.988 0.000 0.000 0.012
#> aberrant_ERR2585307 2 0.0609 0.7813 0.000 0.980 0.000 0.000 0.020
#> aberrant_ERR2585301 2 0.5010 0.2652 0.000 0.572 0.000 0.036 0.392
#> aberrant_ERR2585326 2 0.0609 0.7809 0.000 0.980 0.000 0.000 0.020
#> aberrant_ERR2585331 2 0.0898 0.7775 0.000 0.972 0.008 0.000 0.020
#> aberrant_ERR2585346 4 0.0162 0.7069 0.004 0.000 0.000 0.996 0.000
#> aberrant_ERR2585314 2 0.0404 0.7816 0.000 0.988 0.000 0.000 0.012
#> aberrant_ERR2585298 3 0.0703 0.8527 0.000 0.000 0.976 0.000 0.024
#> aberrant_ERR2585345 2 0.0290 0.7810 0.000 0.992 0.000 0.000 0.008
#> aberrant_ERR2585299 1 0.2017 0.8995 0.912 0.000 0.008 0.000 0.080
#> aberrant_ERR2585309 1 0.0510 0.9019 0.984 0.000 0.000 0.000 0.016
#> aberrant_ERR2585303 2 0.4302 0.6166 0.000 0.744 0.000 0.048 0.208
#> aberrant_ERR2585313 2 0.0963 0.7807 0.000 0.964 0.000 0.000 0.036
#> aberrant_ERR2585318 5 0.6539 0.4600 0.000 0.368 0.000 0.200 0.432
#> aberrant_ERR2585328 4 0.5874 0.0661 0.000 0.108 0.000 0.528 0.364
#> aberrant_ERR2585330 2 0.4206 0.5570 0.000 0.708 0.000 0.020 0.272
#> aberrant_ERR2585293 4 0.0162 0.7069 0.004 0.000 0.000 0.996 0.000
#> aberrant_ERR2585342 5 0.6641 0.4634 0.000 0.368 0.000 0.224 0.408
#> aberrant_ERR2585348 4 0.3999 0.4963 0.000 0.000 0.000 0.656 0.344
#> aberrant_ERR2585352 2 0.2690 0.7222 0.000 0.844 0.000 0.000 0.156
#> aberrant_ERR2585308 1 0.0880 0.9027 0.968 0.000 0.000 0.000 0.032
#> aberrant_ERR2585349 2 0.4335 0.5715 0.000 0.760 0.168 0.000 0.072
#> aberrant_ERR2585316 4 0.1270 0.7119 0.000 0.000 0.000 0.948 0.052
#> aberrant_ERR2585306 4 0.4455 0.5544 0.068 0.000 0.000 0.744 0.188
#> aberrant_ERR2585324 2 0.4047 0.5063 0.000 0.676 0.000 0.004 0.320
#> aberrant_ERR2585310 2 0.6799 0.1023 0.020 0.468 0.352 0.000 0.160
#> aberrant_ERR2585296 3 0.3272 0.8217 0.012 0.032 0.856 0.000 0.100
#> aberrant_ERR2585275 4 0.0162 0.7072 0.000 0.000 0.000 0.996 0.004
#> aberrant_ERR2585311 5 0.5895 0.0737 0.000 0.100 0.000 0.444 0.456
#> aberrant_ERR2585292 4 0.0162 0.7069 0.004 0.000 0.000 0.996 0.000
#> aberrant_ERR2585282 4 0.3774 0.5949 0.000 0.000 0.000 0.704 0.296
#> aberrant_ERR2585305 5 0.6451 0.3469 0.004 0.388 0.000 0.156 0.452
#> aberrant_ERR2585278 2 0.2561 0.7285 0.000 0.856 0.000 0.000 0.144
#> aberrant_ERR2585347 4 0.2471 0.6877 0.000 0.000 0.000 0.864 0.136
#> aberrant_ERR2585332 4 0.3508 0.6342 0.000 0.000 0.000 0.748 0.252
#> aberrant_ERR2585280 2 0.6718 -0.4476 0.000 0.384 0.000 0.248 0.368
#> aberrant_ERR2585304 2 0.2144 0.7407 0.000 0.912 0.068 0.000 0.020
#> aberrant_ERR2585322 2 0.1478 0.7738 0.000 0.936 0.000 0.000 0.064
#> aberrant_ERR2585279 2 0.1981 0.7416 0.000 0.920 0.064 0.000 0.016
#> aberrant_ERR2585277 2 0.0404 0.7809 0.000 0.988 0.000 0.000 0.012
#> aberrant_ERR2585295 4 0.4168 0.5814 0.000 0.044 0.000 0.756 0.200
#> aberrant_ERR2585333 4 0.4400 0.5412 0.000 0.020 0.000 0.672 0.308
#> aberrant_ERR2585285 2 0.4400 0.4988 0.000 0.672 0.000 0.020 0.308
#> aberrant_ERR2585286 2 0.0703 0.7818 0.000 0.976 0.000 0.000 0.024
#> aberrant_ERR2585294 2 0.6452 -0.1826 0.000 0.476 0.000 0.196 0.328
#> aberrant_ERR2585300 4 0.3612 0.6042 0.000 0.000 0.000 0.732 0.268
#> aberrant_ERR2585334 2 0.0898 0.7781 0.000 0.972 0.008 0.000 0.020
#> aberrant_ERR2585361 2 0.6610 -0.4018 0.000 0.428 0.000 0.220 0.352
#> aberrant_ERR2585372 5 0.6132 0.2618 0.000 0.128 0.000 0.432 0.440
#> round_ERR2585217 3 0.0693 0.8526 0.000 0.012 0.980 0.000 0.008
#> round_ERR2585205 1 0.1341 0.9029 0.944 0.000 0.000 0.000 0.056
#> round_ERR2585214 3 0.0798 0.8517 0.000 0.016 0.976 0.000 0.008
#> round_ERR2585202 3 0.4402 0.6483 0.000 0.204 0.740 0.000 0.056
#> aberrant_ERR2585367 2 0.6619 -0.4098 0.000 0.420 0.000 0.220 0.360
#> round_ERR2585220 1 0.4767 0.7163 0.720 0.000 0.192 0.000 0.088
#> round_ERR2585238 1 0.1121 0.9034 0.956 0.000 0.000 0.000 0.044
#> aberrant_ERR2585276 4 0.6724 -0.4088 0.000 0.296 0.000 0.420 0.284
#> round_ERR2585218 1 0.1197 0.9037 0.952 0.000 0.000 0.000 0.048
#> aberrant_ERR2585363 2 0.3366 0.6564 0.000 0.768 0.000 0.000 0.232
#> round_ERR2585201 3 0.0794 0.8525 0.000 0.000 0.972 0.000 0.028
#> round_ERR2585210 1 0.1121 0.9030 0.956 0.000 0.000 0.000 0.044
#> aberrant_ERR2585362 5 0.6772 0.4850 0.000 0.280 0.000 0.332 0.388
#> aberrant_ERR2585360 5 0.6600 0.4402 0.000 0.212 0.000 0.380 0.408
#> round_ERR2585209 3 0.0963 0.8534 0.000 0.000 0.964 0.000 0.036
#> round_ERR2585242 3 0.1282 0.8519 0.000 0.004 0.952 0.000 0.044
#> round_ERR2585216 1 0.4411 0.7930 0.764 0.000 0.120 0.000 0.116
#> round_ERR2585219 1 0.2795 0.8804 0.872 0.000 0.028 0.000 0.100
#> round_ERR2585237 3 0.0912 0.8508 0.000 0.016 0.972 0.000 0.012
#> round_ERR2585198 3 0.0912 0.8509 0.000 0.016 0.972 0.000 0.012
#> round_ERR2585211 1 0.1544 0.9005 0.932 0.000 0.000 0.000 0.068
#> round_ERR2585206 1 0.0963 0.9037 0.964 0.000 0.000 0.000 0.036
#> aberrant_ERR2585281 2 0.5755 0.2765 0.000 0.624 0.004 0.240 0.132
#> round_ERR2585212 1 0.6158 0.2262 0.480 0.000 0.384 0.000 0.136
#> round_ERR2585221 1 0.1121 0.9038 0.956 0.000 0.000 0.000 0.044
#> round_ERR2585243 1 0.1341 0.9037 0.944 0.000 0.000 0.000 0.056
#> round_ERR2585204 3 0.0898 0.8488 0.000 0.020 0.972 0.000 0.008
#> round_ERR2585213 3 0.4268 0.4523 0.000 0.344 0.648 0.000 0.008
#> aberrant_ERR2585373 4 0.5204 0.2877 0.000 0.052 0.000 0.580 0.368
#> aberrant_ERR2585358 4 0.4165 0.5360 0.000 0.008 0.000 0.672 0.320
#> aberrant_ERR2585365 2 0.4243 0.5417 0.000 0.712 0.000 0.024 0.264
#> aberrant_ERR2585359 4 0.3336 0.6577 0.000 0.000 0.000 0.772 0.228
#> aberrant_ERR2585370 2 0.0162 0.7806 0.000 0.996 0.000 0.000 0.004
#> round_ERR2585215 1 0.0880 0.9027 0.968 0.000 0.000 0.000 0.032
#> round_ERR2585262 3 0.5220 0.7150 0.004 0.056 0.744 0.056 0.140
#> round_ERR2585199 3 0.4067 0.5321 0.000 0.300 0.692 0.000 0.008
#> aberrant_ERR2585369 5 0.6235 0.4874 0.000 0.344 0.000 0.156 0.500
#> round_ERR2585208 1 0.0404 0.9009 0.988 0.000 0.000 0.000 0.012
#> round_ERR2585252 1 0.0510 0.9002 0.984 0.000 0.000 0.000 0.016
#> round_ERR2585236 1 0.3724 0.8291 0.844 0.000 0.036 0.068 0.052
#> aberrant_ERR2585284 4 0.0000 0.7073 0.000 0.000 0.000 1.000 0.000
#> round_ERR2585224 1 0.0566 0.9016 0.984 0.000 0.000 0.004 0.012
#> round_ERR2585260 1 0.1270 0.9039 0.948 0.000 0.000 0.000 0.052
#> round_ERR2585229 1 0.1270 0.9041 0.948 0.000 0.000 0.000 0.052
#> aberrant_ERR2585364 4 0.1121 0.7099 0.000 0.000 0.000 0.956 0.044
#> round_ERR2585253 1 0.0609 0.9011 0.980 0.000 0.000 0.000 0.020
#> aberrant_ERR2585368 2 0.0510 0.7793 0.000 0.984 0.000 0.000 0.016
#> aberrant_ERR2585371 2 0.0510 0.7793 0.000 0.984 0.000 0.000 0.016
#> round_ERR2585239 1 0.2305 0.8952 0.896 0.000 0.012 0.000 0.092
#> round_ERR2585273 1 0.2351 0.8911 0.896 0.000 0.016 0.000 0.088
#> round_ERR2585256 3 0.1357 0.8508 0.004 0.000 0.948 0.000 0.048
#> round_ERR2585272 1 0.2520 0.8838 0.896 0.000 0.048 0.000 0.056
#> round_ERR2585246 1 0.1478 0.9025 0.936 0.000 0.000 0.000 0.064
#> round_ERR2585261 3 0.0880 0.8533 0.000 0.000 0.968 0.000 0.032
#> round_ERR2585254 3 0.0693 0.8524 0.000 0.008 0.980 0.000 0.012
#> round_ERR2585225 3 0.4429 0.6953 0.192 0.000 0.744 0.000 0.064
#> round_ERR2585235 1 0.2236 0.8948 0.908 0.000 0.024 0.000 0.068
#> round_ERR2585271 1 0.1410 0.9024 0.940 0.000 0.000 0.000 0.060
#> round_ERR2585251 3 0.5519 0.3761 0.332 0.000 0.584 0.000 0.084
#> round_ERR2585255 3 0.1571 0.8473 0.004 0.000 0.936 0.000 0.060
#> round_ERR2585257 3 0.1697 0.8479 0.008 0.000 0.932 0.000 0.060
#> round_ERR2585226 1 0.3861 0.8115 0.804 0.000 0.128 0.000 0.068
#> round_ERR2585265 1 0.4593 0.7854 0.748 0.000 0.124 0.000 0.128
#> round_ERR2585259 1 0.5927 0.3757 0.540 0.000 0.340 0.000 0.120
#> round_ERR2585247 1 0.1410 0.9029 0.940 0.000 0.000 0.000 0.060
#> round_ERR2585241 1 0.1410 0.9033 0.940 0.000 0.000 0.000 0.060
#> round_ERR2585263 1 0.6248 0.2053 0.468 0.000 0.384 0.000 0.148
#> round_ERR2585264 1 0.0404 0.8999 0.988 0.000 0.000 0.000 0.012
#> round_ERR2585233 3 0.5843 0.2153 0.388 0.000 0.512 0.000 0.100
#> round_ERR2585223 1 0.1121 0.9041 0.956 0.000 0.000 0.000 0.044
#> round_ERR2585234 3 0.0404 0.8526 0.000 0.000 0.988 0.000 0.012
#> round_ERR2585222 1 0.2561 0.8892 0.884 0.000 0.020 0.000 0.096
#> round_ERR2585228 1 0.1851 0.8987 0.912 0.000 0.000 0.000 0.088
#> round_ERR2585248 1 0.0671 0.9010 0.980 0.000 0.000 0.004 0.016
#> round_ERR2585240 3 0.3180 0.8005 0.076 0.000 0.856 0.000 0.068
#> round_ERR2585270 1 0.4545 0.7913 0.752 0.000 0.116 0.000 0.132
#> round_ERR2585232 3 0.5112 0.5787 0.256 0.000 0.664 0.000 0.080
#> aberrant_ERR2585341 2 0.4953 0.5285 0.000 0.712 0.000 0.124 0.164
#> aberrant_ERR2585355 2 0.1341 0.7776 0.000 0.944 0.000 0.000 0.056
#> round_ERR2585227 1 0.5887 0.1193 0.476 0.000 0.424 0.000 0.100
#> aberrant_ERR2585351 2 0.4655 0.4420 0.000 0.644 0.000 0.028 0.328
#> round_ERR2585269 1 0.0404 0.9011 0.988 0.000 0.000 0.000 0.012
#> aberrant_ERR2585357 2 0.0404 0.7810 0.000 0.988 0.000 0.000 0.012
#> aberrant_ERR2585350 2 0.0703 0.7810 0.000 0.976 0.000 0.000 0.024
#> round_ERR2585250 1 0.5665 0.6270 0.648 0.000 0.220 0.008 0.124
#> round_ERR2585245 1 0.0290 0.9006 0.992 0.000 0.000 0.000 0.008
#> aberrant_ERR2585353 4 0.4505 0.4489 0.000 0.012 0.000 0.604 0.384
#> round_ERR2585258 1 0.3812 0.8322 0.812 0.000 0.092 0.000 0.096
#> aberrant_ERR2585354 5 0.5895 0.1539 0.000 0.100 0.000 0.440 0.460
#> round_ERR2585249 1 0.0404 0.9004 0.988 0.000 0.000 0.000 0.012
#> round_ERR2585268 1 0.5493 0.5692 0.628 0.000 0.264 0.000 0.108
#> aberrant_ERR2585356 4 0.3274 0.6498 0.000 0.000 0.000 0.780 0.220
#> round_ERR2585266 3 0.1697 0.8481 0.008 0.000 0.932 0.000 0.060
#> round_ERR2585231 1 0.0609 0.9014 0.980 0.000 0.000 0.000 0.020
#> round_ERR2585230 1 0.1851 0.9014 0.912 0.000 0.000 0.000 0.088
#> round_ERR2585267 1 0.1357 0.9042 0.948 0.000 0.000 0.004 0.048
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 5 0.6719 0.2991 0.000 0.080 0.000 0.272 0.484 NA
#> aberrant_ERR2585338 2 0.1745 0.7704 0.000 0.924 0.000 0.000 0.056 NA
#> aberrant_ERR2585325 5 0.6743 0.2996 0.000 0.080 0.000 0.272 0.480 NA
#> aberrant_ERR2585283 4 0.0146 0.6661 0.000 0.000 0.000 0.996 0.000 NA
#> aberrant_ERR2585343 4 0.4085 0.6013 0.000 0.000 0.000 0.736 0.192 NA
#> aberrant_ERR2585329 2 0.0820 0.7736 0.000 0.972 0.000 0.000 0.012 NA
#> aberrant_ERR2585317 2 0.0725 0.7748 0.000 0.976 0.000 0.000 0.012 NA
#> aberrant_ERR2585339 2 0.1745 0.7651 0.000 0.920 0.000 0.000 0.068 NA
#> aberrant_ERR2585335 2 0.3991 0.6517 0.000 0.756 0.000 0.000 0.156 NA
#> aberrant_ERR2585287 4 0.0820 0.6662 0.000 0.000 0.000 0.972 0.016 NA
#> aberrant_ERR2585321 5 0.5423 0.0615 0.000 0.028 0.000 0.356 0.552 NA
#> aberrant_ERR2585297 1 0.1841 0.8623 0.920 0.000 0.000 0.008 0.008 NA
#> aberrant_ERR2585337 2 0.0820 0.7746 0.000 0.972 0.000 0.000 0.016 NA
#> aberrant_ERR2585319 2 0.5988 0.0545 0.000 0.456 0.000 0.004 0.332 NA
#> aberrant_ERR2585315 2 0.3221 0.7219 0.000 0.828 0.000 0.000 0.096 NA
#> aberrant_ERR2585336 2 0.0603 0.7745 0.000 0.980 0.000 0.000 0.016 NA
#> aberrant_ERR2585307 2 0.1088 0.7753 0.000 0.960 0.000 0.000 0.016 NA
#> aberrant_ERR2585301 2 0.6490 -0.0575 0.000 0.440 0.000 0.040 0.344 NA
#> aberrant_ERR2585326 2 0.0909 0.7742 0.000 0.968 0.000 0.000 0.012 NA
#> aberrant_ERR2585331 2 0.0520 0.7731 0.000 0.984 0.008 0.000 0.000 NA
#> aberrant_ERR2585346 4 0.0520 0.6668 0.000 0.000 0.000 0.984 0.008 NA
#> aberrant_ERR2585314 2 0.1478 0.7734 0.000 0.944 0.004 0.000 0.020 NA
#> aberrant_ERR2585298 3 0.1625 0.7847 0.000 0.000 0.928 0.000 0.012 NA
#> aberrant_ERR2585345 2 0.0622 0.7731 0.000 0.980 0.000 0.000 0.008 NA
#> aberrant_ERR2585299 1 0.2163 0.8634 0.892 0.000 0.004 0.000 0.008 NA
#> aberrant_ERR2585309 1 0.1349 0.8600 0.940 0.000 0.000 0.004 0.000 NA
#> aberrant_ERR2585303 2 0.4731 0.5604 0.000 0.684 0.000 0.020 0.236 NA
#> aberrant_ERR2585313 2 0.1644 0.7718 0.000 0.932 0.000 0.000 0.040 NA
#> aberrant_ERR2585318 5 0.6702 0.4982 0.000 0.304 0.000 0.108 0.476 NA
#> aberrant_ERR2585328 5 0.6659 0.0928 0.000 0.108 0.008 0.400 0.416 NA
#> aberrant_ERR2585330 2 0.5931 0.2878 0.000 0.544 0.000 0.036 0.304 NA
#> aberrant_ERR2585293 4 0.0436 0.6663 0.004 0.000 0.000 0.988 0.004 NA
#> aberrant_ERR2585342 5 0.7167 0.4866 0.000 0.296 0.000 0.168 0.412 NA
#> aberrant_ERR2585348 4 0.5566 0.2465 0.000 0.016 0.000 0.508 0.384 NA
#> aberrant_ERR2585352 2 0.3978 0.6587 0.000 0.756 0.000 0.000 0.160 NA
#> aberrant_ERR2585308 1 0.1219 0.8591 0.948 0.000 0.000 0.000 0.004 NA
#> aberrant_ERR2585349 2 0.4582 0.5666 0.000 0.736 0.164 0.000 0.056 NA
#> aberrant_ERR2585316 4 0.1895 0.6667 0.000 0.000 0.000 0.912 0.072 NA
#> aberrant_ERR2585306 4 0.6488 0.3462 0.092 0.000 0.000 0.540 0.232 NA
#> aberrant_ERR2585324 2 0.5988 0.0545 0.000 0.456 0.000 0.004 0.332 NA
#> aberrant_ERR2585310 2 0.7913 -0.0508 0.028 0.372 0.228 0.004 0.108 NA
#> aberrant_ERR2585296 3 0.5382 0.7016 0.020 0.056 0.672 0.000 0.040 NA
#> aberrant_ERR2585275 4 0.0508 0.6678 0.000 0.000 0.000 0.984 0.012 NA
#> aberrant_ERR2585311 5 0.5805 0.3098 0.000 0.072 0.000 0.244 0.604 NA
#> aberrant_ERR2585292 4 0.0436 0.6663 0.004 0.000 0.000 0.988 0.004 NA
#> aberrant_ERR2585282 4 0.4968 0.3879 0.000 0.000 0.000 0.556 0.368 NA
#> aberrant_ERR2585305 5 0.7752 0.4601 0.024 0.256 0.016 0.088 0.436 NA
#> aberrant_ERR2585278 2 0.3927 0.6625 0.000 0.756 0.000 0.000 0.172 NA
#> aberrant_ERR2585347 4 0.3370 0.6331 0.000 0.000 0.000 0.804 0.148 NA
#> aberrant_ERR2585332 4 0.4655 0.5108 0.000 0.000 0.000 0.632 0.300 NA
#> aberrant_ERR2585280 5 0.7581 0.4199 0.000 0.264 0.000 0.256 0.320 NA
#> aberrant_ERR2585304 2 0.2879 0.7189 0.000 0.868 0.072 0.000 0.016 NA
#> aberrant_ERR2585322 2 0.2712 0.7454 0.000 0.864 0.000 0.000 0.088 NA
#> aberrant_ERR2585279 2 0.2320 0.7259 0.000 0.892 0.080 0.000 0.004 NA
#> aberrant_ERR2585277 2 0.0603 0.7733 0.000 0.980 0.000 0.000 0.016 NA
#> aberrant_ERR2585295 4 0.5240 0.4489 0.000 0.056 0.000 0.676 0.192 NA
#> aberrant_ERR2585333 4 0.5760 0.3501 0.000 0.040 0.000 0.548 0.328 NA
#> aberrant_ERR2585285 2 0.5532 0.2585 0.000 0.540 0.000 0.016 0.348 NA
#> aberrant_ERR2585286 2 0.1492 0.7730 0.000 0.940 0.000 0.000 0.036 NA
#> aberrant_ERR2585294 5 0.6982 0.3235 0.000 0.352 0.000 0.100 0.396 NA
#> aberrant_ERR2585300 4 0.5166 0.3432 0.000 0.000 0.000 0.524 0.384 NA
#> aberrant_ERR2585334 2 0.1944 0.7593 0.000 0.924 0.036 0.000 0.016 NA
#> aberrant_ERR2585361 5 0.6997 0.4813 0.000 0.328 0.000 0.172 0.408 NA
#> aberrant_ERR2585372 5 0.6561 0.2797 0.000 0.096 0.000 0.332 0.472 NA
#> round_ERR2585217 3 0.2432 0.7811 0.000 0.024 0.876 0.000 0.000 NA
#> round_ERR2585205 1 0.1644 0.8616 0.920 0.000 0.000 0.000 0.004 NA
#> round_ERR2585214 3 0.1738 0.7824 0.000 0.016 0.928 0.000 0.004 NA
#> round_ERR2585202 3 0.5495 0.4954 0.000 0.264 0.608 0.000 0.028 NA
#> aberrant_ERR2585367 5 0.7022 0.4620 0.000 0.332 0.000 0.184 0.396 NA
#> round_ERR2585220 1 0.5359 0.6451 0.620 0.000 0.176 0.000 0.008 NA
#> round_ERR2585238 1 0.1700 0.8643 0.916 0.000 0.000 0.000 0.004 NA
#> aberrant_ERR2585276 4 0.7541 -0.3616 0.000 0.224 0.000 0.312 0.308 NA
#> round_ERR2585218 1 0.2147 0.8640 0.896 0.000 0.000 0.000 0.020 NA
#> aberrant_ERR2585363 2 0.4749 0.5175 0.000 0.656 0.000 0.008 0.268 NA
#> round_ERR2585201 3 0.1584 0.7842 0.000 0.000 0.928 0.000 0.008 NA
#> round_ERR2585210 1 0.2100 0.8600 0.884 0.000 0.000 0.000 0.004 NA
#> aberrant_ERR2585362 5 0.6797 0.4728 0.000 0.220 0.000 0.208 0.488 NA
#> aberrant_ERR2585360 5 0.6783 0.4630 0.000 0.180 0.000 0.224 0.500 NA
#> round_ERR2585209 3 0.2612 0.7804 0.016 0.000 0.868 0.000 0.008 NA
#> round_ERR2585242 3 0.2404 0.7836 0.000 0.000 0.872 0.000 0.016 NA
#> round_ERR2585216 1 0.4657 0.7504 0.684 0.000 0.092 0.000 0.004 NA
#> round_ERR2585219 1 0.3395 0.8315 0.812 0.000 0.048 0.000 0.004 NA
#> round_ERR2585237 3 0.2994 0.7645 0.000 0.064 0.852 0.000 0.004 NA
#> round_ERR2585198 3 0.2237 0.7677 0.000 0.068 0.896 0.000 0.000 NA
#> round_ERR2585211 1 0.1806 0.8605 0.908 0.000 0.000 0.000 0.004 NA
#> round_ERR2585206 1 0.1434 0.8601 0.940 0.000 0.000 0.000 0.012 NA
#> aberrant_ERR2585281 2 0.6895 0.1518 0.000 0.532 0.016 0.192 0.172 NA
#> round_ERR2585212 1 0.6101 0.3007 0.464 0.000 0.300 0.000 0.008 NA
#> round_ERR2585221 1 0.1387 0.8630 0.932 0.000 0.000 0.000 0.000 NA
#> round_ERR2585243 1 0.2400 0.8597 0.872 0.000 0.000 0.004 0.008 NA
#> round_ERR2585204 3 0.2519 0.7616 0.000 0.068 0.884 0.000 0.004 NA
#> round_ERR2585213 3 0.4758 0.1289 0.000 0.460 0.500 0.000 0.008 NA
#> aberrant_ERR2585373 5 0.5678 -0.0725 0.000 0.048 0.000 0.432 0.468 NA
#> aberrant_ERR2585358 4 0.5303 0.3338 0.000 0.020 0.000 0.532 0.388 NA
#> aberrant_ERR2585365 2 0.5092 0.3879 0.000 0.612 0.000 0.036 0.312 NA
#> aberrant_ERR2585359 4 0.4445 0.5414 0.000 0.000 0.000 0.656 0.288 NA
#> aberrant_ERR2585370 2 0.0291 0.7734 0.000 0.992 0.000 0.000 0.004 NA
#> round_ERR2585215 1 0.1866 0.8648 0.908 0.000 0.000 0.000 0.008 NA
#> round_ERR2585262 3 0.7247 0.5218 0.008 0.068 0.556 0.068 0.136 NA
#> round_ERR2585199 3 0.4721 0.4190 0.000 0.356 0.592 0.000 0.004 NA
#> aberrant_ERR2585369 5 0.5875 0.5252 0.000 0.208 0.000 0.092 0.616 NA
#> round_ERR2585208 1 0.1285 0.8620 0.944 0.000 0.000 0.000 0.004 NA
#> round_ERR2585252 1 0.1285 0.8621 0.944 0.000 0.000 0.000 0.004 NA
#> round_ERR2585236 1 0.5923 0.6965 0.660 0.000 0.060 0.104 0.028 NA
#> aberrant_ERR2585284 4 0.0520 0.6669 0.000 0.000 0.000 0.984 0.008 NA
#> round_ERR2585224 1 0.1500 0.8573 0.936 0.000 0.000 0.012 0.000 NA
#> round_ERR2585260 1 0.2544 0.8595 0.864 0.000 0.012 0.000 0.004 NA
#> round_ERR2585229 1 0.1327 0.8643 0.936 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585364 4 0.2214 0.6599 0.000 0.000 0.000 0.888 0.096 NA
#> round_ERR2585253 1 0.0547 0.8537 0.980 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585368 2 0.0767 0.7722 0.000 0.976 0.004 0.000 0.012 NA
#> aberrant_ERR2585371 2 0.0767 0.7722 0.000 0.976 0.004 0.000 0.012 NA
#> round_ERR2585239 1 0.3802 0.8155 0.772 0.000 0.036 0.000 0.012 NA
#> round_ERR2585273 1 0.3172 0.8454 0.820 0.000 0.016 0.000 0.012 NA
#> round_ERR2585256 3 0.2504 0.7808 0.004 0.000 0.856 0.000 0.004 NA
#> round_ERR2585272 1 0.3898 0.8200 0.780 0.000 0.060 0.000 0.012 NA
#> round_ERR2585246 1 0.2261 0.8612 0.884 0.000 0.000 0.004 0.008 NA
#> round_ERR2585261 3 0.2504 0.7833 0.000 0.004 0.856 0.000 0.004 NA
#> round_ERR2585254 3 0.2443 0.7837 0.000 0.020 0.880 0.000 0.004 NA
#> round_ERR2585225 3 0.5542 0.6641 0.132 0.000 0.668 0.028 0.016 NA
#> round_ERR2585235 1 0.3982 0.8181 0.788 0.000 0.048 0.008 0.016 NA
#> round_ERR2585271 1 0.2631 0.8526 0.840 0.000 0.008 0.000 0.000 NA
#> round_ERR2585251 3 0.6197 0.2935 0.272 0.000 0.440 0.000 0.008 NA
#> round_ERR2585255 3 0.2121 0.7801 0.000 0.000 0.892 0.000 0.012 NA
#> round_ERR2585257 3 0.3577 0.7638 0.020 0.000 0.792 0.000 0.020 NA
#> round_ERR2585226 1 0.4861 0.7446 0.696 0.000 0.116 0.000 0.016 NA
#> round_ERR2585265 1 0.5613 0.6283 0.572 0.000 0.144 0.000 0.012 NA
#> round_ERR2585259 1 0.6181 0.4571 0.516 0.000 0.236 0.004 0.016 NA
#> round_ERR2585247 1 0.2196 0.8620 0.884 0.000 0.000 0.004 0.004 NA
#> round_ERR2585241 1 0.2020 0.8637 0.896 0.000 0.008 0.000 0.000 NA
#> round_ERR2585263 1 0.6297 0.1093 0.384 0.000 0.328 0.000 0.008 NA
#> round_ERR2585264 1 0.1524 0.8605 0.932 0.000 0.000 0.008 0.000 NA
#> round_ERR2585233 3 0.6542 0.0901 0.360 0.000 0.404 0.004 0.024 NA
#> round_ERR2585223 1 0.1908 0.8634 0.900 0.000 0.000 0.000 0.004 NA
#> round_ERR2585234 3 0.1398 0.7842 0.000 0.008 0.940 0.000 0.000 NA
#> round_ERR2585222 1 0.3636 0.8188 0.764 0.000 0.012 0.000 0.016 NA
#> round_ERR2585228 1 0.2500 0.8584 0.868 0.000 0.004 0.000 0.012 NA
#> round_ERR2585248 1 0.1124 0.8598 0.956 0.000 0.000 0.008 0.000 NA
#> round_ERR2585240 3 0.4307 0.7327 0.068 0.000 0.744 0.000 0.016 NA
#> round_ERR2585270 1 0.5432 0.6607 0.608 0.000 0.140 0.000 0.012 NA
#> round_ERR2585232 3 0.5404 0.5718 0.216 0.000 0.600 0.000 0.004 NA
#> aberrant_ERR2585341 2 0.5754 0.4263 0.000 0.624 0.000 0.068 0.212 NA
#> aberrant_ERR2585355 2 0.1367 0.7706 0.000 0.944 0.000 0.000 0.044 NA
#> round_ERR2585227 1 0.6353 0.2939 0.464 0.000 0.292 0.000 0.024 NA
#> aberrant_ERR2585351 2 0.5895 -0.0310 0.000 0.480 0.000 0.056 0.400 NA
#> round_ERR2585269 1 0.1219 0.8592 0.948 0.000 0.000 0.000 0.004 NA
#> aberrant_ERR2585357 2 0.0520 0.7741 0.000 0.984 0.000 0.000 0.008 NA
#> aberrant_ERR2585350 2 0.1408 0.7736 0.000 0.944 0.000 0.000 0.036 NA
#> round_ERR2585250 1 0.6079 0.5636 0.548 0.000 0.156 0.008 0.020 NA
#> round_ERR2585245 1 0.1003 0.8560 0.964 0.000 0.000 0.004 0.004 NA
#> aberrant_ERR2585353 4 0.5173 0.2376 0.000 0.008 0.000 0.476 0.452 NA
#> round_ERR2585258 1 0.5184 0.7160 0.644 0.000 0.108 0.000 0.016 NA
#> aberrant_ERR2585354 5 0.5829 0.2004 0.000 0.064 0.000 0.320 0.552 NA
#> round_ERR2585249 1 0.1155 0.8583 0.956 0.000 0.000 0.004 0.004 NA
#> round_ERR2585268 1 0.6700 0.4346 0.492 0.000 0.196 0.012 0.040 NA
#> aberrant_ERR2585356 4 0.4671 0.4981 0.000 0.000 0.000 0.628 0.304 NA
#> round_ERR2585266 3 0.2804 0.7736 0.004 0.000 0.852 0.000 0.024 NA
#> round_ERR2585231 1 0.1265 0.8552 0.948 0.000 0.000 0.008 0.000 NA
#> round_ERR2585230 1 0.3199 0.8508 0.836 0.000 0.024 0.004 0.012 NA
#> round_ERR2585267 1 0.2058 0.8611 0.908 0.000 0.000 0.012 0.008 NA
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> MAD:skmeans 155 3.94e-19 2
#> MAD:skmeans 148 5.16e-22 3
#> MAD:skmeans 147 7.14e-27 4
#> MAD:skmeans 127 1.30e-22 5
#> MAD:skmeans 113 8.42e-19 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "pam"]
# you can also extract it by
# res = res_list["MAD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.703 0.911 0.945 0.148 0.904 0.904
#> 3 3 0.367 0.607 0.844 1.150 0.829 0.811
#> 4 4 0.308 0.659 0.815 0.201 0.950 0.934
#> 5 5 0.295 0.606 0.794 0.088 0.965 0.953
#> 6 6 0.415 0.646 0.831 0.366 0.691 0.586
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 1 0.5737 0.879 0.864 0.136
#> aberrant_ERR2585338 1 0.0000 0.946 1.000 0.000
#> aberrant_ERR2585325 1 0.5519 0.881 0.872 0.128
#> aberrant_ERR2585283 2 0.0000 0.861 0.000 1.000
#> aberrant_ERR2585343 1 0.6531 0.843 0.832 0.168
#> aberrant_ERR2585329 1 0.1843 0.942 0.972 0.028
#> aberrant_ERR2585317 1 0.1633 0.943 0.976 0.024
#> aberrant_ERR2585339 1 0.3114 0.931 0.944 0.056
#> aberrant_ERR2585335 1 0.5059 0.894 0.888 0.112
#> aberrant_ERR2585287 2 0.9170 0.556 0.332 0.668
#> aberrant_ERR2585321 1 0.5737 0.874 0.864 0.136
#> aberrant_ERR2585297 1 0.1184 0.943 0.984 0.016
#> aberrant_ERR2585337 1 0.1414 0.944 0.980 0.020
#> aberrant_ERR2585319 1 0.5519 0.881 0.872 0.128
#> aberrant_ERR2585315 1 0.5178 0.891 0.884 0.116
#> aberrant_ERR2585336 1 0.2603 0.936 0.956 0.044
#> aberrant_ERR2585307 1 0.2043 0.941 0.968 0.032
#> aberrant_ERR2585301 1 0.2236 0.940 0.964 0.036
#> aberrant_ERR2585326 1 0.1633 0.943 0.976 0.024
#> aberrant_ERR2585331 1 0.0000 0.946 1.000 0.000
#> aberrant_ERR2585346 2 0.0376 0.862 0.004 0.996
#> aberrant_ERR2585314 1 0.2423 0.937 0.960 0.040
#> aberrant_ERR2585298 1 0.0376 0.946 0.996 0.004
#> aberrant_ERR2585345 1 0.2423 0.937 0.960 0.040
#> aberrant_ERR2585299 1 0.0672 0.947 0.992 0.008
#> aberrant_ERR2585309 1 0.0938 0.944 0.988 0.012
#> aberrant_ERR2585303 1 0.1843 0.942 0.972 0.028
#> aberrant_ERR2585313 1 0.4815 0.901 0.896 0.104
#> aberrant_ERR2585318 1 0.5178 0.892 0.884 0.116
#> aberrant_ERR2585328 1 0.4161 0.915 0.916 0.084
#> aberrant_ERR2585330 1 0.5737 0.874 0.864 0.136
#> aberrant_ERR2585293 2 0.0376 0.861 0.004 0.996
#> aberrant_ERR2585342 1 0.5737 0.874 0.864 0.136
#> aberrant_ERR2585348 1 0.5629 0.877 0.868 0.132
#> aberrant_ERR2585352 1 0.2603 0.936 0.956 0.044
#> aberrant_ERR2585308 1 0.4161 0.920 0.916 0.084
#> aberrant_ERR2585349 1 0.0938 0.945 0.988 0.012
#> aberrant_ERR2585316 1 0.6712 0.824 0.824 0.176
#> aberrant_ERR2585306 1 0.4298 0.912 0.912 0.088
#> aberrant_ERR2585324 1 0.5294 0.888 0.880 0.120
#> aberrant_ERR2585310 1 0.0938 0.946 0.988 0.012
#> aberrant_ERR2585296 1 0.0000 0.946 1.000 0.000
#> aberrant_ERR2585275 2 0.9170 0.555 0.332 0.668
#> aberrant_ERR2585311 1 0.5519 0.882 0.872 0.128
#> aberrant_ERR2585292 2 0.0376 0.861 0.004 0.996
#> aberrant_ERR2585282 1 0.5842 0.875 0.860 0.140
#> aberrant_ERR2585305 1 0.5408 0.886 0.876 0.124
#> aberrant_ERR2585278 1 0.2423 0.937 0.960 0.040
#> aberrant_ERR2585347 1 0.6247 0.857 0.844 0.156
#> aberrant_ERR2585332 1 0.6148 0.860 0.848 0.152
#> aberrant_ERR2585280 1 0.5737 0.874 0.864 0.136
#> aberrant_ERR2585304 1 0.0376 0.946 0.996 0.004
#> aberrant_ERR2585322 1 0.1843 0.942 0.972 0.028
#> aberrant_ERR2585279 1 0.0376 0.946 0.996 0.004
#> aberrant_ERR2585277 1 0.1184 0.945 0.984 0.016
#> aberrant_ERR2585295 1 0.4690 0.903 0.900 0.100
#> aberrant_ERR2585333 1 0.6148 0.860 0.848 0.152
#> aberrant_ERR2585285 1 0.5408 0.885 0.876 0.124
#> aberrant_ERR2585286 1 0.2236 0.939 0.964 0.036
#> aberrant_ERR2585294 1 0.0672 0.946 0.992 0.008
#> aberrant_ERR2585300 1 0.5294 0.892 0.880 0.120
#> aberrant_ERR2585334 1 0.0000 0.946 1.000 0.000
#> aberrant_ERR2585361 1 0.5178 0.891 0.884 0.116
#> aberrant_ERR2585372 1 0.5842 0.871 0.860 0.140
#> round_ERR2585217 1 0.0672 0.945 0.992 0.008
#> round_ERR2585205 1 0.0938 0.944 0.988 0.012
#> round_ERR2585214 1 0.0000 0.946 1.000 0.000
#> round_ERR2585202 1 0.0000 0.946 1.000 0.000
#> aberrant_ERR2585367 1 0.1633 0.944 0.976 0.024
#> round_ERR2585220 1 0.0672 0.945 0.992 0.008
#> round_ERR2585238 1 0.0938 0.944 0.988 0.012
#> aberrant_ERR2585276 1 0.4815 0.902 0.896 0.104
#> round_ERR2585218 1 0.0938 0.944 0.988 0.012
#> aberrant_ERR2585363 1 0.4815 0.902 0.896 0.104
#> round_ERR2585201 1 0.0376 0.946 0.996 0.004
#> round_ERR2585210 1 0.0938 0.944 0.988 0.012
#> aberrant_ERR2585362 1 0.3114 0.935 0.944 0.056
#> aberrant_ERR2585360 1 0.5059 0.897 0.888 0.112
#> round_ERR2585209 1 0.0376 0.946 0.996 0.004
#> round_ERR2585242 1 0.0000 0.946 1.000 0.000
#> round_ERR2585216 1 0.0938 0.944 0.988 0.012
#> round_ERR2585219 1 0.0938 0.944 0.988 0.012
#> round_ERR2585237 1 0.0376 0.946 0.996 0.004
#> round_ERR2585198 1 0.0000 0.946 1.000 0.000
#> round_ERR2585211 1 0.0938 0.944 0.988 0.012
#> round_ERR2585206 1 0.1184 0.944 0.984 0.016
#> aberrant_ERR2585281 1 0.1633 0.943 0.976 0.024
#> round_ERR2585212 1 0.0938 0.944 0.988 0.012
#> round_ERR2585221 1 0.0672 0.946 0.992 0.008
#> round_ERR2585243 1 0.0938 0.944 0.988 0.012
#> round_ERR2585204 1 0.0000 0.946 1.000 0.000
#> round_ERR2585213 1 0.0000 0.946 1.000 0.000
#> aberrant_ERR2585373 1 0.5519 0.883 0.872 0.128
#> aberrant_ERR2585358 1 0.6801 0.829 0.820 0.180
#> aberrant_ERR2585365 1 0.1843 0.942 0.972 0.028
#> aberrant_ERR2585359 1 0.9661 0.379 0.608 0.392
#> aberrant_ERR2585370 1 0.1414 0.944 0.980 0.020
#> round_ERR2585215 1 0.1414 0.943 0.980 0.020
#> round_ERR2585262 1 0.0376 0.946 0.996 0.004
#> round_ERR2585199 1 0.0000 0.946 1.000 0.000
#> aberrant_ERR2585369 1 0.3431 0.927 0.936 0.064
#> round_ERR2585208 1 0.0938 0.944 0.988 0.012
#> round_ERR2585252 1 0.0938 0.944 0.988 0.012
#> round_ERR2585236 1 0.1633 0.946 0.976 0.024
#> aberrant_ERR2585284 2 0.0376 0.862 0.004 0.996
#> round_ERR2585224 1 0.7139 0.812 0.804 0.196
#> round_ERR2585260 1 0.0938 0.946 0.988 0.012
#> round_ERR2585229 1 0.1184 0.946 0.984 0.016
#> aberrant_ERR2585364 2 0.7219 0.747 0.200 0.800
#> round_ERR2585253 1 0.2603 0.931 0.956 0.044
#> aberrant_ERR2585368 1 0.0000 0.946 1.000 0.000
#> aberrant_ERR2585371 1 0.0000 0.946 1.000 0.000
#> round_ERR2585239 1 0.0938 0.944 0.988 0.012
#> round_ERR2585273 1 0.0938 0.944 0.988 0.012
#> round_ERR2585256 1 0.0672 0.945 0.992 0.008
#> round_ERR2585272 1 0.0938 0.944 0.988 0.012
#> round_ERR2585246 1 0.1184 0.945 0.984 0.016
#> round_ERR2585261 1 0.0376 0.946 0.996 0.004
#> round_ERR2585254 1 0.0376 0.946 0.996 0.004
#> round_ERR2585225 1 0.0672 0.946 0.992 0.008
#> round_ERR2585235 1 0.0938 0.944 0.988 0.012
#> round_ERR2585271 1 0.0938 0.944 0.988 0.012
#> round_ERR2585251 1 0.0672 0.945 0.992 0.008
#> round_ERR2585255 1 0.0376 0.946 0.996 0.004
#> round_ERR2585257 1 0.0376 0.946 0.996 0.004
#> round_ERR2585226 1 0.0376 0.946 0.996 0.004
#> round_ERR2585265 1 0.0672 0.945 0.992 0.008
#> round_ERR2585259 1 0.0938 0.944 0.988 0.012
#> round_ERR2585247 1 0.0672 0.946 0.992 0.008
#> round_ERR2585241 1 0.0938 0.944 0.988 0.012
#> round_ERR2585263 1 0.0376 0.946 0.996 0.004
#> round_ERR2585264 1 0.9522 0.281 0.628 0.372
#> round_ERR2585233 1 0.0376 0.946 0.996 0.004
#> round_ERR2585223 1 0.0938 0.944 0.988 0.012
#> round_ERR2585234 1 0.0000 0.946 1.000 0.000
#> round_ERR2585222 1 0.0000 0.946 1.000 0.000
#> round_ERR2585228 1 0.0938 0.944 0.988 0.012
#> round_ERR2585248 1 0.5842 0.829 0.860 0.140
#> round_ERR2585240 1 0.0376 0.946 0.996 0.004
#> round_ERR2585270 1 0.0672 0.945 0.992 0.008
#> round_ERR2585232 1 0.0376 0.946 0.996 0.004
#> aberrant_ERR2585341 1 0.2423 0.939 0.960 0.040
#> aberrant_ERR2585355 1 0.1843 0.942 0.972 0.028
#> round_ERR2585227 1 0.0672 0.946 0.992 0.008
#> aberrant_ERR2585351 1 0.4298 0.914 0.912 0.088
#> round_ERR2585269 1 0.2423 0.931 0.960 0.040
#> aberrant_ERR2585357 1 0.1414 0.944 0.980 0.020
#> aberrant_ERR2585350 1 0.1843 0.942 0.972 0.028
#> round_ERR2585250 1 0.0938 0.946 0.988 0.012
#> round_ERR2585245 1 0.3733 0.908 0.928 0.072
#> aberrant_ERR2585353 1 0.5737 0.874 0.864 0.136
#> round_ERR2585258 1 0.1184 0.946 0.984 0.016
#> aberrant_ERR2585354 1 0.4298 0.914 0.912 0.088
#> round_ERR2585249 1 0.2778 0.932 0.952 0.048
#> round_ERR2585268 1 0.0376 0.946 0.996 0.004
#> aberrant_ERR2585356 1 0.5519 0.881 0.872 0.128
#> round_ERR2585266 1 0.0000 0.946 1.000 0.000
#> round_ERR2585231 1 0.2778 0.927 0.952 0.048
#> round_ERR2585230 1 0.0938 0.944 0.988 0.012
#> round_ERR2585267 1 0.0938 0.944 0.988 0.012
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.6079 0.5364 0.388 0.612 0.000
#> aberrant_ERR2585338 1 0.1031 0.7801 0.976 0.024 0.000
#> aberrant_ERR2585325 2 0.6215 0.4766 0.428 0.572 0.000
#> aberrant_ERR2585283 3 0.0000 0.8124 0.000 0.000 1.000
#> aberrant_ERR2585343 2 0.5327 0.7000 0.272 0.728 0.000
#> aberrant_ERR2585329 1 0.5810 0.3037 0.664 0.336 0.000
#> aberrant_ERR2585317 1 0.5810 0.3036 0.664 0.336 0.000
#> aberrant_ERR2585339 1 0.4974 0.5422 0.764 0.236 0.000
#> aberrant_ERR2585335 1 0.6026 0.1549 0.624 0.376 0.000
#> aberrant_ERR2585287 3 0.8396 0.5392 0.180 0.196 0.624
#> aberrant_ERR2585321 1 0.6225 -0.1489 0.568 0.432 0.000
#> aberrant_ERR2585297 1 0.0424 0.7819 0.992 0.008 0.000
#> aberrant_ERR2585337 1 0.5560 0.3990 0.700 0.300 0.000
#> aberrant_ERR2585319 2 0.5650 0.7523 0.312 0.688 0.000
#> aberrant_ERR2585315 1 0.5706 0.3587 0.680 0.320 0.000
#> aberrant_ERR2585336 1 0.5810 0.3053 0.664 0.336 0.000
#> aberrant_ERR2585307 1 0.4002 0.6654 0.840 0.160 0.000
#> aberrant_ERR2585301 1 0.3619 0.6929 0.864 0.136 0.000
#> aberrant_ERR2585326 1 0.5760 0.3287 0.672 0.328 0.000
#> aberrant_ERR2585331 1 0.0747 0.7829 0.984 0.016 0.000
#> aberrant_ERR2585346 3 0.0892 0.8049 0.000 0.020 0.980
#> aberrant_ERR2585314 1 0.5785 0.3078 0.668 0.332 0.000
#> aberrant_ERR2585298 1 0.0000 0.7845 1.000 0.000 0.000
#> aberrant_ERR2585345 1 0.5810 0.3033 0.664 0.336 0.000
#> aberrant_ERR2585299 1 0.1163 0.7821 0.972 0.028 0.000
#> aberrant_ERR2585309 1 0.0424 0.7819 0.992 0.008 0.000
#> aberrant_ERR2585303 1 0.4452 0.6204 0.808 0.192 0.000
#> aberrant_ERR2585313 1 0.6008 0.1710 0.628 0.372 0.000
#> aberrant_ERR2585318 2 0.6045 0.7361 0.380 0.620 0.000
#> aberrant_ERR2585328 1 0.5431 0.4464 0.716 0.284 0.000
#> aberrant_ERR2585330 1 0.6274 -0.2844 0.544 0.456 0.000
#> aberrant_ERR2585293 3 0.0000 0.8124 0.000 0.000 1.000
#> aberrant_ERR2585342 2 0.5882 0.7571 0.348 0.652 0.000
#> aberrant_ERR2585348 1 0.6126 -0.1588 0.600 0.400 0.000
#> aberrant_ERR2585352 1 0.6045 0.1155 0.620 0.380 0.000
#> aberrant_ERR2585308 1 0.4399 0.7020 0.864 0.044 0.092
#> aberrant_ERR2585349 1 0.3752 0.6700 0.856 0.144 0.000
#> aberrant_ERR2585316 1 0.6586 0.4438 0.728 0.216 0.056
#> aberrant_ERR2585306 1 0.4353 0.6660 0.836 0.156 0.008
#> aberrant_ERR2585324 2 0.5905 0.7424 0.352 0.648 0.000
#> aberrant_ERR2585310 1 0.2625 0.7412 0.916 0.084 0.000
#> aberrant_ERR2585296 1 0.0237 0.7849 0.996 0.004 0.000
#> aberrant_ERR2585275 3 0.5929 0.1061 0.320 0.004 0.676
#> aberrant_ERR2585311 1 0.6180 -0.0436 0.584 0.416 0.000
#> aberrant_ERR2585292 3 0.0000 0.8124 0.000 0.000 1.000
#> aberrant_ERR2585282 1 0.5785 0.1837 0.668 0.332 0.000
#> aberrant_ERR2585305 1 0.5988 0.1880 0.632 0.368 0.000
#> aberrant_ERR2585278 1 0.5098 0.5053 0.752 0.248 0.000
#> aberrant_ERR2585347 1 0.6148 0.1629 0.640 0.356 0.004
#> aberrant_ERR2585332 2 0.5650 0.7552 0.312 0.688 0.000
#> aberrant_ERR2585280 1 0.5678 0.3665 0.684 0.316 0.000
#> aberrant_ERR2585304 1 0.3412 0.7018 0.876 0.124 0.000
#> aberrant_ERR2585322 1 0.3879 0.6749 0.848 0.152 0.000
#> aberrant_ERR2585279 1 0.2796 0.7340 0.908 0.092 0.000
#> aberrant_ERR2585277 1 0.4654 0.5989 0.792 0.208 0.000
#> aberrant_ERR2585295 1 0.4555 0.6155 0.800 0.200 0.000
#> aberrant_ERR2585333 2 0.6280 0.5840 0.460 0.540 0.000
#> aberrant_ERR2585285 2 0.6180 0.6792 0.416 0.584 0.000
#> aberrant_ERR2585286 1 0.5363 0.4642 0.724 0.276 0.000
#> aberrant_ERR2585294 1 0.4062 0.6615 0.836 0.164 0.000
#> aberrant_ERR2585300 1 0.6667 0.1007 0.616 0.368 0.016
#> aberrant_ERR2585334 1 0.0000 0.7845 1.000 0.000 0.000
#> aberrant_ERR2585361 2 0.6309 0.4451 0.500 0.500 0.000
#> aberrant_ERR2585372 2 0.5948 0.7524 0.360 0.640 0.000
#> round_ERR2585217 1 0.0237 0.7834 0.996 0.004 0.000
#> round_ERR2585205 1 0.0424 0.7819 0.992 0.008 0.000
#> round_ERR2585214 1 0.0000 0.7845 1.000 0.000 0.000
#> round_ERR2585202 1 0.0424 0.7847 0.992 0.008 0.000
#> aberrant_ERR2585367 1 0.2711 0.7292 0.912 0.088 0.000
#> round_ERR2585220 1 0.0237 0.7834 0.996 0.004 0.000
#> round_ERR2585238 1 0.1643 0.7750 0.956 0.044 0.000
#> aberrant_ERR2585276 1 0.5926 0.2376 0.644 0.356 0.000
#> round_ERR2585218 1 0.0237 0.7834 0.996 0.004 0.000
#> aberrant_ERR2585363 1 0.5948 0.1789 0.640 0.360 0.000
#> round_ERR2585201 1 0.0000 0.7845 1.000 0.000 0.000
#> round_ERR2585210 1 0.0424 0.7819 0.992 0.008 0.000
#> aberrant_ERR2585362 1 0.5678 0.3638 0.684 0.316 0.000
#> aberrant_ERR2585360 1 0.5948 0.2358 0.640 0.360 0.000
#> round_ERR2585209 1 0.0000 0.7845 1.000 0.000 0.000
#> round_ERR2585242 1 0.0237 0.7837 0.996 0.004 0.000
#> round_ERR2585216 1 0.0237 0.7834 0.996 0.004 0.000
#> round_ERR2585219 1 0.0424 0.7819 0.992 0.008 0.000
#> round_ERR2585237 1 0.0000 0.7845 1.000 0.000 0.000
#> round_ERR2585198 1 0.0000 0.7845 1.000 0.000 0.000
#> round_ERR2585211 1 0.0424 0.7819 0.992 0.008 0.000
#> round_ERR2585206 1 0.0592 0.7832 0.988 0.012 0.000
#> aberrant_ERR2585281 1 0.2448 0.7503 0.924 0.076 0.000
#> round_ERR2585212 1 0.0592 0.7851 0.988 0.012 0.000
#> round_ERR2585221 1 0.0747 0.7839 0.984 0.016 0.000
#> round_ERR2585243 1 0.0237 0.7834 0.996 0.004 0.000
#> round_ERR2585204 1 0.0000 0.7845 1.000 0.000 0.000
#> round_ERR2585213 1 0.0000 0.7845 1.000 0.000 0.000
#> aberrant_ERR2585373 2 0.6008 0.7440 0.372 0.628 0.000
#> aberrant_ERR2585358 2 0.4749 0.2454 0.116 0.844 0.040
#> aberrant_ERR2585365 1 0.5835 0.2865 0.660 0.340 0.000
#> aberrant_ERR2585359 2 0.8349 0.3854 0.220 0.624 0.156
#> aberrant_ERR2585370 1 0.5733 0.3390 0.676 0.324 0.000
#> round_ERR2585215 1 0.0848 0.7802 0.984 0.008 0.008
#> round_ERR2585262 1 0.0000 0.7845 1.000 0.000 0.000
#> round_ERR2585199 1 0.0000 0.7845 1.000 0.000 0.000
#> aberrant_ERR2585369 1 0.5650 0.3638 0.688 0.312 0.000
#> round_ERR2585208 1 0.0424 0.7819 0.992 0.008 0.000
#> round_ERR2585252 1 0.0424 0.7819 0.992 0.008 0.000
#> round_ERR2585236 1 0.0829 0.7824 0.984 0.012 0.004
#> aberrant_ERR2585284 3 0.0000 0.8124 0.000 0.000 1.000
#> round_ERR2585224 1 0.6678 0.4792 0.728 0.064 0.208
#> round_ERR2585260 1 0.0892 0.7835 0.980 0.020 0.000
#> round_ERR2585229 1 0.1031 0.7834 0.976 0.024 0.000
#> aberrant_ERR2585364 3 0.8676 0.5246 0.116 0.352 0.532
#> round_ERR2585253 1 0.1950 0.7632 0.952 0.008 0.040
#> aberrant_ERR2585368 1 0.0424 0.7841 0.992 0.008 0.000
#> aberrant_ERR2585371 1 0.0237 0.7846 0.996 0.004 0.000
#> round_ERR2585239 1 0.0424 0.7819 0.992 0.008 0.000
#> round_ERR2585273 1 0.0892 0.7840 0.980 0.020 0.000
#> round_ERR2585256 1 0.0237 0.7834 0.996 0.004 0.000
#> round_ERR2585272 1 0.0424 0.7819 0.992 0.008 0.000
#> round_ERR2585246 1 0.1964 0.7689 0.944 0.056 0.000
#> round_ERR2585261 1 0.0000 0.7845 1.000 0.000 0.000
#> round_ERR2585254 1 0.0237 0.7847 0.996 0.004 0.000
#> round_ERR2585225 1 0.0000 0.7845 1.000 0.000 0.000
#> round_ERR2585235 1 0.1031 0.7835 0.976 0.024 0.000
#> round_ERR2585271 1 0.0424 0.7819 0.992 0.008 0.000
#> round_ERR2585251 1 0.0592 0.7834 0.988 0.012 0.000
#> round_ERR2585255 1 0.0000 0.7845 1.000 0.000 0.000
#> round_ERR2585257 1 0.0000 0.7845 1.000 0.000 0.000
#> round_ERR2585226 1 0.2261 0.7503 0.932 0.068 0.000
#> round_ERR2585265 1 0.1411 0.7774 0.964 0.036 0.000
#> round_ERR2585259 1 0.0424 0.7819 0.992 0.008 0.000
#> round_ERR2585247 1 0.0592 0.7834 0.988 0.012 0.000
#> round_ERR2585241 1 0.0424 0.7819 0.992 0.008 0.000
#> round_ERR2585263 1 0.0237 0.7848 0.996 0.004 0.000
#> round_ERR2585264 1 0.6682 -0.2478 0.504 0.008 0.488
#> round_ERR2585233 1 0.0000 0.7845 1.000 0.000 0.000
#> round_ERR2585223 1 0.0424 0.7819 0.992 0.008 0.000
#> round_ERR2585234 1 0.0237 0.7847 0.996 0.004 0.000
#> round_ERR2585222 1 0.0000 0.7845 1.000 0.000 0.000
#> round_ERR2585228 1 0.0424 0.7819 0.992 0.008 0.000
#> round_ERR2585248 1 0.5443 0.4437 0.736 0.004 0.260
#> round_ERR2585240 1 0.0000 0.7845 1.000 0.000 0.000
#> round_ERR2585270 1 0.1411 0.7782 0.964 0.036 0.000
#> round_ERR2585232 1 0.0000 0.7845 1.000 0.000 0.000
#> aberrant_ERR2585341 1 0.3752 0.6736 0.856 0.144 0.000
#> aberrant_ERR2585355 1 0.5529 0.4134 0.704 0.296 0.000
#> round_ERR2585227 1 0.1031 0.7822 0.976 0.024 0.000
#> aberrant_ERR2585351 1 0.5948 0.2199 0.640 0.360 0.000
#> round_ERR2585269 1 0.2280 0.7533 0.940 0.008 0.052
#> aberrant_ERR2585357 1 0.5810 0.3036 0.664 0.336 0.000
#> aberrant_ERR2585350 1 0.5733 0.3401 0.676 0.324 0.000
#> round_ERR2585250 1 0.0747 0.7846 0.984 0.016 0.000
#> round_ERR2585245 1 0.4861 0.5790 0.800 0.008 0.192
#> aberrant_ERR2585353 2 0.6309 0.4564 0.496 0.504 0.000
#> round_ERR2585258 1 0.1163 0.7825 0.972 0.028 0.000
#> aberrant_ERR2585354 1 0.5560 0.3962 0.700 0.300 0.000
#> round_ERR2585249 1 0.2749 0.7444 0.924 0.012 0.064
#> round_ERR2585268 1 0.3192 0.7143 0.888 0.112 0.000
#> aberrant_ERR2585356 1 0.6495 -0.4050 0.536 0.460 0.004
#> round_ERR2585266 1 0.0000 0.7845 1.000 0.000 0.000
#> round_ERR2585231 1 0.2584 0.7425 0.928 0.008 0.064
#> round_ERR2585230 1 0.0424 0.7819 0.992 0.008 0.000
#> round_ERR2585267 1 0.0424 0.7819 0.992 0.008 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 3 0.3945 -0.0648 0.004 0.216 0.780 0.000
#> aberrant_ERR2585338 1 0.0817 0.8159 0.976 0.000 0.024 0.000
#> aberrant_ERR2585325 3 0.4123 -0.0664 0.008 0.220 0.772 0.000
#> aberrant_ERR2585283 4 0.0000 0.7711 0.000 0.000 0.000 1.000
#> aberrant_ERR2585343 2 0.4877 0.3613 0.044 0.752 0.204 0.000
#> aberrant_ERR2585329 1 0.4585 0.6289 0.668 0.000 0.332 0.000
#> aberrant_ERR2585317 1 0.4585 0.6274 0.668 0.000 0.332 0.000
#> aberrant_ERR2585339 1 0.4088 0.7341 0.764 0.004 0.232 0.000
#> aberrant_ERR2585335 1 0.4950 0.5765 0.620 0.004 0.376 0.000
#> aberrant_ERR2585287 4 0.8463 0.3916 0.076 0.116 0.360 0.448
#> aberrant_ERR2585321 1 0.6617 0.3937 0.532 0.088 0.380 0.000
#> aberrant_ERR2585297 1 0.2011 0.8084 0.920 0.080 0.000 0.000
#> aberrant_ERR2585337 1 0.4331 0.6763 0.712 0.000 0.288 0.000
#> aberrant_ERR2585319 2 0.4877 0.3929 0.008 0.664 0.328 0.000
#> aberrant_ERR2585315 1 0.5075 0.6175 0.644 0.012 0.344 0.000
#> aberrant_ERR2585336 1 0.4605 0.6231 0.664 0.000 0.336 0.000
#> aberrant_ERR2585307 1 0.3569 0.7471 0.804 0.000 0.196 0.000
#> aberrant_ERR2585301 1 0.3681 0.7537 0.816 0.008 0.176 0.000
#> aberrant_ERR2585326 1 0.4564 0.6325 0.672 0.000 0.328 0.000
#> aberrant_ERR2585331 1 0.0592 0.8150 0.984 0.000 0.016 0.000
#> aberrant_ERR2585346 4 0.1411 0.7421 0.000 0.020 0.020 0.960
#> aberrant_ERR2585314 1 0.4522 0.6382 0.680 0.000 0.320 0.000
#> aberrant_ERR2585298 1 0.0000 0.8130 1.000 0.000 0.000 0.000
#> aberrant_ERR2585345 1 0.4585 0.6288 0.668 0.000 0.332 0.000
#> aberrant_ERR2585299 1 0.2773 0.8149 0.900 0.072 0.028 0.000
#> aberrant_ERR2585309 1 0.2281 0.8028 0.904 0.096 0.000 0.000
#> aberrant_ERR2585303 1 0.3764 0.7303 0.784 0.000 0.216 0.000
#> aberrant_ERR2585313 1 0.4936 0.5797 0.624 0.004 0.372 0.000
#> aberrant_ERR2585318 3 0.7800 -0.2847 0.248 0.372 0.380 0.000
#> aberrant_ERR2585328 1 0.4483 0.6885 0.712 0.004 0.284 0.000
#> aberrant_ERR2585330 1 0.6972 0.3492 0.520 0.124 0.356 0.000
#> aberrant_ERR2585293 4 0.0000 0.7711 0.000 0.000 0.000 1.000
#> aberrant_ERR2585342 2 0.7136 0.3246 0.136 0.488 0.376 0.000
#> aberrant_ERR2585348 1 0.7595 -0.1869 0.428 0.200 0.372 0.000
#> aberrant_ERR2585352 1 0.6071 0.5331 0.612 0.064 0.324 0.000
#> aberrant_ERR2585308 1 0.4802 0.7817 0.800 0.132 0.052 0.016
#> aberrant_ERR2585349 1 0.2530 0.7973 0.888 0.000 0.112 0.000
#> aberrant_ERR2585316 1 0.6667 0.6299 0.684 0.140 0.144 0.032
#> aberrant_ERR2585306 1 0.5011 0.7280 0.748 0.040 0.208 0.004
#> aberrant_ERR2585324 2 0.5489 0.4117 0.040 0.664 0.296 0.000
#> aberrant_ERR2585310 1 0.2589 0.7901 0.884 0.000 0.116 0.000
#> aberrant_ERR2585296 1 0.0188 0.8138 0.996 0.000 0.004 0.000
#> aberrant_ERR2585275 4 0.4699 -0.0310 0.320 0.004 0.000 0.676
#> aberrant_ERR2585311 1 0.6055 0.5037 0.576 0.052 0.372 0.000
#> aberrant_ERR2585292 4 0.0000 0.7711 0.000 0.000 0.000 1.000
#> aberrant_ERR2585282 1 0.6917 0.2951 0.568 0.288 0.144 0.000
#> aberrant_ERR2585305 1 0.4920 0.5917 0.628 0.004 0.368 0.000
#> aberrant_ERR2585278 1 0.3801 0.7384 0.780 0.000 0.220 0.000
#> aberrant_ERR2585347 1 0.6675 0.5293 0.612 0.116 0.268 0.004
#> aberrant_ERR2585332 2 0.7199 0.2636 0.232 0.552 0.216 0.000
#> aberrant_ERR2585280 1 0.6370 0.5795 0.620 0.100 0.280 0.000
#> aberrant_ERR2585304 1 0.3219 0.7615 0.836 0.000 0.164 0.000
#> aberrant_ERR2585322 1 0.3528 0.7480 0.808 0.000 0.192 0.000
#> aberrant_ERR2585279 1 0.2469 0.7935 0.892 0.000 0.108 0.000
#> aberrant_ERR2585277 1 0.3837 0.7351 0.776 0.000 0.224 0.000
#> aberrant_ERR2585295 1 0.4868 0.7277 0.748 0.040 0.212 0.000
#> aberrant_ERR2585333 1 0.7723 -0.0805 0.420 0.232 0.348 0.000
#> aberrant_ERR2585285 3 0.7883 -0.1684 0.328 0.292 0.380 0.000
#> aberrant_ERR2585286 1 0.4304 0.6841 0.716 0.000 0.284 0.000
#> aberrant_ERR2585294 1 0.3528 0.7522 0.808 0.000 0.192 0.000
#> aberrant_ERR2585300 1 0.7275 0.4929 0.560 0.136 0.292 0.012
#> aberrant_ERR2585334 1 0.0000 0.8130 1.000 0.000 0.000 0.000
#> aberrant_ERR2585361 1 0.7740 -0.0732 0.432 0.248 0.320 0.000
#> aberrant_ERR2585372 2 0.7415 0.2684 0.216 0.512 0.272 0.000
#> round_ERR2585217 1 0.0336 0.8125 0.992 0.008 0.000 0.000
#> round_ERR2585205 1 0.0817 0.8141 0.976 0.024 0.000 0.000
#> round_ERR2585214 1 0.0000 0.8130 1.000 0.000 0.000 0.000
#> round_ERR2585202 1 0.0336 0.8144 0.992 0.000 0.008 0.000
#> aberrant_ERR2585367 1 0.2984 0.7996 0.888 0.028 0.084 0.000
#> round_ERR2585220 1 0.1211 0.8132 0.960 0.040 0.000 0.000
#> round_ERR2585238 1 0.3787 0.7958 0.840 0.124 0.036 0.000
#> aberrant_ERR2585276 1 0.5577 0.6242 0.636 0.036 0.328 0.000
#> round_ERR2585218 1 0.2469 0.8010 0.892 0.108 0.000 0.000
#> aberrant_ERR2585363 1 0.6016 0.5791 0.632 0.068 0.300 0.000
#> round_ERR2585201 1 0.0000 0.8130 1.000 0.000 0.000 0.000
#> round_ERR2585210 1 0.0921 0.8130 0.972 0.028 0.000 0.000
#> aberrant_ERR2585362 1 0.4677 0.6482 0.680 0.004 0.316 0.000
#> aberrant_ERR2585360 1 0.5289 0.6165 0.636 0.020 0.344 0.000
#> round_ERR2585209 1 0.0188 0.8131 0.996 0.004 0.000 0.000
#> round_ERR2585242 1 0.0188 0.8131 0.996 0.004 0.000 0.000
#> round_ERR2585216 1 0.0188 0.8126 0.996 0.004 0.000 0.000
#> round_ERR2585219 1 0.1557 0.8116 0.944 0.056 0.000 0.000
#> round_ERR2585237 1 0.0000 0.8130 1.000 0.000 0.000 0.000
#> round_ERR2585198 1 0.0000 0.8130 1.000 0.000 0.000 0.000
#> round_ERR2585211 1 0.1792 0.8100 0.932 0.068 0.000 0.000
#> round_ERR2585206 1 0.2944 0.7914 0.868 0.128 0.004 0.000
#> aberrant_ERR2585281 1 0.2011 0.8083 0.920 0.000 0.080 0.000
#> round_ERR2585212 1 0.2125 0.8122 0.920 0.076 0.004 0.000
#> round_ERR2585221 1 0.3032 0.7945 0.868 0.124 0.008 0.000
#> round_ERR2585243 1 0.1022 0.8148 0.968 0.032 0.000 0.000
#> round_ERR2585204 1 0.0000 0.8130 1.000 0.000 0.000 0.000
#> round_ERR2585213 1 0.0000 0.8130 1.000 0.000 0.000 0.000
#> aberrant_ERR2585373 2 0.7554 0.1979 0.192 0.432 0.376 0.000
#> aberrant_ERR2585358 2 0.3863 0.3011 0.004 0.812 0.176 0.008
#> aberrant_ERR2585365 1 0.5254 0.6359 0.672 0.028 0.300 0.000
#> aberrant_ERR2585359 2 0.8869 0.3317 0.156 0.452 0.300 0.092
#> aberrant_ERR2585370 1 0.4522 0.6421 0.680 0.000 0.320 0.000
#> round_ERR2585215 1 0.2944 0.7899 0.868 0.128 0.000 0.004
#> round_ERR2585262 1 0.0000 0.8130 1.000 0.000 0.000 0.000
#> round_ERR2585199 1 0.0000 0.8130 1.000 0.000 0.000 0.000
#> aberrant_ERR2585369 1 0.4356 0.6784 0.708 0.000 0.292 0.000
#> round_ERR2585208 1 0.2469 0.8012 0.892 0.108 0.000 0.000
#> round_ERR2585252 1 0.2760 0.7907 0.872 0.128 0.000 0.000
#> round_ERR2585236 1 0.2831 0.7973 0.876 0.120 0.004 0.000
#> aberrant_ERR2585284 4 0.0000 0.7711 0.000 0.000 0.000 1.000
#> round_ERR2585224 1 0.6669 0.7188 0.704 0.132 0.084 0.080
#> round_ERR2585260 1 0.2522 0.8138 0.908 0.076 0.016 0.000
#> round_ERR2585229 1 0.2909 0.8096 0.888 0.092 0.020 0.000
#> aberrant_ERR2585364 2 0.5856 -0.1951 0.000 0.556 0.036 0.408
#> round_ERR2585253 1 0.3443 0.7838 0.848 0.136 0.000 0.016
#> aberrant_ERR2585368 1 0.0524 0.8140 0.988 0.004 0.008 0.000
#> aberrant_ERR2585371 1 0.0376 0.8134 0.992 0.004 0.004 0.000
#> round_ERR2585239 1 0.2011 0.8094 0.920 0.080 0.000 0.000
#> round_ERR2585273 1 0.2867 0.8037 0.884 0.104 0.012 0.000
#> round_ERR2585256 1 0.0188 0.8126 0.996 0.004 0.000 0.000
#> round_ERR2585272 1 0.1022 0.8130 0.968 0.032 0.000 0.000
#> round_ERR2585246 1 0.4127 0.7930 0.824 0.124 0.052 0.000
#> round_ERR2585261 1 0.0000 0.8130 1.000 0.000 0.000 0.000
#> round_ERR2585254 1 0.0376 0.8142 0.992 0.004 0.004 0.000
#> round_ERR2585225 1 0.0000 0.8130 1.000 0.000 0.000 0.000
#> round_ERR2585235 1 0.2988 0.8004 0.876 0.112 0.012 0.000
#> round_ERR2585271 1 0.1302 0.8138 0.956 0.044 0.000 0.000
#> round_ERR2585251 1 0.0895 0.8149 0.976 0.020 0.004 0.000
#> round_ERR2585255 1 0.0000 0.8130 1.000 0.000 0.000 0.000
#> round_ERR2585257 1 0.0000 0.8130 1.000 0.000 0.000 0.000
#> round_ERR2585226 1 0.2011 0.8015 0.920 0.000 0.080 0.000
#> round_ERR2585265 1 0.2943 0.8128 0.892 0.076 0.032 0.000
#> round_ERR2585259 1 0.1118 0.8145 0.964 0.036 0.000 0.000
#> round_ERR2585247 1 0.2654 0.7998 0.888 0.108 0.004 0.000
#> round_ERR2585241 1 0.2408 0.8022 0.896 0.104 0.000 0.000
#> round_ERR2585263 1 0.0524 0.8151 0.988 0.008 0.004 0.000
#> round_ERR2585264 1 0.7043 0.2589 0.504 0.128 0.000 0.368
#> round_ERR2585233 1 0.0000 0.8130 1.000 0.000 0.000 0.000
#> round_ERR2585223 1 0.2281 0.8033 0.904 0.096 0.000 0.000
#> round_ERR2585234 1 0.0188 0.8136 0.996 0.000 0.004 0.000
#> round_ERR2585222 1 0.0000 0.8130 1.000 0.000 0.000 0.000
#> round_ERR2585228 1 0.2281 0.8022 0.904 0.096 0.000 0.000
#> round_ERR2585248 1 0.5395 0.6951 0.736 0.092 0.000 0.172
#> round_ERR2585240 1 0.0000 0.8130 1.000 0.000 0.000 0.000
#> round_ERR2585270 1 0.2408 0.8184 0.920 0.044 0.036 0.000
#> round_ERR2585232 1 0.0000 0.8130 1.000 0.000 0.000 0.000
#> aberrant_ERR2585341 1 0.3443 0.7802 0.848 0.016 0.136 0.000
#> aberrant_ERR2585355 1 0.4331 0.6768 0.712 0.000 0.288 0.000
#> round_ERR2585227 1 0.1297 0.8183 0.964 0.016 0.020 0.000
#> aberrant_ERR2585351 1 0.4746 0.5897 0.632 0.000 0.368 0.000
#> round_ERR2585269 1 0.3271 0.7866 0.856 0.132 0.000 0.012
#> aberrant_ERR2585357 1 0.4585 0.6274 0.668 0.000 0.332 0.000
#> aberrant_ERR2585350 1 0.4454 0.6567 0.692 0.000 0.308 0.000
#> round_ERR2585250 1 0.1510 0.8186 0.956 0.028 0.016 0.000
#> round_ERR2585245 1 0.4552 0.7592 0.800 0.128 0.000 0.072
#> aberrant_ERR2585353 1 0.7581 0.0529 0.440 0.200 0.360 0.000
#> round_ERR2585258 1 0.3160 0.8031 0.872 0.108 0.020 0.000
#> aberrant_ERR2585354 1 0.5664 0.6883 0.696 0.076 0.228 0.000
#> round_ERR2585249 1 0.3606 0.7836 0.844 0.132 0.000 0.024
#> round_ERR2585268 1 0.3157 0.7753 0.852 0.004 0.144 0.000
#> aberrant_ERR2585356 2 0.7815 0.1105 0.328 0.444 0.224 0.004
#> round_ERR2585266 1 0.0000 0.8130 1.000 0.000 0.000 0.000
#> round_ERR2585231 1 0.3308 0.7958 0.872 0.092 0.000 0.036
#> round_ERR2585230 1 0.1389 0.8133 0.952 0.048 0.000 0.000
#> round_ERR2585267 1 0.2401 0.8072 0.904 0.092 0.004 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 3 0.4171 0.7250 0.000 0.104 0.784 0.000 0.112
#> aberrant_ERR2585338 1 0.0794 0.7665 0.972 0.028 0.000 0.000 0.000
#> aberrant_ERR2585325 3 0.4149 0.7242 0.000 0.088 0.784 0.000 0.128
#> aberrant_ERR2585283 4 0.0000 0.6850 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585343 5 0.4612 0.1219 0.044 0.112 0.060 0.000 0.784
#> aberrant_ERR2585329 1 0.4126 0.5574 0.620 0.380 0.000 0.000 0.000
#> aberrant_ERR2585317 1 0.4138 0.5513 0.616 0.384 0.000 0.000 0.000
#> aberrant_ERR2585339 1 0.3728 0.6950 0.748 0.244 0.000 0.000 0.008
#> aberrant_ERR2585335 1 0.4517 0.5356 0.600 0.388 0.000 0.000 0.012
#> aberrant_ERR2585287 3 0.6747 0.2275 0.076 0.008 0.452 0.424 0.040
#> aberrant_ERR2585321 1 0.5778 0.3955 0.528 0.376 0.000 0.000 0.096
#> aberrant_ERR2585297 1 0.1908 0.7603 0.908 0.000 0.092 0.000 0.000
#> aberrant_ERR2585337 1 0.3949 0.6048 0.668 0.332 0.000 0.000 0.000
#> aberrant_ERR2585319 5 0.1544 0.0533 0.000 0.068 0.000 0.000 0.932
#> aberrant_ERR2585315 1 0.4437 0.6245 0.664 0.316 0.000 0.000 0.020
#> aberrant_ERR2585336 1 0.4126 0.5586 0.620 0.380 0.000 0.000 0.000
#> aberrant_ERR2585307 1 0.2966 0.7175 0.816 0.184 0.000 0.000 0.000
#> aberrant_ERR2585301 1 0.3224 0.7241 0.824 0.160 0.000 0.000 0.016
#> aberrant_ERR2585326 1 0.4074 0.5696 0.636 0.364 0.000 0.000 0.000
#> aberrant_ERR2585331 1 0.0609 0.7637 0.980 0.020 0.000 0.000 0.000
#> aberrant_ERR2585346 4 0.1270 0.6182 0.000 0.000 0.000 0.948 0.052
#> aberrant_ERR2585314 1 0.4101 0.5610 0.628 0.372 0.000 0.000 0.000
#> aberrant_ERR2585298 1 0.0000 0.7597 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585345 1 0.4126 0.5578 0.620 0.380 0.000 0.000 0.000
#> aberrant_ERR2585299 1 0.2773 0.7626 0.868 0.020 0.112 0.000 0.000
#> aberrant_ERR2585309 1 0.2516 0.7484 0.860 0.000 0.140 0.000 0.000
#> aberrant_ERR2585303 1 0.3210 0.6898 0.788 0.212 0.000 0.000 0.000
#> aberrant_ERR2585313 1 0.4517 0.5355 0.600 0.388 0.000 0.000 0.012
#> aberrant_ERR2585318 5 0.6647 0.1981 0.224 0.384 0.000 0.000 0.392
#> aberrant_ERR2585328 1 0.3906 0.6541 0.704 0.292 0.000 0.000 0.004
#> aberrant_ERR2585330 1 0.6540 0.1674 0.472 0.300 0.000 0.000 0.228
#> aberrant_ERR2585293 4 0.0000 0.6850 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585342 5 0.6077 0.3744 0.124 0.396 0.000 0.000 0.480
#> aberrant_ERR2585348 1 0.8028 -0.2548 0.416 0.124 0.272 0.000 0.188
#> aberrant_ERR2585352 1 0.5396 0.4406 0.560 0.376 0.000 0.000 0.064
#> aberrant_ERR2585308 1 0.3829 0.7218 0.776 0.028 0.196 0.000 0.000
#> aberrant_ERR2585349 1 0.2813 0.7300 0.832 0.168 0.000 0.000 0.000
#> aberrant_ERR2585316 1 0.6131 0.5264 0.644 0.092 0.032 0.008 0.224
#> aberrant_ERR2585306 1 0.4922 0.6804 0.732 0.156 0.008 0.000 0.104
#> aberrant_ERR2585324 5 0.1638 0.0554 0.004 0.064 0.000 0.000 0.932
#> aberrant_ERR2585310 1 0.2127 0.7508 0.892 0.108 0.000 0.000 0.000
#> aberrant_ERR2585296 1 0.0162 0.7609 0.996 0.004 0.000 0.000 0.000
#> aberrant_ERR2585275 4 0.4200 -0.1582 0.320 0.000 0.004 0.672 0.004
#> aberrant_ERR2585311 1 0.5274 0.4885 0.572 0.372 0.000 0.000 0.056
#> aberrant_ERR2585292 4 0.0000 0.6850 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585282 5 0.5499 -0.1044 0.472 0.028 0.020 0.000 0.480
#> aberrant_ERR2585305 1 0.4651 0.5515 0.608 0.372 0.000 0.000 0.020
#> aberrant_ERR2585278 1 0.3684 0.6590 0.720 0.280 0.000 0.000 0.000
#> aberrant_ERR2585347 1 0.6417 0.4727 0.588 0.268 0.044 0.000 0.100
#> aberrant_ERR2585332 5 0.6826 0.3153 0.232 0.060 0.136 0.000 0.572
#> aberrant_ERR2585280 1 0.6036 0.4374 0.576 0.140 0.004 0.000 0.280
#> aberrant_ERR2585304 1 0.2561 0.7295 0.856 0.144 0.000 0.000 0.000
#> aberrant_ERR2585322 1 0.2852 0.7227 0.828 0.172 0.000 0.000 0.000
#> aberrant_ERR2585279 1 0.1965 0.7535 0.904 0.096 0.000 0.000 0.000
#> aberrant_ERR2585277 1 0.3452 0.6945 0.756 0.244 0.000 0.000 0.000
#> aberrant_ERR2585295 1 0.4840 0.6721 0.724 0.124 0.000 0.000 0.152
#> aberrant_ERR2585333 1 0.6888 -0.1405 0.400 0.276 0.004 0.000 0.320
#> aberrant_ERR2585285 2 0.6779 -0.3064 0.324 0.388 0.000 0.000 0.288
#> aberrant_ERR2585286 1 0.3837 0.6400 0.692 0.308 0.000 0.000 0.000
#> aberrant_ERR2585294 1 0.2966 0.7206 0.816 0.184 0.000 0.000 0.000
#> aberrant_ERR2585300 1 0.6965 0.4527 0.552 0.272 0.048 0.008 0.120
#> aberrant_ERR2585334 1 0.0000 0.7597 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585361 1 0.7550 -0.0519 0.420 0.304 0.052 0.000 0.224
#> aberrant_ERR2585372 5 0.6358 0.3681 0.212 0.176 0.020 0.000 0.592
#> round_ERR2585217 1 0.0162 0.7602 0.996 0.004 0.000 0.000 0.000
#> round_ERR2585205 1 0.0510 0.7623 0.984 0.000 0.016 0.000 0.000
#> round_ERR2585214 1 0.0000 0.7597 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585202 1 0.0404 0.7631 0.988 0.012 0.000 0.000 0.000
#> aberrant_ERR2585367 1 0.3216 0.7517 0.856 0.096 0.004 0.000 0.044
#> round_ERR2585220 1 0.1638 0.7662 0.932 0.004 0.064 0.000 0.000
#> round_ERR2585238 1 0.3612 0.7366 0.800 0.028 0.172 0.000 0.000
#> aberrant_ERR2585276 1 0.4772 0.5766 0.624 0.352 0.012 0.000 0.012
#> round_ERR2585218 1 0.2732 0.7414 0.840 0.000 0.160 0.000 0.000
#> aberrant_ERR2585363 1 0.5640 0.4905 0.592 0.304 0.000 0.000 0.104
#> round_ERR2585201 1 0.0000 0.7597 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585210 1 0.0609 0.7628 0.980 0.000 0.020 0.000 0.000
#> aberrant_ERR2585362 1 0.4045 0.5822 0.644 0.356 0.000 0.000 0.000
#> aberrant_ERR2585360 1 0.4701 0.5604 0.612 0.368 0.004 0.000 0.016
#> round_ERR2585209 1 0.0162 0.7602 0.996 0.004 0.000 0.000 0.000
#> round_ERR2585242 1 0.0162 0.7595 0.996 0.000 0.004 0.000 0.000
#> round_ERR2585216 1 0.0000 0.7597 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585219 1 0.1478 0.7641 0.936 0.000 0.064 0.000 0.000
#> round_ERR2585237 1 0.0000 0.7597 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585198 1 0.0000 0.7597 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585211 1 0.1732 0.7637 0.920 0.000 0.080 0.000 0.000
#> round_ERR2585206 1 0.2966 0.7276 0.816 0.000 0.184 0.000 0.000
#> aberrant_ERR2585281 1 0.1671 0.7641 0.924 0.076 0.000 0.000 0.000
#> round_ERR2585212 1 0.2077 0.7652 0.908 0.008 0.084 0.000 0.000
#> round_ERR2585221 1 0.3003 0.7259 0.812 0.000 0.188 0.000 0.000
#> round_ERR2585243 1 0.0703 0.7632 0.976 0.000 0.024 0.000 0.000
#> round_ERR2585204 1 0.0000 0.7597 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585213 1 0.0000 0.7597 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585373 5 0.6428 0.3034 0.176 0.384 0.000 0.000 0.440
#> aberrant_ERR2585358 5 0.3401 -0.0769 0.000 0.064 0.096 0.000 0.840
#> aberrant_ERR2585365 1 0.4696 0.5457 0.616 0.360 0.000 0.000 0.024
#> aberrant_ERR2585359 5 0.8811 0.3229 0.152 0.248 0.132 0.052 0.416
#> aberrant_ERR2585370 1 0.4060 0.5770 0.640 0.360 0.000 0.000 0.000
#> round_ERR2585215 1 0.3048 0.7308 0.820 0.000 0.176 0.004 0.000
#> round_ERR2585262 1 0.0000 0.7597 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585199 1 0.0162 0.7602 0.996 0.004 0.000 0.000 0.000
#> aberrant_ERR2585369 1 0.3913 0.6194 0.676 0.324 0.000 0.000 0.000
#> round_ERR2585208 1 0.2280 0.7563 0.880 0.000 0.120 0.000 0.000
#> round_ERR2585252 1 0.3039 0.7235 0.808 0.000 0.192 0.000 0.000
#> round_ERR2585236 1 0.2648 0.7449 0.848 0.000 0.152 0.000 0.000
#> aberrant_ERR2585284 4 0.0000 0.6850 0.000 0.000 0.000 1.000 0.000
#> round_ERR2585224 1 0.5093 0.6939 0.716 0.068 0.196 0.020 0.000
#> round_ERR2585260 1 0.2304 0.7634 0.892 0.008 0.100 0.000 0.000
#> round_ERR2585229 1 0.2909 0.7537 0.848 0.012 0.140 0.000 0.000
#> aberrant_ERR2585364 5 0.4482 -0.2537 0.000 0.012 0.000 0.376 0.612
#> round_ERR2585253 1 0.3231 0.7193 0.800 0.000 0.196 0.004 0.000
#> aberrant_ERR2585368 2 0.4801 0.2935 0.372 0.604 0.020 0.000 0.004
#> aberrant_ERR2585371 2 0.4813 0.2966 0.376 0.600 0.020 0.000 0.004
#> round_ERR2585239 1 0.2230 0.7578 0.884 0.000 0.116 0.000 0.000
#> round_ERR2585273 1 0.2929 0.7451 0.840 0.008 0.152 0.000 0.000
#> round_ERR2585256 1 0.0000 0.7597 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585272 1 0.1043 0.7655 0.960 0.000 0.040 0.000 0.000
#> round_ERR2585246 1 0.3958 0.7259 0.776 0.040 0.184 0.000 0.000
#> round_ERR2585261 1 0.0000 0.7597 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585254 1 0.0290 0.7615 0.992 0.008 0.000 0.000 0.000
#> round_ERR2585225 1 0.0000 0.7597 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585235 1 0.3048 0.7343 0.820 0.004 0.176 0.000 0.000
#> round_ERR2585271 1 0.1043 0.7646 0.960 0.000 0.040 0.000 0.000
#> round_ERR2585251 1 0.0798 0.7647 0.976 0.008 0.016 0.000 0.000
#> round_ERR2585255 1 0.0162 0.7602 0.996 0.004 0.000 0.000 0.000
#> round_ERR2585257 1 0.0000 0.7597 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585226 1 0.1892 0.7536 0.916 0.080 0.004 0.000 0.000
#> round_ERR2585265 1 0.3051 0.7594 0.852 0.028 0.120 0.000 0.000
#> round_ERR2585259 1 0.1121 0.7652 0.956 0.000 0.044 0.000 0.000
#> round_ERR2585247 1 0.2806 0.7436 0.844 0.004 0.152 0.000 0.000
#> round_ERR2585241 1 0.2424 0.7533 0.868 0.000 0.132 0.000 0.000
#> round_ERR2585263 1 0.0566 0.7645 0.984 0.012 0.004 0.000 0.000
#> round_ERR2585264 1 0.6374 0.2035 0.504 0.000 0.196 0.300 0.000
#> round_ERR2585233 1 0.0000 0.7597 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585223 1 0.2280 0.7554 0.880 0.000 0.120 0.000 0.000
#> round_ERR2585234 1 0.0290 0.7615 0.992 0.008 0.000 0.000 0.000
#> round_ERR2585222 1 0.0000 0.7597 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585228 1 0.2561 0.7456 0.856 0.000 0.144 0.000 0.000
#> round_ERR2585248 1 0.4704 0.6679 0.736 0.000 0.152 0.112 0.000
#> round_ERR2585240 1 0.0000 0.7597 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585270 1 0.2209 0.7720 0.912 0.032 0.056 0.000 0.000
#> round_ERR2585232 1 0.0000 0.7597 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585341 1 0.3299 0.7394 0.828 0.152 0.004 0.000 0.016
#> aberrant_ERR2585355 1 0.3999 0.5941 0.656 0.344 0.000 0.000 0.000
#> round_ERR2585227 1 0.1117 0.7689 0.964 0.020 0.016 0.000 0.000
#> aberrant_ERR2585351 1 0.4192 0.5309 0.596 0.404 0.000 0.000 0.000
#> round_ERR2585269 1 0.3231 0.7193 0.800 0.000 0.196 0.004 0.000
#> aberrant_ERR2585357 1 0.4126 0.5561 0.620 0.380 0.000 0.000 0.000
#> aberrant_ERR2585350 1 0.4060 0.5785 0.640 0.360 0.000 0.000 0.000
#> round_ERR2585250 1 0.1300 0.7702 0.956 0.016 0.028 0.000 0.000
#> round_ERR2585245 1 0.3318 0.7201 0.800 0.000 0.192 0.008 0.000
#> aberrant_ERR2585353 1 0.6588 0.0544 0.436 0.348 0.000 0.000 0.216
#> round_ERR2585258 1 0.2971 0.7436 0.836 0.008 0.156 0.000 0.000
#> aberrant_ERR2585354 1 0.5340 0.5903 0.648 0.280 0.012 0.000 0.060
#> round_ERR2585249 1 0.3074 0.7211 0.804 0.000 0.196 0.000 0.000
#> round_ERR2585268 1 0.2424 0.7355 0.868 0.132 0.000 0.000 0.000
#> aberrant_ERR2585356 5 0.5385 0.2020 0.288 0.088 0.000 0.000 0.624
#> round_ERR2585266 1 0.0000 0.7597 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585231 1 0.3016 0.7466 0.848 0.000 0.132 0.020 0.000
#> round_ERR2585230 1 0.1410 0.7650 0.940 0.000 0.060 0.000 0.000
#> round_ERR2585267 1 0.2424 0.7520 0.868 0.000 0.132 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 6 0.0000 0.74357 0.000 0.000 0.000 0.000 0.000 1.000
#> aberrant_ERR2585338 1 0.0547 0.84528 0.980 0.000 0.000 0.000 0.020 0.000
#> aberrant_ERR2585325 6 0.0000 0.74357 0.000 0.000 0.000 0.000 0.000 1.000
#> aberrant_ERR2585283 4 0.0000 0.72048 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585343 5 0.5560 -0.15710 0.012 0.000 0.416 0.000 0.476 0.096
#> aberrant_ERR2585329 5 0.0632 0.64705 0.024 0.000 0.000 0.000 0.976 0.000
#> aberrant_ERR2585317 5 0.0713 0.65031 0.028 0.000 0.000 0.000 0.972 0.000
#> aberrant_ERR2585339 1 0.3531 0.53973 0.672 0.000 0.000 0.000 0.328 0.000
#> aberrant_ERR2585335 5 0.1910 0.65764 0.108 0.000 0.000 0.000 0.892 0.000
#> aberrant_ERR2585287 6 0.5135 0.37562 0.060 0.000 0.000 0.348 0.016 0.576
#> aberrant_ERR2585321 5 0.1794 0.64997 0.036 0.000 0.040 0.000 0.924 0.000
#> aberrant_ERR2585297 1 0.1663 0.84304 0.912 0.088 0.000 0.000 0.000 0.000
#> aberrant_ERR2585337 5 0.3823 0.29785 0.436 0.000 0.000 0.000 0.564 0.000
#> aberrant_ERR2585319 3 0.0260 0.09598 0.000 0.000 0.992 0.000 0.008 0.000
#> aberrant_ERR2585315 5 0.3690 0.44774 0.308 0.000 0.008 0.000 0.684 0.000
#> aberrant_ERR2585336 5 0.1007 0.66059 0.044 0.000 0.000 0.000 0.956 0.000
#> aberrant_ERR2585307 1 0.3288 0.59442 0.724 0.000 0.000 0.000 0.276 0.000
#> aberrant_ERR2585301 1 0.4121 0.30292 0.604 0.000 0.016 0.000 0.380 0.000
#> aberrant_ERR2585326 5 0.1075 0.65406 0.048 0.000 0.000 0.000 0.952 0.000
#> aberrant_ERR2585331 1 0.0632 0.84351 0.976 0.000 0.000 0.000 0.024 0.000
#> aberrant_ERR2585346 4 0.1327 0.65241 0.000 0.000 0.064 0.936 0.000 0.000
#> aberrant_ERR2585314 5 0.2300 0.64634 0.144 0.000 0.000 0.000 0.856 0.000
#> aberrant_ERR2585298 1 0.0000 0.84393 1.000 0.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585345 5 0.2416 0.63372 0.156 0.000 0.000 0.000 0.844 0.000
#> aberrant_ERR2585299 1 0.2752 0.83280 0.856 0.108 0.000 0.000 0.036 0.000
#> aberrant_ERR2585309 1 0.2378 0.82288 0.848 0.152 0.000 0.000 0.000 0.000
#> aberrant_ERR2585303 1 0.3998 -0.13363 0.504 0.000 0.004 0.000 0.492 0.000
#> aberrant_ERR2585313 5 0.1285 0.66333 0.052 0.000 0.004 0.000 0.944 0.000
#> aberrant_ERR2585318 5 0.2039 0.61661 0.020 0.000 0.076 0.000 0.904 0.000
#> aberrant_ERR2585328 1 0.3636 0.54921 0.676 0.000 0.004 0.000 0.320 0.000
#> aberrant_ERR2585330 5 0.3485 0.52364 0.028 0.004 0.184 0.000 0.784 0.000
#> aberrant_ERR2585293 4 0.0000 0.72048 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585342 5 0.0291 0.62732 0.004 0.000 0.004 0.000 0.992 0.000
#> aberrant_ERR2585348 5 0.6588 0.01880 0.168 0.000 0.052 0.000 0.444 0.336
#> aberrant_ERR2585352 5 0.1531 0.66666 0.068 0.000 0.004 0.000 0.928 0.000
#> aberrant_ERR2585308 1 0.5708 0.40527 0.520 0.216 0.000 0.000 0.264 0.000
#> aberrant_ERR2585349 1 0.2762 0.72959 0.804 0.000 0.000 0.000 0.196 0.000
#> aberrant_ERR2585316 1 0.6497 0.39152 0.568 0.000 0.184 0.004 0.144 0.100
#> aberrant_ERR2585306 1 0.5636 -0.08967 0.456 0.012 0.104 0.000 0.428 0.000
#> aberrant_ERR2585324 3 0.0260 0.09598 0.000 0.000 0.992 0.000 0.008 0.000
#> aberrant_ERR2585310 1 0.2003 0.79927 0.884 0.000 0.000 0.000 0.116 0.000
#> aberrant_ERR2585296 1 0.0260 0.84410 0.992 0.000 0.000 0.000 0.008 0.000
#> aberrant_ERR2585275 4 0.3636 -0.01209 0.320 0.000 0.000 0.676 0.000 0.004
#> aberrant_ERR2585311 5 0.2669 0.62733 0.156 0.000 0.008 0.000 0.836 0.000
#> aberrant_ERR2585292 4 0.0000 0.72048 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585282 1 0.5303 -0.00286 0.464 0.000 0.456 0.000 0.012 0.068
#> aberrant_ERR2585305 5 0.3707 0.44092 0.312 0.000 0.008 0.000 0.680 0.000
#> aberrant_ERR2585278 5 0.3727 0.35871 0.388 0.000 0.000 0.000 0.612 0.000
#> aberrant_ERR2585347 5 0.4452 0.55261 0.096 0.000 0.040 0.000 0.760 0.104
#> aberrant_ERR2585332 3 0.6669 0.18059 0.060 0.000 0.432 0.000 0.344 0.164
#> aberrant_ERR2585280 3 0.6313 -0.03881 0.344 0.000 0.364 0.000 0.284 0.008
#> aberrant_ERR2585304 1 0.3797 0.17619 0.580 0.000 0.000 0.000 0.420 0.000
#> aberrant_ERR2585322 1 0.3747 0.28369 0.604 0.000 0.000 0.000 0.396 0.000
#> aberrant_ERR2585279 1 0.1814 0.80440 0.900 0.000 0.000 0.000 0.100 0.000
#> aberrant_ERR2585277 1 0.3774 0.32168 0.592 0.000 0.000 0.000 0.408 0.000
#> aberrant_ERR2585295 1 0.4996 0.55586 0.644 0.000 0.156 0.000 0.200 0.000
#> aberrant_ERR2585333 5 0.5007 0.51729 0.076 0.000 0.092 0.000 0.720 0.112
#> aberrant_ERR2585285 5 0.0363 0.63653 0.012 0.000 0.000 0.000 0.988 0.000
#> aberrant_ERR2585286 1 0.3672 0.44299 0.632 0.000 0.000 0.000 0.368 0.000
#> aberrant_ERR2585294 1 0.3828 0.15056 0.560 0.000 0.000 0.000 0.440 0.000
#> aberrant_ERR2585300 5 0.4125 0.59575 0.080 0.044 0.076 0.004 0.796 0.000
#> aberrant_ERR2585334 1 0.0000 0.84393 1.000 0.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585361 5 0.4679 0.57450 0.100 0.000 0.084 0.000 0.748 0.068
#> aberrant_ERR2585372 5 0.7185 -0.23235 0.104 0.000 0.308 0.000 0.388 0.200
#> round_ERR2585217 1 0.0260 0.84405 0.992 0.000 0.000 0.000 0.008 0.000
#> round_ERR2585205 1 0.0363 0.84617 0.988 0.012 0.000 0.000 0.000 0.000
#> round_ERR2585214 1 0.0000 0.84393 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585202 1 0.0547 0.84542 0.980 0.000 0.000 0.000 0.020 0.000
#> aberrant_ERR2585367 1 0.3240 0.78059 0.812 0.000 0.000 0.000 0.040 0.148
#> round_ERR2585220 1 0.1643 0.84574 0.924 0.068 0.000 0.000 0.008 0.000
#> round_ERR2585238 1 0.3418 0.79890 0.784 0.184 0.000 0.000 0.032 0.000
#> aberrant_ERR2585276 5 0.3903 0.44318 0.304 0.012 0.004 0.000 0.680 0.000
#> round_ERR2585218 1 0.2454 0.81993 0.840 0.160 0.000 0.000 0.000 0.000
#> aberrant_ERR2585363 5 0.4075 0.50546 0.240 0.000 0.048 0.000 0.712 0.000
#> round_ERR2585201 1 0.0000 0.84393 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585210 1 0.0458 0.84684 0.984 0.016 0.000 0.000 0.000 0.000
#> aberrant_ERR2585362 5 0.3531 0.44709 0.328 0.000 0.000 0.000 0.672 0.000
#> aberrant_ERR2585360 5 0.2772 0.60340 0.180 0.000 0.000 0.000 0.816 0.004
#> round_ERR2585209 1 0.0260 0.84405 0.992 0.000 0.000 0.000 0.008 0.000
#> round_ERR2585242 1 0.0000 0.84393 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585216 1 0.0000 0.84393 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585219 1 0.1327 0.84648 0.936 0.064 0.000 0.000 0.000 0.000
#> round_ERR2585237 1 0.0000 0.84393 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585198 1 0.0000 0.84393 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585211 1 0.1610 0.84449 0.916 0.084 0.000 0.000 0.000 0.000
#> round_ERR2585206 1 0.2730 0.80253 0.808 0.192 0.000 0.000 0.000 0.000
#> aberrant_ERR2585281 1 0.1610 0.82273 0.916 0.000 0.000 0.000 0.084 0.000
#> round_ERR2585212 1 0.2060 0.84408 0.900 0.084 0.000 0.000 0.016 0.000
#> round_ERR2585221 1 0.2823 0.79403 0.796 0.204 0.000 0.000 0.000 0.000
#> round_ERR2585243 1 0.0632 0.84649 0.976 0.024 0.000 0.000 0.000 0.000
#> round_ERR2585204 1 0.0146 0.84405 0.996 0.000 0.000 0.000 0.004 0.000
#> round_ERR2585213 1 0.0000 0.84393 1.000 0.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585373 5 0.0767 0.62863 0.008 0.000 0.012 0.000 0.976 0.004
#> aberrant_ERR2585358 3 0.5887 0.04432 0.000 0.004 0.484 0.000 0.320 0.192
#> aberrant_ERR2585365 5 0.1327 0.66576 0.064 0.000 0.000 0.000 0.936 0.000
#> aberrant_ERR2585359 5 0.4961 0.40240 0.008 0.000 0.080 0.048 0.728 0.136
#> aberrant_ERR2585370 5 0.3747 0.37030 0.396 0.000 0.000 0.000 0.604 0.000
#> round_ERR2585215 1 0.2772 0.80833 0.816 0.180 0.000 0.004 0.000 0.000
#> round_ERR2585262 1 0.0000 0.84393 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585199 1 0.0260 0.84405 0.992 0.000 0.000 0.000 0.008 0.000
#> aberrant_ERR2585369 5 0.2664 0.62430 0.184 0.000 0.000 0.000 0.816 0.000
#> round_ERR2585208 1 0.2219 0.83020 0.864 0.136 0.000 0.000 0.000 0.000
#> round_ERR2585252 1 0.2883 0.78824 0.788 0.212 0.000 0.000 0.000 0.000
#> round_ERR2585236 1 0.2520 0.82428 0.844 0.152 0.000 0.000 0.000 0.004
#> aberrant_ERR2585284 4 0.0260 0.71703 0.000 0.000 0.000 0.992 0.008 0.000
#> round_ERR2585224 1 0.5708 0.57177 0.584 0.216 0.000 0.016 0.184 0.000
#> round_ERR2585260 1 0.2070 0.84177 0.892 0.100 0.000 0.000 0.008 0.000
#> round_ERR2585229 1 0.2821 0.82144 0.832 0.152 0.000 0.000 0.016 0.000
#> aberrant_ERR2585364 3 0.4872 -0.15106 0.000 0.000 0.548 0.388 0.064 0.000
#> round_ERR2585253 1 0.2912 0.78543 0.784 0.216 0.000 0.000 0.000 0.000
#> aberrant_ERR2585368 2 0.3359 1.00000 0.196 0.784 0.008 0.000 0.012 0.000
#> aberrant_ERR2585371 2 0.3359 1.00000 0.196 0.784 0.008 0.000 0.012 0.000
#> round_ERR2585239 1 0.1957 0.83791 0.888 0.112 0.000 0.000 0.000 0.000
#> round_ERR2585273 1 0.2631 0.82292 0.840 0.152 0.000 0.000 0.008 0.000
#> round_ERR2585256 1 0.0000 0.84393 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585272 1 0.0865 0.84840 0.964 0.036 0.000 0.000 0.000 0.000
#> round_ERR2585246 1 0.4474 0.73155 0.704 0.188 0.000 0.000 0.108 0.000
#> round_ERR2585261 1 0.0000 0.84393 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585254 1 0.0363 0.84451 0.988 0.000 0.000 0.000 0.012 0.000
#> round_ERR2585225 1 0.0000 0.84393 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585235 1 0.2772 0.81062 0.816 0.180 0.000 0.000 0.004 0.000
#> round_ERR2585271 1 0.0865 0.84791 0.964 0.036 0.000 0.000 0.000 0.000
#> round_ERR2585251 1 0.0622 0.84763 0.980 0.012 0.000 0.000 0.008 0.000
#> round_ERR2585255 1 0.0146 0.84405 0.996 0.000 0.000 0.000 0.004 0.000
#> round_ERR2585257 1 0.0000 0.84393 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585226 1 0.1806 0.80856 0.908 0.004 0.000 0.000 0.088 0.000
#> round_ERR2585265 1 0.2843 0.82975 0.848 0.116 0.000 0.000 0.036 0.000
#> round_ERR2585259 1 0.0937 0.84818 0.960 0.040 0.000 0.000 0.000 0.000
#> round_ERR2585247 1 0.2520 0.82259 0.844 0.152 0.000 0.000 0.004 0.000
#> round_ERR2585241 1 0.2178 0.83315 0.868 0.132 0.000 0.000 0.000 0.000
#> round_ERR2585263 1 0.0603 0.84660 0.980 0.004 0.000 0.000 0.016 0.000
#> round_ERR2585264 1 0.5783 0.33264 0.496 0.212 0.000 0.292 0.000 0.000
#> round_ERR2585233 1 0.0000 0.84393 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585223 1 0.2178 0.83170 0.868 0.132 0.000 0.000 0.000 0.000
#> round_ERR2585234 1 0.0363 0.84451 0.988 0.000 0.000 0.000 0.012 0.000
#> round_ERR2585222 1 0.0000 0.84393 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585228 1 0.2260 0.82712 0.860 0.140 0.000 0.000 0.000 0.000
#> round_ERR2585248 1 0.4351 0.72609 0.720 0.172 0.000 0.108 0.000 0.000
#> round_ERR2585240 1 0.0000 0.84393 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585270 1 0.2058 0.84477 0.908 0.056 0.000 0.000 0.036 0.000
#> round_ERR2585232 1 0.0000 0.84393 1.000 0.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585341 1 0.3996 0.69693 0.752 0.000 0.000 0.000 0.168 0.080
#> aberrant_ERR2585355 5 0.4199 0.37103 0.380 0.020 0.000 0.000 0.600 0.000
#> round_ERR2585227 1 0.1088 0.84782 0.960 0.016 0.000 0.000 0.024 0.000
#> aberrant_ERR2585351 5 0.0547 0.64382 0.020 0.000 0.000 0.000 0.980 0.000
#> round_ERR2585269 1 0.2912 0.78543 0.784 0.216 0.000 0.000 0.000 0.000
#> aberrant_ERR2585357 5 0.0790 0.65344 0.032 0.000 0.000 0.000 0.968 0.000
#> aberrant_ERR2585350 5 0.2527 0.62743 0.168 0.000 0.000 0.000 0.832 0.000
#> round_ERR2585250 1 0.1245 0.84967 0.952 0.032 0.000 0.000 0.016 0.000
#> round_ERR2585245 1 0.3023 0.78594 0.784 0.212 0.000 0.004 0.000 0.000
#> aberrant_ERR2585353 5 0.3758 0.58981 0.080 0.000 0.080 0.000 0.812 0.028
#> round_ERR2585258 1 0.2631 0.82292 0.840 0.152 0.000 0.000 0.008 0.000
#> aberrant_ERR2585354 5 0.5054 0.29879 0.392 0.004 0.032 0.000 0.552 0.020
#> round_ERR2585249 1 0.2912 0.78543 0.784 0.216 0.000 0.000 0.000 0.000
#> round_ERR2585268 1 0.2854 0.68336 0.792 0.000 0.000 0.000 0.208 0.000
#> aberrant_ERR2585356 3 0.5534 0.13913 0.248 0.000 0.556 0.000 0.196 0.000
#> round_ERR2585266 1 0.0000 0.84393 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585231 1 0.2907 0.81360 0.828 0.152 0.000 0.020 0.000 0.000
#> round_ERR2585230 1 0.1204 0.84746 0.944 0.056 0.000 0.000 0.000 0.000
#> round_ERR2585267 1 0.2219 0.83070 0.864 0.136 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> MAD:pam 158 2.40e-02 2
#> MAD:pam 115 1.12e-07 3
#> MAD:pam 134 4.97e-02 4
#> MAD:pam 129 1.08e-02 5
#> MAD:pam 126 7.21e-14 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "mclust"]
# you can also extract it by
# res = res_list["MAD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.988 0.995 0.5035 0.497 0.497
#> 3 3 0.766 0.800 0.891 0.1714 0.941 0.881
#> 4 4 0.845 0.908 0.937 0.1116 0.886 0.747
#> 5 5 0.794 0.818 0.907 0.1256 0.909 0.738
#> 6 6 0.748 0.631 0.834 0.0623 0.950 0.817
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585338 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585325 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585283 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585343 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585329 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585317 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585339 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585335 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585287 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585321 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585297 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585319 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585315 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585336 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585307 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585301 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585326 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585331 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585346 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585314 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585298 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585345 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585299 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585303 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585313 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585318 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585328 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585330 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585293 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585342 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585348 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585352 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585308 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585349 2 0.3879 0.916 0.076 0.924
#> aberrant_ERR2585316 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585306 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585324 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585310 1 0.7299 0.744 0.796 0.204
#> aberrant_ERR2585296 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585275 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585311 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585292 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585282 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585305 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585278 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585347 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585332 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585280 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585304 2 0.8813 0.571 0.300 0.700
#> aberrant_ERR2585322 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585279 2 0.5178 0.868 0.116 0.884
#> aberrant_ERR2585277 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585295 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585333 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585285 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585286 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585294 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585300 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585334 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585361 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585372 2 0.0000 0.994 0.000 1.000
#> round_ERR2585217 1 0.0000 0.995 1.000 0.000
#> round_ERR2585205 1 0.0000 0.995 1.000 0.000
#> round_ERR2585214 1 0.0000 0.995 1.000 0.000
#> round_ERR2585202 1 0.0376 0.991 0.996 0.004
#> aberrant_ERR2585367 2 0.0000 0.994 0.000 1.000
#> round_ERR2585220 1 0.0000 0.995 1.000 0.000
#> round_ERR2585238 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585276 2 0.0000 0.994 0.000 1.000
#> round_ERR2585218 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.994 0.000 1.000
#> round_ERR2585201 1 0.0000 0.995 1.000 0.000
#> round_ERR2585210 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585362 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585360 2 0.0000 0.994 0.000 1.000
#> round_ERR2585209 1 0.0000 0.995 1.000 0.000
#> round_ERR2585242 1 0.0000 0.995 1.000 0.000
#> round_ERR2585216 1 0.0000 0.995 1.000 0.000
#> round_ERR2585219 1 0.0000 0.995 1.000 0.000
#> round_ERR2585237 1 0.0000 0.995 1.000 0.000
#> round_ERR2585198 1 0.0000 0.995 1.000 0.000
#> round_ERR2585211 1 0.0000 0.995 1.000 0.000
#> round_ERR2585206 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585281 2 0.0000 0.994 0.000 1.000
#> round_ERR2585212 1 0.0000 0.995 1.000 0.000
#> round_ERR2585221 1 0.0000 0.995 1.000 0.000
#> round_ERR2585243 1 0.0000 0.995 1.000 0.000
#> round_ERR2585204 1 0.0000 0.995 1.000 0.000
#> round_ERR2585213 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585373 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585358 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585365 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585359 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585370 2 0.0000 0.994 0.000 1.000
#> round_ERR2585215 1 0.0000 0.995 1.000 0.000
#> round_ERR2585262 1 0.6343 0.809 0.840 0.160
#> round_ERR2585199 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585369 2 0.0000 0.994 0.000 1.000
#> round_ERR2585208 1 0.0000 0.995 1.000 0.000
#> round_ERR2585252 1 0.0000 0.995 1.000 0.000
#> round_ERR2585236 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585284 2 0.0000 0.994 0.000 1.000
#> round_ERR2585224 1 0.0000 0.995 1.000 0.000
#> round_ERR2585260 1 0.0000 0.995 1.000 0.000
#> round_ERR2585229 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585364 2 0.0000 0.994 0.000 1.000
#> round_ERR2585253 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585371 2 0.0000 0.994 0.000 1.000
#> round_ERR2585239 1 0.0000 0.995 1.000 0.000
#> round_ERR2585273 1 0.0000 0.995 1.000 0.000
#> round_ERR2585256 1 0.0000 0.995 1.000 0.000
#> round_ERR2585272 1 0.0000 0.995 1.000 0.000
#> round_ERR2585246 1 0.0000 0.995 1.000 0.000
#> round_ERR2585261 1 0.0000 0.995 1.000 0.000
#> round_ERR2585254 1 0.0000 0.995 1.000 0.000
#> round_ERR2585225 1 0.0000 0.995 1.000 0.000
#> round_ERR2585235 1 0.0000 0.995 1.000 0.000
#> round_ERR2585271 1 0.0000 0.995 1.000 0.000
#> round_ERR2585251 1 0.0000 0.995 1.000 0.000
#> round_ERR2585255 1 0.0000 0.995 1.000 0.000
#> round_ERR2585257 1 0.0000 0.995 1.000 0.000
#> round_ERR2585226 1 0.0000 0.995 1.000 0.000
#> round_ERR2585265 1 0.0000 0.995 1.000 0.000
#> round_ERR2585259 1 0.0000 0.995 1.000 0.000
#> round_ERR2585247 1 0.0000 0.995 1.000 0.000
#> round_ERR2585241 1 0.0000 0.995 1.000 0.000
#> round_ERR2585263 1 0.0000 0.995 1.000 0.000
#> round_ERR2585264 1 0.0000 0.995 1.000 0.000
#> round_ERR2585233 1 0.0000 0.995 1.000 0.000
#> round_ERR2585223 1 0.0000 0.995 1.000 0.000
#> round_ERR2585234 1 0.0000 0.995 1.000 0.000
#> round_ERR2585222 1 0.0000 0.995 1.000 0.000
#> round_ERR2585228 1 0.0000 0.995 1.000 0.000
#> round_ERR2585248 1 0.0000 0.995 1.000 0.000
#> round_ERR2585240 1 0.0000 0.995 1.000 0.000
#> round_ERR2585270 1 0.0000 0.995 1.000 0.000
#> round_ERR2585232 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585341 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585355 2 0.0000 0.994 0.000 1.000
#> round_ERR2585227 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585351 2 0.0000 0.994 0.000 1.000
#> round_ERR2585269 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.994 0.000 1.000
#> aberrant_ERR2585350 2 0.0000 0.994 0.000 1.000
#> round_ERR2585250 1 0.0000 0.995 1.000 0.000
#> round_ERR2585245 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.994 0.000 1.000
#> round_ERR2585258 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.994 0.000 1.000
#> round_ERR2585249 1 0.0000 0.995 1.000 0.000
#> round_ERR2585268 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585356 2 0.0000 0.994 0.000 1.000
#> round_ERR2585266 1 0.0000 0.995 1.000 0.000
#> round_ERR2585231 1 0.0000 0.995 1.000 0.000
#> round_ERR2585230 1 0.0000 0.995 1.000 0.000
#> round_ERR2585267 1 0.0000 0.995 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.1163 0.8727 0.000 0.972 0.028
#> aberrant_ERR2585338 2 0.0592 0.8784 0.000 0.988 0.012
#> aberrant_ERR2585325 2 0.1163 0.8727 0.000 0.972 0.028
#> aberrant_ERR2585283 3 0.6168 0.6458 0.000 0.412 0.588
#> aberrant_ERR2585343 2 0.1163 0.8736 0.000 0.972 0.028
#> aberrant_ERR2585329 2 0.1289 0.8706 0.000 0.968 0.032
#> aberrant_ERR2585317 2 0.1163 0.8726 0.000 0.972 0.028
#> aberrant_ERR2585339 2 0.0424 0.8799 0.000 0.992 0.008
#> aberrant_ERR2585335 2 0.1031 0.8753 0.000 0.976 0.024
#> aberrant_ERR2585287 3 0.6225 0.6172 0.000 0.432 0.568
#> aberrant_ERR2585321 2 0.0747 0.8789 0.000 0.984 0.016
#> aberrant_ERR2585297 1 0.0000 0.9083 1.000 0.000 0.000
#> aberrant_ERR2585337 2 0.1163 0.8726 0.000 0.972 0.028
#> aberrant_ERR2585319 2 0.0892 0.8767 0.000 0.980 0.020
#> aberrant_ERR2585315 2 0.0592 0.8782 0.000 0.988 0.012
#> aberrant_ERR2585336 2 0.1163 0.8726 0.000 0.972 0.028
#> aberrant_ERR2585307 2 0.4750 0.6508 0.000 0.784 0.216
#> aberrant_ERR2585301 2 0.2261 0.8544 0.000 0.932 0.068
#> aberrant_ERR2585326 2 0.1163 0.8726 0.000 0.972 0.028
#> aberrant_ERR2585331 2 0.4796 0.6372 0.000 0.780 0.220
#> aberrant_ERR2585346 3 0.6225 0.6192 0.000 0.432 0.568
#> aberrant_ERR2585314 2 0.6180 0.0633 0.000 0.584 0.416
#> aberrant_ERR2585298 1 0.4974 0.8194 0.764 0.000 0.236
#> aberrant_ERR2585345 2 0.1529 0.8665 0.000 0.960 0.040
#> aberrant_ERR2585299 1 0.0237 0.9066 0.996 0.000 0.004
#> aberrant_ERR2585309 1 0.0592 0.9029 0.988 0.000 0.012
#> aberrant_ERR2585303 2 0.0747 0.8777 0.000 0.984 0.016
#> aberrant_ERR2585313 2 0.1031 0.8790 0.000 0.976 0.024
#> aberrant_ERR2585318 2 0.1163 0.8741 0.000 0.972 0.028
#> aberrant_ERR2585328 2 0.5016 0.5716 0.000 0.760 0.240
#> aberrant_ERR2585330 2 0.0892 0.8767 0.000 0.980 0.020
#> aberrant_ERR2585293 3 0.6140 0.6503 0.000 0.404 0.596
#> aberrant_ERR2585342 2 0.0592 0.8785 0.000 0.988 0.012
#> aberrant_ERR2585348 2 0.4291 0.6911 0.000 0.820 0.180
#> aberrant_ERR2585352 2 0.1031 0.8798 0.000 0.976 0.024
#> aberrant_ERR2585308 1 0.0237 0.9066 0.996 0.000 0.004
#> aberrant_ERR2585349 2 0.6654 -0.1165 0.008 0.536 0.456
#> aberrant_ERR2585316 2 0.5397 0.4667 0.000 0.720 0.280
#> aberrant_ERR2585306 2 0.5621 0.3969 0.000 0.692 0.308
#> aberrant_ERR2585324 2 0.1031 0.8787 0.000 0.976 0.024
#> aberrant_ERR2585310 3 0.7112 0.0711 0.308 0.044 0.648
#> aberrant_ERR2585296 1 0.5678 0.7511 0.684 0.000 0.316
#> aberrant_ERR2585275 3 0.6204 0.6330 0.000 0.424 0.576
#> aberrant_ERR2585311 2 0.1031 0.8757 0.000 0.976 0.024
#> aberrant_ERR2585292 3 0.6140 0.6503 0.000 0.404 0.596
#> aberrant_ERR2585282 2 0.2959 0.8128 0.000 0.900 0.100
#> aberrant_ERR2585305 2 0.6095 0.1409 0.000 0.608 0.392
#> aberrant_ERR2585278 2 0.0747 0.8780 0.000 0.984 0.016
#> aberrant_ERR2585347 2 0.5327 0.4810 0.000 0.728 0.272
#> aberrant_ERR2585332 2 0.1031 0.8737 0.000 0.976 0.024
#> aberrant_ERR2585280 2 0.0892 0.8759 0.000 0.980 0.020
#> aberrant_ERR2585304 3 0.7389 0.2655 0.032 0.464 0.504
#> aberrant_ERR2585322 2 0.0892 0.8776 0.000 0.980 0.020
#> aberrant_ERR2585279 2 0.6460 -0.0365 0.004 0.556 0.440
#> aberrant_ERR2585277 2 0.0592 0.8800 0.000 0.988 0.012
#> aberrant_ERR2585295 2 0.4702 0.6207 0.000 0.788 0.212
#> aberrant_ERR2585333 2 0.0892 0.8784 0.000 0.980 0.020
#> aberrant_ERR2585285 2 0.0747 0.8778 0.000 0.984 0.016
#> aberrant_ERR2585286 2 0.0592 0.8800 0.000 0.988 0.012
#> aberrant_ERR2585294 2 0.1643 0.8693 0.000 0.956 0.044
#> aberrant_ERR2585300 2 0.1163 0.8773 0.000 0.972 0.028
#> aberrant_ERR2585334 2 0.6095 0.1765 0.000 0.608 0.392
#> aberrant_ERR2585361 2 0.0892 0.8759 0.000 0.980 0.020
#> aberrant_ERR2585372 2 0.0892 0.8772 0.000 0.980 0.020
#> round_ERR2585217 1 0.4974 0.8194 0.764 0.000 0.236
#> round_ERR2585205 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585214 1 0.4974 0.8194 0.764 0.000 0.236
#> round_ERR2585202 1 0.6476 0.5502 0.548 0.004 0.448
#> aberrant_ERR2585367 2 0.1163 0.8718 0.000 0.972 0.028
#> round_ERR2585220 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585238 1 0.0000 0.9083 1.000 0.000 0.000
#> aberrant_ERR2585276 2 0.3816 0.7533 0.000 0.852 0.148
#> round_ERR2585218 1 0.0000 0.9083 1.000 0.000 0.000
#> aberrant_ERR2585363 2 0.0592 0.8782 0.000 0.988 0.012
#> round_ERR2585201 1 0.4974 0.8194 0.764 0.000 0.236
#> round_ERR2585210 1 0.1031 0.9029 0.976 0.000 0.024
#> aberrant_ERR2585362 2 0.4235 0.7025 0.000 0.824 0.176
#> aberrant_ERR2585360 2 0.1860 0.8664 0.000 0.948 0.052
#> round_ERR2585209 1 0.4555 0.8386 0.800 0.000 0.200
#> round_ERR2585242 1 0.4974 0.8194 0.764 0.000 0.236
#> round_ERR2585216 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585219 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585237 1 0.4974 0.8194 0.764 0.000 0.236
#> round_ERR2585198 1 0.4974 0.8194 0.764 0.000 0.236
#> round_ERR2585211 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.9083 1.000 0.000 0.000
#> aberrant_ERR2585281 2 0.4842 0.5962 0.000 0.776 0.224
#> round_ERR2585212 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585221 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585243 1 0.1964 0.8762 0.944 0.000 0.056
#> round_ERR2585204 1 0.4974 0.8194 0.764 0.000 0.236
#> round_ERR2585213 1 0.6235 0.5804 0.564 0.000 0.436
#> aberrant_ERR2585373 2 0.0892 0.8795 0.000 0.980 0.020
#> aberrant_ERR2585358 2 0.0892 0.8784 0.000 0.980 0.020
#> aberrant_ERR2585365 2 0.0747 0.8772 0.000 0.984 0.016
#> aberrant_ERR2585359 2 0.1031 0.8757 0.000 0.976 0.024
#> aberrant_ERR2585370 2 0.1289 0.8738 0.000 0.968 0.032
#> round_ERR2585215 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585262 3 0.6917 -0.1232 0.368 0.024 0.608
#> round_ERR2585199 1 0.5178 0.8059 0.744 0.000 0.256
#> aberrant_ERR2585369 2 0.0747 0.8778 0.000 0.984 0.016
#> round_ERR2585208 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585236 1 0.5327 0.7937 0.728 0.000 0.272
#> aberrant_ERR2585284 3 0.6140 0.6503 0.000 0.404 0.596
#> round_ERR2585224 1 0.0592 0.9029 0.988 0.000 0.012
#> round_ERR2585260 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585229 1 0.0000 0.9083 1.000 0.000 0.000
#> aberrant_ERR2585364 2 0.3686 0.7631 0.000 0.860 0.140
#> round_ERR2585253 1 0.0000 0.9083 1.000 0.000 0.000
#> aberrant_ERR2585368 2 0.1860 0.8554 0.000 0.948 0.052
#> aberrant_ERR2585371 2 0.2066 0.8471 0.000 0.940 0.060
#> round_ERR2585239 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585273 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585256 1 0.4750 0.8305 0.784 0.000 0.216
#> round_ERR2585272 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585246 1 0.2261 0.8674 0.932 0.000 0.068
#> round_ERR2585261 1 0.4931 0.8218 0.768 0.000 0.232
#> round_ERR2585254 1 0.4974 0.8194 0.764 0.000 0.236
#> round_ERR2585225 1 0.5216 0.8021 0.740 0.000 0.260
#> round_ERR2585235 1 0.2878 0.8806 0.904 0.000 0.096
#> round_ERR2585271 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585251 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585255 1 0.5058 0.8141 0.756 0.000 0.244
#> round_ERR2585257 1 0.5098 0.8114 0.752 0.000 0.248
#> round_ERR2585226 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585265 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585259 1 0.4121 0.8528 0.832 0.000 0.168
#> round_ERR2585247 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585241 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585263 1 0.0424 0.9068 0.992 0.000 0.008
#> round_ERR2585264 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585233 1 0.4974 0.8194 0.764 0.000 0.236
#> round_ERR2585223 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585234 1 0.4974 0.8194 0.764 0.000 0.236
#> round_ERR2585222 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585228 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585248 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585240 1 0.4178 0.8515 0.828 0.000 0.172
#> round_ERR2585270 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585232 1 0.2711 0.8831 0.912 0.000 0.088
#> aberrant_ERR2585341 2 0.1031 0.8750 0.000 0.976 0.024
#> aberrant_ERR2585355 2 0.0892 0.8803 0.000 0.980 0.020
#> round_ERR2585227 1 0.0000 0.9083 1.000 0.000 0.000
#> aberrant_ERR2585351 2 0.0424 0.8795 0.000 0.992 0.008
#> round_ERR2585269 1 0.0000 0.9083 1.000 0.000 0.000
#> aberrant_ERR2585357 2 0.1753 0.8592 0.000 0.952 0.048
#> aberrant_ERR2585350 2 0.1031 0.8747 0.000 0.976 0.024
#> round_ERR2585250 1 0.4062 0.8552 0.836 0.000 0.164
#> round_ERR2585245 1 0.0000 0.9083 1.000 0.000 0.000
#> aberrant_ERR2585353 2 0.0747 0.8802 0.000 0.984 0.016
#> round_ERR2585258 1 0.0000 0.9083 1.000 0.000 0.000
#> aberrant_ERR2585354 2 0.1163 0.8773 0.000 0.972 0.028
#> round_ERR2585249 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585268 1 0.5327 0.7942 0.728 0.000 0.272
#> aberrant_ERR2585356 2 0.0892 0.8784 0.000 0.980 0.020
#> round_ERR2585266 1 0.4974 0.8194 0.764 0.000 0.236
#> round_ERR2585231 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585230 1 0.0000 0.9083 1.000 0.000 0.000
#> round_ERR2585267 1 0.0000 0.9083 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 2 0.1474 0.9366 0.000 0.948 0.000 0.052
#> aberrant_ERR2585338 2 0.1940 0.9310 0.000 0.924 0.000 0.076
#> aberrant_ERR2585325 2 0.1557 0.9368 0.000 0.944 0.000 0.056
#> aberrant_ERR2585283 4 0.2647 0.8807 0.000 0.120 0.000 0.880
#> aberrant_ERR2585343 2 0.0707 0.9472 0.000 0.980 0.000 0.020
#> aberrant_ERR2585329 2 0.1929 0.9373 0.000 0.940 0.024 0.036
#> aberrant_ERR2585317 2 0.1411 0.9437 0.000 0.960 0.020 0.020
#> aberrant_ERR2585339 2 0.1474 0.9437 0.000 0.948 0.000 0.052
#> aberrant_ERR2585335 2 0.0469 0.9471 0.000 0.988 0.000 0.012
#> aberrant_ERR2585287 4 0.3486 0.8606 0.000 0.188 0.000 0.812
#> aberrant_ERR2585321 2 0.0469 0.9468 0.000 0.988 0.000 0.012
#> aberrant_ERR2585297 1 0.0188 0.9687 0.996 0.000 0.004 0.000
#> aberrant_ERR2585337 2 0.1733 0.9405 0.000 0.948 0.024 0.028
#> aberrant_ERR2585319 2 0.0336 0.9469 0.000 0.992 0.000 0.008
#> aberrant_ERR2585315 2 0.0469 0.9482 0.000 0.988 0.000 0.012
#> aberrant_ERR2585336 2 0.1488 0.9444 0.000 0.956 0.012 0.032
#> aberrant_ERR2585307 2 0.2111 0.9334 0.000 0.932 0.024 0.044
#> aberrant_ERR2585301 2 0.1256 0.9435 0.000 0.964 0.008 0.028
#> aberrant_ERR2585326 2 0.1624 0.9420 0.000 0.952 0.020 0.028
#> aberrant_ERR2585331 2 0.3400 0.9018 0.000 0.872 0.064 0.064
#> aberrant_ERR2585346 4 0.3726 0.8466 0.000 0.212 0.000 0.788
#> aberrant_ERR2585314 2 0.2840 0.9095 0.000 0.900 0.056 0.044
#> aberrant_ERR2585298 3 0.2530 0.9028 0.112 0.000 0.888 0.000
#> aberrant_ERR2585345 2 0.2111 0.9334 0.000 0.932 0.024 0.044
#> aberrant_ERR2585299 1 0.0188 0.9687 0.996 0.000 0.004 0.000
#> aberrant_ERR2585309 1 0.0188 0.9687 0.996 0.000 0.004 0.000
#> aberrant_ERR2585303 2 0.1867 0.9332 0.000 0.928 0.000 0.072
#> aberrant_ERR2585313 2 0.1118 0.9464 0.000 0.964 0.000 0.036
#> aberrant_ERR2585318 2 0.1109 0.9440 0.000 0.968 0.004 0.028
#> aberrant_ERR2585328 2 0.1637 0.9393 0.000 0.940 0.000 0.060
#> aberrant_ERR2585330 2 0.0469 0.9468 0.000 0.988 0.000 0.012
#> aberrant_ERR2585293 4 0.1557 0.8633 0.000 0.056 0.000 0.944
#> aberrant_ERR2585342 2 0.0469 0.9468 0.000 0.988 0.000 0.012
#> aberrant_ERR2585348 2 0.1716 0.9313 0.000 0.936 0.000 0.064
#> aberrant_ERR2585352 2 0.0188 0.9482 0.000 0.996 0.000 0.004
#> aberrant_ERR2585308 1 0.0188 0.9687 0.996 0.000 0.004 0.000
#> aberrant_ERR2585349 2 0.4079 0.7751 0.000 0.800 0.180 0.020
#> aberrant_ERR2585316 2 0.1792 0.9253 0.000 0.932 0.000 0.068
#> aberrant_ERR2585306 2 0.1022 0.9449 0.000 0.968 0.000 0.032
#> aberrant_ERR2585324 2 0.0469 0.9468 0.000 0.988 0.000 0.012
#> aberrant_ERR2585310 3 0.2297 0.8037 0.032 0.012 0.932 0.024
#> aberrant_ERR2585296 3 0.2345 0.8870 0.100 0.000 0.900 0.000
#> aberrant_ERR2585275 4 0.3837 0.8229 0.000 0.224 0.000 0.776
#> aberrant_ERR2585311 2 0.0707 0.9465 0.000 0.980 0.000 0.020
#> aberrant_ERR2585292 4 0.1557 0.8633 0.000 0.056 0.000 0.944
#> aberrant_ERR2585282 2 0.0469 0.9468 0.000 0.988 0.000 0.012
#> aberrant_ERR2585305 2 0.1624 0.9386 0.000 0.952 0.020 0.028
#> aberrant_ERR2585278 2 0.0469 0.9468 0.000 0.988 0.000 0.012
#> aberrant_ERR2585347 2 0.1637 0.9347 0.000 0.940 0.000 0.060
#> aberrant_ERR2585332 2 0.1474 0.9401 0.000 0.948 0.000 0.052
#> aberrant_ERR2585280 2 0.1302 0.9402 0.000 0.956 0.000 0.044
#> aberrant_ERR2585304 2 0.5937 0.0650 0.000 0.492 0.472 0.036
#> aberrant_ERR2585322 2 0.1022 0.9469 0.000 0.968 0.000 0.032
#> aberrant_ERR2585279 2 0.4206 0.8284 0.000 0.816 0.136 0.048
#> aberrant_ERR2585277 2 0.2011 0.9301 0.000 0.920 0.000 0.080
#> aberrant_ERR2585295 2 0.1637 0.9365 0.000 0.940 0.000 0.060
#> aberrant_ERR2585333 2 0.1022 0.9449 0.000 0.968 0.000 0.032
#> aberrant_ERR2585285 2 0.0592 0.9466 0.000 0.984 0.000 0.016
#> aberrant_ERR2585286 2 0.2011 0.9291 0.000 0.920 0.000 0.080
#> aberrant_ERR2585294 2 0.1004 0.9457 0.000 0.972 0.004 0.024
#> aberrant_ERR2585300 2 0.0592 0.9473 0.000 0.984 0.000 0.016
#> aberrant_ERR2585334 2 0.3542 0.8968 0.000 0.864 0.060 0.076
#> aberrant_ERR2585361 2 0.1792 0.9317 0.000 0.932 0.000 0.068
#> aberrant_ERR2585372 2 0.0469 0.9468 0.000 0.988 0.000 0.012
#> round_ERR2585217 3 0.2704 0.8990 0.124 0.000 0.876 0.000
#> round_ERR2585205 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585214 3 0.2469 0.9025 0.108 0.000 0.892 0.000
#> round_ERR2585202 3 0.1114 0.7988 0.016 0.008 0.972 0.004
#> aberrant_ERR2585367 2 0.2011 0.9291 0.000 0.920 0.000 0.080
#> round_ERR2585220 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585238 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> aberrant_ERR2585276 2 0.1109 0.9445 0.000 0.968 0.004 0.028
#> round_ERR2585218 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> aberrant_ERR2585363 2 0.0469 0.9468 0.000 0.988 0.000 0.012
#> round_ERR2585201 3 0.2469 0.9025 0.108 0.000 0.892 0.000
#> round_ERR2585210 1 0.0336 0.9647 0.992 0.000 0.008 0.000
#> aberrant_ERR2585362 2 0.0336 0.9487 0.000 0.992 0.008 0.000
#> aberrant_ERR2585360 2 0.0336 0.9475 0.000 0.992 0.000 0.008
#> round_ERR2585209 1 0.4967 -0.0816 0.548 0.000 0.452 0.000
#> round_ERR2585242 3 0.2589 0.9019 0.116 0.000 0.884 0.000
#> round_ERR2585216 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585219 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585237 3 0.2469 0.9025 0.108 0.000 0.892 0.000
#> round_ERR2585198 3 0.2530 0.9027 0.112 0.000 0.888 0.000
#> round_ERR2585211 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> aberrant_ERR2585281 2 0.1940 0.9310 0.000 0.924 0.000 0.076
#> round_ERR2585212 1 0.0188 0.9683 0.996 0.000 0.004 0.000
#> round_ERR2585221 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585243 1 0.0188 0.9687 0.996 0.000 0.004 0.000
#> round_ERR2585204 3 0.2469 0.9025 0.108 0.000 0.892 0.000
#> round_ERR2585213 3 0.0895 0.8097 0.020 0.000 0.976 0.004
#> aberrant_ERR2585373 2 0.0921 0.9444 0.000 0.972 0.000 0.028
#> aberrant_ERR2585358 2 0.1474 0.9454 0.000 0.948 0.000 0.052
#> aberrant_ERR2585365 2 0.1940 0.9310 0.000 0.924 0.000 0.076
#> aberrant_ERR2585359 2 0.0707 0.9465 0.000 0.980 0.000 0.020
#> aberrant_ERR2585370 2 0.2021 0.9388 0.000 0.936 0.024 0.040
#> round_ERR2585215 1 0.0188 0.9683 0.996 0.000 0.004 0.000
#> round_ERR2585262 3 0.2207 0.8512 0.056 0.012 0.928 0.004
#> round_ERR2585199 3 0.1902 0.8633 0.064 0.000 0.932 0.004
#> aberrant_ERR2585369 2 0.0469 0.9468 0.000 0.988 0.000 0.012
#> round_ERR2585208 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585236 3 0.2760 0.8879 0.128 0.000 0.872 0.000
#> aberrant_ERR2585284 4 0.1557 0.8633 0.000 0.056 0.000 0.944
#> round_ERR2585224 1 0.0188 0.9687 0.996 0.000 0.004 0.000
#> round_ERR2585260 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585229 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> aberrant_ERR2585364 2 0.1474 0.9313 0.000 0.948 0.000 0.052
#> round_ERR2585253 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> aberrant_ERR2585368 2 0.2021 0.9381 0.000 0.936 0.024 0.040
#> aberrant_ERR2585371 2 0.2111 0.9370 0.000 0.932 0.024 0.044
#> round_ERR2585239 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585273 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585256 3 0.3764 0.8187 0.216 0.000 0.784 0.000
#> round_ERR2585272 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585246 1 0.0188 0.9687 0.996 0.000 0.004 0.000
#> round_ERR2585261 3 0.3266 0.8679 0.168 0.000 0.832 0.000
#> round_ERR2585254 3 0.3356 0.8603 0.176 0.000 0.824 0.000
#> round_ERR2585225 3 0.2408 0.9009 0.104 0.000 0.896 0.000
#> round_ERR2585235 1 0.4643 0.3272 0.656 0.000 0.344 0.000
#> round_ERR2585271 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585251 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585255 3 0.2408 0.9009 0.104 0.000 0.896 0.000
#> round_ERR2585257 3 0.2408 0.9009 0.104 0.000 0.896 0.000
#> round_ERR2585226 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585265 1 0.0336 0.9645 0.992 0.000 0.008 0.000
#> round_ERR2585259 3 0.4948 0.4451 0.440 0.000 0.560 0.000
#> round_ERR2585247 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585241 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585263 1 0.1022 0.9386 0.968 0.000 0.032 0.000
#> round_ERR2585264 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585233 3 0.4888 0.4949 0.412 0.000 0.588 0.000
#> round_ERR2585223 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585234 3 0.2530 0.9027 0.112 0.000 0.888 0.000
#> round_ERR2585222 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585228 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585248 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585240 1 0.3873 0.6515 0.772 0.000 0.228 0.000
#> round_ERR2585270 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585232 1 0.2589 0.8381 0.884 0.000 0.116 0.000
#> aberrant_ERR2585341 2 0.2011 0.9291 0.000 0.920 0.000 0.080
#> aberrant_ERR2585355 2 0.2081 0.9283 0.000 0.916 0.000 0.084
#> round_ERR2585227 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> aberrant_ERR2585351 2 0.0469 0.9468 0.000 0.988 0.000 0.012
#> round_ERR2585269 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> aberrant_ERR2585357 2 0.2111 0.9352 0.000 0.932 0.024 0.044
#> aberrant_ERR2585350 2 0.1489 0.9449 0.000 0.952 0.004 0.044
#> round_ERR2585250 3 0.4730 0.6080 0.364 0.000 0.636 0.000
#> round_ERR2585245 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> aberrant_ERR2585353 2 0.0336 0.9469 0.000 0.992 0.000 0.008
#> round_ERR2585258 1 0.0336 0.9645 0.992 0.000 0.008 0.000
#> aberrant_ERR2585354 2 0.0376 0.9482 0.000 0.992 0.004 0.004
#> round_ERR2585249 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585268 3 0.3123 0.8735 0.156 0.000 0.844 0.000
#> aberrant_ERR2585356 2 0.0921 0.9444 0.000 0.972 0.000 0.028
#> round_ERR2585266 3 0.2647 0.9006 0.120 0.000 0.880 0.000
#> round_ERR2585231 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585230 1 0.0000 0.9713 1.000 0.000 0.000 0.000
#> round_ERR2585267 1 0.0000 0.9713 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 5 0.2450 0.826 0.000 0.076 0.000 0.028 0.896
#> aberrant_ERR2585338 5 0.4930 0.288 0.000 0.388 0.000 0.032 0.580
#> aberrant_ERR2585325 5 0.2597 0.816 0.000 0.092 0.000 0.024 0.884
#> aberrant_ERR2585283 4 0.0566 0.923 0.000 0.004 0.000 0.984 0.012
#> aberrant_ERR2585343 5 0.0162 0.852 0.000 0.000 0.000 0.004 0.996
#> aberrant_ERR2585329 2 0.2929 0.836 0.000 0.820 0.000 0.000 0.180
#> aberrant_ERR2585317 2 0.2966 0.835 0.000 0.816 0.000 0.000 0.184
#> aberrant_ERR2585339 5 0.4425 0.540 0.000 0.296 0.000 0.024 0.680
#> aberrant_ERR2585335 5 0.3452 0.559 0.000 0.244 0.000 0.000 0.756
#> aberrant_ERR2585287 4 0.0955 0.917 0.000 0.004 0.000 0.968 0.028
#> aberrant_ERR2585321 5 0.0162 0.852 0.000 0.000 0.000 0.004 0.996
#> aberrant_ERR2585297 1 0.0162 0.970 0.996 0.000 0.004 0.000 0.000
#> aberrant_ERR2585337 2 0.3003 0.834 0.000 0.812 0.000 0.000 0.188
#> aberrant_ERR2585319 5 0.0162 0.852 0.000 0.000 0.000 0.004 0.996
#> aberrant_ERR2585315 5 0.2719 0.791 0.000 0.144 0.000 0.004 0.852
#> aberrant_ERR2585336 2 0.3661 0.743 0.000 0.724 0.000 0.000 0.276
#> aberrant_ERR2585307 2 0.2646 0.787 0.000 0.868 0.004 0.004 0.124
#> aberrant_ERR2585301 5 0.3508 0.537 0.000 0.252 0.000 0.000 0.748
#> aberrant_ERR2585326 2 0.3074 0.832 0.000 0.804 0.000 0.000 0.196
#> aberrant_ERR2585331 2 0.3421 0.817 0.000 0.824 0.008 0.016 0.152
#> aberrant_ERR2585346 4 0.1892 0.878 0.000 0.004 0.000 0.916 0.080
#> aberrant_ERR2585314 2 0.2833 0.635 0.000 0.864 0.004 0.012 0.120
#> aberrant_ERR2585298 3 0.0510 0.865 0.016 0.000 0.984 0.000 0.000
#> aberrant_ERR2585345 2 0.3039 0.833 0.000 0.808 0.000 0.000 0.192
#> aberrant_ERR2585299 1 0.0162 0.970 0.996 0.000 0.004 0.000 0.000
#> aberrant_ERR2585309 1 0.0162 0.970 0.996 0.000 0.004 0.000 0.000
#> aberrant_ERR2585303 5 0.3910 0.710 0.000 0.196 0.000 0.032 0.772
#> aberrant_ERR2585313 5 0.3912 0.669 0.000 0.228 0.000 0.020 0.752
#> aberrant_ERR2585318 5 0.0794 0.843 0.000 0.028 0.000 0.000 0.972
#> aberrant_ERR2585328 5 0.3760 0.728 0.000 0.188 0.000 0.028 0.784
#> aberrant_ERR2585330 5 0.0162 0.852 0.000 0.000 0.000 0.004 0.996
#> aberrant_ERR2585293 4 0.0324 0.923 0.000 0.004 0.000 0.992 0.004
#> aberrant_ERR2585342 5 0.0000 0.852 0.000 0.000 0.000 0.000 1.000
#> aberrant_ERR2585348 5 0.2850 0.813 0.000 0.092 0.000 0.036 0.872
#> aberrant_ERR2585352 5 0.1444 0.849 0.000 0.040 0.000 0.012 0.948
#> aberrant_ERR2585308 1 0.0000 0.971 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585349 2 0.1815 0.653 0.000 0.940 0.020 0.016 0.024
#> aberrant_ERR2585316 5 0.0865 0.851 0.000 0.004 0.000 0.024 0.972
#> aberrant_ERR2585306 5 0.3563 0.632 0.000 0.208 0.000 0.012 0.780
#> aberrant_ERR2585324 5 0.0324 0.852 0.000 0.004 0.000 0.004 0.992
#> aberrant_ERR2585310 3 0.6250 0.532 0.120 0.268 0.592 0.016 0.004
#> aberrant_ERR2585296 3 0.2228 0.829 0.076 0.012 0.908 0.004 0.000
#> aberrant_ERR2585275 4 0.3455 0.666 0.000 0.008 0.000 0.784 0.208
#> aberrant_ERR2585311 5 0.0000 0.852 0.000 0.000 0.000 0.000 1.000
#> aberrant_ERR2585292 4 0.0324 0.923 0.000 0.004 0.000 0.992 0.004
#> aberrant_ERR2585282 5 0.0000 0.852 0.000 0.000 0.000 0.000 1.000
#> aberrant_ERR2585305 2 0.4676 0.363 0.000 0.592 0.004 0.012 0.392
#> aberrant_ERR2585278 5 0.2020 0.802 0.000 0.100 0.000 0.000 0.900
#> aberrant_ERR2585347 5 0.1997 0.840 0.000 0.036 0.000 0.040 0.924
#> aberrant_ERR2585332 5 0.0798 0.852 0.000 0.008 0.000 0.016 0.976
#> aberrant_ERR2585280 5 0.1300 0.850 0.000 0.028 0.000 0.016 0.956
#> aberrant_ERR2585304 2 0.3685 0.495 0.000 0.816 0.148 0.016 0.020
#> aberrant_ERR2585322 5 0.4582 0.188 0.000 0.416 0.000 0.012 0.572
#> aberrant_ERR2585279 2 0.1524 0.647 0.000 0.952 0.016 0.016 0.016
#> aberrant_ERR2585277 2 0.5019 0.280 0.000 0.532 0.000 0.032 0.436
#> aberrant_ERR2585295 5 0.3055 0.780 0.000 0.144 0.000 0.016 0.840
#> aberrant_ERR2585333 5 0.0162 0.852 0.000 0.000 0.000 0.004 0.996
#> aberrant_ERR2585285 5 0.0324 0.853 0.000 0.004 0.000 0.004 0.992
#> aberrant_ERR2585286 5 0.4794 0.419 0.000 0.344 0.000 0.032 0.624
#> aberrant_ERR2585294 5 0.2852 0.697 0.000 0.172 0.000 0.000 0.828
#> aberrant_ERR2585300 5 0.0162 0.852 0.000 0.004 0.000 0.000 0.996
#> aberrant_ERR2585334 2 0.3463 0.820 0.000 0.820 0.008 0.016 0.156
#> aberrant_ERR2585361 5 0.3115 0.798 0.000 0.112 0.000 0.036 0.852
#> aberrant_ERR2585372 5 0.0162 0.852 0.000 0.000 0.000 0.004 0.996
#> round_ERR2585217 3 0.0609 0.865 0.020 0.000 0.980 0.000 0.000
#> round_ERR2585205 1 0.0162 0.971 0.996 0.004 0.000 0.000 0.000
#> round_ERR2585214 3 0.0162 0.860 0.004 0.000 0.996 0.000 0.000
#> round_ERR2585202 3 0.2570 0.802 0.008 0.108 0.880 0.004 0.000
#> aberrant_ERR2585367 5 0.4010 0.694 0.000 0.208 0.000 0.032 0.760
#> round_ERR2585220 1 0.0162 0.970 0.996 0.000 0.000 0.004 0.000
#> round_ERR2585238 1 0.0162 0.970 0.996 0.000 0.000 0.004 0.000
#> aberrant_ERR2585276 5 0.2074 0.780 0.000 0.104 0.000 0.000 0.896
#> round_ERR2585218 1 0.0162 0.971 0.996 0.004 0.000 0.000 0.000
#> aberrant_ERR2585363 5 0.1205 0.849 0.000 0.040 0.000 0.004 0.956
#> round_ERR2585201 3 0.0609 0.864 0.020 0.000 0.980 0.000 0.000
#> round_ERR2585210 1 0.0000 0.971 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585362 5 0.1792 0.836 0.000 0.084 0.000 0.000 0.916
#> aberrant_ERR2585360 5 0.1197 0.829 0.000 0.048 0.000 0.000 0.952
#> round_ERR2585209 3 0.3837 0.557 0.308 0.000 0.692 0.000 0.000
#> round_ERR2585242 3 0.0510 0.865 0.016 0.000 0.984 0.000 0.000
#> round_ERR2585216 1 0.0162 0.970 0.996 0.000 0.000 0.004 0.000
#> round_ERR2585219 1 0.0162 0.970 0.996 0.000 0.000 0.004 0.000
#> round_ERR2585237 3 0.0404 0.863 0.012 0.000 0.988 0.000 0.000
#> round_ERR2585198 3 0.0510 0.865 0.016 0.000 0.984 0.000 0.000
#> round_ERR2585211 1 0.0324 0.969 0.992 0.004 0.004 0.000 0.000
#> round_ERR2585206 1 0.0162 0.971 0.996 0.004 0.000 0.000 0.000
#> aberrant_ERR2585281 5 0.4428 0.593 0.000 0.268 0.000 0.032 0.700
#> round_ERR2585212 1 0.1041 0.942 0.964 0.000 0.032 0.004 0.000
#> round_ERR2585221 1 0.0000 0.971 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585243 1 0.0162 0.970 0.996 0.000 0.004 0.000 0.000
#> round_ERR2585204 3 0.0162 0.860 0.004 0.000 0.996 0.000 0.000
#> round_ERR2585213 3 0.2497 0.797 0.004 0.112 0.880 0.004 0.000
#> aberrant_ERR2585373 5 0.0162 0.852 0.000 0.000 0.000 0.004 0.996
#> aberrant_ERR2585358 5 0.0162 0.852 0.000 0.000 0.000 0.004 0.996
#> aberrant_ERR2585365 5 0.3944 0.704 0.000 0.200 0.000 0.032 0.768
#> aberrant_ERR2585359 5 0.0290 0.853 0.000 0.000 0.000 0.008 0.992
#> aberrant_ERR2585370 2 0.3607 0.785 0.000 0.752 0.000 0.004 0.244
#> round_ERR2585215 1 0.0324 0.969 0.992 0.004 0.004 0.000 0.000
#> round_ERR2585262 3 0.3527 0.708 0.000 0.192 0.792 0.016 0.000
#> round_ERR2585199 3 0.0613 0.860 0.008 0.004 0.984 0.004 0.000
#> aberrant_ERR2585369 5 0.0324 0.853 0.000 0.004 0.000 0.004 0.992
#> round_ERR2585208 1 0.0324 0.969 0.992 0.004 0.004 0.000 0.000
#> round_ERR2585252 1 0.0162 0.971 0.996 0.004 0.000 0.000 0.000
#> round_ERR2585236 3 0.3607 0.643 0.244 0.004 0.752 0.000 0.000
#> aberrant_ERR2585284 4 0.0324 0.923 0.000 0.004 0.000 0.992 0.004
#> round_ERR2585224 1 0.0162 0.970 0.996 0.000 0.004 0.000 0.000
#> round_ERR2585260 1 0.0000 0.971 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585229 1 0.0000 0.971 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585364 5 0.0794 0.849 0.000 0.000 0.000 0.028 0.972
#> round_ERR2585253 1 0.0451 0.967 0.988 0.008 0.004 0.000 0.000
#> aberrant_ERR2585368 2 0.2929 0.836 0.000 0.820 0.000 0.000 0.180
#> aberrant_ERR2585371 2 0.2929 0.836 0.000 0.820 0.000 0.000 0.180
#> round_ERR2585239 1 0.0000 0.971 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585273 1 0.0000 0.971 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585256 3 0.1410 0.850 0.060 0.000 0.940 0.000 0.000
#> round_ERR2585272 1 0.0451 0.968 0.988 0.004 0.008 0.000 0.000
#> round_ERR2585246 1 0.0162 0.970 0.996 0.000 0.004 0.000 0.000
#> round_ERR2585261 3 0.0963 0.861 0.036 0.000 0.964 0.000 0.000
#> round_ERR2585254 3 0.1197 0.856 0.048 0.000 0.952 0.000 0.000
#> round_ERR2585225 3 0.0290 0.863 0.008 0.000 0.992 0.000 0.000
#> round_ERR2585235 1 0.4192 0.221 0.596 0.000 0.404 0.000 0.000
#> round_ERR2585271 1 0.0000 0.971 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585251 1 0.0162 0.970 0.996 0.000 0.000 0.004 0.000
#> round_ERR2585255 3 0.0290 0.863 0.008 0.000 0.992 0.000 0.000
#> round_ERR2585257 3 0.0290 0.863 0.008 0.000 0.992 0.000 0.000
#> round_ERR2585226 1 0.0162 0.970 0.996 0.000 0.000 0.004 0.000
#> round_ERR2585265 1 0.0162 0.970 0.996 0.000 0.000 0.004 0.000
#> round_ERR2585259 3 0.4225 0.498 0.364 0.000 0.632 0.004 0.000
#> round_ERR2585247 1 0.0000 0.971 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585241 1 0.0162 0.971 0.996 0.004 0.000 0.000 0.000
#> round_ERR2585263 1 0.1502 0.917 0.940 0.000 0.056 0.004 0.000
#> round_ERR2585264 1 0.0451 0.967 0.988 0.008 0.004 0.000 0.000
#> round_ERR2585233 3 0.2813 0.742 0.168 0.000 0.832 0.000 0.000
#> round_ERR2585223 1 0.0162 0.970 0.996 0.000 0.000 0.004 0.000
#> round_ERR2585234 3 0.0290 0.863 0.008 0.000 0.992 0.000 0.000
#> round_ERR2585222 1 0.0162 0.970 0.996 0.000 0.004 0.000 0.000
#> round_ERR2585228 1 0.0162 0.970 0.996 0.000 0.000 0.004 0.000
#> round_ERR2585248 1 0.0451 0.967 0.988 0.008 0.004 0.000 0.000
#> round_ERR2585240 1 0.4088 0.377 0.632 0.000 0.368 0.000 0.000
#> round_ERR2585270 1 0.0162 0.970 0.996 0.000 0.000 0.004 0.000
#> round_ERR2585232 1 0.3586 0.615 0.736 0.000 0.264 0.000 0.000
#> aberrant_ERR2585341 5 0.4380 0.608 0.000 0.260 0.000 0.032 0.708
#> aberrant_ERR2585355 5 0.4774 0.380 0.000 0.360 0.000 0.028 0.612
#> round_ERR2585227 1 0.0451 0.967 0.988 0.000 0.008 0.004 0.000
#> aberrant_ERR2585351 5 0.0404 0.854 0.000 0.012 0.000 0.000 0.988
#> round_ERR2585269 1 0.0162 0.971 0.996 0.004 0.000 0.000 0.000
#> aberrant_ERR2585357 2 0.2929 0.836 0.000 0.820 0.000 0.000 0.180
#> aberrant_ERR2585350 2 0.4201 0.635 0.000 0.664 0.000 0.008 0.328
#> round_ERR2585250 3 0.4350 0.402 0.408 0.000 0.588 0.004 0.000
#> round_ERR2585245 1 0.0290 0.969 0.992 0.008 0.000 0.000 0.000
#> aberrant_ERR2585353 5 0.0404 0.853 0.000 0.012 0.000 0.000 0.988
#> round_ERR2585258 1 0.0162 0.970 0.996 0.000 0.000 0.004 0.000
#> aberrant_ERR2585354 5 0.0290 0.853 0.000 0.008 0.000 0.000 0.992
#> round_ERR2585249 1 0.0162 0.971 0.996 0.004 0.000 0.000 0.000
#> round_ERR2585268 3 0.2690 0.759 0.156 0.000 0.844 0.000 0.000
#> aberrant_ERR2585356 5 0.0162 0.852 0.000 0.000 0.000 0.004 0.996
#> round_ERR2585266 3 0.0510 0.865 0.016 0.000 0.984 0.000 0.000
#> round_ERR2585231 1 0.0324 0.969 0.992 0.004 0.004 0.000 0.000
#> round_ERR2585230 1 0.0162 0.970 0.996 0.000 0.000 0.004 0.000
#> round_ERR2585267 1 0.0324 0.969 0.992 0.004 0.004 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 5 0.4804 -0.458 0.000 0.032 0.000 0.012 0.540 0.416
#> aberrant_ERR2585338 2 0.5679 0.254 0.000 0.580 0.000 0.012 0.204 0.204
#> aberrant_ERR2585325 5 0.4935 -0.521 0.000 0.040 0.000 0.012 0.524 0.424
#> aberrant_ERR2585283 4 0.0820 0.902 0.000 0.000 0.000 0.972 0.016 0.012
#> aberrant_ERR2585343 5 0.2053 0.593 0.000 0.000 0.000 0.004 0.888 0.108
#> aberrant_ERR2585329 2 0.0865 0.786 0.000 0.964 0.000 0.000 0.036 0.000
#> aberrant_ERR2585317 2 0.0865 0.786 0.000 0.964 0.000 0.000 0.036 0.000
#> aberrant_ERR2585339 5 0.6160 -0.460 0.000 0.340 0.000 0.008 0.428 0.224
#> aberrant_ERR2585335 5 0.3052 0.506 0.000 0.216 0.000 0.000 0.780 0.004
#> aberrant_ERR2585287 4 0.2826 0.853 0.000 0.000 0.000 0.856 0.052 0.092
#> aberrant_ERR2585321 5 0.0547 0.652 0.000 0.020 0.000 0.000 0.980 0.000
#> aberrant_ERR2585297 1 0.0865 0.915 0.964 0.000 0.000 0.000 0.000 0.036
#> aberrant_ERR2585337 2 0.0865 0.786 0.000 0.964 0.000 0.000 0.036 0.000
#> aberrant_ERR2585319 5 0.1442 0.649 0.000 0.040 0.000 0.004 0.944 0.012
#> aberrant_ERR2585315 5 0.3712 0.471 0.000 0.204 0.000 0.004 0.760 0.032
#> aberrant_ERR2585336 2 0.1779 0.775 0.000 0.920 0.000 0.000 0.064 0.016
#> aberrant_ERR2585307 2 0.3752 0.754 0.000 0.808 0.004 0.012 0.072 0.104
#> aberrant_ERR2585301 5 0.3102 0.559 0.000 0.156 0.000 0.000 0.816 0.028
#> aberrant_ERR2585326 2 0.0790 0.786 0.000 0.968 0.000 0.000 0.032 0.000
#> aberrant_ERR2585331 2 0.3539 0.763 0.000 0.832 0.004 0.024 0.056 0.084
#> aberrant_ERR2585346 4 0.2309 0.866 0.000 0.000 0.000 0.888 0.084 0.028
#> aberrant_ERR2585314 2 0.4871 0.652 0.000 0.708 0.004 0.024 0.084 0.180
#> aberrant_ERR2585298 3 0.0146 0.821 0.004 0.000 0.996 0.000 0.000 0.000
#> aberrant_ERR2585345 2 0.0790 0.786 0.000 0.968 0.000 0.000 0.032 0.000
#> aberrant_ERR2585299 1 0.0547 0.914 0.980 0.000 0.000 0.000 0.000 0.020
#> aberrant_ERR2585309 1 0.2135 0.887 0.872 0.000 0.000 0.000 0.000 0.128
#> aberrant_ERR2585303 5 0.5893 -0.858 0.000 0.140 0.000 0.012 0.428 0.420
#> aberrant_ERR2585313 5 0.6003 -0.413 0.000 0.368 0.000 0.008 0.444 0.180
#> aberrant_ERR2585318 5 0.2019 0.623 0.000 0.088 0.000 0.000 0.900 0.012
#> aberrant_ERR2585328 5 0.5674 -0.801 0.000 0.120 0.000 0.008 0.448 0.424
#> aberrant_ERR2585330 5 0.1237 0.651 0.000 0.020 0.000 0.004 0.956 0.020
#> aberrant_ERR2585293 4 0.0146 0.901 0.000 0.000 0.000 0.996 0.000 0.004
#> aberrant_ERR2585342 5 0.0865 0.640 0.000 0.000 0.000 0.000 0.964 0.036
#> aberrant_ERR2585348 5 0.5178 -0.567 0.000 0.052 0.000 0.016 0.508 0.424
#> aberrant_ERR2585352 5 0.3086 0.616 0.000 0.080 0.000 0.012 0.852 0.056
#> aberrant_ERR2585308 1 0.2631 0.861 0.820 0.000 0.000 0.000 0.000 0.180
#> aberrant_ERR2585349 2 0.4453 0.651 0.000 0.720 0.008 0.028 0.024 0.220
#> aberrant_ERR2585316 5 0.2376 0.605 0.000 0.000 0.000 0.044 0.888 0.068
#> aberrant_ERR2585306 5 0.4187 0.495 0.000 0.080 0.000 0.024 0.772 0.124
#> aberrant_ERR2585324 5 0.1829 0.639 0.000 0.064 0.000 0.004 0.920 0.012
#> aberrant_ERR2585310 3 0.7573 0.330 0.124 0.160 0.444 0.028 0.000 0.244
#> aberrant_ERR2585296 3 0.3213 0.762 0.076 0.000 0.836 0.004 0.000 0.084
#> aberrant_ERR2585275 4 0.3960 0.672 0.000 0.000 0.000 0.752 0.176 0.072
#> aberrant_ERR2585311 5 0.1074 0.650 0.000 0.028 0.000 0.000 0.960 0.012
#> aberrant_ERR2585292 4 0.0146 0.901 0.000 0.000 0.000 0.996 0.000 0.004
#> aberrant_ERR2585282 5 0.1714 0.612 0.000 0.000 0.000 0.000 0.908 0.092
#> aberrant_ERR2585305 5 0.5690 0.265 0.000 0.192 0.000 0.024 0.604 0.180
#> aberrant_ERR2585278 5 0.2814 0.558 0.000 0.172 0.000 0.000 0.820 0.008
#> aberrant_ERR2585347 5 0.4334 -0.302 0.000 0.000 0.000 0.024 0.568 0.408
#> aberrant_ERR2585332 5 0.3541 0.303 0.000 0.000 0.000 0.012 0.728 0.260
#> aberrant_ERR2585280 5 0.2730 0.524 0.000 0.000 0.000 0.012 0.836 0.152
#> aberrant_ERR2585304 2 0.5606 0.551 0.000 0.636 0.084 0.024 0.020 0.236
#> aberrant_ERR2585322 2 0.4884 0.422 0.000 0.652 0.000 0.000 0.220 0.128
#> aberrant_ERR2585279 2 0.4239 0.667 0.000 0.744 0.008 0.024 0.024 0.200
#> aberrant_ERR2585277 2 0.5453 0.362 0.000 0.616 0.000 0.012 0.192 0.180
#> aberrant_ERR2585295 5 0.5375 -0.707 0.000 0.096 0.000 0.004 0.484 0.416
#> aberrant_ERR2585333 5 0.0767 0.652 0.000 0.008 0.000 0.004 0.976 0.012
#> aberrant_ERR2585285 5 0.1116 0.645 0.000 0.008 0.000 0.004 0.960 0.028
#> aberrant_ERR2585286 2 0.6345 -0.433 0.000 0.392 0.000 0.012 0.260 0.336
#> aberrant_ERR2585294 5 0.2760 0.595 0.000 0.116 0.000 0.004 0.856 0.024
#> aberrant_ERR2585300 5 0.0909 0.651 0.000 0.020 0.000 0.000 0.968 0.012
#> aberrant_ERR2585334 2 0.3766 0.756 0.000 0.812 0.004 0.024 0.052 0.108
#> aberrant_ERR2585361 5 0.5228 -0.588 0.000 0.056 0.000 0.016 0.504 0.424
#> aberrant_ERR2585372 5 0.1700 0.618 0.000 0.000 0.000 0.004 0.916 0.080
#> round_ERR2585217 3 0.0508 0.822 0.012 0.000 0.984 0.000 0.000 0.004
#> round_ERR2585205 1 0.0790 0.915 0.968 0.000 0.000 0.000 0.000 0.032
#> round_ERR2585214 3 0.0146 0.821 0.004 0.000 0.996 0.000 0.000 0.000
#> round_ERR2585202 3 0.3596 0.720 0.004 0.040 0.796 0.004 0.000 0.156
#> aberrant_ERR2585367 6 0.5731 0.761 0.000 0.116 0.000 0.012 0.432 0.440
#> round_ERR2585220 1 0.1327 0.901 0.936 0.000 0.000 0.000 0.000 0.064
#> round_ERR2585238 1 0.1007 0.914 0.956 0.000 0.000 0.000 0.000 0.044
#> aberrant_ERR2585276 5 0.2679 0.604 0.000 0.096 0.000 0.004 0.868 0.032
#> round_ERR2585218 1 0.0865 0.913 0.964 0.000 0.000 0.000 0.000 0.036
#> aberrant_ERR2585363 5 0.2795 0.586 0.000 0.044 0.000 0.000 0.856 0.100
#> round_ERR2585201 3 0.0405 0.821 0.008 0.000 0.988 0.000 0.000 0.004
#> round_ERR2585210 1 0.0717 0.913 0.976 0.000 0.016 0.000 0.000 0.008
#> aberrant_ERR2585362 5 0.4881 -0.129 0.000 0.068 0.000 0.004 0.604 0.324
#> aberrant_ERR2585360 5 0.2250 0.617 0.000 0.092 0.000 0.000 0.888 0.020
#> round_ERR2585209 3 0.3371 0.684 0.200 0.000 0.780 0.004 0.000 0.016
#> round_ERR2585242 3 0.0260 0.822 0.008 0.000 0.992 0.000 0.000 0.000
#> round_ERR2585216 1 0.1007 0.909 0.956 0.000 0.000 0.000 0.000 0.044
#> round_ERR2585219 1 0.0937 0.909 0.960 0.000 0.000 0.000 0.000 0.040
#> round_ERR2585237 3 0.0405 0.820 0.004 0.000 0.988 0.000 0.000 0.008
#> round_ERR2585198 3 0.0405 0.821 0.008 0.000 0.988 0.000 0.000 0.004
#> round_ERR2585211 1 0.2178 0.889 0.868 0.000 0.000 0.000 0.000 0.132
#> round_ERR2585206 1 0.1663 0.903 0.912 0.000 0.000 0.000 0.000 0.088
#> aberrant_ERR2585281 6 0.6368 0.844 0.000 0.196 0.000 0.024 0.360 0.420
#> round_ERR2585212 1 0.2328 0.871 0.892 0.000 0.052 0.000 0.000 0.056
#> round_ERR2585221 1 0.2003 0.892 0.884 0.000 0.000 0.000 0.000 0.116
#> round_ERR2585243 1 0.0914 0.911 0.968 0.000 0.016 0.000 0.000 0.016
#> round_ERR2585204 3 0.0146 0.821 0.004 0.000 0.996 0.000 0.000 0.000
#> round_ERR2585213 3 0.2981 0.726 0.000 0.020 0.820 0.000 0.000 0.160
#> aberrant_ERR2585373 5 0.1059 0.653 0.000 0.016 0.000 0.004 0.964 0.016
#> aberrant_ERR2585358 5 0.1812 0.617 0.000 0.000 0.000 0.008 0.912 0.080
#> aberrant_ERR2585365 5 0.5899 -0.800 0.000 0.144 0.000 0.012 0.460 0.384
#> aberrant_ERR2585359 5 0.1471 0.626 0.000 0.000 0.000 0.004 0.932 0.064
#> aberrant_ERR2585370 2 0.1812 0.767 0.000 0.912 0.000 0.000 0.080 0.008
#> round_ERR2585215 1 0.2178 0.889 0.868 0.000 0.000 0.000 0.000 0.132
#> round_ERR2585262 3 0.4417 0.638 0.000 0.044 0.736 0.024 0.004 0.192
#> round_ERR2585199 3 0.1429 0.806 0.004 0.000 0.940 0.004 0.000 0.052
#> aberrant_ERR2585369 5 0.1390 0.648 0.000 0.016 0.000 0.004 0.948 0.032
#> round_ERR2585208 1 0.2092 0.890 0.876 0.000 0.000 0.000 0.000 0.124
#> round_ERR2585252 1 0.2527 0.867 0.832 0.000 0.000 0.000 0.000 0.168
#> round_ERR2585236 3 0.4449 0.607 0.272 0.000 0.672 0.004 0.000 0.052
#> aberrant_ERR2585284 4 0.0146 0.901 0.000 0.000 0.000 0.996 0.000 0.004
#> round_ERR2585224 1 0.2823 0.843 0.796 0.000 0.000 0.000 0.000 0.204
#> round_ERR2585260 1 0.1007 0.910 0.956 0.000 0.000 0.000 0.000 0.044
#> round_ERR2585229 1 0.1267 0.909 0.940 0.000 0.000 0.000 0.000 0.060
#> aberrant_ERR2585364 5 0.3092 0.555 0.000 0.000 0.000 0.104 0.836 0.060
#> round_ERR2585253 1 0.2941 0.833 0.780 0.000 0.000 0.000 0.000 0.220
#> aberrant_ERR2585368 2 0.0935 0.786 0.000 0.964 0.000 0.000 0.032 0.004
#> aberrant_ERR2585371 2 0.0935 0.786 0.000 0.964 0.000 0.000 0.032 0.004
#> round_ERR2585239 1 0.1010 0.912 0.960 0.000 0.004 0.000 0.000 0.036
#> round_ERR2585273 1 0.0790 0.913 0.968 0.000 0.000 0.000 0.000 0.032
#> round_ERR2585256 3 0.1442 0.814 0.040 0.000 0.944 0.004 0.000 0.012
#> round_ERR2585272 1 0.1245 0.909 0.952 0.000 0.016 0.000 0.000 0.032
#> round_ERR2585246 1 0.0790 0.915 0.968 0.000 0.000 0.000 0.000 0.032
#> round_ERR2585261 3 0.1036 0.820 0.024 0.000 0.964 0.004 0.000 0.008
#> round_ERR2585254 3 0.0922 0.819 0.024 0.000 0.968 0.004 0.000 0.004
#> round_ERR2585225 3 0.0260 0.821 0.008 0.000 0.992 0.000 0.000 0.000
#> round_ERR2585235 3 0.4264 0.228 0.488 0.000 0.496 0.000 0.000 0.016
#> round_ERR2585271 1 0.0865 0.914 0.964 0.000 0.000 0.000 0.000 0.036
#> round_ERR2585251 1 0.1387 0.901 0.932 0.000 0.000 0.000 0.000 0.068
#> round_ERR2585255 3 0.0146 0.821 0.004 0.000 0.996 0.000 0.000 0.000
#> round_ERR2585257 3 0.0146 0.821 0.004 0.000 0.996 0.000 0.000 0.000
#> round_ERR2585226 1 0.1267 0.903 0.940 0.000 0.000 0.000 0.000 0.060
#> round_ERR2585265 1 0.1141 0.906 0.948 0.000 0.000 0.000 0.000 0.052
#> round_ERR2585259 3 0.4118 0.538 0.352 0.000 0.628 0.000 0.000 0.020
#> round_ERR2585247 1 0.0713 0.915 0.972 0.000 0.000 0.000 0.000 0.028
#> round_ERR2585241 1 0.0713 0.914 0.972 0.000 0.000 0.000 0.000 0.028
#> round_ERR2585263 1 0.3332 0.752 0.808 0.000 0.144 0.000 0.000 0.048
#> round_ERR2585264 1 0.2941 0.833 0.780 0.000 0.000 0.000 0.000 0.220
#> round_ERR2585233 3 0.1806 0.787 0.088 0.000 0.908 0.000 0.000 0.004
#> round_ERR2585223 1 0.0632 0.914 0.976 0.000 0.000 0.000 0.000 0.024
#> round_ERR2585234 3 0.0146 0.821 0.004 0.000 0.996 0.000 0.000 0.000
#> round_ERR2585222 1 0.1082 0.909 0.956 0.000 0.004 0.000 0.000 0.040
#> round_ERR2585228 1 0.0632 0.912 0.976 0.000 0.000 0.000 0.000 0.024
#> round_ERR2585248 1 0.2912 0.835 0.784 0.000 0.000 0.000 0.000 0.216
#> round_ERR2585240 3 0.4262 0.089 0.476 0.000 0.508 0.000 0.000 0.016
#> round_ERR2585270 1 0.1204 0.904 0.944 0.000 0.000 0.000 0.000 0.056
#> round_ERR2585232 1 0.3615 0.562 0.700 0.000 0.292 0.000 0.000 0.008
#> aberrant_ERR2585341 6 0.6108 0.868 0.000 0.184 0.000 0.012 0.368 0.436
#> aberrant_ERR2585355 2 0.6261 -0.300 0.000 0.436 0.000 0.012 0.244 0.308
#> round_ERR2585227 1 0.1524 0.901 0.932 0.000 0.008 0.000 0.000 0.060
#> aberrant_ERR2585351 5 0.2007 0.650 0.000 0.044 0.000 0.004 0.916 0.036
#> round_ERR2585269 1 0.2730 0.852 0.808 0.000 0.000 0.000 0.000 0.192
#> aberrant_ERR2585357 2 0.0790 0.786 0.000 0.968 0.000 0.000 0.032 0.000
#> aberrant_ERR2585350 2 0.2839 0.736 0.000 0.860 0.000 0.004 0.092 0.044
#> round_ERR2585250 3 0.4641 0.450 0.396 0.000 0.564 0.004 0.000 0.036
#> round_ERR2585245 1 0.2912 0.835 0.784 0.000 0.000 0.000 0.000 0.216
#> aberrant_ERR2585353 5 0.2823 0.452 0.000 0.000 0.000 0.000 0.796 0.204
#> round_ERR2585258 1 0.0713 0.912 0.972 0.000 0.000 0.000 0.000 0.028
#> aberrant_ERR2585354 5 0.0891 0.653 0.000 0.024 0.000 0.000 0.968 0.008
#> round_ERR2585249 1 0.2883 0.838 0.788 0.000 0.000 0.000 0.000 0.212
#> round_ERR2585268 3 0.3744 0.645 0.256 0.000 0.724 0.004 0.000 0.016
#> aberrant_ERR2585356 5 0.1173 0.651 0.000 0.016 0.000 0.008 0.960 0.016
#> round_ERR2585266 3 0.0260 0.822 0.008 0.000 0.992 0.000 0.000 0.000
#> round_ERR2585231 1 0.2941 0.833 0.780 0.000 0.000 0.000 0.000 0.220
#> round_ERR2585230 1 0.0937 0.909 0.960 0.000 0.000 0.000 0.000 0.040
#> round_ERR2585267 1 0.1910 0.898 0.892 0.000 0.000 0.000 0.000 0.108
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> MAD:mclust 160 3.08e-30 2
#> MAD:mclust 149 2.89e-28 3
#> MAD:mclust 155 7.53e-28 4
#> MAD:mclust 149 9.56e-26 5
#> MAD:mclust 134 1.62e-22 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "NMF"]
# you can also extract it by
# res = res_list["MAD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'MAD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.983 0.993 0.5026 0.498 0.498
#> 3 3 0.650 0.717 0.854 0.2623 0.800 0.617
#> 4 4 0.640 0.775 0.866 0.0621 0.904 0.759
#> 5 5 0.575 0.634 0.808 0.0700 0.987 0.964
#> 6 6 0.578 0.431 0.707 0.0604 0.962 0.891
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585338 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585325 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585283 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585343 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585329 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585317 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585339 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585335 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585287 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585321 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585297 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585319 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585315 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585336 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585307 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585301 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585326 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585331 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585346 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585314 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585298 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585345 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585299 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585303 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585313 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585318 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585328 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585330 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585293 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585342 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585348 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585352 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585308 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585349 2 0.0376 0.986 0.004 0.996
#> aberrant_ERR2585316 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585306 2 0.0672 0.983 0.008 0.992
#> aberrant_ERR2585324 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585310 1 0.2423 0.957 0.960 0.040
#> aberrant_ERR2585296 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585275 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585311 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585292 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585282 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585305 2 0.2423 0.952 0.040 0.960
#> aberrant_ERR2585278 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585347 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585332 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585280 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585304 2 0.1633 0.968 0.024 0.976
#> aberrant_ERR2585322 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585279 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585277 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585295 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585333 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585285 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585286 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585294 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585300 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585334 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585361 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585372 2 0.0000 0.990 0.000 1.000
#> round_ERR2585217 1 0.0000 0.995 1.000 0.000
#> round_ERR2585205 1 0.0000 0.995 1.000 0.000
#> round_ERR2585214 1 0.3274 0.936 0.940 0.060
#> round_ERR2585202 2 0.9661 0.357 0.392 0.608
#> aberrant_ERR2585367 2 0.0000 0.990 0.000 1.000
#> round_ERR2585220 1 0.0000 0.995 1.000 0.000
#> round_ERR2585238 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585276 2 0.0000 0.990 0.000 1.000
#> round_ERR2585218 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.990 0.000 1.000
#> round_ERR2585201 1 0.0000 0.995 1.000 0.000
#> round_ERR2585210 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585362 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585360 2 0.0000 0.990 0.000 1.000
#> round_ERR2585209 1 0.0000 0.995 1.000 0.000
#> round_ERR2585242 1 0.0000 0.995 1.000 0.000
#> round_ERR2585216 1 0.0000 0.995 1.000 0.000
#> round_ERR2585219 1 0.0000 0.995 1.000 0.000
#> round_ERR2585237 1 0.0672 0.988 0.992 0.008
#> round_ERR2585198 1 0.0000 0.995 1.000 0.000
#> round_ERR2585211 1 0.0000 0.995 1.000 0.000
#> round_ERR2585206 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585281 2 0.0000 0.990 0.000 1.000
#> round_ERR2585212 1 0.0000 0.995 1.000 0.000
#> round_ERR2585221 1 0.0000 0.995 1.000 0.000
#> round_ERR2585243 1 0.0000 0.995 1.000 0.000
#> round_ERR2585204 1 0.7139 0.756 0.804 0.196
#> round_ERR2585213 2 0.2948 0.939 0.052 0.948
#> aberrant_ERR2585373 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585358 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585365 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585359 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585370 2 0.0000 0.990 0.000 1.000
#> round_ERR2585215 1 0.0000 0.995 1.000 0.000
#> round_ERR2585262 2 0.8909 0.559 0.308 0.692
#> round_ERR2585199 1 0.1843 0.970 0.972 0.028
#> aberrant_ERR2585369 2 0.0000 0.990 0.000 1.000
#> round_ERR2585208 1 0.0000 0.995 1.000 0.000
#> round_ERR2585252 1 0.0000 0.995 1.000 0.000
#> round_ERR2585236 1 0.0938 0.985 0.988 0.012
#> aberrant_ERR2585284 2 0.0000 0.990 0.000 1.000
#> round_ERR2585224 1 0.0000 0.995 1.000 0.000
#> round_ERR2585260 1 0.0000 0.995 1.000 0.000
#> round_ERR2585229 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585364 2 0.0000 0.990 0.000 1.000
#> round_ERR2585253 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585371 2 0.0000 0.990 0.000 1.000
#> round_ERR2585239 1 0.0000 0.995 1.000 0.000
#> round_ERR2585273 1 0.0000 0.995 1.000 0.000
#> round_ERR2585256 1 0.0000 0.995 1.000 0.000
#> round_ERR2585272 1 0.0000 0.995 1.000 0.000
#> round_ERR2585246 1 0.0000 0.995 1.000 0.000
#> round_ERR2585261 1 0.0000 0.995 1.000 0.000
#> round_ERR2585254 1 0.0000 0.995 1.000 0.000
#> round_ERR2585225 1 0.0000 0.995 1.000 0.000
#> round_ERR2585235 1 0.0000 0.995 1.000 0.000
#> round_ERR2585271 1 0.0000 0.995 1.000 0.000
#> round_ERR2585251 1 0.0000 0.995 1.000 0.000
#> round_ERR2585255 1 0.0376 0.992 0.996 0.004
#> round_ERR2585257 1 0.0000 0.995 1.000 0.000
#> round_ERR2585226 1 0.0000 0.995 1.000 0.000
#> round_ERR2585265 1 0.0000 0.995 1.000 0.000
#> round_ERR2585259 1 0.0000 0.995 1.000 0.000
#> round_ERR2585247 1 0.0000 0.995 1.000 0.000
#> round_ERR2585241 1 0.0000 0.995 1.000 0.000
#> round_ERR2585263 1 0.0000 0.995 1.000 0.000
#> round_ERR2585264 1 0.0000 0.995 1.000 0.000
#> round_ERR2585233 1 0.0000 0.995 1.000 0.000
#> round_ERR2585223 1 0.0000 0.995 1.000 0.000
#> round_ERR2585234 1 0.0000 0.995 1.000 0.000
#> round_ERR2585222 1 0.0000 0.995 1.000 0.000
#> round_ERR2585228 1 0.0000 0.995 1.000 0.000
#> round_ERR2585248 1 0.0000 0.995 1.000 0.000
#> round_ERR2585240 1 0.0000 0.995 1.000 0.000
#> round_ERR2585270 1 0.0000 0.995 1.000 0.000
#> round_ERR2585232 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585341 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585355 2 0.0000 0.990 0.000 1.000
#> round_ERR2585227 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585351 2 0.0000 0.990 0.000 1.000
#> round_ERR2585269 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.990 0.000 1.000
#> aberrant_ERR2585350 2 0.0000 0.990 0.000 1.000
#> round_ERR2585250 1 0.0000 0.995 1.000 0.000
#> round_ERR2585245 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.990 0.000 1.000
#> round_ERR2585258 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.990 0.000 1.000
#> round_ERR2585249 1 0.0000 0.995 1.000 0.000
#> round_ERR2585268 1 0.0000 0.995 1.000 0.000
#> aberrant_ERR2585356 2 0.0000 0.990 0.000 1.000
#> round_ERR2585266 1 0.0000 0.995 1.000 0.000
#> round_ERR2585231 1 0.0000 0.995 1.000 0.000
#> round_ERR2585230 1 0.0000 0.995 1.000 0.000
#> round_ERR2585267 1 0.0000 0.995 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.5905 0.5757 0.000 0.648 0.352
#> aberrant_ERR2585338 3 0.5138 0.5411 0.000 0.252 0.748
#> aberrant_ERR2585325 2 0.5785 0.6035 0.000 0.668 0.332
#> aberrant_ERR2585283 2 0.0000 0.7107 0.000 1.000 0.000
#> aberrant_ERR2585343 2 0.3619 0.7356 0.000 0.864 0.136
#> aberrant_ERR2585329 3 0.6286 -0.0673 0.000 0.464 0.536
#> aberrant_ERR2585317 2 0.6252 0.3831 0.000 0.556 0.444
#> aberrant_ERR2585339 2 0.5926 0.5696 0.000 0.644 0.356
#> aberrant_ERR2585335 2 0.4399 0.7179 0.000 0.812 0.188
#> aberrant_ERR2585287 2 0.3340 0.7246 0.000 0.880 0.120
#> aberrant_ERR2585321 2 0.3192 0.7380 0.000 0.888 0.112
#> aberrant_ERR2585297 1 0.0000 0.9645 1.000 0.000 0.000
#> aberrant_ERR2585337 2 0.6180 0.4577 0.000 0.584 0.416
#> aberrant_ERR2585319 2 0.0892 0.7212 0.000 0.980 0.020
#> aberrant_ERR2585315 2 0.2537 0.7380 0.000 0.920 0.080
#> aberrant_ERR2585336 3 0.6235 0.0589 0.000 0.436 0.564
#> aberrant_ERR2585307 2 0.6299 0.2804 0.000 0.524 0.476
#> aberrant_ERR2585301 2 0.4002 0.7293 0.000 0.840 0.160
#> aberrant_ERR2585326 2 0.6111 0.5003 0.000 0.604 0.396
#> aberrant_ERR2585331 3 0.0892 0.7046 0.000 0.020 0.980
#> aberrant_ERR2585346 2 0.0237 0.7083 0.000 0.996 0.004
#> aberrant_ERR2585314 2 0.6215 0.4304 0.000 0.572 0.428
#> aberrant_ERR2585298 3 0.4121 0.6415 0.168 0.000 0.832
#> aberrant_ERR2585345 2 0.6280 0.3379 0.000 0.540 0.460
#> aberrant_ERR2585299 1 0.0000 0.9645 1.000 0.000 0.000
#> aberrant_ERR2585309 1 0.0237 0.9636 0.996 0.000 0.004
#> aberrant_ERR2585303 2 0.6168 0.4697 0.000 0.588 0.412
#> aberrant_ERR2585313 2 0.6192 0.4500 0.000 0.580 0.420
#> aberrant_ERR2585318 2 0.2878 0.7382 0.000 0.904 0.096
#> aberrant_ERR2585328 3 0.5926 0.3264 0.000 0.356 0.644
#> aberrant_ERR2585330 2 0.2448 0.7373 0.000 0.924 0.076
#> aberrant_ERR2585293 2 0.3412 0.6099 0.000 0.876 0.124
#> aberrant_ERR2585342 2 0.2537 0.7379 0.000 0.920 0.080
#> aberrant_ERR2585348 3 0.6244 0.0422 0.000 0.440 0.560
#> aberrant_ERR2585352 2 0.5859 0.5853 0.000 0.656 0.344
#> aberrant_ERR2585308 1 0.0237 0.9636 0.996 0.000 0.004
#> aberrant_ERR2585349 3 0.1031 0.7056 0.000 0.024 0.976
#> aberrant_ERR2585316 2 0.0892 0.7218 0.000 0.980 0.020
#> aberrant_ERR2585306 2 0.0983 0.7009 0.016 0.980 0.004
#> aberrant_ERR2585324 2 0.0747 0.7196 0.000 0.984 0.016
#> aberrant_ERR2585310 1 0.0475 0.9618 0.992 0.004 0.004
#> aberrant_ERR2585296 1 0.3752 0.8456 0.856 0.000 0.144
#> aberrant_ERR2585275 2 0.0000 0.7107 0.000 1.000 0.000
#> aberrant_ERR2585311 2 0.2537 0.7380 0.000 0.920 0.080
#> aberrant_ERR2585292 2 0.3412 0.6099 0.000 0.876 0.124
#> aberrant_ERR2585282 2 0.5497 0.6432 0.000 0.708 0.292
#> aberrant_ERR2585305 2 0.4834 0.5387 0.204 0.792 0.004
#> aberrant_ERR2585278 2 0.4178 0.7258 0.000 0.828 0.172
#> aberrant_ERR2585347 2 0.6154 0.4791 0.000 0.592 0.408
#> aberrant_ERR2585332 2 0.5058 0.6858 0.000 0.756 0.244
#> aberrant_ERR2585280 2 0.0892 0.7208 0.000 0.980 0.020
#> aberrant_ERR2585304 3 0.8934 0.4406 0.236 0.196 0.568
#> aberrant_ERR2585322 2 0.6295 0.2945 0.000 0.528 0.472
#> aberrant_ERR2585279 3 0.0424 0.7044 0.000 0.008 0.992
#> aberrant_ERR2585277 3 0.5431 0.4811 0.000 0.284 0.716
#> aberrant_ERR2585295 3 0.6267 -0.0206 0.000 0.452 0.548
#> aberrant_ERR2585333 2 0.0000 0.7107 0.000 1.000 0.000
#> aberrant_ERR2585285 2 0.4178 0.7257 0.000 0.828 0.172
#> aberrant_ERR2585286 3 0.3551 0.6693 0.000 0.132 0.868
#> aberrant_ERR2585294 2 0.0424 0.7159 0.000 0.992 0.008
#> aberrant_ERR2585300 2 0.0237 0.7083 0.000 0.996 0.004
#> aberrant_ERR2585334 3 0.0592 0.7044 0.000 0.012 0.988
#> aberrant_ERR2585361 2 0.5926 0.5714 0.000 0.644 0.356
#> aberrant_ERR2585372 2 0.4235 0.7244 0.000 0.824 0.176
#> round_ERR2585217 3 0.4346 0.6369 0.184 0.000 0.816
#> round_ERR2585205 1 0.0000 0.9645 1.000 0.000 0.000
#> round_ERR2585214 3 0.1163 0.7087 0.028 0.000 0.972
#> round_ERR2585202 3 0.2261 0.7088 0.068 0.000 0.932
#> aberrant_ERR2585367 2 0.6215 0.4306 0.000 0.572 0.428
#> round_ERR2585220 1 0.0000 0.9645 1.000 0.000 0.000
#> round_ERR2585238 1 0.0000 0.9645 1.000 0.000 0.000
#> aberrant_ERR2585276 2 0.0424 0.7158 0.000 0.992 0.008
#> round_ERR2585218 1 0.0237 0.9640 0.996 0.000 0.004
#> aberrant_ERR2585363 2 0.6225 0.4171 0.000 0.568 0.432
#> round_ERR2585201 3 0.2959 0.6870 0.100 0.000 0.900
#> round_ERR2585210 1 0.1163 0.9541 0.972 0.000 0.028
#> aberrant_ERR2585362 3 0.6168 0.1546 0.000 0.412 0.588
#> aberrant_ERR2585360 2 0.5327 0.6613 0.000 0.728 0.272
#> round_ERR2585209 1 0.4062 0.8360 0.836 0.000 0.164
#> round_ERR2585242 1 0.5397 0.6766 0.720 0.000 0.280
#> round_ERR2585216 1 0.1031 0.9562 0.976 0.000 0.024
#> round_ERR2585219 1 0.0000 0.9645 1.000 0.000 0.000
#> round_ERR2585237 3 0.2796 0.6991 0.092 0.000 0.908
#> round_ERR2585198 1 0.5968 0.5303 0.636 0.000 0.364
#> round_ERR2585211 1 0.0237 0.9640 0.996 0.000 0.004
#> round_ERR2585206 1 0.0237 0.9640 0.996 0.000 0.004
#> aberrant_ERR2585281 3 0.4605 0.6089 0.000 0.204 0.796
#> round_ERR2585212 1 0.1163 0.9541 0.972 0.000 0.028
#> round_ERR2585221 1 0.0237 0.9636 0.996 0.000 0.004
#> round_ERR2585243 1 0.0237 0.9636 0.996 0.000 0.004
#> round_ERR2585204 3 0.1163 0.7095 0.028 0.000 0.972
#> round_ERR2585213 3 0.0424 0.7051 0.008 0.000 0.992
#> aberrant_ERR2585373 2 0.0424 0.7158 0.000 0.992 0.008
#> aberrant_ERR2585358 2 0.2356 0.7367 0.000 0.928 0.072
#> aberrant_ERR2585365 3 0.6309 -0.2053 0.000 0.496 0.504
#> aberrant_ERR2585359 2 0.0747 0.7199 0.000 0.984 0.016
#> aberrant_ERR2585370 3 0.6295 -0.1073 0.000 0.472 0.528
#> round_ERR2585215 1 0.0592 0.9615 0.988 0.000 0.012
#> round_ERR2585262 3 0.1129 0.7103 0.020 0.004 0.976
#> round_ERR2585199 3 0.2959 0.6964 0.100 0.000 0.900
#> aberrant_ERR2585369 2 0.5706 0.6135 0.000 0.680 0.320
#> round_ERR2585208 1 0.0000 0.9645 1.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.9645 1.000 0.000 0.000
#> round_ERR2585236 1 0.5016 0.7235 0.760 0.000 0.240
#> aberrant_ERR2585284 2 0.6307 0.2166 0.000 0.512 0.488
#> round_ERR2585224 1 0.0661 0.9590 0.988 0.008 0.004
#> round_ERR2585260 1 0.0000 0.9645 1.000 0.000 0.000
#> round_ERR2585229 1 0.0000 0.9645 1.000 0.000 0.000
#> aberrant_ERR2585364 2 0.0237 0.7083 0.000 0.996 0.004
#> round_ERR2585253 1 0.0237 0.9636 0.996 0.000 0.004
#> aberrant_ERR2585368 3 0.3941 0.6521 0.000 0.156 0.844
#> aberrant_ERR2585371 3 0.3551 0.6702 0.000 0.132 0.868
#> round_ERR2585239 1 0.0000 0.9645 1.000 0.000 0.000
#> round_ERR2585273 1 0.0000 0.9645 1.000 0.000 0.000
#> round_ERR2585256 1 0.1163 0.9541 0.972 0.000 0.028
#> round_ERR2585272 1 0.0592 0.9615 0.988 0.000 0.012
#> round_ERR2585246 1 0.0237 0.9636 0.996 0.000 0.004
#> round_ERR2585261 1 0.4235 0.8193 0.824 0.000 0.176
#> round_ERR2585254 1 0.1163 0.9542 0.972 0.000 0.028
#> round_ERR2585225 3 0.2066 0.7016 0.060 0.000 0.940
#> round_ERR2585235 1 0.1031 0.9562 0.976 0.000 0.024
#> round_ERR2585271 1 0.0237 0.9640 0.996 0.000 0.004
#> round_ERR2585251 1 0.0000 0.9645 1.000 0.000 0.000
#> round_ERR2585255 3 0.1643 0.7091 0.044 0.000 0.956
#> round_ERR2585257 3 0.2711 0.6982 0.088 0.000 0.912
#> round_ERR2585226 1 0.0237 0.9636 0.996 0.000 0.004
#> round_ERR2585265 1 0.0000 0.9645 1.000 0.000 0.000
#> round_ERR2585259 1 0.3879 0.8443 0.848 0.000 0.152
#> round_ERR2585247 1 0.0237 0.9636 0.996 0.000 0.004
#> round_ERR2585241 1 0.0424 0.9629 0.992 0.000 0.008
#> round_ERR2585263 1 0.1411 0.9495 0.964 0.000 0.036
#> round_ERR2585264 1 0.0237 0.9636 0.996 0.000 0.004
#> round_ERR2585233 3 0.5291 0.5507 0.268 0.000 0.732
#> round_ERR2585223 1 0.0237 0.9636 0.996 0.000 0.004
#> round_ERR2585234 3 0.3551 0.6658 0.132 0.000 0.868
#> round_ERR2585222 1 0.0000 0.9645 1.000 0.000 0.000
#> round_ERR2585228 1 0.0237 0.9640 0.996 0.000 0.004
#> round_ERR2585248 1 0.0000 0.9645 1.000 0.000 0.000
#> round_ERR2585240 1 0.1529 0.9474 0.960 0.000 0.040
#> round_ERR2585270 1 0.0424 0.9629 0.992 0.000 0.008
#> round_ERR2585232 1 0.0747 0.9597 0.984 0.000 0.016
#> aberrant_ERR2585341 3 0.5650 0.4329 0.000 0.312 0.688
#> aberrant_ERR2585355 3 0.3686 0.6662 0.000 0.140 0.860
#> round_ERR2585227 1 0.0000 0.9645 1.000 0.000 0.000
#> aberrant_ERR2585351 2 0.6111 0.4973 0.000 0.604 0.396
#> round_ERR2585269 1 0.0237 0.9636 0.996 0.000 0.004
#> aberrant_ERR2585357 3 0.6235 0.0538 0.000 0.436 0.564
#> aberrant_ERR2585350 2 0.6026 0.5393 0.000 0.624 0.376
#> round_ERR2585250 1 0.1964 0.9353 0.944 0.000 0.056
#> round_ERR2585245 1 0.0237 0.9636 0.996 0.000 0.004
#> aberrant_ERR2585353 2 0.4702 0.7045 0.000 0.788 0.212
#> round_ERR2585258 1 0.0000 0.9645 1.000 0.000 0.000
#> aberrant_ERR2585354 2 0.3619 0.7347 0.000 0.864 0.136
#> round_ERR2585249 1 0.0237 0.9636 0.996 0.000 0.004
#> round_ERR2585268 1 0.3038 0.8965 0.896 0.000 0.104
#> aberrant_ERR2585356 2 0.0237 0.7083 0.000 0.996 0.004
#> round_ERR2585266 1 0.5254 0.7080 0.736 0.000 0.264
#> round_ERR2585231 1 0.0000 0.9645 1.000 0.000 0.000
#> round_ERR2585230 1 0.0237 0.9640 0.996 0.000 0.004
#> round_ERR2585267 1 0.0237 0.9640 0.996 0.000 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 2 0.3899 0.81134 0.000 0.840 0.108 0.052
#> aberrant_ERR2585338 3 0.4941 0.09716 0.000 0.436 0.564 0.000
#> aberrant_ERR2585325 2 0.4114 0.80770 0.000 0.828 0.112 0.060
#> aberrant_ERR2585283 2 0.5125 0.46049 0.000 0.604 0.008 0.388
#> aberrant_ERR2585343 2 0.1118 0.82493 0.000 0.964 0.000 0.036
#> aberrant_ERR2585329 2 0.3910 0.77646 0.000 0.820 0.156 0.024
#> aberrant_ERR2585317 2 0.2546 0.82005 0.000 0.912 0.060 0.028
#> aberrant_ERR2585339 2 0.4359 0.80928 0.000 0.816 0.100 0.084
#> aberrant_ERR2585335 2 0.1256 0.82413 0.000 0.964 0.008 0.028
#> aberrant_ERR2585287 2 0.7159 0.44900 0.000 0.548 0.180 0.272
#> aberrant_ERR2585321 2 0.1771 0.81948 0.004 0.948 0.012 0.036
#> aberrant_ERR2585297 1 0.1706 0.90873 0.948 0.000 0.016 0.036
#> aberrant_ERR2585337 2 0.2222 0.82388 0.000 0.924 0.060 0.016
#> aberrant_ERR2585319 2 0.1743 0.81899 0.000 0.940 0.004 0.056
#> aberrant_ERR2585315 2 0.3764 0.73827 0.000 0.784 0.000 0.216
#> aberrant_ERR2585336 2 0.4594 0.65906 0.000 0.712 0.280 0.008
#> aberrant_ERR2585307 2 0.4458 0.80684 0.000 0.808 0.116 0.076
#> aberrant_ERR2585301 2 0.2255 0.82086 0.000 0.920 0.012 0.068
#> aberrant_ERR2585326 2 0.2596 0.82727 0.000 0.908 0.068 0.024
#> aberrant_ERR2585331 3 0.1716 0.75483 0.000 0.064 0.936 0.000
#> aberrant_ERR2585346 2 0.4647 0.64039 0.000 0.704 0.008 0.288
#> aberrant_ERR2585314 2 0.1975 0.82626 0.000 0.936 0.048 0.016
#> aberrant_ERR2585298 3 0.2197 0.74160 0.080 0.004 0.916 0.000
#> aberrant_ERR2585345 2 0.3900 0.78860 0.000 0.816 0.164 0.020
#> aberrant_ERR2585299 1 0.0336 0.91908 0.992 0.000 0.000 0.008
#> aberrant_ERR2585309 1 0.2950 0.88315 0.900 0.012 0.020 0.068
#> aberrant_ERR2585303 2 0.4642 0.71499 0.000 0.740 0.240 0.020
#> aberrant_ERR2585313 2 0.3160 0.81199 0.000 0.872 0.108 0.020
#> aberrant_ERR2585318 2 0.1938 0.81736 0.000 0.936 0.012 0.052
#> aberrant_ERR2585328 2 0.4642 0.69494 0.000 0.740 0.240 0.020
#> aberrant_ERR2585330 2 0.2704 0.80339 0.000 0.876 0.000 0.124
#> aberrant_ERR2585293 4 0.2859 1.00000 0.000 0.112 0.008 0.880
#> aberrant_ERR2585342 2 0.2125 0.82380 0.000 0.920 0.004 0.076
#> aberrant_ERR2585348 2 0.3831 0.74671 0.000 0.792 0.204 0.004
#> aberrant_ERR2585352 2 0.2021 0.82854 0.000 0.936 0.040 0.024
#> aberrant_ERR2585308 1 0.2275 0.89802 0.928 0.004 0.020 0.048
#> aberrant_ERR2585349 3 0.4139 0.66906 0.000 0.176 0.800 0.024
#> aberrant_ERR2585316 2 0.2593 0.80445 0.000 0.892 0.004 0.104
#> aberrant_ERR2585306 2 0.6511 0.49352 0.060 0.636 0.024 0.280
#> aberrant_ERR2585324 2 0.2714 0.80573 0.000 0.884 0.004 0.112
#> aberrant_ERR2585310 1 0.3746 0.84924 0.868 0.072 0.020 0.040
#> aberrant_ERR2585296 1 0.4037 0.85872 0.848 0.040 0.096 0.016
#> aberrant_ERR2585275 2 0.4914 0.59588 0.000 0.676 0.012 0.312
#> aberrant_ERR2585311 2 0.1888 0.81723 0.000 0.940 0.016 0.044
#> aberrant_ERR2585292 4 0.2859 1.00000 0.000 0.112 0.008 0.880
#> aberrant_ERR2585282 2 0.1733 0.82887 0.000 0.948 0.024 0.028
#> aberrant_ERR2585305 2 0.3396 0.78259 0.024 0.884 0.024 0.068
#> aberrant_ERR2585278 2 0.1824 0.82377 0.000 0.936 0.004 0.060
#> aberrant_ERR2585347 2 0.3919 0.81335 0.000 0.840 0.104 0.056
#> aberrant_ERR2585332 2 0.1936 0.82869 0.000 0.940 0.028 0.032
#> aberrant_ERR2585280 2 0.4567 0.68561 0.000 0.716 0.008 0.276
#> aberrant_ERR2585304 2 0.7872 -0.10113 0.280 0.376 0.344 0.000
#> aberrant_ERR2585322 2 0.4914 0.74095 0.000 0.748 0.208 0.044
#> aberrant_ERR2585279 3 0.1118 0.76567 0.000 0.036 0.964 0.000
#> aberrant_ERR2585277 3 0.4406 0.51034 0.000 0.300 0.700 0.000
#> aberrant_ERR2585295 2 0.5663 0.31382 0.000 0.536 0.440 0.024
#> aberrant_ERR2585333 2 0.4391 0.69545 0.000 0.740 0.008 0.252
#> aberrant_ERR2585285 2 0.1042 0.82724 0.000 0.972 0.008 0.020
#> aberrant_ERR2585286 3 0.3688 0.64046 0.000 0.208 0.792 0.000
#> aberrant_ERR2585294 2 0.4248 0.71552 0.000 0.768 0.012 0.220
#> aberrant_ERR2585300 2 0.4054 0.75497 0.000 0.796 0.016 0.188
#> aberrant_ERR2585334 3 0.1389 0.76202 0.000 0.048 0.952 0.000
#> aberrant_ERR2585361 2 0.3471 0.82437 0.000 0.868 0.072 0.060
#> aberrant_ERR2585372 2 0.2256 0.83105 0.000 0.924 0.020 0.056
#> round_ERR2585217 3 0.3470 0.69945 0.132 0.008 0.852 0.008
#> round_ERR2585205 1 0.0895 0.91859 0.976 0.000 0.020 0.004
#> round_ERR2585214 3 0.1509 0.76774 0.012 0.020 0.960 0.008
#> round_ERR2585202 3 0.2297 0.76997 0.024 0.044 0.928 0.004
#> aberrant_ERR2585367 2 0.4617 0.75198 0.000 0.764 0.204 0.032
#> round_ERR2585220 1 0.1520 0.92082 0.956 0.000 0.024 0.020
#> round_ERR2585238 1 0.0336 0.91772 0.992 0.000 0.000 0.008
#> aberrant_ERR2585276 2 0.4019 0.73190 0.000 0.792 0.012 0.196
#> round_ERR2585218 1 0.1406 0.91851 0.960 0.000 0.024 0.016
#> aberrant_ERR2585363 2 0.2466 0.82150 0.000 0.916 0.056 0.028
#> round_ERR2585201 3 0.2125 0.74489 0.076 0.000 0.920 0.004
#> round_ERR2585210 1 0.3991 0.86515 0.860 0.044 0.064 0.032
#> aberrant_ERR2585362 2 0.3910 0.77360 0.000 0.820 0.156 0.024
#> aberrant_ERR2585360 2 0.1824 0.82863 0.000 0.936 0.004 0.060
#> round_ERR2585209 1 0.5016 0.40845 0.600 0.000 0.396 0.004
#> round_ERR2585242 3 0.4103 0.53213 0.256 0.000 0.744 0.000
#> round_ERR2585216 1 0.2222 0.90618 0.924 0.000 0.060 0.016
#> round_ERR2585219 1 0.1284 0.91877 0.964 0.000 0.024 0.012
#> round_ERR2585237 3 0.2485 0.76035 0.064 0.016 0.916 0.004
#> round_ERR2585198 3 0.3907 0.57008 0.232 0.000 0.768 0.000
#> round_ERR2585211 1 0.1247 0.91973 0.968 0.004 0.016 0.012
#> round_ERR2585206 1 0.0779 0.91966 0.980 0.000 0.016 0.004
#> aberrant_ERR2585281 3 0.4313 0.56072 0.000 0.260 0.736 0.004
#> round_ERR2585212 1 0.2124 0.90369 0.924 0.000 0.068 0.008
#> round_ERR2585221 1 0.2667 0.89130 0.912 0.008 0.020 0.060
#> round_ERR2585243 1 0.2131 0.90561 0.936 0.008 0.016 0.040
#> round_ERR2585204 3 0.1369 0.76671 0.016 0.016 0.964 0.004
#> round_ERR2585213 3 0.1118 0.76567 0.000 0.036 0.964 0.000
#> aberrant_ERR2585373 2 0.1807 0.81433 0.000 0.940 0.008 0.052
#> aberrant_ERR2585358 2 0.2124 0.82553 0.000 0.924 0.008 0.068
#> aberrant_ERR2585365 2 0.3597 0.79114 0.000 0.836 0.148 0.016
#> aberrant_ERR2585359 2 0.2125 0.81826 0.000 0.920 0.004 0.076
#> aberrant_ERR2585370 2 0.4697 0.55547 0.000 0.644 0.356 0.000
#> round_ERR2585215 1 0.2563 0.90634 0.916 0.012 0.060 0.012
#> round_ERR2585262 3 0.2011 0.75570 0.000 0.080 0.920 0.000
#> round_ERR2585199 3 0.2368 0.77082 0.032 0.032 0.928 0.008
#> aberrant_ERR2585369 2 0.1297 0.82571 0.000 0.964 0.016 0.020
#> round_ERR2585208 1 0.0672 0.91779 0.984 0.000 0.008 0.008
#> round_ERR2585252 1 0.1042 0.91516 0.972 0.000 0.008 0.020
#> round_ERR2585236 1 0.5306 0.57455 0.664 0.020 0.312 0.004
#> aberrant_ERR2585284 2 0.6603 0.47951 0.000 0.572 0.328 0.100
#> round_ERR2585224 1 0.3536 0.86316 0.876 0.028 0.020 0.076
#> round_ERR2585260 1 0.1059 0.91928 0.972 0.000 0.016 0.012
#> round_ERR2585229 1 0.0469 0.91922 0.988 0.000 0.000 0.012
#> aberrant_ERR2585364 2 0.3901 0.76333 0.004 0.816 0.012 0.168
#> round_ERR2585253 1 0.0672 0.91954 0.984 0.000 0.008 0.008
#> aberrant_ERR2585368 3 0.3688 0.64534 0.000 0.208 0.792 0.000
#> aberrant_ERR2585371 3 0.3266 0.68324 0.000 0.168 0.832 0.000
#> round_ERR2585239 1 0.1209 0.91712 0.964 0.000 0.032 0.004
#> round_ERR2585273 1 0.2021 0.90707 0.932 0.000 0.012 0.056
#> round_ERR2585256 1 0.2334 0.89299 0.908 0.000 0.088 0.004
#> round_ERR2585272 1 0.1576 0.91139 0.948 0.000 0.048 0.004
#> round_ERR2585246 1 0.3250 0.87280 0.888 0.016 0.024 0.072
#> round_ERR2585261 1 0.5028 0.39549 0.596 0.000 0.400 0.004
#> round_ERR2585254 1 0.2197 0.89738 0.916 0.000 0.080 0.004
#> round_ERR2585225 3 0.1209 0.76287 0.032 0.004 0.964 0.000
#> round_ERR2585235 1 0.2198 0.90320 0.920 0.000 0.072 0.008
#> round_ERR2585271 1 0.1209 0.91654 0.964 0.000 0.032 0.004
#> round_ERR2585251 1 0.0921 0.91811 0.972 0.000 0.028 0.000
#> round_ERR2585255 3 0.1339 0.76479 0.024 0.008 0.964 0.004
#> round_ERR2585257 3 0.2586 0.73970 0.092 0.004 0.900 0.004
#> round_ERR2585226 1 0.1488 0.91313 0.956 0.000 0.012 0.032
#> round_ERR2585265 1 0.0804 0.91962 0.980 0.000 0.012 0.008
#> round_ERR2585259 1 0.5125 0.41866 0.604 0.000 0.388 0.008
#> round_ERR2585247 1 0.1722 0.90841 0.944 0.000 0.008 0.048
#> round_ERR2585241 1 0.1677 0.91394 0.948 0.000 0.040 0.012
#> round_ERR2585263 1 0.6019 0.72765 0.736 0.088 0.140 0.036
#> round_ERR2585264 1 0.1109 0.91547 0.968 0.000 0.004 0.028
#> round_ERR2585233 3 0.3032 0.70817 0.124 0.008 0.868 0.000
#> round_ERR2585223 1 0.0895 0.91513 0.976 0.000 0.004 0.020
#> round_ERR2585234 3 0.2088 0.75022 0.064 0.004 0.928 0.004
#> round_ERR2585222 1 0.1151 0.91911 0.968 0.000 0.024 0.008
#> round_ERR2585228 1 0.1059 0.91955 0.972 0.000 0.016 0.012
#> round_ERR2585248 1 0.0804 0.91852 0.980 0.000 0.008 0.012
#> round_ERR2585240 1 0.3157 0.85894 0.852 0.000 0.144 0.004
#> round_ERR2585270 1 0.1545 0.91467 0.952 0.000 0.040 0.008
#> round_ERR2585232 1 0.2198 0.90671 0.920 0.000 0.072 0.008
#> aberrant_ERR2585341 3 0.5277 -0.00459 0.000 0.460 0.532 0.008
#> aberrant_ERR2585355 3 0.3942 0.61162 0.000 0.236 0.764 0.000
#> round_ERR2585227 1 0.1706 0.91713 0.948 0.000 0.016 0.036
#> aberrant_ERR2585351 2 0.1610 0.82604 0.000 0.952 0.032 0.016
#> round_ERR2585269 1 0.2335 0.89560 0.920 0.000 0.020 0.060
#> aberrant_ERR2585357 2 0.4509 0.65522 0.000 0.708 0.288 0.004
#> aberrant_ERR2585350 2 0.3372 0.82397 0.000 0.868 0.096 0.036
#> round_ERR2585250 1 0.3366 0.87634 0.872 0.008 0.100 0.020
#> round_ERR2585245 1 0.2363 0.89456 0.920 0.000 0.024 0.056
#> aberrant_ERR2585353 2 0.2002 0.82978 0.000 0.936 0.020 0.044
#> round_ERR2585258 1 0.1174 0.91805 0.968 0.000 0.012 0.020
#> aberrant_ERR2585354 2 0.1356 0.82644 0.000 0.960 0.008 0.032
#> round_ERR2585249 1 0.1820 0.90521 0.944 0.000 0.020 0.036
#> round_ERR2585268 1 0.3105 0.85546 0.856 0.000 0.140 0.004
#> aberrant_ERR2585356 2 0.2515 0.80492 0.004 0.912 0.012 0.072
#> round_ERR2585266 3 0.4977 0.01684 0.460 0.000 0.540 0.000
#> round_ERR2585231 1 0.2561 0.89247 0.912 0.004 0.016 0.068
#> round_ERR2585230 1 0.1489 0.91642 0.952 0.000 0.044 0.004
#> round_ERR2585267 1 0.2282 0.91212 0.924 0.000 0.024 0.052
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 2 0.5335 0.4291 0.000 0.668 0.040 0.032 0.260
#> aberrant_ERR2585338 3 0.5009 0.0444 0.000 0.428 0.540 0.000 0.032
#> aberrant_ERR2585325 2 0.5623 0.3963 0.000 0.656 0.056 0.036 0.252
#> aberrant_ERR2585283 2 0.5733 -0.0532 0.000 0.608 0.000 0.256 0.136
#> aberrant_ERR2585343 2 0.1630 0.6744 0.000 0.944 0.004 0.016 0.036
#> aberrant_ERR2585329 2 0.3792 0.6512 0.000 0.832 0.100 0.024 0.044
#> aberrant_ERR2585317 2 0.2786 0.6731 0.000 0.884 0.020 0.012 0.084
#> aberrant_ERR2585339 2 0.3155 0.6461 0.000 0.864 0.096 0.020 0.020
#> aberrant_ERR2585335 2 0.2416 0.6705 0.000 0.888 0.000 0.012 0.100
#> aberrant_ERR2585287 2 0.7722 -0.1195 0.000 0.480 0.244 0.132 0.144
#> aberrant_ERR2585321 2 0.2488 0.6627 0.000 0.872 0.000 0.004 0.124
#> aberrant_ERR2585297 1 0.3636 0.7617 0.728 0.000 0.000 0.000 0.272
#> aberrant_ERR2585337 2 0.1997 0.6753 0.000 0.932 0.028 0.016 0.024
#> aberrant_ERR2585319 2 0.1809 0.6488 0.000 0.928 0.000 0.012 0.060
#> aberrant_ERR2585315 2 0.4557 0.4607 0.000 0.764 0.008 0.088 0.140
#> aberrant_ERR2585336 2 0.4468 0.5282 0.000 0.740 0.212 0.008 0.040
#> aberrant_ERR2585307 2 0.4419 0.5751 0.000 0.780 0.140 0.016 0.064
#> aberrant_ERR2585301 2 0.3013 0.6071 0.000 0.832 0.000 0.008 0.160
#> aberrant_ERR2585326 2 0.2885 0.6652 0.000 0.880 0.064 0.004 0.052
#> aberrant_ERR2585331 3 0.0912 0.7837 0.000 0.012 0.972 0.000 0.016
#> aberrant_ERR2585346 2 0.5273 0.0554 0.000 0.652 0.008 0.064 0.276
#> aberrant_ERR2585314 2 0.3843 0.6089 0.000 0.788 0.016 0.012 0.184
#> aberrant_ERR2585298 3 0.1282 0.7884 0.044 0.000 0.952 0.000 0.004
#> aberrant_ERR2585345 2 0.3243 0.6427 0.000 0.848 0.116 0.004 0.032
#> aberrant_ERR2585299 1 0.1671 0.8264 0.924 0.000 0.000 0.000 0.076
#> aberrant_ERR2585309 1 0.4009 0.7303 0.684 0.000 0.004 0.000 0.312
#> aberrant_ERR2585303 2 0.4743 0.4017 0.000 0.700 0.248 0.004 0.048
#> aberrant_ERR2585313 2 0.2499 0.6801 0.000 0.908 0.040 0.016 0.036
#> aberrant_ERR2585318 2 0.2377 0.6621 0.000 0.872 0.000 0.000 0.128
#> aberrant_ERR2585328 2 0.5699 0.4748 0.000 0.680 0.148 0.024 0.148
#> aberrant_ERR2585330 2 0.2770 0.6265 0.000 0.880 0.000 0.044 0.076
#> aberrant_ERR2585293 4 0.1205 1.0000 0.000 0.040 0.000 0.956 0.004
#> aberrant_ERR2585342 2 0.2669 0.6318 0.000 0.876 0.000 0.020 0.104
#> aberrant_ERR2585348 2 0.4604 0.6038 0.000 0.760 0.072 0.012 0.156
#> aberrant_ERR2585352 2 0.2569 0.6750 0.000 0.896 0.016 0.012 0.076
#> aberrant_ERR2585308 1 0.3300 0.7921 0.792 0.000 0.004 0.000 0.204
#> aberrant_ERR2585349 3 0.5976 0.5343 0.008 0.164 0.660 0.016 0.152
#> aberrant_ERR2585316 2 0.2491 0.6461 0.000 0.896 0.000 0.036 0.068
#> aberrant_ERR2585306 5 0.5903 0.0000 0.028 0.460 0.000 0.044 0.468
#> aberrant_ERR2585324 2 0.2669 0.6184 0.000 0.876 0.000 0.020 0.104
#> aberrant_ERR2585310 1 0.5427 0.6263 0.596 0.052 0.004 0.004 0.344
#> aberrant_ERR2585296 1 0.6062 0.6790 0.688 0.064 0.056 0.020 0.172
#> aberrant_ERR2585275 2 0.5980 -0.2268 0.000 0.588 0.004 0.140 0.268
#> aberrant_ERR2585311 2 0.2358 0.6528 0.000 0.888 0.000 0.008 0.104
#> aberrant_ERR2585292 4 0.1205 1.0000 0.000 0.040 0.000 0.956 0.004
#> aberrant_ERR2585282 2 0.3768 0.6403 0.000 0.808 0.016 0.020 0.156
#> aberrant_ERR2585305 2 0.4518 -0.0413 0.004 0.660 0.000 0.016 0.320
#> aberrant_ERR2585278 2 0.2482 0.6298 0.000 0.892 0.000 0.024 0.084
#> aberrant_ERR2585347 2 0.4308 0.6344 0.000 0.804 0.068 0.032 0.096
#> aberrant_ERR2585332 2 0.3197 0.6620 0.000 0.852 0.008 0.024 0.116
#> aberrant_ERR2585280 2 0.5926 0.3077 0.000 0.684 0.060 0.116 0.140
#> aberrant_ERR2585304 3 0.8027 -0.0971 0.144 0.276 0.420 0.000 0.160
#> aberrant_ERR2585322 2 0.5246 0.4223 0.000 0.696 0.216 0.020 0.068
#> aberrant_ERR2585279 3 0.0324 0.7875 0.000 0.004 0.992 0.000 0.004
#> aberrant_ERR2585277 3 0.3171 0.6713 0.000 0.176 0.816 0.000 0.008
#> aberrant_ERR2585295 2 0.6870 -0.0709 0.000 0.444 0.400 0.040 0.116
#> aberrant_ERR2585333 2 0.3620 0.5554 0.000 0.824 0.000 0.068 0.108
#> aberrant_ERR2585285 2 0.2352 0.6677 0.000 0.896 0.004 0.008 0.092
#> aberrant_ERR2585286 3 0.2407 0.7539 0.000 0.088 0.896 0.004 0.012
#> aberrant_ERR2585294 2 0.4668 0.2290 0.000 0.684 0.000 0.044 0.272
#> aberrant_ERR2585300 2 0.4428 0.2678 0.000 0.700 0.000 0.032 0.268
#> aberrant_ERR2585334 3 0.0579 0.7858 0.000 0.008 0.984 0.000 0.008
#> aberrant_ERR2585361 2 0.2819 0.6851 0.000 0.884 0.024 0.012 0.080
#> aberrant_ERR2585372 2 0.3174 0.6583 0.000 0.844 0.004 0.020 0.132
#> round_ERR2585217 3 0.6082 0.4092 0.308 0.012 0.580 0.004 0.096
#> round_ERR2585205 1 0.1202 0.8199 0.960 0.000 0.004 0.004 0.032
#> round_ERR2585214 3 0.0609 0.7920 0.020 0.000 0.980 0.000 0.000
#> round_ERR2585202 3 0.2187 0.7878 0.024 0.008 0.924 0.004 0.040
#> aberrant_ERR2585367 2 0.4948 0.5528 0.000 0.736 0.164 0.016 0.084
#> round_ERR2585220 1 0.1952 0.8296 0.912 0.000 0.004 0.000 0.084
#> round_ERR2585238 1 0.1908 0.8254 0.908 0.000 0.000 0.000 0.092
#> aberrant_ERR2585276 2 0.5030 -0.1728 0.000 0.604 0.000 0.044 0.352
#> round_ERR2585218 1 0.2052 0.8149 0.912 0.000 0.004 0.004 0.080
#> aberrant_ERR2585363 2 0.3849 0.6293 0.000 0.800 0.016 0.020 0.164
#> round_ERR2585201 3 0.2920 0.7318 0.132 0.000 0.852 0.000 0.016
#> round_ERR2585210 1 0.4663 0.7108 0.752 0.012 0.028 0.016 0.192
#> aberrant_ERR2585362 2 0.4826 0.5730 0.000 0.744 0.056 0.024 0.176
#> aberrant_ERR2585360 2 0.2982 0.6638 0.000 0.860 0.004 0.020 0.116
#> round_ERR2585209 1 0.4818 0.5735 0.688 0.000 0.260 0.004 0.048
#> round_ERR2585242 3 0.2389 0.7413 0.116 0.000 0.880 0.004 0.000
#> round_ERR2585216 1 0.2554 0.8078 0.896 0.000 0.020 0.008 0.076
#> round_ERR2585219 1 0.1798 0.8187 0.928 0.000 0.004 0.004 0.064
#> round_ERR2585237 3 0.5022 0.6063 0.192 0.004 0.716 0.004 0.084
#> round_ERR2585198 3 0.2929 0.6896 0.180 0.000 0.820 0.000 0.000
#> round_ERR2585211 1 0.2362 0.8098 0.900 0.000 0.008 0.008 0.084
#> round_ERR2585206 1 0.1124 0.8245 0.960 0.000 0.000 0.004 0.036
#> aberrant_ERR2585281 3 0.4106 0.6512 0.000 0.136 0.792 0.004 0.068
#> round_ERR2585212 1 0.3002 0.8018 0.876 0.000 0.048 0.008 0.068
#> round_ERR2585221 1 0.3949 0.7148 0.668 0.000 0.000 0.000 0.332
#> round_ERR2585243 1 0.4169 0.7640 0.720 0.008 0.004 0.004 0.264
#> round_ERR2585204 3 0.0671 0.7911 0.016 0.000 0.980 0.000 0.004
#> round_ERR2585213 3 0.0404 0.7902 0.012 0.000 0.988 0.000 0.000
#> aberrant_ERR2585373 2 0.2172 0.6716 0.000 0.908 0.000 0.016 0.076
#> aberrant_ERR2585358 2 0.2568 0.6768 0.000 0.888 0.004 0.016 0.092
#> aberrant_ERR2585365 2 0.4427 0.6271 0.000 0.776 0.088 0.008 0.128
#> aberrant_ERR2585359 2 0.2795 0.6729 0.000 0.872 0.000 0.028 0.100
#> aberrant_ERR2585370 2 0.4668 0.2727 0.000 0.624 0.352 0.000 0.024
#> round_ERR2585215 1 0.2967 0.7974 0.868 0.000 0.016 0.012 0.104
#> round_ERR2585262 3 0.2153 0.7721 0.000 0.040 0.916 0.000 0.044
#> round_ERR2585199 3 0.3342 0.7456 0.100 0.000 0.848 0.004 0.048
#> aberrant_ERR2585369 2 0.2445 0.6681 0.000 0.884 0.004 0.004 0.108
#> round_ERR2585208 1 0.1792 0.8273 0.916 0.000 0.000 0.000 0.084
#> round_ERR2585252 1 0.1792 0.8253 0.916 0.000 0.000 0.000 0.084
#> round_ERR2585236 1 0.5508 0.2300 0.528 0.008 0.416 0.000 0.048
#> aberrant_ERR2585284 2 0.7139 0.0828 0.000 0.524 0.264 0.064 0.148
#> round_ERR2585224 1 0.4791 0.5404 0.524 0.012 0.000 0.004 0.460
#> round_ERR2585260 1 0.2280 0.8197 0.880 0.000 0.000 0.000 0.120
#> round_ERR2585229 1 0.1704 0.8243 0.928 0.000 0.004 0.000 0.068
#> aberrant_ERR2585364 2 0.3546 0.6335 0.000 0.832 0.004 0.048 0.116
#> round_ERR2585253 1 0.1591 0.8268 0.940 0.000 0.004 0.004 0.052
#> aberrant_ERR2585368 3 0.2358 0.7458 0.000 0.104 0.888 0.000 0.008
#> aberrant_ERR2585371 3 0.1830 0.7679 0.000 0.068 0.924 0.000 0.008
#> round_ERR2585239 1 0.1522 0.8263 0.944 0.000 0.012 0.000 0.044
#> round_ERR2585273 1 0.3452 0.7787 0.756 0.000 0.000 0.000 0.244
#> round_ERR2585256 1 0.2234 0.8150 0.916 0.000 0.036 0.004 0.044
#> round_ERR2585272 1 0.1461 0.8264 0.952 0.000 0.016 0.004 0.028
#> round_ERR2585246 1 0.4630 0.6280 0.588 0.016 0.000 0.000 0.396
#> round_ERR2585261 1 0.4934 0.3886 0.600 0.000 0.364 0.000 0.036
#> round_ERR2585254 1 0.1990 0.8151 0.928 0.000 0.040 0.004 0.028
#> round_ERR2585225 3 0.0671 0.7910 0.016 0.000 0.980 0.000 0.004
#> round_ERR2585235 1 0.2237 0.8307 0.916 0.000 0.040 0.004 0.040
#> round_ERR2585271 1 0.2179 0.8291 0.912 0.000 0.008 0.008 0.072
#> round_ERR2585251 1 0.1205 0.8279 0.956 0.000 0.004 0.000 0.040
#> round_ERR2585255 3 0.0671 0.7914 0.016 0.000 0.980 0.004 0.000
#> round_ERR2585257 3 0.2367 0.7760 0.072 0.004 0.904 0.000 0.020
#> round_ERR2585226 1 0.3550 0.7758 0.760 0.000 0.004 0.000 0.236
#> round_ERR2585265 1 0.1043 0.8272 0.960 0.000 0.000 0.000 0.040
#> round_ERR2585259 1 0.5216 0.5669 0.668 0.000 0.248 0.004 0.080
#> round_ERR2585247 1 0.3814 0.7580 0.720 0.000 0.004 0.000 0.276
#> round_ERR2585241 1 0.2577 0.8057 0.892 0.000 0.016 0.008 0.084
#> round_ERR2585263 1 0.6297 0.6118 0.664 0.028 0.076 0.040 0.192
#> round_ERR2585264 1 0.2389 0.8260 0.880 0.000 0.004 0.000 0.116
#> round_ERR2585233 3 0.1768 0.7791 0.072 0.000 0.924 0.000 0.004
#> round_ERR2585223 1 0.2852 0.8073 0.828 0.000 0.000 0.000 0.172
#> round_ERR2585234 3 0.1205 0.7887 0.040 0.000 0.956 0.000 0.004
#> round_ERR2585222 1 0.2831 0.8281 0.868 0.004 0.008 0.004 0.116
#> round_ERR2585228 1 0.1662 0.8200 0.936 0.000 0.004 0.004 0.056
#> round_ERR2585248 1 0.2170 0.8295 0.904 0.000 0.004 0.004 0.088
#> round_ERR2585240 1 0.5285 0.4920 0.584 0.000 0.356 0.000 0.060
#> round_ERR2585270 1 0.2208 0.8133 0.908 0.000 0.020 0.000 0.072
#> round_ERR2585232 1 0.2922 0.8231 0.872 0.000 0.072 0.000 0.056
#> aberrant_ERR2585341 3 0.5324 0.1386 0.000 0.380 0.568 0.004 0.048
#> aberrant_ERR2585355 3 0.2674 0.7283 0.000 0.120 0.868 0.000 0.012
#> round_ERR2585227 1 0.3074 0.7959 0.804 0.000 0.000 0.000 0.196
#> aberrant_ERR2585351 2 0.3441 0.6366 0.000 0.824 0.004 0.024 0.148
#> round_ERR2585269 1 0.3661 0.7623 0.724 0.000 0.000 0.000 0.276
#> aberrant_ERR2585357 2 0.5165 0.4514 0.000 0.684 0.240 0.012 0.064
#> aberrant_ERR2585350 2 0.2005 0.6810 0.000 0.924 0.056 0.004 0.016
#> round_ERR2585250 1 0.6007 0.6768 0.668 0.008 0.152 0.024 0.148
#> round_ERR2585245 1 0.3990 0.7278 0.688 0.000 0.000 0.004 0.308
#> aberrant_ERR2585353 2 0.2295 0.6768 0.000 0.900 0.004 0.008 0.088
#> round_ERR2585258 1 0.3462 0.7991 0.792 0.000 0.012 0.000 0.196
#> aberrant_ERR2585354 2 0.2920 0.6638 0.000 0.852 0.000 0.016 0.132
#> round_ERR2585249 1 0.3752 0.7439 0.708 0.000 0.000 0.000 0.292
#> round_ERR2585268 1 0.4219 0.7575 0.772 0.000 0.156 0.000 0.072
#> aberrant_ERR2585356 2 0.3053 0.5986 0.000 0.828 0.000 0.008 0.164
#> round_ERR2585266 3 0.4042 0.5769 0.212 0.000 0.756 0.000 0.032
#> round_ERR2585231 1 0.4166 0.7000 0.648 0.000 0.004 0.000 0.348
#> round_ERR2585230 1 0.3140 0.8237 0.856 0.004 0.020 0.004 0.116
#> round_ERR2585267 1 0.3957 0.7576 0.712 0.000 0.008 0.000 0.280
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 6 0.6153 0.929694 0.000 0.132 0.032 0.000 0.376 0.460
#> aberrant_ERR2585338 3 0.4420 0.300426 0.000 0.000 0.644 0.000 0.308 0.048
#> aberrant_ERR2585325 6 0.6271 0.929800 0.000 0.140 0.036 0.000 0.384 0.440
#> aberrant_ERR2585283 5 0.6288 0.126522 0.000 0.060 0.000 0.128 0.528 0.284
#> aberrant_ERR2585343 5 0.2482 0.535402 0.000 0.004 0.000 0.000 0.848 0.148
#> aberrant_ERR2585329 5 0.4009 0.520365 0.000 0.024 0.076 0.000 0.788 0.112
#> aberrant_ERR2585317 5 0.2595 0.497036 0.000 0.000 0.004 0.000 0.836 0.160
#> aberrant_ERR2585339 5 0.3052 0.540054 0.000 0.000 0.064 0.008 0.852 0.076
#> aberrant_ERR2585335 5 0.2703 0.497403 0.000 0.004 0.000 0.000 0.824 0.172
#> aberrant_ERR2585287 5 0.7900 -0.342455 0.000 0.056 0.208 0.076 0.336 0.324
#> aberrant_ERR2585321 5 0.3122 0.497276 0.000 0.020 0.000 0.000 0.804 0.176
#> aberrant_ERR2585297 1 0.4024 -0.018333 0.592 0.400 0.000 0.004 0.000 0.004
#> aberrant_ERR2585337 5 0.2100 0.561398 0.000 0.000 0.004 0.000 0.884 0.112
#> aberrant_ERR2585319 5 0.3479 0.487592 0.000 0.012 0.000 0.008 0.768 0.212
#> aberrant_ERR2585315 5 0.5016 0.404664 0.000 0.036 0.020 0.036 0.696 0.212
#> aberrant_ERR2585336 5 0.4371 0.397159 0.000 0.004 0.168 0.000 0.728 0.100
#> aberrant_ERR2585307 5 0.5572 0.218661 0.000 0.052 0.216 0.004 0.644 0.084
#> aberrant_ERR2585301 5 0.3875 0.512974 0.000 0.092 0.000 0.004 0.780 0.124
#> aberrant_ERR2585326 5 0.3138 0.542840 0.000 0.004 0.060 0.000 0.840 0.096
#> aberrant_ERR2585331 3 0.0520 0.788221 0.000 0.008 0.984 0.000 0.000 0.008
#> aberrant_ERR2585346 5 0.6129 0.052541 0.000 0.116 0.012 0.024 0.520 0.328
#> aberrant_ERR2585314 5 0.4713 0.185672 0.000 0.072 0.004 0.000 0.652 0.272
#> aberrant_ERR2585298 3 0.1628 0.788142 0.036 0.012 0.940 0.004 0.000 0.008
#> aberrant_ERR2585345 5 0.3439 0.496079 0.000 0.000 0.120 0.000 0.808 0.072
#> aberrant_ERR2585299 1 0.2954 0.611958 0.852 0.096 0.000 0.004 0.000 0.048
#> aberrant_ERR2585309 1 0.3991 -0.254844 0.524 0.472 0.000 0.000 0.000 0.004
#> aberrant_ERR2585303 5 0.4749 0.240145 0.000 0.000 0.260 0.000 0.648 0.092
#> aberrant_ERR2585313 5 0.3155 0.515916 0.000 0.000 0.036 0.004 0.828 0.132
#> aberrant_ERR2585318 5 0.2841 0.500296 0.000 0.012 0.000 0.000 0.824 0.164
#> aberrant_ERR2585328 5 0.6007 0.045068 0.004 0.064 0.088 0.000 0.584 0.260
#> aberrant_ERR2585330 5 0.2949 0.534792 0.000 0.008 0.000 0.028 0.848 0.116
#> aberrant_ERR2585293 4 0.0405 1.000000 0.000 0.000 0.000 0.988 0.004 0.008
#> aberrant_ERR2585342 5 0.2905 0.532109 0.000 0.012 0.000 0.008 0.836 0.144
#> aberrant_ERR2585348 5 0.4414 0.282556 0.000 0.016 0.028 0.000 0.672 0.284
#> aberrant_ERR2585352 5 0.2737 0.507573 0.000 0.004 0.004 0.000 0.832 0.160
#> aberrant_ERR2585308 1 0.3728 0.235718 0.652 0.344 0.000 0.000 0.000 0.004
#> aberrant_ERR2585349 3 0.7405 0.248012 0.040 0.104 0.468 0.000 0.120 0.268
#> aberrant_ERR2585316 5 0.3568 0.493139 0.000 0.020 0.000 0.012 0.780 0.188
#> aberrant_ERR2585306 2 0.6205 -0.312664 0.024 0.516 0.000 0.012 0.320 0.128
#> aberrant_ERR2585324 5 0.4489 0.373448 0.000 0.044 0.000 0.012 0.680 0.264
#> aberrant_ERR2585310 1 0.6524 -0.151856 0.464 0.340 0.000 0.000 0.068 0.128
#> aberrant_ERR2585296 1 0.7109 0.283038 0.512 0.148 0.052 0.000 0.052 0.236
#> aberrant_ERR2585275 5 0.6487 0.000433 0.000 0.124 0.008 0.048 0.480 0.340
#> aberrant_ERR2585311 5 0.2793 0.559451 0.000 0.028 0.000 0.004 0.856 0.112
#> aberrant_ERR2585292 4 0.0405 1.000000 0.000 0.000 0.000 0.988 0.004 0.008
#> aberrant_ERR2585282 5 0.4177 0.266571 0.000 0.032 0.004 0.000 0.684 0.280
#> aberrant_ERR2585305 5 0.5542 0.239930 0.004 0.212 0.000 0.004 0.596 0.184
#> aberrant_ERR2585278 5 0.3109 0.514880 0.000 0.016 0.000 0.004 0.812 0.168
#> aberrant_ERR2585347 5 0.5157 0.265542 0.000 0.056 0.028 0.008 0.656 0.252
#> aberrant_ERR2585332 5 0.3314 0.425836 0.000 0.012 0.000 0.000 0.764 0.224
#> aberrant_ERR2585280 5 0.5957 0.214621 0.000 0.040 0.028 0.060 0.588 0.284
#> aberrant_ERR2585304 3 0.7880 0.099549 0.076 0.124 0.452 0.000 0.216 0.132
#> aberrant_ERR2585322 5 0.5000 0.338527 0.000 0.000 0.144 0.008 0.668 0.180
#> aberrant_ERR2585279 3 0.0291 0.790899 0.000 0.004 0.992 0.000 0.000 0.004
#> aberrant_ERR2585277 3 0.3127 0.716941 0.000 0.000 0.840 0.004 0.100 0.056
#> aberrant_ERR2585295 5 0.6993 -0.285670 0.000 0.028 0.292 0.016 0.372 0.292
#> aberrant_ERR2585333 5 0.4332 0.485372 0.000 0.044 0.000 0.044 0.756 0.156
#> aberrant_ERR2585285 5 0.3098 0.547583 0.000 0.024 0.000 0.000 0.812 0.164
#> aberrant_ERR2585286 3 0.1864 0.775882 0.000 0.004 0.924 0.000 0.032 0.040
#> aberrant_ERR2585294 5 0.5575 0.320685 0.000 0.116 0.000 0.036 0.624 0.224
#> aberrant_ERR2585300 5 0.5252 0.368017 0.000 0.172 0.000 0.016 0.652 0.160
#> aberrant_ERR2585334 3 0.0405 0.788009 0.000 0.004 0.988 0.000 0.000 0.008
#> aberrant_ERR2585361 5 0.3037 0.539203 0.000 0.008 0.012 0.008 0.840 0.132
#> aberrant_ERR2585372 5 0.3952 0.247857 0.000 0.020 0.000 0.000 0.672 0.308
#> round_ERR2585217 3 0.6836 0.287431 0.316 0.080 0.480 0.000 0.016 0.108
#> round_ERR2585205 1 0.2402 0.612506 0.900 0.060 0.008 0.008 0.000 0.024
#> round_ERR2585214 3 0.0653 0.793047 0.012 0.004 0.980 0.000 0.000 0.004
#> round_ERR2585202 3 0.3530 0.749882 0.032 0.064 0.840 0.000 0.008 0.056
#> aberrant_ERR2585367 5 0.4902 0.310292 0.000 0.008 0.128 0.004 0.692 0.168
#> round_ERR2585220 1 0.2615 0.575938 0.852 0.136 0.004 0.000 0.000 0.008
#> round_ERR2585238 1 0.2669 0.545309 0.836 0.156 0.000 0.000 0.000 0.008
#> aberrant_ERR2585276 5 0.6402 0.103350 0.008 0.172 0.004 0.024 0.520 0.272
#> round_ERR2585218 1 0.2668 0.613473 0.872 0.096 0.004 0.004 0.000 0.024
#> aberrant_ERR2585363 5 0.4165 0.311961 0.000 0.028 0.004 0.000 0.676 0.292
#> round_ERR2585201 3 0.2538 0.736680 0.124 0.016 0.860 0.000 0.000 0.000
#> round_ERR2585210 1 0.5337 0.472006 0.660 0.200 0.016 0.004 0.004 0.116
#> aberrant_ERR2585362 5 0.4605 0.218797 0.000 0.024 0.024 0.000 0.644 0.308
#> aberrant_ERR2585360 5 0.3819 0.461384 0.000 0.040 0.004 0.000 0.756 0.200
#> round_ERR2585209 1 0.4507 0.510558 0.736 0.076 0.164 0.000 0.000 0.024
#> round_ERR2585242 3 0.1312 0.793523 0.020 0.008 0.956 0.004 0.000 0.012
#> round_ERR2585216 1 0.4230 0.582381 0.780 0.112 0.020 0.008 0.000 0.080
#> round_ERR2585219 1 0.2238 0.612047 0.904 0.068 0.004 0.008 0.000 0.016
#> round_ERR2585237 3 0.6376 0.387956 0.280 0.084 0.544 0.000 0.008 0.084
#> round_ERR2585198 3 0.2466 0.744742 0.112 0.008 0.872 0.000 0.000 0.008
#> round_ERR2585211 1 0.3366 0.596390 0.824 0.128 0.008 0.004 0.000 0.036
#> round_ERR2585206 1 0.1411 0.610528 0.936 0.060 0.000 0.004 0.000 0.000
#> aberrant_ERR2585281 3 0.3256 0.720196 0.000 0.016 0.840 0.000 0.048 0.096
#> round_ERR2585212 1 0.3608 0.585255 0.816 0.124 0.032 0.004 0.000 0.024
#> round_ERR2585221 1 0.4126 -0.312392 0.512 0.480 0.000 0.004 0.000 0.004
#> round_ERR2585243 1 0.4371 0.042264 0.580 0.396 0.000 0.000 0.004 0.020
#> round_ERR2585204 3 0.0405 0.791963 0.008 0.004 0.988 0.000 0.000 0.000
#> round_ERR2585213 3 0.0665 0.793436 0.004 0.008 0.980 0.000 0.000 0.008
#> aberrant_ERR2585373 5 0.1787 0.558961 0.000 0.008 0.000 0.004 0.920 0.068
#> aberrant_ERR2585358 5 0.2346 0.536322 0.000 0.008 0.000 0.000 0.868 0.124
#> aberrant_ERR2585365 5 0.4616 0.334834 0.000 0.004 0.084 0.000 0.684 0.228
#> aberrant_ERR2585359 5 0.3261 0.487509 0.000 0.012 0.000 0.012 0.804 0.172
#> aberrant_ERR2585370 3 0.5126 -0.213367 0.000 0.008 0.484 0.000 0.448 0.060
#> round_ERR2585215 1 0.4595 0.560425 0.732 0.180 0.016 0.004 0.004 0.064
#> round_ERR2585262 3 0.2698 0.760009 0.000 0.040 0.880 0.000 0.016 0.064
#> round_ERR2585199 3 0.3827 0.713716 0.100 0.044 0.808 0.000 0.000 0.048
#> aberrant_ERR2585369 5 0.2431 0.523815 0.000 0.008 0.000 0.000 0.860 0.132
#> round_ERR2585208 1 0.2773 0.552189 0.836 0.152 0.000 0.008 0.000 0.004
#> round_ERR2585252 1 0.3012 0.496309 0.796 0.196 0.000 0.000 0.000 0.008
#> round_ERR2585236 1 0.7005 0.238332 0.476 0.148 0.276 0.004 0.004 0.092
#> aberrant_ERR2585284 5 0.7639 -0.364756 0.000 0.044 0.196 0.076 0.420 0.264
#> round_ERR2585224 2 0.4333 0.374242 0.380 0.596 0.000 0.000 0.004 0.020
#> round_ERR2585260 1 0.3329 0.443658 0.756 0.236 0.000 0.004 0.000 0.004
#> round_ERR2585229 1 0.2917 0.611772 0.852 0.104 0.000 0.004 0.000 0.040
#> aberrant_ERR2585364 5 0.3633 0.539793 0.000 0.028 0.000 0.024 0.800 0.148
#> round_ERR2585253 1 0.2876 0.600496 0.844 0.132 0.000 0.008 0.000 0.016
#> aberrant_ERR2585368 3 0.1716 0.783986 0.000 0.004 0.932 0.000 0.036 0.028
#> aberrant_ERR2585371 3 0.1321 0.788261 0.000 0.004 0.952 0.000 0.020 0.024
#> round_ERR2585239 1 0.3381 0.617345 0.836 0.096 0.008 0.008 0.000 0.052
#> round_ERR2585273 1 0.4010 -0.036164 0.584 0.408 0.000 0.000 0.000 0.008
#> round_ERR2585256 1 0.2501 0.617563 0.896 0.040 0.048 0.004 0.000 0.012
#> round_ERR2585272 1 0.2325 0.605336 0.900 0.068 0.020 0.004 0.000 0.008
#> round_ERR2585246 2 0.4076 0.304905 0.452 0.540 0.000 0.000 0.000 0.008
#> round_ERR2585261 1 0.5589 0.300923 0.552 0.060 0.344 0.000 0.000 0.044
#> round_ERR2585254 1 0.2351 0.614787 0.900 0.036 0.052 0.000 0.000 0.012
#> round_ERR2585225 3 0.0363 0.791482 0.000 0.000 0.988 0.000 0.000 0.012
#> round_ERR2585235 1 0.3744 0.599251 0.800 0.140 0.040 0.004 0.000 0.016
#> round_ERR2585271 1 0.2857 0.613473 0.856 0.112 0.004 0.004 0.000 0.024
#> round_ERR2585251 1 0.2261 0.583712 0.884 0.104 0.004 0.000 0.000 0.008
#> round_ERR2585255 3 0.0767 0.793391 0.012 0.000 0.976 0.008 0.000 0.004
#> round_ERR2585257 3 0.2624 0.766069 0.080 0.016 0.880 0.000 0.000 0.024
#> round_ERR2585226 1 0.3823 -0.114899 0.564 0.436 0.000 0.000 0.000 0.000
#> round_ERR2585265 1 0.1812 0.592621 0.912 0.080 0.000 0.000 0.000 0.008
#> round_ERR2585259 1 0.6202 0.419182 0.612 0.116 0.180 0.004 0.004 0.084
#> round_ERR2585247 1 0.3866 -0.265805 0.516 0.484 0.000 0.000 0.000 0.000
#> round_ERR2585241 1 0.3711 0.585134 0.796 0.152 0.008 0.008 0.000 0.036
#> round_ERR2585263 1 0.6668 0.370816 0.552 0.216 0.048 0.000 0.028 0.156
#> round_ERR2585264 1 0.3650 0.503374 0.756 0.216 0.000 0.004 0.000 0.024
#> round_ERR2585233 3 0.1862 0.787708 0.044 0.008 0.928 0.004 0.000 0.016
#> round_ERR2585223 1 0.3488 0.423312 0.744 0.244 0.000 0.004 0.000 0.008
#> round_ERR2585234 3 0.0837 0.793717 0.020 0.004 0.972 0.000 0.000 0.004
#> round_ERR2585222 1 0.4000 0.600098 0.792 0.124 0.012 0.004 0.004 0.064
#> round_ERR2585228 1 0.2717 0.608667 0.868 0.100 0.004 0.004 0.000 0.024
#> round_ERR2585248 1 0.3056 0.598443 0.832 0.140 0.000 0.012 0.000 0.016
#> round_ERR2585240 3 0.5210 0.286768 0.324 0.068 0.592 0.004 0.000 0.012
#> round_ERR2585270 1 0.3636 0.595614 0.812 0.108 0.016 0.000 0.000 0.064
#> round_ERR2585232 1 0.4206 0.540893 0.756 0.108 0.128 0.000 0.000 0.008
#> aberrant_ERR2585341 3 0.5524 0.357198 0.000 0.032 0.628 0.000 0.220 0.120
#> aberrant_ERR2585355 3 0.1984 0.770351 0.000 0.000 0.912 0.000 0.056 0.032
#> round_ERR2585227 1 0.3899 0.143774 0.628 0.364 0.008 0.000 0.000 0.000
#> aberrant_ERR2585351 5 0.4023 0.335863 0.000 0.028 0.004 0.000 0.704 0.264
#> round_ERR2585269 1 0.3961 -0.149216 0.556 0.440 0.000 0.000 0.000 0.004
#> aberrant_ERR2585357 5 0.5926 -0.060389 0.000 0.016 0.244 0.008 0.572 0.160
#> aberrant_ERR2585350 5 0.2095 0.553191 0.000 0.000 0.016 0.004 0.904 0.076
#> round_ERR2585250 1 0.6657 0.373002 0.556 0.168 0.180 0.004 0.004 0.088
#> round_ERR2585245 1 0.3982 -0.225765 0.536 0.460 0.000 0.000 0.000 0.004
#> aberrant_ERR2585353 5 0.1918 0.549415 0.000 0.008 0.000 0.000 0.904 0.088
#> round_ERR2585258 1 0.3707 0.282799 0.680 0.312 0.000 0.000 0.000 0.008
#> aberrant_ERR2585354 5 0.3806 0.428888 0.004 0.008 0.000 0.012 0.736 0.240
#> round_ERR2585249 1 0.4205 -0.110945 0.564 0.420 0.000 0.000 0.000 0.016
#> round_ERR2585268 1 0.5915 0.466320 0.640 0.116 0.148 0.000 0.004 0.092
#> aberrant_ERR2585356 5 0.3525 0.543670 0.000 0.068 0.000 0.004 0.808 0.120
#> round_ERR2585266 3 0.2290 0.746722 0.084 0.020 0.892 0.000 0.000 0.004
#> round_ERR2585231 2 0.3993 0.243677 0.476 0.520 0.000 0.000 0.000 0.004
#> round_ERR2585230 1 0.4299 0.584850 0.748 0.164 0.008 0.004 0.000 0.076
#> round_ERR2585267 1 0.4389 -0.248608 0.512 0.468 0.004 0.000 0.000 0.016
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> MAD:NMF 159 2.51e-28 2
#> MAD:NMF 135 6.36e-20 3
#> MAD:NMF 148 1.48e-21 4
#> MAD:NMF 133 2.15e-18 5
#> MAD:NMF 82 5.63e-11 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "hclust"]
# you can also extract it by
# res = res_list["ATC:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.424 0.654 0.828 0.2769 0.916 0.916
#> 3 3 0.607 0.759 0.902 0.7498 0.583 0.545
#> 4 4 0.578 0.738 0.894 0.0159 1.000 0.999
#> 5 5 0.768 0.775 0.880 0.3077 0.777 0.577
#> 6 6 0.625 0.708 0.841 0.0680 0.972 0.916
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585338 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585325 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585283 2 0.9944 0.938 0.456 0.544
#> aberrant_ERR2585343 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585329 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585317 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585339 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585335 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585287 2 0.9833 0.556 0.424 0.576
#> aberrant_ERR2585321 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585297 1 0.1843 0.315 0.972 0.028
#> aberrant_ERR2585337 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585319 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585315 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585336 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585307 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585301 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585326 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585331 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585346 2 0.9944 0.938 0.456 0.544
#> aberrant_ERR2585314 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585298 1 0.9661 0.757 0.608 0.392
#> aberrant_ERR2585345 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585299 1 0.2778 0.267 0.952 0.048
#> aberrant_ERR2585309 1 0.2948 0.237 0.948 0.052
#> aberrant_ERR2585303 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585313 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585318 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585328 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585330 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585293 2 0.9944 0.938 0.456 0.544
#> aberrant_ERR2585342 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585348 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585352 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585308 1 0.2948 0.237 0.948 0.052
#> aberrant_ERR2585349 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585316 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585306 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585324 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585310 1 0.9988 0.782 0.520 0.480
#> aberrant_ERR2585296 1 0.8555 0.690 0.720 0.280
#> aberrant_ERR2585275 2 0.9944 0.938 0.456 0.544
#> aberrant_ERR2585311 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585292 2 0.9944 0.938 0.456 0.544
#> aberrant_ERR2585282 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585305 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585278 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585347 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585332 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585280 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585304 1 0.9881 0.773 0.564 0.436
#> aberrant_ERR2585322 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585279 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585277 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585295 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585333 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585285 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585286 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585294 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585300 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585334 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585361 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585372 1 1.0000 0.785 0.504 0.496
#> round_ERR2585217 1 0.9933 0.776 0.548 0.452
#> round_ERR2585205 1 0.0672 0.341 0.992 0.008
#> round_ERR2585214 1 0.9881 0.772 0.564 0.436
#> round_ERR2585202 1 0.9881 0.772 0.564 0.436
#> aberrant_ERR2585367 1 1.0000 0.785 0.504 0.496
#> round_ERR2585220 1 0.4022 0.482 0.920 0.080
#> round_ERR2585238 1 0.2236 0.278 0.964 0.036
#> aberrant_ERR2585276 1 1.0000 0.785 0.504 0.496
#> round_ERR2585218 1 0.0672 0.341 0.992 0.008
#> aberrant_ERR2585363 1 1.0000 0.785 0.504 0.496
#> round_ERR2585201 1 0.9754 0.763 0.592 0.408
#> round_ERR2585210 1 0.0376 0.365 0.996 0.004
#> aberrant_ERR2585362 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585360 1 1.0000 0.785 0.504 0.496
#> round_ERR2585209 1 0.9522 0.749 0.628 0.372
#> round_ERR2585242 1 0.9580 0.752 0.620 0.380
#> round_ERR2585216 1 0.5294 0.533 0.880 0.120
#> round_ERR2585219 1 0.4939 0.519 0.892 0.108
#> round_ERR2585237 1 0.9815 0.768 0.580 0.420
#> round_ERR2585198 1 0.9850 0.770 0.572 0.428
#> round_ERR2585211 1 0.0376 0.349 0.996 0.004
#> round_ERR2585206 1 0.0672 0.341 0.992 0.008
#> aberrant_ERR2585281 1 1.0000 0.785 0.504 0.496
#> round_ERR2585212 1 0.5294 0.534 0.880 0.120
#> round_ERR2585221 1 0.2043 0.288 0.968 0.032
#> round_ERR2585243 1 0.0376 0.350 0.996 0.004
#> round_ERR2585204 1 0.9881 0.772 0.564 0.436
#> round_ERR2585213 1 0.9881 0.772 0.564 0.436
#> aberrant_ERR2585373 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585358 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585365 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585359 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585370 1 1.0000 0.785 0.504 0.496
#> round_ERR2585215 1 0.0000 0.357 1.000 0.000
#> round_ERR2585262 1 0.9983 0.782 0.524 0.476
#> round_ERR2585199 1 0.9850 0.770 0.572 0.428
#> aberrant_ERR2585369 1 1.0000 0.785 0.504 0.496
#> round_ERR2585208 1 0.0938 0.333 0.988 0.012
#> round_ERR2585252 1 0.2948 0.237 0.948 0.052
#> round_ERR2585236 1 0.9286 0.733 0.656 0.344
#> aberrant_ERR2585284 2 0.9815 0.908 0.420 0.580
#> round_ERR2585224 1 0.2948 0.237 0.948 0.052
#> round_ERR2585260 1 0.1414 0.379 0.980 0.020
#> round_ERR2585229 1 0.2778 0.267 0.952 0.048
#> aberrant_ERR2585364 1 1.0000 0.785 0.504 0.496
#> round_ERR2585253 1 0.2948 0.237 0.948 0.052
#> aberrant_ERR2585368 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585371 1 1.0000 0.785 0.504 0.496
#> round_ERR2585239 1 0.2778 0.437 0.952 0.048
#> round_ERR2585273 1 0.3114 0.369 0.944 0.056
#> round_ERR2585256 1 0.8955 0.713 0.688 0.312
#> round_ERR2585272 1 0.0938 0.379 0.988 0.012
#> round_ERR2585246 1 0.2236 0.278 0.964 0.036
#> round_ERR2585261 1 0.9286 0.733 0.656 0.344
#> round_ERR2585254 1 0.9460 0.745 0.636 0.364
#> round_ERR2585225 1 0.9635 0.755 0.612 0.388
#> round_ERR2585235 1 0.8861 0.708 0.696 0.304
#> round_ERR2585271 1 0.1633 0.400 0.976 0.024
#> round_ERR2585251 1 0.6048 0.566 0.852 0.148
#> round_ERR2585255 1 0.9775 0.765 0.588 0.412
#> round_ERR2585257 1 0.9795 0.766 0.584 0.416
#> round_ERR2585226 1 0.5519 0.543 0.872 0.128
#> round_ERR2585265 1 0.2236 0.419 0.964 0.036
#> round_ERR2585259 1 0.9491 0.746 0.632 0.368
#> round_ERR2585247 1 0.2043 0.339 0.968 0.032
#> round_ERR2585241 1 0.1184 0.324 0.984 0.016
#> round_ERR2585263 1 0.7299 0.624 0.796 0.204
#> round_ERR2585264 1 0.2948 0.237 0.948 0.052
#> round_ERR2585233 1 0.9635 0.755 0.612 0.388
#> round_ERR2585223 1 0.1843 0.393 0.972 0.028
#> round_ERR2585234 1 0.9850 0.770 0.572 0.428
#> round_ERR2585222 1 0.2603 0.431 0.956 0.044
#> round_ERR2585228 1 0.2236 0.393 0.964 0.036
#> round_ERR2585248 1 0.2948 0.237 0.948 0.052
#> round_ERR2585240 1 0.8713 0.699 0.708 0.292
#> round_ERR2585270 1 0.5178 0.529 0.884 0.116
#> round_ERR2585232 1 0.8207 0.671 0.744 0.256
#> aberrant_ERR2585341 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585355 1 1.0000 0.785 0.504 0.496
#> round_ERR2585227 1 0.5737 0.552 0.864 0.136
#> aberrant_ERR2585351 1 1.0000 0.785 0.504 0.496
#> round_ERR2585269 1 0.2948 0.237 0.948 0.052
#> aberrant_ERR2585357 1 1.0000 0.785 0.504 0.496
#> aberrant_ERR2585350 1 1.0000 0.785 0.504 0.496
#> round_ERR2585250 1 0.8608 0.693 0.716 0.284
#> round_ERR2585245 1 0.2948 0.237 0.948 0.052
#> aberrant_ERR2585353 1 1.0000 0.785 0.504 0.496
#> round_ERR2585258 1 0.1843 0.393 0.972 0.028
#> aberrant_ERR2585354 1 1.0000 0.785 0.504 0.496
#> round_ERR2585249 1 0.2948 0.237 0.948 0.052
#> round_ERR2585268 1 0.8016 0.660 0.756 0.244
#> aberrant_ERR2585356 1 1.0000 0.785 0.504 0.496
#> round_ERR2585266 1 0.9393 0.741 0.644 0.356
#> round_ERR2585231 1 0.2948 0.237 0.948 0.052
#> round_ERR2585230 1 0.2423 0.425 0.960 0.040
#> round_ERR2585267 1 0.2948 0.237 0.948 0.052
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585338 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585325 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585283 3 0.0000 0.92330 0.000 0.000 1.000
#> aberrant_ERR2585343 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585329 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585317 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585339 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585335 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585287 3 0.5465 0.60190 0.000 0.288 0.712
#> aberrant_ERR2585321 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585297 1 0.1289 0.83245 0.968 0.032 0.000
#> aberrant_ERR2585337 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585319 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585315 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585336 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585307 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585301 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585326 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585331 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585346 3 0.0000 0.92330 0.000 0.000 1.000
#> aberrant_ERR2585314 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585298 2 0.5968 0.42282 0.364 0.636 0.000
#> aberrant_ERR2585345 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585299 1 0.0592 0.82051 0.988 0.012 0.000
#> aberrant_ERR2585309 1 0.0000 0.81106 1.000 0.000 0.000
#> aberrant_ERR2585303 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585313 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585318 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585328 2 0.0237 0.87614 0.004 0.996 0.000
#> aberrant_ERR2585330 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585293 3 0.0000 0.92330 0.000 0.000 1.000
#> aberrant_ERR2585342 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585348 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585352 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585308 1 0.0000 0.81106 1.000 0.000 0.000
#> aberrant_ERR2585349 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585316 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585306 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585324 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585310 2 0.1643 0.84784 0.044 0.956 0.000
#> aberrant_ERR2585296 1 0.6295 0.15512 0.528 0.472 0.000
#> aberrant_ERR2585275 3 0.0000 0.92330 0.000 0.000 1.000
#> aberrant_ERR2585311 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585292 3 0.0000 0.92330 0.000 0.000 1.000
#> aberrant_ERR2585282 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585305 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585278 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585347 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585332 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585280 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585304 2 0.5678 0.52385 0.316 0.684 0.000
#> aberrant_ERR2585322 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585279 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585277 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585295 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585333 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585285 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585286 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585294 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585300 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585334 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585361 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585372 2 0.0000 0.87885 0.000 1.000 0.000
#> round_ERR2585217 2 0.4291 0.71598 0.180 0.820 0.000
#> round_ERR2585205 1 0.1643 0.83707 0.956 0.044 0.000
#> round_ERR2585214 2 0.5650 0.53060 0.312 0.688 0.000
#> round_ERR2585202 2 0.5560 0.55041 0.300 0.700 0.000
#> aberrant_ERR2585367 2 0.0000 0.87885 0.000 1.000 0.000
#> round_ERR2585220 1 0.4291 0.77178 0.820 0.180 0.000
#> round_ERR2585238 1 0.0747 0.82389 0.984 0.016 0.000
#> aberrant_ERR2585276 2 0.0000 0.87885 0.000 1.000 0.000
#> round_ERR2585218 1 0.1643 0.83707 0.956 0.044 0.000
#> aberrant_ERR2585363 2 0.0000 0.87885 0.000 1.000 0.000
#> round_ERR2585201 2 0.5835 0.47678 0.340 0.660 0.000
#> round_ERR2585210 1 0.1964 0.83779 0.944 0.056 0.000
#> aberrant_ERR2585362 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585360 2 0.0000 0.87885 0.000 1.000 0.000
#> round_ERR2585209 2 0.6095 0.35414 0.392 0.608 0.000
#> round_ERR2585242 2 0.6026 0.39312 0.376 0.624 0.000
#> round_ERR2585216 1 0.4399 0.76208 0.812 0.188 0.000
#> round_ERR2585219 1 0.4796 0.73546 0.780 0.220 0.000
#> round_ERR2585237 2 0.5859 0.46903 0.344 0.656 0.000
#> round_ERR2585198 2 0.5733 0.50897 0.324 0.676 0.000
#> round_ERR2585211 1 0.1753 0.83750 0.952 0.048 0.000
#> round_ERR2585206 1 0.1643 0.83707 0.956 0.044 0.000
#> aberrant_ERR2585281 2 0.0000 0.87885 0.000 1.000 0.000
#> round_ERR2585212 1 0.5216 0.69336 0.740 0.260 0.000
#> round_ERR2585221 1 0.0892 0.82639 0.980 0.020 0.000
#> round_ERR2585243 1 0.1753 0.83774 0.952 0.048 0.000
#> round_ERR2585204 2 0.5650 0.53060 0.312 0.688 0.000
#> round_ERR2585213 2 0.5650 0.53060 0.312 0.688 0.000
#> aberrant_ERR2585373 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585358 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585365 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585359 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585370 2 0.0000 0.87885 0.000 1.000 0.000
#> round_ERR2585215 1 0.1860 0.83767 0.948 0.052 0.000
#> round_ERR2585262 2 0.3941 0.74300 0.156 0.844 0.000
#> round_ERR2585199 2 0.5733 0.50897 0.324 0.676 0.000
#> aberrant_ERR2585369 2 0.0000 0.87885 0.000 1.000 0.000
#> round_ERR2585208 1 0.1529 0.83585 0.960 0.040 0.000
#> round_ERR2585252 1 0.0000 0.81106 1.000 0.000 0.000
#> round_ERR2585236 2 0.6204 0.25243 0.424 0.576 0.000
#> aberrant_ERR2585284 3 0.2165 0.87840 0.000 0.064 0.936
#> round_ERR2585224 1 0.0000 0.81106 1.000 0.000 0.000
#> round_ERR2585260 1 0.2356 0.83586 0.928 0.072 0.000
#> round_ERR2585229 1 0.0592 0.82057 0.988 0.012 0.000
#> aberrant_ERR2585364 2 0.0000 0.87885 0.000 1.000 0.000
#> round_ERR2585253 1 0.0000 0.81106 1.000 0.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585371 2 0.0000 0.87885 0.000 1.000 0.000
#> round_ERR2585239 1 0.3752 0.80040 0.856 0.144 0.000
#> round_ERR2585273 1 0.2261 0.83351 0.932 0.068 0.000
#> round_ERR2585256 2 0.6307 -0.00725 0.488 0.512 0.000
#> round_ERR2585272 1 0.2537 0.83403 0.920 0.080 0.000
#> round_ERR2585246 1 0.0747 0.82389 0.984 0.016 0.000
#> round_ERR2585261 2 0.6215 0.23767 0.428 0.572 0.000
#> round_ERR2585254 2 0.6095 0.35275 0.392 0.608 0.000
#> round_ERR2585225 2 0.6008 0.40364 0.372 0.628 0.000
#> round_ERR2585235 2 0.6280 0.11832 0.460 0.540 0.000
#> round_ERR2585271 1 0.3038 0.82494 0.896 0.104 0.000
#> round_ERR2585251 1 0.5098 0.70615 0.752 0.248 0.000
#> round_ERR2585255 2 0.5810 0.48538 0.336 0.664 0.000
#> round_ERR2585257 2 0.5810 0.48525 0.336 0.664 0.000
#> round_ERR2585226 1 0.4974 0.71801 0.764 0.236 0.000
#> round_ERR2585265 1 0.3267 0.81886 0.884 0.116 0.000
#> round_ERR2585259 2 0.6111 0.33870 0.396 0.604 0.000
#> round_ERR2585247 1 0.1643 0.83628 0.956 0.044 0.000
#> round_ERR2585241 1 0.1411 0.83438 0.964 0.036 0.000
#> round_ERR2585263 1 0.5968 0.48445 0.636 0.364 0.000
#> round_ERR2585264 1 0.0000 0.81106 1.000 0.000 0.000
#> round_ERR2585233 2 0.5988 0.41319 0.368 0.632 0.000
#> round_ERR2585223 1 0.2796 0.82988 0.908 0.092 0.000
#> round_ERR2585234 2 0.5706 0.51628 0.320 0.680 0.000
#> round_ERR2585222 1 0.3619 0.80619 0.864 0.136 0.000
#> round_ERR2585228 1 0.2796 0.82969 0.908 0.092 0.000
#> round_ERR2585248 1 0.0000 0.81106 1.000 0.000 0.000
#> round_ERR2585240 1 0.6299 0.13100 0.524 0.476 0.000
#> round_ERR2585270 1 0.5363 0.66653 0.724 0.276 0.000
#> round_ERR2585232 1 0.6225 0.29235 0.568 0.432 0.000
#> aberrant_ERR2585341 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585355 2 0.0000 0.87885 0.000 1.000 0.000
#> round_ERR2585227 1 0.5138 0.70086 0.748 0.252 0.000
#> aberrant_ERR2585351 2 0.0000 0.87885 0.000 1.000 0.000
#> round_ERR2585269 1 0.0000 0.81106 1.000 0.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.87885 0.000 1.000 0.000
#> aberrant_ERR2585350 2 0.0000 0.87885 0.000 1.000 0.000
#> round_ERR2585250 1 0.6299 0.13951 0.524 0.476 0.000
#> round_ERR2585245 1 0.0000 0.81106 1.000 0.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.87885 0.000 1.000 0.000
#> round_ERR2585258 1 0.2796 0.82988 0.908 0.092 0.000
#> aberrant_ERR2585354 2 0.0000 0.87885 0.000 1.000 0.000
#> round_ERR2585249 1 0.0000 0.81106 1.000 0.000 0.000
#> round_ERR2585268 1 0.6215 0.30975 0.572 0.428 0.000
#> aberrant_ERR2585356 2 0.0000 0.87885 0.000 1.000 0.000
#> round_ERR2585266 2 0.6252 0.18534 0.444 0.556 0.000
#> round_ERR2585231 1 0.0000 0.81106 1.000 0.000 0.000
#> round_ERR2585230 1 0.3619 0.80595 0.864 0.136 0.000
#> round_ERR2585267 1 0.0000 0.81106 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585338 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585325 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585283 4 0.0000 0.77425 0.000 0.000 0.000 1.000
#> aberrant_ERR2585343 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585329 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585317 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585339 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585335 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585287 4 0.4331 0.14637 0.000 0.288 0.000 0.712
#> aberrant_ERR2585321 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585297 1 0.1724 0.80555 0.948 0.032 0.020 0.000
#> aberrant_ERR2585337 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585319 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585315 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585336 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585307 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585301 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585326 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585331 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585346 4 0.0000 0.77425 0.000 0.000 0.000 1.000
#> aberrant_ERR2585314 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585298 2 0.5143 0.42747 0.360 0.628 0.012 0.000
#> aberrant_ERR2585345 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585299 1 0.1284 0.79109 0.964 0.012 0.024 0.000
#> aberrant_ERR2585309 1 0.1211 0.77406 0.960 0.000 0.040 0.000
#> aberrant_ERR2585303 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585313 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585318 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585328 2 0.0188 0.87637 0.004 0.996 0.000 0.000
#> aberrant_ERR2585330 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585293 4 0.0000 0.77425 0.000 0.000 0.000 1.000
#> aberrant_ERR2585342 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585348 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585352 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585308 1 0.1302 0.77180 0.956 0.000 0.044 0.000
#> aberrant_ERR2585349 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585316 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585306 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585324 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585310 2 0.1302 0.84894 0.044 0.956 0.000 0.000
#> aberrant_ERR2585296 1 0.5396 0.14426 0.524 0.464 0.012 0.000
#> aberrant_ERR2585275 4 0.0000 0.77425 0.000 0.000 0.000 1.000
#> aberrant_ERR2585311 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585292 4 0.0000 0.77425 0.000 0.000 0.000 1.000
#> aberrant_ERR2585282 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585305 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585278 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585347 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585332 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585280 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585304 2 0.4792 0.52994 0.312 0.680 0.008 0.000
#> aberrant_ERR2585322 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585279 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585277 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585295 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585333 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585285 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585286 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585294 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585300 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585334 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585361 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585372 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> round_ERR2585217 2 0.3808 0.71590 0.176 0.812 0.012 0.000
#> round_ERR2585205 1 0.2111 0.81156 0.932 0.044 0.024 0.000
#> round_ERR2585214 2 0.4891 0.53374 0.308 0.680 0.012 0.000
#> round_ERR2585202 2 0.4820 0.55332 0.296 0.692 0.012 0.000
#> aberrant_ERR2585367 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> round_ERR2585220 1 0.3852 0.74922 0.808 0.180 0.012 0.000
#> round_ERR2585238 1 0.1406 0.79473 0.960 0.016 0.024 0.000
#> aberrant_ERR2585276 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> round_ERR2585218 1 0.2002 0.81161 0.936 0.044 0.020 0.000
#> aberrant_ERR2585363 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> round_ERR2585201 2 0.5038 0.48065 0.336 0.652 0.012 0.000
#> round_ERR2585210 1 0.1743 0.81206 0.940 0.056 0.004 0.000
#> aberrant_ERR2585362 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585360 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> round_ERR2585209 2 0.5244 0.35979 0.388 0.600 0.012 0.000
#> round_ERR2585242 2 0.5189 0.39821 0.372 0.616 0.012 0.000
#> round_ERR2585216 1 0.3668 0.73492 0.808 0.188 0.004 0.000
#> round_ERR2585219 1 0.4123 0.70836 0.772 0.220 0.008 0.000
#> round_ERR2585237 2 0.5057 0.47304 0.340 0.648 0.012 0.000
#> round_ERR2585198 2 0.4957 0.51238 0.320 0.668 0.012 0.000
#> round_ERR2585211 1 0.1854 0.81211 0.940 0.048 0.012 0.000
#> round_ERR2585206 1 0.2111 0.81154 0.932 0.044 0.024 0.000
#> aberrant_ERR2585281 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> round_ERR2585212 1 0.4313 0.66458 0.736 0.260 0.004 0.000
#> round_ERR2585221 1 0.1724 0.79513 0.948 0.020 0.032 0.000
#> round_ERR2585243 1 0.1722 0.81241 0.944 0.048 0.008 0.000
#> round_ERR2585204 2 0.4891 0.53374 0.308 0.680 0.012 0.000
#> round_ERR2585213 2 0.4891 0.53374 0.308 0.680 0.012 0.000
#> aberrant_ERR2585373 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585358 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585365 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585359 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585370 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> round_ERR2585215 1 0.1661 0.81172 0.944 0.052 0.004 0.000
#> round_ERR2585262 2 0.3401 0.74216 0.152 0.840 0.008 0.000
#> round_ERR2585199 2 0.4957 0.51238 0.320 0.668 0.012 0.000
#> aberrant_ERR2585369 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> round_ERR2585208 1 0.1913 0.81043 0.940 0.040 0.020 0.000
#> round_ERR2585252 1 0.1474 0.76743 0.948 0.000 0.052 0.000
#> round_ERR2585236 2 0.5329 0.25963 0.420 0.568 0.012 0.000
#> aberrant_ERR2585284 3 0.1888 0.00000 0.000 0.016 0.940 0.044
#> round_ERR2585224 1 0.1557 0.76454 0.944 0.000 0.056 0.000
#> round_ERR2585260 1 0.2563 0.81328 0.908 0.072 0.020 0.000
#> round_ERR2585229 1 0.1854 0.78486 0.940 0.012 0.048 0.000
#> aberrant_ERR2585364 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> round_ERR2585253 1 0.1557 0.76454 0.944 0.000 0.056 0.000
#> aberrant_ERR2585368 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585371 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> round_ERR2585239 1 0.3377 0.77788 0.848 0.140 0.012 0.000
#> round_ERR2585273 1 0.2596 0.80953 0.908 0.068 0.024 0.000
#> round_ERR2585256 2 0.5407 0.00414 0.484 0.504 0.012 0.000
#> round_ERR2585272 1 0.2197 0.80859 0.916 0.080 0.004 0.000
#> round_ERR2585246 1 0.1706 0.79085 0.948 0.016 0.036 0.000
#> round_ERR2585261 2 0.5337 0.24509 0.424 0.564 0.012 0.000
#> round_ERR2585254 2 0.5244 0.35843 0.388 0.600 0.012 0.000
#> round_ERR2585225 2 0.5174 0.40858 0.368 0.620 0.012 0.000
#> round_ERR2585235 2 0.5388 0.12759 0.456 0.532 0.012 0.000
#> round_ERR2585271 1 0.2408 0.79980 0.896 0.104 0.000 0.000
#> round_ERR2585251 1 0.4295 0.68269 0.752 0.240 0.008 0.000
#> round_ERR2585255 2 0.5018 0.48914 0.332 0.656 0.012 0.000
#> round_ERR2585257 2 0.5018 0.48900 0.332 0.656 0.012 0.000
#> round_ERR2585226 1 0.4194 0.69458 0.764 0.228 0.008 0.000
#> round_ERR2585265 1 0.3047 0.79684 0.872 0.116 0.012 0.000
#> round_ERR2585259 2 0.5256 0.34460 0.392 0.596 0.012 0.000
#> round_ERR2585247 1 0.2002 0.80991 0.936 0.044 0.020 0.000
#> round_ERR2585241 1 0.1584 0.80904 0.952 0.036 0.012 0.000
#> round_ERR2585263 1 0.5024 0.46974 0.632 0.360 0.008 0.000
#> round_ERR2585264 1 0.1557 0.76454 0.944 0.000 0.056 0.000
#> round_ERR2585233 2 0.5159 0.41798 0.364 0.624 0.012 0.000
#> round_ERR2585223 1 0.3015 0.80775 0.884 0.092 0.024 0.000
#> round_ERR2585234 2 0.4936 0.51958 0.316 0.672 0.012 0.000
#> round_ERR2585222 1 0.3271 0.78404 0.856 0.132 0.012 0.000
#> round_ERR2585228 1 0.2949 0.80715 0.888 0.088 0.024 0.000
#> round_ERR2585248 1 0.1557 0.76454 0.944 0.000 0.056 0.000
#> round_ERR2585240 1 0.5399 0.12063 0.520 0.468 0.012 0.000
#> round_ERR2585270 1 0.4428 0.64515 0.720 0.276 0.004 0.000
#> round_ERR2585232 1 0.5337 0.28272 0.564 0.424 0.012 0.000
#> aberrant_ERR2585341 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585355 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> round_ERR2585227 1 0.4328 0.67728 0.748 0.244 0.008 0.000
#> aberrant_ERR2585351 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> round_ERR2585269 1 0.1389 0.76970 0.952 0.000 0.048 0.000
#> aberrant_ERR2585357 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> aberrant_ERR2585350 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> round_ERR2585250 1 0.5399 0.12867 0.520 0.468 0.012 0.000
#> round_ERR2585245 1 0.1557 0.76454 0.944 0.000 0.056 0.000
#> aberrant_ERR2585353 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> round_ERR2585258 1 0.3015 0.80775 0.884 0.092 0.024 0.000
#> aberrant_ERR2585354 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> round_ERR2585249 1 0.1389 0.76979 0.952 0.000 0.048 0.000
#> round_ERR2585268 1 0.5329 0.29980 0.568 0.420 0.012 0.000
#> aberrant_ERR2585356 2 0.0000 0.87910 0.000 1.000 0.000 0.000
#> round_ERR2585266 2 0.5366 0.19342 0.440 0.548 0.012 0.000
#> round_ERR2585231 1 0.1389 0.76979 0.952 0.000 0.048 0.000
#> round_ERR2585230 1 0.3390 0.78237 0.852 0.132 0.016 0.000
#> round_ERR2585267 1 0.1389 0.76979 0.952 0.000 0.048 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 2 0.0794 0.9738 0.000 0.972 0.028 0.000 0
#> aberrant_ERR2585338 2 0.0963 0.9690 0.000 0.964 0.036 0.000 0
#> aberrant_ERR2585325 2 0.0794 0.9738 0.000 0.972 0.028 0.000 0
#> aberrant_ERR2585283 4 0.0000 0.8007 0.000 0.000 0.000 1.000 0
#> aberrant_ERR2585343 2 0.0162 0.9725 0.000 0.996 0.004 0.000 0
#> aberrant_ERR2585329 2 0.0609 0.9759 0.000 0.980 0.020 0.000 0
#> aberrant_ERR2585317 2 0.0609 0.9759 0.000 0.980 0.020 0.000 0
#> aberrant_ERR2585339 2 0.0963 0.9690 0.000 0.964 0.036 0.000 0
#> aberrant_ERR2585335 2 0.0162 0.9757 0.000 0.996 0.004 0.000 0
#> aberrant_ERR2585287 4 0.3730 0.2155 0.000 0.288 0.000 0.712 0
#> aberrant_ERR2585321 2 0.0162 0.9725 0.000 0.996 0.004 0.000 0
#> aberrant_ERR2585297 1 0.3684 0.7612 0.720 0.000 0.280 0.000 0
#> aberrant_ERR2585337 2 0.0510 0.9767 0.000 0.984 0.016 0.000 0
#> aberrant_ERR2585319 2 0.0162 0.9757 0.000 0.996 0.004 0.000 0
#> aberrant_ERR2585315 2 0.0290 0.9764 0.000 0.992 0.008 0.000 0
#> aberrant_ERR2585336 2 0.0703 0.9744 0.000 0.976 0.024 0.000 0
#> aberrant_ERR2585307 2 0.0510 0.9761 0.000 0.984 0.016 0.000 0
#> aberrant_ERR2585301 2 0.0290 0.9727 0.000 0.992 0.008 0.000 0
#> aberrant_ERR2585326 2 0.0609 0.9759 0.000 0.980 0.020 0.000 0
#> aberrant_ERR2585331 2 0.0963 0.9690 0.000 0.964 0.036 0.000 0
#> aberrant_ERR2585346 4 0.0000 0.8007 0.000 0.000 0.000 1.000 0
#> aberrant_ERR2585314 2 0.0404 0.9755 0.000 0.988 0.012 0.000 0
#> aberrant_ERR2585298 3 0.0451 0.7448 0.004 0.008 0.988 0.000 0
#> aberrant_ERR2585345 2 0.0609 0.9759 0.000 0.980 0.020 0.000 0
#> aberrant_ERR2585299 1 0.3039 0.7651 0.808 0.000 0.192 0.000 0
#> aberrant_ERR2585309 1 0.2471 0.7506 0.864 0.000 0.136 0.000 0
#> aberrant_ERR2585303 2 0.0880 0.9712 0.000 0.968 0.032 0.000 0
#> aberrant_ERR2585313 2 0.0609 0.9757 0.000 0.980 0.020 0.000 0
#> aberrant_ERR2585318 2 0.0162 0.9758 0.000 0.996 0.004 0.000 0
#> aberrant_ERR2585328 2 0.2471 0.8597 0.000 0.864 0.136 0.000 0
#> aberrant_ERR2585330 2 0.0000 0.9743 0.000 1.000 0.000 0.000 0
#> aberrant_ERR2585293 4 0.0000 0.8007 0.000 0.000 0.000 1.000 0
#> aberrant_ERR2585342 2 0.0000 0.9743 0.000 1.000 0.000 0.000 0
#> aberrant_ERR2585348 2 0.0794 0.9734 0.000 0.972 0.028 0.000 0
#> aberrant_ERR2585352 2 0.0162 0.9758 0.000 0.996 0.004 0.000 0
#> aberrant_ERR2585308 1 0.2329 0.7456 0.876 0.000 0.124 0.000 0
#> aberrant_ERR2585349 2 0.3003 0.7841 0.000 0.812 0.188 0.000 0
#> aberrant_ERR2585316 2 0.0404 0.9745 0.000 0.988 0.012 0.000 0
#> aberrant_ERR2585306 2 0.0510 0.9705 0.000 0.984 0.016 0.000 0
#> aberrant_ERR2585324 2 0.0162 0.9757 0.000 0.996 0.004 0.000 0
#> aberrant_ERR2585310 2 0.2424 0.8347 0.000 0.868 0.132 0.000 0
#> aberrant_ERR2585296 3 0.2773 0.6474 0.164 0.000 0.836 0.000 0
#> aberrant_ERR2585275 4 0.0000 0.8007 0.000 0.000 0.000 1.000 0
#> aberrant_ERR2585311 2 0.0162 0.9725 0.000 0.996 0.004 0.000 0
#> aberrant_ERR2585292 4 0.0000 0.8007 0.000 0.000 0.000 1.000 0
#> aberrant_ERR2585282 2 0.0703 0.9748 0.000 0.976 0.024 0.000 0
#> aberrant_ERR2585305 2 0.0290 0.9727 0.000 0.992 0.008 0.000 0
#> aberrant_ERR2585278 2 0.0404 0.9768 0.000 0.988 0.012 0.000 0
#> aberrant_ERR2585347 2 0.0703 0.9752 0.000 0.976 0.024 0.000 0
#> aberrant_ERR2585332 2 0.0404 0.9745 0.000 0.988 0.012 0.000 0
#> aberrant_ERR2585280 2 0.0794 0.9738 0.000 0.972 0.028 0.000 0
#> aberrant_ERR2585304 3 0.1965 0.7030 0.000 0.096 0.904 0.000 0
#> aberrant_ERR2585322 2 0.0510 0.9767 0.000 0.984 0.016 0.000 0
#> aberrant_ERR2585279 2 0.1732 0.9306 0.000 0.920 0.080 0.000 0
#> aberrant_ERR2585277 2 0.1270 0.9575 0.000 0.948 0.052 0.000 0
#> aberrant_ERR2585295 2 0.0880 0.9718 0.000 0.968 0.032 0.000 0
#> aberrant_ERR2585333 2 0.0404 0.9760 0.000 0.988 0.012 0.000 0
#> aberrant_ERR2585285 2 0.0404 0.9768 0.000 0.988 0.012 0.000 0
#> aberrant_ERR2585286 2 0.1270 0.9575 0.000 0.948 0.052 0.000 0
#> aberrant_ERR2585294 2 0.0290 0.9727 0.000 0.992 0.008 0.000 0
#> aberrant_ERR2585300 2 0.0404 0.9717 0.000 0.988 0.012 0.000 0
#> aberrant_ERR2585334 2 0.1270 0.9575 0.000 0.948 0.052 0.000 0
#> aberrant_ERR2585361 2 0.0290 0.9764 0.000 0.992 0.008 0.000 0
#> aberrant_ERR2585372 2 0.0404 0.9765 0.000 0.988 0.012 0.000 0
#> round_ERR2585217 3 0.3424 0.4526 0.000 0.240 0.760 0.000 0
#> round_ERR2585205 1 0.3857 0.7480 0.688 0.000 0.312 0.000 0
#> round_ERR2585214 3 0.1341 0.7315 0.000 0.056 0.944 0.000 0
#> round_ERR2585202 3 0.1908 0.6982 0.000 0.092 0.908 0.000 0
#> aberrant_ERR2585367 2 0.0794 0.9734 0.000 0.972 0.028 0.000 0
#> round_ERR2585220 3 0.4306 -0.3638 0.492 0.000 0.508 0.000 0
#> round_ERR2585238 1 0.3109 0.7681 0.800 0.000 0.200 0.000 0
#> aberrant_ERR2585276 2 0.0290 0.9727 0.000 0.992 0.008 0.000 0
#> round_ERR2585218 1 0.3837 0.7488 0.692 0.000 0.308 0.000 0
#> aberrant_ERR2585363 2 0.0162 0.9758 0.000 0.996 0.004 0.000 0
#> round_ERR2585201 3 0.0794 0.7452 0.000 0.028 0.972 0.000 0
#> round_ERR2585210 1 0.4150 0.6706 0.612 0.000 0.388 0.000 0
#> aberrant_ERR2585362 2 0.1851 0.9192 0.000 0.912 0.088 0.000 0
#> aberrant_ERR2585360 2 0.0162 0.9725 0.000 0.996 0.004 0.000 0
#> round_ERR2585209 3 0.1364 0.7472 0.036 0.012 0.952 0.000 0
#> round_ERR2585242 3 0.0798 0.7466 0.016 0.008 0.976 0.000 0
#> round_ERR2585216 3 0.4287 -0.2800 0.460 0.000 0.540 0.000 0
#> round_ERR2585219 3 0.4262 -0.1647 0.440 0.000 0.560 0.000 0
#> round_ERR2585237 3 0.1444 0.7463 0.012 0.040 0.948 0.000 0
#> round_ERR2585198 3 0.1197 0.7391 0.000 0.048 0.952 0.000 0
#> round_ERR2585211 1 0.3983 0.7242 0.660 0.000 0.340 0.000 0
#> round_ERR2585206 1 0.3796 0.7528 0.700 0.000 0.300 0.000 0
#> aberrant_ERR2585281 2 0.0880 0.9718 0.000 0.968 0.032 0.000 0
#> round_ERR2585212 3 0.4150 0.0816 0.388 0.000 0.612 0.000 0
#> round_ERR2585221 1 0.3003 0.7654 0.812 0.000 0.188 0.000 0
#> round_ERR2585243 1 0.3999 0.7211 0.656 0.000 0.344 0.000 0
#> round_ERR2585204 3 0.1341 0.7315 0.000 0.056 0.944 0.000 0
#> round_ERR2585213 3 0.1341 0.7315 0.000 0.056 0.944 0.000 0
#> aberrant_ERR2585373 2 0.0290 0.9762 0.000 0.992 0.008 0.000 0
#> aberrant_ERR2585358 2 0.0162 0.9758 0.000 0.996 0.004 0.000 0
#> aberrant_ERR2585365 2 0.0290 0.9764 0.000 0.992 0.008 0.000 0
#> aberrant_ERR2585359 2 0.0162 0.9725 0.000 0.996 0.004 0.000 0
#> aberrant_ERR2585370 2 0.0609 0.9759 0.000 0.980 0.020 0.000 0
#> round_ERR2585215 1 0.4114 0.6824 0.624 0.000 0.376 0.000 0
#> round_ERR2585262 3 0.3684 0.3753 0.000 0.280 0.720 0.000 0
#> round_ERR2585199 3 0.1197 0.7391 0.000 0.048 0.952 0.000 0
#> aberrant_ERR2585369 2 0.0000 0.9743 0.000 1.000 0.000 0.000 0
#> round_ERR2585208 1 0.3796 0.7533 0.700 0.000 0.300 0.000 0
#> round_ERR2585252 1 0.1732 0.7252 0.920 0.000 0.080 0.000 0
#> round_ERR2585236 3 0.1571 0.7366 0.060 0.004 0.936 0.000 0
#> aberrant_ERR2585284 5 0.0000 0.0000 0.000 0.000 0.000 0.000 1
#> round_ERR2585224 1 0.0000 0.6407 1.000 0.000 0.000 0.000 0
#> round_ERR2585260 1 0.3966 0.7286 0.664 0.000 0.336 0.000 0
#> round_ERR2585229 1 0.2773 0.7584 0.836 0.000 0.164 0.000 0
#> aberrant_ERR2585364 2 0.0510 0.9705 0.000 0.984 0.016 0.000 0
#> round_ERR2585253 1 0.0162 0.6461 0.996 0.000 0.004 0.000 0
#> aberrant_ERR2585368 2 0.0963 0.9690 0.000 0.964 0.036 0.000 0
#> aberrant_ERR2585371 2 0.0963 0.9690 0.000 0.964 0.036 0.000 0
#> round_ERR2585239 1 0.4291 0.4943 0.536 0.000 0.464 0.000 0
#> round_ERR2585273 1 0.3816 0.7462 0.696 0.000 0.304 0.000 0
#> round_ERR2585256 3 0.2488 0.6919 0.124 0.004 0.872 0.000 0
#> round_ERR2585272 1 0.4201 0.6344 0.592 0.000 0.408 0.000 0
#> round_ERR2585246 1 0.3039 0.7667 0.808 0.000 0.192 0.000 0
#> round_ERR2585261 3 0.1830 0.7347 0.068 0.008 0.924 0.000 0
#> round_ERR2585254 3 0.1648 0.7485 0.040 0.020 0.940 0.000 0
#> round_ERR2585225 3 0.0451 0.7445 0.008 0.004 0.988 0.000 0
#> round_ERR2585235 3 0.2124 0.7162 0.096 0.004 0.900 0.000 0
#> round_ERR2585271 1 0.4249 0.5819 0.568 0.000 0.432 0.000 0
#> round_ERR2585251 3 0.4227 -0.0538 0.420 0.000 0.580 0.000 0
#> round_ERR2585255 3 0.0880 0.7444 0.000 0.032 0.968 0.000 0
#> round_ERR2585257 3 0.1043 0.7447 0.000 0.040 0.960 0.000 0
#> round_ERR2585226 3 0.4249 -0.1027 0.432 0.000 0.568 0.000 0
#> round_ERR2585265 1 0.4227 0.6024 0.580 0.000 0.420 0.000 0
#> round_ERR2585259 3 0.1041 0.7441 0.032 0.004 0.964 0.000 0
#> round_ERR2585247 1 0.3612 0.7623 0.732 0.000 0.268 0.000 0
#> round_ERR2585241 1 0.3837 0.7524 0.692 0.000 0.308 0.000 0
#> round_ERR2585263 3 0.3661 0.4477 0.276 0.000 0.724 0.000 0
#> round_ERR2585264 1 0.0000 0.6407 1.000 0.000 0.000 0.000 0
#> round_ERR2585233 3 0.0324 0.7434 0.004 0.004 0.992 0.000 0
#> round_ERR2585223 1 0.4074 0.6891 0.636 0.000 0.364 0.000 0
#> round_ERR2585234 3 0.1197 0.7378 0.000 0.048 0.952 0.000 0
#> round_ERR2585222 1 0.4287 0.5108 0.540 0.000 0.460 0.000 0
#> round_ERR2585228 1 0.4101 0.6851 0.628 0.000 0.372 0.000 0
#> round_ERR2585248 1 0.0000 0.6407 1.000 0.000 0.000 0.000 0
#> round_ERR2585240 3 0.3010 0.6380 0.172 0.004 0.824 0.000 0
#> round_ERR2585270 3 0.4088 0.1601 0.368 0.000 0.632 0.000 0
#> round_ERR2585232 3 0.3461 0.5628 0.224 0.004 0.772 0.000 0
#> aberrant_ERR2585341 2 0.0794 0.9738 0.000 0.972 0.028 0.000 0
#> aberrant_ERR2585355 2 0.0963 0.9690 0.000 0.964 0.036 0.000 0
#> round_ERR2585227 3 0.4219 -0.0311 0.416 0.000 0.584 0.000 0
#> aberrant_ERR2585351 2 0.0162 0.9758 0.000 0.996 0.004 0.000 0
#> round_ERR2585269 1 0.2074 0.7378 0.896 0.000 0.104 0.000 0
#> aberrant_ERR2585357 2 0.0609 0.9759 0.000 0.980 0.020 0.000 0
#> aberrant_ERR2585350 2 0.0963 0.9690 0.000 0.964 0.036 0.000 0
#> round_ERR2585250 3 0.2890 0.6540 0.160 0.004 0.836 0.000 0
#> round_ERR2585245 1 0.0000 0.6407 1.000 0.000 0.000 0.000 0
#> aberrant_ERR2585353 2 0.0000 0.9743 0.000 1.000 0.000 0.000 0
#> round_ERR2585258 1 0.4074 0.6891 0.636 0.000 0.364 0.000 0
#> aberrant_ERR2585354 2 0.0162 0.9758 0.000 0.996 0.004 0.000 0
#> round_ERR2585249 1 0.1478 0.7139 0.936 0.000 0.064 0.000 0
#> round_ERR2585268 3 0.3177 0.5832 0.208 0.000 0.792 0.000 0
#> aberrant_ERR2585356 2 0.0404 0.9717 0.000 0.988 0.012 0.000 0
#> round_ERR2585266 3 0.2193 0.7183 0.092 0.008 0.900 0.000 0
#> round_ERR2585231 1 0.1197 0.7016 0.952 0.000 0.048 0.000 0
#> round_ERR2585230 1 0.4291 0.4982 0.536 0.000 0.464 0.000 0
#> round_ERR2585267 1 0.1478 0.7133 0.936 0.000 0.064 0.000 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 2 0.2446 0.7890 0.000 0.864 0.012 0.000 0.124 0
#> aberrant_ERR2585338 2 0.1644 0.8398 0.000 0.932 0.028 0.000 0.040 0
#> aberrant_ERR2585325 2 0.2446 0.7890 0.000 0.864 0.012 0.000 0.124 0
#> aberrant_ERR2585283 4 0.0000 0.8935 0.000 0.000 0.000 1.000 0.000 0
#> aberrant_ERR2585343 2 0.1765 0.8494 0.000 0.904 0.000 0.000 0.096 0
#> aberrant_ERR2585329 2 0.1265 0.8606 0.000 0.948 0.008 0.000 0.044 0
#> aberrant_ERR2585317 2 0.1124 0.8599 0.000 0.956 0.008 0.000 0.036 0
#> aberrant_ERR2585339 2 0.1341 0.8510 0.000 0.948 0.024 0.000 0.028 0
#> aberrant_ERR2585335 2 0.1610 0.8437 0.000 0.916 0.000 0.000 0.084 0
#> aberrant_ERR2585287 4 0.4444 0.4825 0.000 0.108 0.000 0.708 0.184 0
#> aberrant_ERR2585321 2 0.2454 0.7839 0.000 0.840 0.000 0.000 0.160 0
#> aberrant_ERR2585297 1 0.3266 0.7564 0.728 0.000 0.272 0.000 0.000 0
#> aberrant_ERR2585337 2 0.1686 0.8581 0.000 0.924 0.012 0.000 0.064 0
#> aberrant_ERR2585319 2 0.1501 0.8465 0.000 0.924 0.000 0.000 0.076 0
#> aberrant_ERR2585315 2 0.1411 0.8559 0.000 0.936 0.004 0.000 0.060 0
#> aberrant_ERR2585336 2 0.1594 0.8607 0.000 0.932 0.016 0.000 0.052 0
#> aberrant_ERR2585307 2 0.1462 0.8647 0.000 0.936 0.008 0.000 0.056 0
#> aberrant_ERR2585301 2 0.2402 0.7930 0.000 0.856 0.004 0.000 0.140 0
#> aberrant_ERR2585326 2 0.1049 0.8601 0.000 0.960 0.008 0.000 0.032 0
#> aberrant_ERR2585331 2 0.1713 0.8378 0.000 0.928 0.028 0.000 0.044 0
#> aberrant_ERR2585346 4 0.1204 0.8601 0.000 0.000 0.000 0.944 0.056 0
#> aberrant_ERR2585314 2 0.1918 0.8421 0.000 0.904 0.008 0.000 0.088 0
#> aberrant_ERR2585298 3 0.0520 0.7606 0.008 0.000 0.984 0.000 0.008 0
#> aberrant_ERR2585345 2 0.0972 0.8601 0.000 0.964 0.008 0.000 0.028 0
#> aberrant_ERR2585299 1 0.2664 0.7576 0.816 0.000 0.184 0.000 0.000 0
#> aberrant_ERR2585309 1 0.2092 0.7415 0.876 0.000 0.124 0.000 0.000 0
#> aberrant_ERR2585303 2 0.1334 0.8525 0.000 0.948 0.020 0.000 0.032 0
#> aberrant_ERR2585313 2 0.1563 0.8596 0.000 0.932 0.012 0.000 0.056 0
#> aberrant_ERR2585318 2 0.1814 0.8377 0.000 0.900 0.000 0.000 0.100 0
#> aberrant_ERR2585328 2 0.2999 0.7260 0.000 0.836 0.124 0.000 0.040 0
#> aberrant_ERR2585330 2 0.1714 0.8402 0.000 0.908 0.000 0.000 0.092 0
#> aberrant_ERR2585293 4 0.0000 0.8935 0.000 0.000 0.000 1.000 0.000 0
#> aberrant_ERR2585342 2 0.1957 0.8390 0.000 0.888 0.000 0.000 0.112 0
#> aberrant_ERR2585348 2 0.1245 0.8547 0.000 0.952 0.016 0.000 0.032 0
#> aberrant_ERR2585352 2 0.1444 0.8555 0.000 0.928 0.000 0.000 0.072 0
#> aberrant_ERR2585308 1 0.1957 0.7363 0.888 0.000 0.112 0.000 0.000 0
#> aberrant_ERR2585349 2 0.3618 0.6065 0.000 0.776 0.176 0.000 0.048 0
#> aberrant_ERR2585316 2 0.3804 -0.1269 0.000 0.576 0.000 0.000 0.424 0
#> aberrant_ERR2585306 5 0.3464 0.9492 0.000 0.312 0.000 0.000 0.688 0
#> aberrant_ERR2585324 2 0.1501 0.8465 0.000 0.924 0.000 0.000 0.076 0
#> aberrant_ERR2585310 2 0.4526 0.4810 0.000 0.704 0.132 0.000 0.164 0
#> aberrant_ERR2585296 3 0.2562 0.6527 0.172 0.000 0.828 0.000 0.000 0
#> aberrant_ERR2585275 4 0.0146 0.8924 0.000 0.000 0.000 0.996 0.004 0
#> aberrant_ERR2585311 2 0.2697 0.7340 0.000 0.812 0.000 0.000 0.188 0
#> aberrant_ERR2585292 4 0.0000 0.8935 0.000 0.000 0.000 1.000 0.000 0
#> aberrant_ERR2585282 2 0.1643 0.8507 0.000 0.924 0.008 0.000 0.068 0
#> aberrant_ERR2585305 2 0.3023 0.6457 0.000 0.768 0.000 0.000 0.232 0
#> aberrant_ERR2585278 2 0.2070 0.8468 0.000 0.892 0.008 0.000 0.100 0
#> aberrant_ERR2585347 2 0.2882 0.7025 0.000 0.812 0.008 0.000 0.180 0
#> aberrant_ERR2585332 2 0.2854 0.7053 0.000 0.792 0.000 0.000 0.208 0
#> aberrant_ERR2585280 2 0.2531 0.7681 0.000 0.856 0.012 0.000 0.132 0
#> aberrant_ERR2585304 3 0.2039 0.7193 0.000 0.076 0.904 0.000 0.020 0
#> aberrant_ERR2585322 2 0.1333 0.8627 0.000 0.944 0.008 0.000 0.048 0
#> aberrant_ERR2585279 2 0.2442 0.8042 0.000 0.884 0.068 0.000 0.048 0
#> aberrant_ERR2585277 2 0.2001 0.8281 0.000 0.912 0.040 0.000 0.048 0
#> aberrant_ERR2585295 2 0.2859 0.7348 0.000 0.828 0.016 0.000 0.156 0
#> aberrant_ERR2585333 2 0.2260 0.8135 0.000 0.860 0.000 0.000 0.140 0
#> aberrant_ERR2585285 2 0.2118 0.8444 0.000 0.888 0.008 0.000 0.104 0
#> aberrant_ERR2585286 2 0.2001 0.8281 0.000 0.912 0.040 0.000 0.048 0
#> aberrant_ERR2585294 2 0.2996 0.6541 0.000 0.772 0.000 0.000 0.228 0
#> aberrant_ERR2585300 5 0.3531 0.9395 0.000 0.328 0.000 0.000 0.672 0
#> aberrant_ERR2585334 2 0.2001 0.8281 0.000 0.912 0.040 0.000 0.048 0
#> aberrant_ERR2585361 2 0.0865 0.8586 0.000 0.964 0.000 0.000 0.036 0
#> aberrant_ERR2585372 2 0.1411 0.8625 0.000 0.936 0.004 0.000 0.060 0
#> round_ERR2585217 3 0.3806 0.4694 0.000 0.200 0.752 0.000 0.048 0
#> round_ERR2585205 1 0.3428 0.7444 0.696 0.000 0.304 0.000 0.000 0
#> round_ERR2585214 3 0.1334 0.7477 0.000 0.032 0.948 0.000 0.020 0
#> round_ERR2585202 3 0.1895 0.7177 0.000 0.072 0.912 0.000 0.016 0
#> aberrant_ERR2585367 2 0.1168 0.8533 0.000 0.956 0.016 0.000 0.028 0
#> round_ERR2585220 1 0.3869 0.3591 0.500 0.000 0.500 0.000 0.000 0
#> round_ERR2585238 1 0.2697 0.7603 0.812 0.000 0.188 0.000 0.000 0
#> aberrant_ERR2585276 2 0.2996 0.6541 0.000 0.772 0.000 0.000 0.228 0
#> round_ERR2585218 1 0.3409 0.7451 0.700 0.000 0.300 0.000 0.000 0
#> aberrant_ERR2585363 2 0.0937 0.8587 0.000 0.960 0.000 0.000 0.040 0
#> round_ERR2585201 3 0.0717 0.7592 0.000 0.008 0.976 0.000 0.016 0
#> round_ERR2585210 1 0.3706 0.6726 0.620 0.000 0.380 0.000 0.000 0
#> aberrant_ERR2585362 2 0.2433 0.8071 0.000 0.884 0.072 0.000 0.044 0
#> aberrant_ERR2585360 2 0.1714 0.8467 0.000 0.908 0.000 0.000 0.092 0
#> round_ERR2585209 3 0.1124 0.7608 0.036 0.000 0.956 0.000 0.008 0
#> round_ERR2585242 3 0.0603 0.7617 0.016 0.000 0.980 0.000 0.004 0
#> round_ERR2585216 3 0.3857 -0.3068 0.468 0.000 0.532 0.000 0.000 0
#> round_ERR2585219 3 0.3838 -0.1957 0.448 0.000 0.552 0.000 0.000 0
#> round_ERR2585237 3 0.1542 0.7607 0.016 0.024 0.944 0.000 0.016 0
#> round_ERR2585198 3 0.1168 0.7545 0.000 0.028 0.956 0.000 0.016 0
#> round_ERR2585211 1 0.3547 0.7224 0.668 0.000 0.332 0.000 0.000 0
#> round_ERR2585206 1 0.3351 0.7499 0.712 0.000 0.288 0.000 0.000 0
#> aberrant_ERR2585281 2 0.2664 0.7685 0.000 0.848 0.016 0.000 0.136 0
#> round_ERR2585212 3 0.3747 0.0525 0.396 0.000 0.604 0.000 0.000 0
#> round_ERR2585221 1 0.2772 0.7574 0.816 0.000 0.180 0.000 0.004 0
#> round_ERR2585243 1 0.3563 0.7198 0.664 0.000 0.336 0.000 0.000 0
#> round_ERR2585204 3 0.1334 0.7477 0.000 0.032 0.948 0.000 0.020 0
#> round_ERR2585213 3 0.1334 0.7477 0.000 0.032 0.948 0.000 0.020 0
#> aberrant_ERR2585373 2 0.2823 0.7081 0.000 0.796 0.000 0.000 0.204 0
#> aberrant_ERR2585358 2 0.1814 0.8467 0.000 0.900 0.000 0.000 0.100 0
#> aberrant_ERR2585365 2 0.1075 0.8602 0.000 0.952 0.000 0.000 0.048 0
#> aberrant_ERR2585359 2 0.3175 0.6091 0.000 0.744 0.000 0.000 0.256 0
#> aberrant_ERR2585370 2 0.0891 0.8580 0.000 0.968 0.008 0.000 0.024 0
#> round_ERR2585215 1 0.3672 0.6837 0.632 0.000 0.368 0.000 0.000 0
#> round_ERR2585262 3 0.4075 0.3711 0.000 0.240 0.712 0.000 0.048 0
#> round_ERR2585199 3 0.1168 0.7545 0.000 0.028 0.956 0.000 0.016 0
#> aberrant_ERR2585369 2 0.1814 0.8383 0.000 0.900 0.000 0.000 0.100 0
#> round_ERR2585208 1 0.3371 0.7492 0.708 0.000 0.292 0.000 0.000 0
#> round_ERR2585252 1 0.1701 0.7144 0.920 0.000 0.072 0.000 0.008 0
#> round_ERR2585236 3 0.1387 0.7482 0.068 0.000 0.932 0.000 0.000 0
#> aberrant_ERR2585284 6 0.0000 0.0000 0.000 0.000 0.000 0.000 0.000 1
#> round_ERR2585224 1 0.0790 0.6061 0.968 0.000 0.000 0.000 0.032 0
#> round_ERR2585260 1 0.3531 0.7270 0.672 0.000 0.328 0.000 0.000 0
#> round_ERR2585229 1 0.2378 0.7498 0.848 0.000 0.152 0.000 0.000 0
#> aberrant_ERR2585364 5 0.3390 0.9283 0.000 0.296 0.000 0.000 0.704 0
#> round_ERR2585253 1 0.0777 0.6214 0.972 0.000 0.004 0.000 0.024 0
#> aberrant_ERR2585368 2 0.1713 0.8378 0.000 0.928 0.028 0.000 0.044 0
#> aberrant_ERR2585371 2 0.1713 0.8378 0.000 0.928 0.028 0.000 0.044 0
#> round_ERR2585239 1 0.3847 0.5085 0.544 0.000 0.456 0.000 0.000 0
#> round_ERR2585273 1 0.3390 0.7430 0.704 0.000 0.296 0.000 0.000 0
#> round_ERR2585256 3 0.2178 0.6987 0.132 0.000 0.868 0.000 0.000 0
#> round_ERR2585272 1 0.3756 0.6392 0.600 0.000 0.400 0.000 0.000 0
#> round_ERR2585246 1 0.2631 0.7588 0.820 0.000 0.180 0.000 0.000 0
#> round_ERR2585261 3 0.1588 0.7464 0.072 0.000 0.924 0.000 0.004 0
#> round_ERR2585254 3 0.1442 0.7624 0.040 0.012 0.944 0.000 0.004 0
#> round_ERR2585225 3 0.0603 0.7612 0.016 0.000 0.980 0.000 0.004 0
#> round_ERR2585235 3 0.1863 0.7249 0.104 0.000 0.896 0.000 0.000 0
#> round_ERR2585271 1 0.3804 0.5904 0.576 0.000 0.424 0.000 0.000 0
#> round_ERR2585251 3 0.3810 -0.0823 0.428 0.000 0.572 0.000 0.000 0
#> round_ERR2585255 3 0.0891 0.7565 0.000 0.008 0.968 0.000 0.024 0
#> round_ERR2585257 3 0.0993 0.7594 0.000 0.024 0.964 0.000 0.012 0
#> round_ERR2585226 3 0.3828 -0.1306 0.440 0.000 0.560 0.000 0.000 0
#> round_ERR2585265 1 0.3782 0.6097 0.588 0.000 0.412 0.000 0.000 0
#> round_ERR2585259 3 0.0937 0.7579 0.040 0.000 0.960 0.000 0.000 0
#> round_ERR2585247 1 0.3198 0.7572 0.740 0.000 0.260 0.000 0.000 0
#> round_ERR2585241 1 0.3409 0.7486 0.700 0.000 0.300 0.000 0.000 0
#> round_ERR2585263 3 0.3330 0.4398 0.284 0.000 0.716 0.000 0.000 0
#> round_ERR2585264 1 0.0790 0.6061 0.968 0.000 0.000 0.000 0.032 0
#> round_ERR2585233 3 0.0520 0.7598 0.008 0.000 0.984 0.000 0.008 0
#> round_ERR2585223 1 0.3620 0.6926 0.648 0.000 0.352 0.000 0.000 0
#> round_ERR2585234 3 0.1176 0.7532 0.000 0.024 0.956 0.000 0.020 0
#> round_ERR2585222 1 0.3843 0.5236 0.548 0.000 0.452 0.000 0.000 0
#> round_ERR2585228 1 0.3659 0.6872 0.636 0.000 0.364 0.000 0.000 0
#> round_ERR2585248 1 0.0790 0.6061 0.968 0.000 0.000 0.000 0.032 0
#> round_ERR2585240 3 0.2562 0.6517 0.172 0.000 0.828 0.000 0.000 0
#> round_ERR2585270 3 0.3695 0.1358 0.376 0.000 0.624 0.000 0.000 0
#> round_ERR2585232 3 0.2969 0.5750 0.224 0.000 0.776 0.000 0.000 0
#> aberrant_ERR2585341 2 0.2402 0.7821 0.000 0.868 0.012 0.000 0.120 0
#> aberrant_ERR2585355 2 0.1644 0.8398 0.000 0.932 0.028 0.000 0.040 0
#> round_ERR2585227 3 0.3804 -0.0592 0.424 0.000 0.576 0.000 0.000 0
#> aberrant_ERR2585351 2 0.1204 0.8575 0.000 0.944 0.000 0.000 0.056 0
#> round_ERR2585269 1 0.1858 0.7268 0.904 0.000 0.092 0.000 0.004 0
#> aberrant_ERR2585357 2 0.1049 0.8593 0.000 0.960 0.008 0.000 0.032 0
#> aberrant_ERR2585350 2 0.1644 0.8441 0.000 0.932 0.028 0.000 0.040 0
#> round_ERR2585250 3 0.2527 0.6584 0.168 0.000 0.832 0.000 0.000 0
#> round_ERR2585245 1 0.0790 0.6061 0.968 0.000 0.000 0.000 0.032 0
#> aberrant_ERR2585353 2 0.1387 0.8583 0.000 0.932 0.000 0.000 0.068 0
#> round_ERR2585258 1 0.3620 0.6926 0.648 0.000 0.352 0.000 0.000 0
#> aberrant_ERR2585354 2 0.1765 0.8400 0.000 0.904 0.000 0.000 0.096 0
#> round_ERR2585249 1 0.1625 0.7037 0.928 0.000 0.060 0.000 0.012 0
#> round_ERR2585268 3 0.2912 0.5840 0.216 0.000 0.784 0.000 0.000 0
#> aberrant_ERR2585356 5 0.3499 0.9453 0.000 0.320 0.000 0.000 0.680 0
#> round_ERR2585266 3 0.1858 0.7337 0.092 0.000 0.904 0.000 0.004 0
#> round_ERR2585231 1 0.1367 0.6868 0.944 0.000 0.044 0.000 0.012 0
#> round_ERR2585230 1 0.3847 0.5122 0.544 0.000 0.456 0.000 0.000 0
#> round_ERR2585267 1 0.1462 0.7024 0.936 0.000 0.056 0.000 0.008 0
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> ATC:hclust 120 1.74e-01 2
#> ATC:hclust 138 7.06e-20 3
#> ATC:hclust 136 1.67e-19 4
#> ATC:hclust 145 1.22e-25 5
#> ATC:hclust 145 7.78e-25 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "kmeans"]
# you can also extract it by
# res = res_list["ATC:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.987 0.982 0.4912 0.498 0.498
#> 3 3 0.834 0.840 0.905 0.1760 0.960 0.919
#> 4 4 0.718 0.847 0.882 0.1591 0.831 0.647
#> 5 5 0.752 0.750 0.816 0.1108 0.892 0.675
#> 6 6 0.702 0.619 0.775 0.0537 0.915 0.678
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585338 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585325 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585283 1 0.634 0.813 0.840 0.160
#> aberrant_ERR2585343 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585329 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585317 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585339 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585335 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585287 2 0.184 0.969 0.028 0.972
#> aberrant_ERR2585321 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585297 1 0.184 0.989 0.972 0.028
#> aberrant_ERR2585337 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585319 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585315 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585336 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585307 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585301 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585326 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585331 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585346 1 0.184 0.954 0.972 0.028
#> aberrant_ERR2585314 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585298 1 0.184 0.989 0.972 0.028
#> aberrant_ERR2585345 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585299 1 0.184 0.989 0.972 0.028
#> aberrant_ERR2585309 1 0.184 0.989 0.972 0.028
#> aberrant_ERR2585303 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585313 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585318 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585328 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585330 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585293 1 0.118 0.960 0.984 0.016
#> aberrant_ERR2585342 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585348 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585352 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585308 1 0.184 0.989 0.972 0.028
#> aberrant_ERR2585349 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585316 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585306 1 0.416 0.937 0.916 0.084
#> aberrant_ERR2585324 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585310 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585296 1 0.184 0.989 0.972 0.028
#> aberrant_ERR2585275 1 0.563 0.849 0.868 0.132
#> aberrant_ERR2585311 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585292 1 0.118 0.960 0.984 0.016
#> aberrant_ERR2585282 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585305 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585278 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585347 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585332 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585280 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585304 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585322 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585279 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585277 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585295 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585333 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585285 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585286 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585294 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585300 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585334 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585361 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585372 2 0.000 0.997 0.000 1.000
#> round_ERR2585217 2 0.260 0.954 0.044 0.956
#> round_ERR2585205 1 0.184 0.989 0.972 0.028
#> round_ERR2585214 2 0.242 0.958 0.040 0.960
#> round_ERR2585202 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585367 2 0.000 0.997 0.000 1.000
#> round_ERR2585220 1 0.184 0.989 0.972 0.028
#> round_ERR2585238 1 0.184 0.989 0.972 0.028
#> aberrant_ERR2585276 2 0.000 0.997 0.000 1.000
#> round_ERR2585218 1 0.184 0.989 0.972 0.028
#> aberrant_ERR2585363 2 0.000 0.997 0.000 1.000
#> round_ERR2585201 1 0.184 0.989 0.972 0.028
#> round_ERR2585210 1 0.184 0.989 0.972 0.028
#> aberrant_ERR2585362 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585360 2 0.000 0.997 0.000 1.000
#> round_ERR2585209 1 0.184 0.989 0.972 0.028
#> round_ERR2585242 1 0.184 0.989 0.972 0.028
#> round_ERR2585216 1 0.184 0.989 0.972 0.028
#> round_ERR2585219 1 0.184 0.989 0.972 0.028
#> round_ERR2585237 2 0.311 0.941 0.056 0.944
#> round_ERR2585198 1 0.738 0.777 0.792 0.208
#> round_ERR2585211 1 0.184 0.989 0.972 0.028
#> round_ERR2585206 1 0.184 0.989 0.972 0.028
#> aberrant_ERR2585281 2 0.000 0.997 0.000 1.000
#> round_ERR2585212 1 0.184 0.989 0.972 0.028
#> round_ERR2585221 1 0.184 0.989 0.972 0.028
#> round_ERR2585243 1 0.184 0.989 0.972 0.028
#> round_ERR2585204 2 0.224 0.962 0.036 0.964
#> round_ERR2585213 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585373 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585358 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585365 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585359 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585370 2 0.000 0.997 0.000 1.000
#> round_ERR2585215 1 0.184 0.989 0.972 0.028
#> round_ERR2585262 2 0.000 0.997 0.000 1.000
#> round_ERR2585199 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585369 2 0.000 0.997 0.000 1.000
#> round_ERR2585208 1 0.184 0.989 0.972 0.028
#> round_ERR2585252 1 0.184 0.989 0.972 0.028
#> round_ERR2585236 1 0.184 0.989 0.972 0.028
#> aberrant_ERR2585284 1 0.680 0.785 0.820 0.180
#> round_ERR2585224 1 0.184 0.989 0.972 0.028
#> round_ERR2585260 1 0.184 0.989 0.972 0.028
#> round_ERR2585229 1 0.184 0.989 0.972 0.028
#> aberrant_ERR2585364 2 0.000 0.997 0.000 1.000
#> round_ERR2585253 1 0.184 0.989 0.972 0.028
#> aberrant_ERR2585368 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585371 2 0.000 0.997 0.000 1.000
#> round_ERR2585239 1 0.184 0.989 0.972 0.028
#> round_ERR2585273 1 0.184 0.989 0.972 0.028
#> round_ERR2585256 1 0.184 0.989 0.972 0.028
#> round_ERR2585272 1 0.184 0.989 0.972 0.028
#> round_ERR2585246 1 0.184 0.989 0.972 0.028
#> round_ERR2585261 1 0.184 0.989 0.972 0.028
#> round_ERR2585254 1 0.184 0.989 0.972 0.028
#> round_ERR2585225 1 0.184 0.989 0.972 0.028
#> round_ERR2585235 1 0.184 0.989 0.972 0.028
#> round_ERR2585271 1 0.184 0.989 0.972 0.028
#> round_ERR2585251 1 0.184 0.989 0.972 0.028
#> round_ERR2585255 1 0.184 0.989 0.972 0.028
#> round_ERR2585257 1 0.184 0.989 0.972 0.028
#> round_ERR2585226 1 0.184 0.989 0.972 0.028
#> round_ERR2585265 1 0.184 0.989 0.972 0.028
#> round_ERR2585259 1 0.184 0.989 0.972 0.028
#> round_ERR2585247 1 0.184 0.989 0.972 0.028
#> round_ERR2585241 1 0.184 0.989 0.972 0.028
#> round_ERR2585263 1 0.184 0.989 0.972 0.028
#> round_ERR2585264 1 0.184 0.989 0.972 0.028
#> round_ERR2585233 1 0.184 0.989 0.972 0.028
#> round_ERR2585223 1 0.184 0.989 0.972 0.028
#> round_ERR2585234 1 0.184 0.989 0.972 0.028
#> round_ERR2585222 1 0.184 0.989 0.972 0.028
#> round_ERR2585228 1 0.184 0.989 0.972 0.028
#> round_ERR2585248 1 0.184 0.989 0.972 0.028
#> round_ERR2585240 1 0.184 0.989 0.972 0.028
#> round_ERR2585270 1 0.184 0.989 0.972 0.028
#> round_ERR2585232 1 0.184 0.989 0.972 0.028
#> aberrant_ERR2585341 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585355 2 0.000 0.997 0.000 1.000
#> round_ERR2585227 1 0.184 0.989 0.972 0.028
#> aberrant_ERR2585351 2 0.000 0.997 0.000 1.000
#> round_ERR2585269 1 0.184 0.989 0.972 0.028
#> aberrant_ERR2585357 2 0.000 0.997 0.000 1.000
#> aberrant_ERR2585350 2 0.000 0.997 0.000 1.000
#> round_ERR2585250 1 0.184 0.989 0.972 0.028
#> round_ERR2585245 1 0.184 0.989 0.972 0.028
#> aberrant_ERR2585353 2 0.000 0.997 0.000 1.000
#> round_ERR2585258 1 0.184 0.989 0.972 0.028
#> aberrant_ERR2585354 2 0.000 0.997 0.000 1.000
#> round_ERR2585249 1 0.184 0.989 0.972 0.028
#> round_ERR2585268 1 0.184 0.989 0.972 0.028
#> aberrant_ERR2585356 2 0.000 0.997 0.000 1.000
#> round_ERR2585266 1 0.184 0.989 0.972 0.028
#> round_ERR2585231 1 0.184 0.989 0.972 0.028
#> round_ERR2585230 1 0.184 0.989 0.972 0.028
#> round_ERR2585267 1 0.184 0.989 0.972 0.028
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585338 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585325 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585283 3 0.1337 0.886 0.016 0.012 0.972
#> aberrant_ERR2585343 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585329 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585317 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585339 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585335 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585287 3 0.5760 0.465 0.000 0.328 0.672
#> aberrant_ERR2585321 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585297 1 0.5465 0.807 0.712 0.000 0.288
#> aberrant_ERR2585337 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585319 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585315 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585336 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585307 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585301 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585326 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585331 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585346 3 0.0747 0.887 0.016 0.000 0.984
#> aberrant_ERR2585314 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585298 1 0.0892 0.753 0.980 0.000 0.020
#> aberrant_ERR2585345 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585299 1 0.5529 0.803 0.704 0.000 0.296
#> aberrant_ERR2585309 1 0.5706 0.790 0.680 0.000 0.320
#> aberrant_ERR2585303 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585313 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585318 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585328 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585330 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585293 3 0.0747 0.887 0.016 0.000 0.984
#> aberrant_ERR2585342 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585348 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585352 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585308 1 0.5706 0.790 0.680 0.000 0.320
#> aberrant_ERR2585349 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585316 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585306 1 0.6565 0.653 0.576 0.008 0.416
#> aberrant_ERR2585324 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585310 2 0.6357 0.565 0.296 0.684 0.020
#> aberrant_ERR2585296 1 0.0892 0.753 0.980 0.000 0.020
#> aberrant_ERR2585275 3 0.0983 0.888 0.016 0.004 0.980
#> aberrant_ERR2585311 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585292 3 0.0747 0.887 0.016 0.000 0.984
#> aberrant_ERR2585282 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585305 2 0.1482 0.917 0.012 0.968 0.020
#> aberrant_ERR2585278 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585347 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585332 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585280 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585304 2 0.6294 0.577 0.288 0.692 0.020
#> aberrant_ERR2585322 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585279 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585277 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585295 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585333 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585285 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585286 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585294 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585300 2 0.1411 0.914 0.000 0.964 0.036
#> aberrant_ERR2585334 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585361 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585372 2 0.0000 0.948 0.000 1.000 0.000
#> round_ERR2585217 2 0.6758 0.452 0.360 0.620 0.020
#> round_ERR2585205 1 0.5465 0.807 0.712 0.000 0.288
#> round_ERR2585214 2 0.6758 0.452 0.360 0.620 0.020
#> round_ERR2585202 2 0.6553 0.519 0.324 0.656 0.020
#> aberrant_ERR2585367 2 0.0000 0.948 0.000 1.000 0.000
#> round_ERR2585220 1 0.3192 0.787 0.888 0.000 0.112
#> round_ERR2585238 1 0.5465 0.807 0.712 0.000 0.288
#> aberrant_ERR2585276 2 0.0000 0.948 0.000 1.000 0.000
#> round_ERR2585218 1 0.5465 0.807 0.712 0.000 0.288
#> aberrant_ERR2585363 2 0.0000 0.948 0.000 1.000 0.000
#> round_ERR2585201 1 0.0892 0.753 0.980 0.000 0.020
#> round_ERR2585210 1 0.5465 0.807 0.712 0.000 0.288
#> aberrant_ERR2585362 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585360 2 0.0000 0.948 0.000 1.000 0.000
#> round_ERR2585209 1 0.0892 0.753 0.980 0.000 0.020
#> round_ERR2585242 1 0.0892 0.753 0.980 0.000 0.020
#> round_ERR2585216 1 0.0747 0.763 0.984 0.000 0.016
#> round_ERR2585219 1 0.3267 0.788 0.884 0.000 0.116
#> round_ERR2585237 2 0.6814 0.427 0.372 0.608 0.020
#> round_ERR2585198 1 0.0892 0.753 0.980 0.000 0.020
#> round_ERR2585211 1 0.5465 0.807 0.712 0.000 0.288
#> round_ERR2585206 1 0.5465 0.807 0.712 0.000 0.288
#> aberrant_ERR2585281 2 0.0000 0.948 0.000 1.000 0.000
#> round_ERR2585212 1 0.0237 0.758 0.996 0.000 0.004
#> round_ERR2585221 1 0.5706 0.790 0.680 0.000 0.320
#> round_ERR2585243 1 0.5465 0.807 0.712 0.000 0.288
#> round_ERR2585204 2 0.6717 0.467 0.352 0.628 0.020
#> round_ERR2585213 2 0.6387 0.559 0.300 0.680 0.020
#> aberrant_ERR2585373 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585358 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585365 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585359 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585370 2 0.0000 0.948 0.000 1.000 0.000
#> round_ERR2585215 1 0.5465 0.807 0.712 0.000 0.288
#> round_ERR2585262 2 0.6445 0.546 0.308 0.672 0.020
#> round_ERR2585199 2 0.6416 0.553 0.304 0.676 0.020
#> aberrant_ERR2585369 2 0.0000 0.948 0.000 1.000 0.000
#> round_ERR2585208 1 0.5678 0.792 0.684 0.000 0.316
#> round_ERR2585252 1 0.5706 0.790 0.680 0.000 0.320
#> round_ERR2585236 1 0.0892 0.753 0.980 0.000 0.020
#> aberrant_ERR2585284 3 0.3155 0.848 0.044 0.040 0.916
#> round_ERR2585224 1 0.5706 0.790 0.680 0.000 0.320
#> round_ERR2585260 1 0.5431 0.808 0.716 0.000 0.284
#> round_ERR2585229 1 0.5706 0.790 0.680 0.000 0.320
#> aberrant_ERR2585364 2 0.0000 0.948 0.000 1.000 0.000
#> round_ERR2585253 1 0.5706 0.790 0.680 0.000 0.320
#> aberrant_ERR2585368 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585371 2 0.0000 0.948 0.000 1.000 0.000
#> round_ERR2585239 1 0.5465 0.807 0.712 0.000 0.288
#> round_ERR2585273 1 0.5016 0.807 0.760 0.000 0.240
#> round_ERR2585256 1 0.0892 0.753 0.980 0.000 0.020
#> round_ERR2585272 1 0.5178 0.808 0.744 0.000 0.256
#> round_ERR2585246 1 0.5706 0.790 0.680 0.000 0.320
#> round_ERR2585261 1 0.0892 0.753 0.980 0.000 0.020
#> round_ERR2585254 1 0.0892 0.753 0.980 0.000 0.020
#> round_ERR2585225 1 0.0892 0.753 0.980 0.000 0.020
#> round_ERR2585235 1 0.0892 0.753 0.980 0.000 0.020
#> round_ERR2585271 1 0.5431 0.808 0.716 0.000 0.284
#> round_ERR2585251 1 0.2448 0.779 0.924 0.000 0.076
#> round_ERR2585255 1 0.0892 0.753 0.980 0.000 0.020
#> round_ERR2585257 1 0.0892 0.753 0.980 0.000 0.020
#> round_ERR2585226 1 0.2878 0.784 0.904 0.000 0.096
#> round_ERR2585265 1 0.4887 0.806 0.772 0.000 0.228
#> round_ERR2585259 1 0.0892 0.753 0.980 0.000 0.020
#> round_ERR2585247 1 0.5465 0.807 0.712 0.000 0.288
#> round_ERR2585241 1 0.5465 0.807 0.712 0.000 0.288
#> round_ERR2585263 1 0.0892 0.753 0.980 0.000 0.020
#> round_ERR2585264 1 0.5706 0.790 0.680 0.000 0.320
#> round_ERR2585233 1 0.0892 0.753 0.980 0.000 0.020
#> round_ERR2585223 1 0.5497 0.805 0.708 0.000 0.292
#> round_ERR2585234 1 0.0892 0.753 0.980 0.000 0.020
#> round_ERR2585222 1 0.5431 0.808 0.716 0.000 0.284
#> round_ERR2585228 1 0.5431 0.808 0.716 0.000 0.284
#> round_ERR2585248 1 0.5706 0.790 0.680 0.000 0.320
#> round_ERR2585240 1 0.0892 0.753 0.980 0.000 0.020
#> round_ERR2585270 1 0.0237 0.760 0.996 0.000 0.004
#> round_ERR2585232 1 0.0892 0.753 0.980 0.000 0.020
#> aberrant_ERR2585341 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585355 2 0.0000 0.948 0.000 1.000 0.000
#> round_ERR2585227 1 0.0592 0.763 0.988 0.000 0.012
#> aberrant_ERR2585351 2 0.0000 0.948 0.000 1.000 0.000
#> round_ERR2585269 1 0.5706 0.790 0.680 0.000 0.320
#> aberrant_ERR2585357 2 0.0000 0.948 0.000 1.000 0.000
#> aberrant_ERR2585350 2 0.0000 0.948 0.000 1.000 0.000
#> round_ERR2585250 1 0.0892 0.753 0.980 0.000 0.020
#> round_ERR2585245 1 0.5706 0.790 0.680 0.000 0.320
#> aberrant_ERR2585353 2 0.0000 0.948 0.000 1.000 0.000
#> round_ERR2585258 1 0.5431 0.808 0.716 0.000 0.284
#> aberrant_ERR2585354 2 0.0000 0.948 0.000 1.000 0.000
#> round_ERR2585249 1 0.5706 0.790 0.680 0.000 0.320
#> round_ERR2585268 1 0.0892 0.753 0.980 0.000 0.020
#> aberrant_ERR2585356 2 0.0000 0.948 0.000 1.000 0.000
#> round_ERR2585266 1 0.0892 0.753 0.980 0.000 0.020
#> round_ERR2585231 1 0.5706 0.790 0.680 0.000 0.320
#> round_ERR2585230 1 0.5465 0.807 0.712 0.000 0.288
#> round_ERR2585267 1 0.5706 0.790 0.680 0.000 0.320
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 2 0.2973 0.879 0.000 0.856 0.144 0.000
#> aberrant_ERR2585338 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> aberrant_ERR2585325 2 0.2973 0.879 0.000 0.856 0.144 0.000
#> aberrant_ERR2585283 4 0.0000 0.995 0.000 0.000 0.000 1.000
#> aberrant_ERR2585343 2 0.0000 0.864 0.000 1.000 0.000 0.000
#> aberrant_ERR2585329 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> aberrant_ERR2585317 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> aberrant_ERR2585339 2 0.4164 0.851 0.000 0.736 0.264 0.000
#> aberrant_ERR2585335 2 0.1867 0.877 0.000 0.928 0.072 0.000
#> aberrant_ERR2585287 4 0.0188 0.993 0.000 0.004 0.000 0.996
#> aberrant_ERR2585321 2 0.0000 0.864 0.000 1.000 0.000 0.000
#> aberrant_ERR2585297 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> aberrant_ERR2585337 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> aberrant_ERR2585319 2 0.1792 0.876 0.000 0.932 0.068 0.000
#> aberrant_ERR2585315 2 0.3569 0.871 0.000 0.804 0.196 0.000
#> aberrant_ERR2585336 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> aberrant_ERR2585307 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> aberrant_ERR2585301 2 0.0000 0.864 0.000 1.000 0.000 0.000
#> aberrant_ERR2585326 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> aberrant_ERR2585331 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> aberrant_ERR2585346 4 0.0000 0.995 0.000 0.000 0.000 1.000
#> aberrant_ERR2585314 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> aberrant_ERR2585298 3 0.4431 0.821 0.304 0.000 0.696 0.000
#> aberrant_ERR2585345 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> aberrant_ERR2585299 1 0.0188 0.951 0.996 0.000 0.000 0.004
#> aberrant_ERR2585309 1 0.0188 0.951 0.996 0.000 0.000 0.004
#> aberrant_ERR2585303 2 0.2647 0.880 0.000 0.880 0.120 0.000
#> aberrant_ERR2585313 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> aberrant_ERR2585318 2 0.0188 0.865 0.000 0.996 0.004 0.000
#> aberrant_ERR2585328 2 0.2760 0.879 0.000 0.872 0.128 0.000
#> aberrant_ERR2585330 2 0.0336 0.867 0.000 0.992 0.008 0.000
#> aberrant_ERR2585293 4 0.0707 0.992 0.000 0.000 0.020 0.980
#> aberrant_ERR2585342 2 0.0000 0.864 0.000 1.000 0.000 0.000
#> aberrant_ERR2585348 2 0.2760 0.879 0.000 0.872 0.128 0.000
#> aberrant_ERR2585352 2 0.3172 0.877 0.000 0.840 0.160 0.000
#> aberrant_ERR2585308 1 0.0188 0.951 0.996 0.000 0.000 0.004
#> aberrant_ERR2585349 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> aberrant_ERR2585316 2 0.0707 0.853 0.000 0.980 0.000 0.020
#> aberrant_ERR2585306 1 0.7037 0.216 0.564 0.268 0.000 0.168
#> aberrant_ERR2585324 2 0.1792 0.876 0.000 0.932 0.068 0.000
#> aberrant_ERR2585310 3 0.4483 0.474 0.004 0.284 0.712 0.000
#> aberrant_ERR2585296 3 0.4431 0.821 0.304 0.000 0.696 0.000
#> aberrant_ERR2585275 4 0.0000 0.995 0.000 0.000 0.000 1.000
#> aberrant_ERR2585311 2 0.0000 0.864 0.000 1.000 0.000 0.000
#> aberrant_ERR2585292 4 0.0707 0.992 0.000 0.000 0.020 0.980
#> aberrant_ERR2585282 2 0.0000 0.864 0.000 1.000 0.000 0.000
#> aberrant_ERR2585305 2 0.0469 0.858 0.000 0.988 0.000 0.012
#> aberrant_ERR2585278 2 0.2081 0.877 0.000 0.916 0.084 0.000
#> aberrant_ERR2585347 2 0.1022 0.872 0.000 0.968 0.032 0.000
#> aberrant_ERR2585332 2 0.0000 0.864 0.000 1.000 0.000 0.000
#> aberrant_ERR2585280 2 0.0188 0.865 0.000 0.996 0.004 0.000
#> aberrant_ERR2585304 3 0.1118 0.610 0.000 0.036 0.964 0.000
#> aberrant_ERR2585322 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> aberrant_ERR2585279 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> aberrant_ERR2585277 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> aberrant_ERR2585295 2 0.3444 0.873 0.000 0.816 0.184 0.000
#> aberrant_ERR2585333 2 0.0000 0.864 0.000 1.000 0.000 0.000
#> aberrant_ERR2585285 2 0.0188 0.865 0.000 0.996 0.004 0.000
#> aberrant_ERR2585286 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> aberrant_ERR2585294 2 0.0469 0.858 0.000 0.988 0.000 0.012
#> aberrant_ERR2585300 2 0.1389 0.832 0.000 0.952 0.000 0.048
#> aberrant_ERR2585334 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> aberrant_ERR2585361 2 0.2469 0.880 0.000 0.892 0.108 0.000
#> aberrant_ERR2585372 2 0.0000 0.864 0.000 1.000 0.000 0.000
#> round_ERR2585217 3 0.1305 0.615 0.004 0.036 0.960 0.000
#> round_ERR2585205 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> round_ERR2585214 3 0.1305 0.615 0.004 0.036 0.960 0.000
#> round_ERR2585202 3 0.1305 0.615 0.004 0.036 0.960 0.000
#> aberrant_ERR2585367 2 0.3123 0.877 0.000 0.844 0.156 0.000
#> round_ERR2585220 1 0.1637 0.881 0.940 0.000 0.060 0.000
#> round_ERR2585238 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> aberrant_ERR2585276 2 0.0469 0.858 0.000 0.988 0.000 0.012
#> round_ERR2585218 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> aberrant_ERR2585363 2 0.3649 0.869 0.000 0.796 0.204 0.000
#> round_ERR2585201 3 0.3486 0.777 0.188 0.000 0.812 0.000
#> round_ERR2585210 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> aberrant_ERR2585362 2 0.2345 0.879 0.000 0.900 0.100 0.000
#> aberrant_ERR2585360 2 0.0000 0.864 0.000 1.000 0.000 0.000
#> round_ERR2585209 3 0.4431 0.821 0.304 0.000 0.696 0.000
#> round_ERR2585242 3 0.4431 0.821 0.304 0.000 0.696 0.000
#> round_ERR2585216 3 0.4477 0.812 0.312 0.000 0.688 0.000
#> round_ERR2585219 1 0.1637 0.882 0.940 0.000 0.060 0.000
#> round_ERR2585237 3 0.1305 0.615 0.004 0.036 0.960 0.000
#> round_ERR2585198 3 0.2868 0.736 0.136 0.000 0.864 0.000
#> round_ERR2585211 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> aberrant_ERR2585281 2 0.3801 0.864 0.000 0.780 0.220 0.000
#> round_ERR2585212 3 0.4477 0.812 0.312 0.000 0.688 0.000
#> round_ERR2585221 1 0.0188 0.951 0.996 0.000 0.000 0.004
#> round_ERR2585243 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> round_ERR2585204 3 0.1305 0.615 0.004 0.036 0.960 0.000
#> round_ERR2585213 3 0.1118 0.610 0.000 0.036 0.964 0.000
#> aberrant_ERR2585373 2 0.0000 0.864 0.000 1.000 0.000 0.000
#> aberrant_ERR2585358 2 0.0000 0.864 0.000 1.000 0.000 0.000
#> aberrant_ERR2585365 2 0.2704 0.879 0.000 0.876 0.124 0.000
#> aberrant_ERR2585359 2 0.0000 0.864 0.000 1.000 0.000 0.000
#> aberrant_ERR2585370 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> round_ERR2585215 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> round_ERR2585262 3 0.1305 0.615 0.004 0.036 0.960 0.000
#> round_ERR2585199 3 0.1118 0.610 0.000 0.036 0.964 0.000
#> aberrant_ERR2585369 2 0.0000 0.864 0.000 1.000 0.000 0.000
#> round_ERR2585208 1 0.0188 0.951 0.996 0.000 0.000 0.004
#> round_ERR2585252 1 0.0188 0.951 0.996 0.000 0.000 0.004
#> round_ERR2585236 3 0.4431 0.821 0.304 0.000 0.696 0.000
#> aberrant_ERR2585284 4 0.0469 0.993 0.000 0.000 0.012 0.988
#> round_ERR2585224 1 0.0376 0.948 0.992 0.000 0.004 0.004
#> round_ERR2585260 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> round_ERR2585229 1 0.0188 0.951 0.996 0.000 0.000 0.004
#> aberrant_ERR2585364 2 0.0707 0.853 0.000 0.980 0.000 0.020
#> round_ERR2585253 1 0.0188 0.951 0.996 0.000 0.000 0.004
#> aberrant_ERR2585368 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> aberrant_ERR2585371 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> round_ERR2585239 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> round_ERR2585273 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> round_ERR2585256 3 0.4431 0.821 0.304 0.000 0.696 0.000
#> round_ERR2585272 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> round_ERR2585246 1 0.0188 0.951 0.996 0.000 0.000 0.004
#> round_ERR2585261 3 0.4431 0.821 0.304 0.000 0.696 0.000
#> round_ERR2585254 3 0.4431 0.821 0.304 0.000 0.696 0.000
#> round_ERR2585225 3 0.4431 0.821 0.304 0.000 0.696 0.000
#> round_ERR2585235 3 0.4431 0.821 0.304 0.000 0.696 0.000
#> round_ERR2585271 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> round_ERR2585251 1 0.4961 -0.257 0.552 0.000 0.448 0.000
#> round_ERR2585255 3 0.3649 0.786 0.204 0.000 0.796 0.000
#> round_ERR2585257 3 0.4406 0.820 0.300 0.000 0.700 0.000
#> round_ERR2585226 1 0.2530 0.802 0.888 0.000 0.112 0.000
#> round_ERR2585265 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> round_ERR2585259 3 0.4431 0.821 0.304 0.000 0.696 0.000
#> round_ERR2585247 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> round_ERR2585241 1 0.0188 0.951 0.996 0.000 0.000 0.004
#> round_ERR2585263 3 0.4431 0.821 0.304 0.000 0.696 0.000
#> round_ERR2585264 1 0.0376 0.948 0.992 0.000 0.004 0.004
#> round_ERR2585233 3 0.4431 0.821 0.304 0.000 0.696 0.000
#> round_ERR2585223 1 0.0188 0.951 0.996 0.000 0.000 0.004
#> round_ERR2585234 3 0.3356 0.768 0.176 0.000 0.824 0.000
#> round_ERR2585222 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> round_ERR2585228 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> round_ERR2585248 1 0.0376 0.948 0.992 0.000 0.004 0.004
#> round_ERR2585240 3 0.4431 0.821 0.304 0.000 0.696 0.000
#> round_ERR2585270 3 0.4585 0.784 0.332 0.000 0.668 0.000
#> round_ERR2585232 3 0.4431 0.821 0.304 0.000 0.696 0.000
#> aberrant_ERR2585341 2 0.4008 0.858 0.000 0.756 0.244 0.000
#> aberrant_ERR2585355 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> round_ERR2585227 1 0.4907 -0.144 0.580 0.000 0.420 0.000
#> aberrant_ERR2585351 2 0.0188 0.865 0.000 0.996 0.004 0.000
#> round_ERR2585269 1 0.0188 0.951 0.996 0.000 0.000 0.004
#> aberrant_ERR2585357 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> aberrant_ERR2585350 2 0.4193 0.850 0.000 0.732 0.268 0.000
#> round_ERR2585250 3 0.4431 0.821 0.304 0.000 0.696 0.000
#> round_ERR2585245 1 0.0376 0.948 0.992 0.000 0.004 0.004
#> aberrant_ERR2585353 2 0.0469 0.868 0.000 0.988 0.012 0.000
#> round_ERR2585258 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.864 0.000 1.000 0.000 0.000
#> round_ERR2585249 1 0.0188 0.951 0.996 0.000 0.000 0.004
#> round_ERR2585268 3 0.4500 0.807 0.316 0.000 0.684 0.000
#> aberrant_ERR2585356 2 0.0707 0.853 0.000 0.980 0.000 0.020
#> round_ERR2585266 3 0.4431 0.821 0.304 0.000 0.696 0.000
#> round_ERR2585231 1 0.0188 0.951 0.996 0.000 0.000 0.004
#> round_ERR2585230 1 0.0000 0.951 1.000 0.000 0.000 0.000
#> round_ERR2585267 1 0.0188 0.951 0.996 0.000 0.000 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 2 0.5188 0.6808 0.000 0.540 0.044 0.000 0.416
#> aberrant_ERR2585338 2 0.3983 0.9380 0.000 0.660 0.000 0.000 0.340
#> aberrant_ERR2585325 2 0.5188 0.6808 0.000 0.540 0.044 0.000 0.416
#> aberrant_ERR2585283 4 0.0162 0.9879 0.000 0.000 0.000 0.996 0.004
#> aberrant_ERR2585343 5 0.0703 0.7561 0.000 0.024 0.000 0.000 0.976
#> aberrant_ERR2585329 2 0.3983 0.9380 0.000 0.660 0.000 0.000 0.340
#> aberrant_ERR2585317 2 0.3983 0.9380 0.000 0.660 0.000 0.000 0.340
#> aberrant_ERR2585339 2 0.3983 0.9380 0.000 0.660 0.000 0.000 0.340
#> aberrant_ERR2585335 5 0.4294 -0.5187 0.000 0.468 0.000 0.000 0.532
#> aberrant_ERR2585287 4 0.1710 0.9693 0.000 0.012 0.024 0.944 0.020
#> aberrant_ERR2585321 5 0.0162 0.7587 0.000 0.004 0.000 0.000 0.996
#> aberrant_ERR2585297 1 0.0703 0.8684 0.976 0.024 0.000 0.000 0.000
#> aberrant_ERR2585337 2 0.3983 0.9380 0.000 0.660 0.000 0.000 0.340
#> aberrant_ERR2585319 5 0.4597 -0.3638 0.000 0.424 0.012 0.000 0.564
#> aberrant_ERR2585315 2 0.4252 0.9322 0.000 0.652 0.008 0.000 0.340
#> aberrant_ERR2585336 2 0.3983 0.9380 0.000 0.660 0.000 0.000 0.340
#> aberrant_ERR2585307 2 0.3983 0.9380 0.000 0.660 0.000 0.000 0.340
#> aberrant_ERR2585301 5 0.0771 0.7572 0.000 0.004 0.020 0.000 0.976
#> aberrant_ERR2585326 2 0.3983 0.9380 0.000 0.660 0.000 0.000 0.340
#> aberrant_ERR2585331 2 0.3932 0.9253 0.000 0.672 0.000 0.000 0.328
#> aberrant_ERR2585346 4 0.0162 0.9879 0.000 0.000 0.000 0.996 0.004
#> aberrant_ERR2585314 2 0.3999 0.9346 0.000 0.656 0.000 0.000 0.344
#> aberrant_ERR2585298 3 0.1942 0.8728 0.068 0.012 0.920 0.000 0.000
#> aberrant_ERR2585345 2 0.3983 0.9380 0.000 0.660 0.000 0.000 0.340
#> aberrant_ERR2585299 1 0.0794 0.8713 0.972 0.028 0.000 0.000 0.000
#> aberrant_ERR2585309 1 0.2852 0.8283 0.828 0.172 0.000 0.000 0.000
#> aberrant_ERR2585303 5 0.4403 -0.4152 0.000 0.436 0.004 0.000 0.560
#> aberrant_ERR2585313 2 0.3983 0.9380 0.000 0.660 0.000 0.000 0.340
#> aberrant_ERR2585318 5 0.2516 0.6188 0.000 0.140 0.000 0.000 0.860
#> aberrant_ERR2585328 5 0.3838 0.3025 0.000 0.280 0.004 0.000 0.716
#> aberrant_ERR2585330 2 0.4305 0.6543 0.000 0.512 0.000 0.000 0.488
#> aberrant_ERR2585293 4 0.0451 0.9852 0.000 0.008 0.004 0.988 0.000
#> aberrant_ERR2585342 5 0.0290 0.7575 0.000 0.008 0.000 0.000 0.992
#> aberrant_ERR2585348 5 0.4410 -0.4280 0.000 0.440 0.004 0.000 0.556
#> aberrant_ERR2585352 2 0.4101 0.9014 0.000 0.628 0.000 0.000 0.372
#> aberrant_ERR2585308 1 0.3366 0.8072 0.784 0.212 0.000 0.004 0.000
#> aberrant_ERR2585349 2 0.3949 0.9290 0.000 0.668 0.000 0.000 0.332
#> aberrant_ERR2585316 5 0.0693 0.7544 0.000 0.000 0.008 0.012 0.980
#> aberrant_ERR2585306 5 0.6719 0.1686 0.120 0.140 0.016 0.080 0.644
#> aberrant_ERR2585324 5 0.4689 -0.3644 0.000 0.424 0.016 0.000 0.560
#> aberrant_ERR2585310 3 0.3409 0.7287 0.000 0.032 0.824 0.000 0.144
#> aberrant_ERR2585296 3 0.2946 0.8603 0.088 0.044 0.868 0.000 0.000
#> aberrant_ERR2585275 4 0.0162 0.9879 0.000 0.000 0.000 0.996 0.004
#> aberrant_ERR2585311 5 0.0290 0.7589 0.000 0.000 0.008 0.000 0.992
#> aberrant_ERR2585292 4 0.0451 0.9852 0.000 0.008 0.004 0.988 0.000
#> aberrant_ERR2585282 5 0.1106 0.7516 0.000 0.024 0.012 0.000 0.964
#> aberrant_ERR2585305 5 0.0912 0.7532 0.000 0.000 0.016 0.012 0.972
#> aberrant_ERR2585278 5 0.4450 -0.5786 0.000 0.488 0.004 0.000 0.508
#> aberrant_ERR2585347 5 0.3710 0.6242 0.000 0.144 0.048 0.000 0.808
#> aberrant_ERR2585332 5 0.0162 0.7592 0.000 0.000 0.004 0.000 0.996
#> aberrant_ERR2585280 5 0.4394 0.4792 0.000 0.220 0.048 0.000 0.732
#> aberrant_ERR2585304 3 0.2020 0.8282 0.000 0.100 0.900 0.000 0.000
#> aberrant_ERR2585322 2 0.3983 0.9380 0.000 0.660 0.000 0.000 0.340
#> aberrant_ERR2585279 2 0.5243 0.6444 0.000 0.680 0.132 0.000 0.188
#> aberrant_ERR2585277 2 0.3966 0.9347 0.000 0.664 0.000 0.000 0.336
#> aberrant_ERR2585295 5 0.5286 -0.4201 0.000 0.448 0.048 0.000 0.504
#> aberrant_ERR2585333 5 0.0671 0.7568 0.000 0.004 0.016 0.000 0.980
#> aberrant_ERR2585285 5 0.4030 -0.0192 0.000 0.352 0.000 0.000 0.648
#> aberrant_ERR2585286 2 0.3966 0.9347 0.000 0.664 0.000 0.000 0.336
#> aberrant_ERR2585294 5 0.0912 0.7532 0.000 0.000 0.016 0.012 0.972
#> aberrant_ERR2585300 5 0.1082 0.7406 0.000 0.000 0.008 0.028 0.964
#> aberrant_ERR2585334 2 0.3913 0.9192 0.000 0.676 0.000 0.000 0.324
#> aberrant_ERR2585361 2 0.4201 0.8510 0.000 0.592 0.000 0.000 0.408
#> aberrant_ERR2585372 5 0.0162 0.7587 0.000 0.004 0.000 0.000 0.996
#> round_ERR2585217 3 0.1792 0.8437 0.000 0.084 0.916 0.000 0.000
#> round_ERR2585205 1 0.0404 0.8700 0.988 0.012 0.000 0.000 0.000
#> round_ERR2585214 3 0.1792 0.8437 0.000 0.084 0.916 0.000 0.000
#> round_ERR2585202 3 0.1792 0.8437 0.000 0.084 0.916 0.000 0.000
#> aberrant_ERR2585367 2 0.4264 0.8939 0.000 0.620 0.004 0.000 0.376
#> round_ERR2585220 1 0.2946 0.7860 0.868 0.044 0.088 0.000 0.000
#> round_ERR2585238 1 0.0703 0.8699 0.976 0.024 0.000 0.000 0.000
#> aberrant_ERR2585276 5 0.0912 0.7532 0.000 0.000 0.016 0.012 0.972
#> round_ERR2585218 1 0.0510 0.8702 0.984 0.016 0.000 0.000 0.000
#> aberrant_ERR2585363 2 0.4074 0.9127 0.000 0.636 0.000 0.000 0.364
#> round_ERR2585201 3 0.2067 0.8650 0.032 0.048 0.920 0.000 0.000
#> round_ERR2585210 1 0.0794 0.8637 0.972 0.028 0.000 0.000 0.000
#> aberrant_ERR2585362 5 0.3766 0.3417 0.000 0.268 0.004 0.000 0.728
#> aberrant_ERR2585360 5 0.0162 0.7587 0.000 0.004 0.000 0.000 0.996
#> round_ERR2585209 3 0.2325 0.8719 0.068 0.028 0.904 0.000 0.000
#> round_ERR2585242 3 0.1830 0.8734 0.068 0.008 0.924 0.000 0.000
#> round_ERR2585216 3 0.4908 0.6578 0.320 0.044 0.636 0.000 0.000
#> round_ERR2585219 1 0.2946 0.7862 0.868 0.044 0.088 0.000 0.000
#> round_ERR2585237 3 0.1792 0.8437 0.000 0.084 0.916 0.000 0.000
#> round_ERR2585198 3 0.2036 0.8608 0.024 0.056 0.920 0.000 0.000
#> round_ERR2585211 1 0.0404 0.8700 0.988 0.012 0.000 0.000 0.000
#> round_ERR2585206 1 0.0404 0.8700 0.988 0.012 0.000 0.000 0.000
#> aberrant_ERR2585281 2 0.4807 0.7087 0.000 0.532 0.020 0.000 0.448
#> round_ERR2585212 3 0.4797 0.6964 0.296 0.044 0.660 0.000 0.000
#> round_ERR2585221 1 0.2732 0.8350 0.840 0.160 0.000 0.000 0.000
#> round_ERR2585243 1 0.1124 0.8612 0.960 0.036 0.004 0.000 0.000
#> round_ERR2585204 3 0.1792 0.8437 0.000 0.084 0.916 0.000 0.000
#> round_ERR2585213 3 0.1792 0.8437 0.000 0.084 0.916 0.000 0.000
#> aberrant_ERR2585373 5 0.0162 0.7585 0.000 0.000 0.000 0.004 0.996
#> aberrant_ERR2585358 5 0.0290 0.7575 0.000 0.008 0.000 0.000 0.992
#> aberrant_ERR2585365 2 0.4235 0.8203 0.000 0.576 0.000 0.000 0.424
#> aberrant_ERR2585359 5 0.0162 0.7585 0.000 0.000 0.000 0.004 0.996
#> aberrant_ERR2585370 2 0.3983 0.9380 0.000 0.660 0.000 0.000 0.340
#> round_ERR2585215 1 0.0880 0.8689 0.968 0.032 0.000 0.000 0.000
#> round_ERR2585262 3 0.1732 0.8439 0.000 0.080 0.920 0.000 0.000
#> round_ERR2585199 3 0.1792 0.8437 0.000 0.084 0.916 0.000 0.000
#> aberrant_ERR2585369 5 0.0404 0.7553 0.000 0.012 0.000 0.000 0.988
#> round_ERR2585208 1 0.2280 0.8493 0.880 0.120 0.000 0.000 0.000
#> round_ERR2585252 1 0.3430 0.8029 0.776 0.220 0.000 0.004 0.000
#> round_ERR2585236 3 0.4096 0.7943 0.200 0.040 0.760 0.000 0.000
#> aberrant_ERR2585284 4 0.1612 0.9768 0.000 0.024 0.016 0.948 0.012
#> round_ERR2585224 1 0.3461 0.8019 0.772 0.224 0.000 0.004 0.000
#> round_ERR2585260 1 0.0404 0.8676 0.988 0.012 0.000 0.000 0.000
#> round_ERR2585229 1 0.2471 0.8433 0.864 0.136 0.000 0.000 0.000
#> aberrant_ERR2585364 5 0.0566 0.7550 0.000 0.000 0.004 0.012 0.984
#> round_ERR2585253 1 0.3430 0.8029 0.776 0.220 0.000 0.004 0.000
#> aberrant_ERR2585368 2 0.3983 0.9380 0.000 0.660 0.000 0.000 0.340
#> aberrant_ERR2585371 2 0.3983 0.9380 0.000 0.660 0.000 0.000 0.340
#> round_ERR2585239 1 0.1597 0.8507 0.940 0.048 0.012 0.000 0.000
#> round_ERR2585273 1 0.1331 0.8590 0.952 0.040 0.008 0.000 0.000
#> round_ERR2585256 3 0.2409 0.8711 0.068 0.032 0.900 0.000 0.000
#> round_ERR2585272 1 0.1281 0.8568 0.956 0.032 0.012 0.000 0.000
#> round_ERR2585246 1 0.2852 0.8297 0.828 0.172 0.000 0.000 0.000
#> round_ERR2585261 3 0.1830 0.8743 0.068 0.008 0.924 0.000 0.000
#> round_ERR2585254 3 0.2491 0.8721 0.068 0.036 0.896 0.000 0.000
#> round_ERR2585225 3 0.1704 0.8745 0.068 0.004 0.928 0.000 0.000
#> round_ERR2585235 3 0.4313 0.7705 0.228 0.040 0.732 0.000 0.000
#> round_ERR2585271 1 0.1195 0.8590 0.960 0.028 0.012 0.000 0.000
#> round_ERR2585251 1 0.5230 -0.1864 0.504 0.044 0.452 0.000 0.000
#> round_ERR2585255 3 0.2077 0.8677 0.040 0.040 0.920 0.000 0.000
#> round_ERR2585257 3 0.1981 0.8727 0.064 0.016 0.920 0.000 0.000
#> round_ERR2585226 1 0.3804 0.6905 0.796 0.044 0.160 0.000 0.000
#> round_ERR2585265 1 0.0992 0.8624 0.968 0.024 0.008 0.000 0.000
#> round_ERR2585259 3 0.2569 0.8688 0.068 0.040 0.892 0.000 0.000
#> round_ERR2585247 1 0.0566 0.8702 0.984 0.012 0.004 0.000 0.000
#> round_ERR2585241 1 0.0510 0.8702 0.984 0.016 0.000 0.000 0.000
#> round_ERR2585263 3 0.3958 0.8112 0.176 0.044 0.780 0.000 0.000
#> round_ERR2585264 1 0.3461 0.8019 0.772 0.224 0.000 0.004 0.000
#> round_ERR2585233 3 0.1830 0.8743 0.068 0.008 0.924 0.000 0.000
#> round_ERR2585223 1 0.1121 0.8674 0.956 0.044 0.000 0.000 0.000
#> round_ERR2585234 3 0.2067 0.8650 0.032 0.048 0.920 0.000 0.000
#> round_ERR2585222 1 0.1597 0.8507 0.940 0.048 0.012 0.000 0.000
#> round_ERR2585228 1 0.0579 0.8669 0.984 0.008 0.008 0.000 0.000
#> round_ERR2585248 1 0.3461 0.8019 0.772 0.224 0.000 0.004 0.000
#> round_ERR2585240 3 0.3771 0.8220 0.164 0.040 0.796 0.000 0.000
#> round_ERR2585270 3 0.4957 0.6336 0.332 0.044 0.624 0.000 0.000
#> round_ERR2585232 3 0.4284 0.7744 0.224 0.040 0.736 0.000 0.000
#> aberrant_ERR2585341 2 0.4770 0.8838 0.000 0.644 0.036 0.000 0.320
#> aberrant_ERR2585355 2 0.3983 0.9380 0.000 0.660 0.000 0.000 0.340
#> round_ERR2585227 1 0.5113 0.1149 0.576 0.044 0.380 0.000 0.000
#> aberrant_ERR2585351 5 0.3074 0.5216 0.000 0.196 0.000 0.000 0.804
#> round_ERR2585269 1 0.3430 0.8029 0.776 0.220 0.000 0.004 0.000
#> aberrant_ERR2585357 2 0.3983 0.9380 0.000 0.660 0.000 0.000 0.340
#> aberrant_ERR2585350 2 0.3983 0.9380 0.000 0.660 0.000 0.000 0.340
#> round_ERR2585250 3 0.3885 0.8126 0.176 0.040 0.784 0.000 0.000
#> round_ERR2585245 1 0.3461 0.8019 0.772 0.224 0.000 0.004 0.000
#> aberrant_ERR2585353 5 0.1908 0.6861 0.000 0.092 0.000 0.000 0.908
#> round_ERR2585258 1 0.0992 0.8710 0.968 0.024 0.008 0.000 0.000
#> aberrant_ERR2585354 5 0.0162 0.7587 0.000 0.004 0.000 0.000 0.996
#> round_ERR2585249 1 0.3461 0.8019 0.772 0.224 0.000 0.004 0.000
#> round_ERR2585268 3 0.4777 0.7001 0.292 0.044 0.664 0.000 0.000
#> aberrant_ERR2585356 5 0.0566 0.7550 0.000 0.000 0.004 0.012 0.984
#> round_ERR2585266 3 0.1704 0.8745 0.068 0.004 0.928 0.000 0.000
#> round_ERR2585231 1 0.3461 0.8019 0.772 0.224 0.000 0.004 0.000
#> round_ERR2585230 1 0.1597 0.8507 0.940 0.048 0.012 0.000 0.000
#> round_ERR2585267 1 0.3430 0.8044 0.776 0.220 0.000 0.004 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 2 0.5388 0.494 0.000 0.604 0.004 0.000 0.196 0.196
#> aberrant_ERR2585338 2 0.0260 0.854 0.000 0.992 0.000 0.000 0.000 0.008
#> aberrant_ERR2585325 2 0.5337 0.502 0.000 0.612 0.004 0.000 0.192 0.192
#> aberrant_ERR2585283 4 0.0000 0.971 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585343 5 0.3213 0.840 0.000 0.160 0.000 0.000 0.808 0.032
#> aberrant_ERR2585329 2 0.0000 0.855 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585317 2 0.0000 0.855 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585339 2 0.0146 0.854 0.000 0.996 0.000 0.000 0.000 0.004
#> aberrant_ERR2585335 2 0.3360 0.613 0.000 0.732 0.000 0.000 0.264 0.004
#> aberrant_ERR2585287 4 0.2112 0.936 0.000 0.000 0.000 0.896 0.016 0.088
#> aberrant_ERR2585321 5 0.3717 0.858 0.000 0.148 0.000 0.000 0.780 0.072
#> aberrant_ERR2585297 1 0.1794 0.508 0.924 0.000 0.000 0.000 0.036 0.040
#> aberrant_ERR2585337 2 0.0000 0.855 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585319 2 0.4533 0.484 0.000 0.652 0.000 0.000 0.284 0.064
#> aberrant_ERR2585315 2 0.0806 0.849 0.000 0.972 0.000 0.000 0.008 0.020
#> aberrant_ERR2585336 2 0.0000 0.855 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585307 2 0.0000 0.855 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585301 5 0.4059 0.848 0.000 0.148 0.000 0.000 0.752 0.100
#> aberrant_ERR2585326 2 0.0000 0.855 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585331 2 0.0508 0.846 0.000 0.984 0.004 0.000 0.012 0.000
#> aberrant_ERR2585346 4 0.0000 0.971 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585314 2 0.0713 0.847 0.000 0.972 0.000 0.000 0.028 0.000
#> aberrant_ERR2585298 3 0.0692 0.868 0.020 0.000 0.976 0.000 0.004 0.000
#> aberrant_ERR2585345 2 0.0000 0.855 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585299 1 0.1908 0.486 0.916 0.000 0.000 0.000 0.028 0.056
#> aberrant_ERR2585309 1 0.4228 -0.738 0.588 0.000 0.000 0.000 0.020 0.392
#> aberrant_ERR2585303 2 0.4071 0.588 0.000 0.712 0.004 0.000 0.248 0.036
#> aberrant_ERR2585313 2 0.0000 0.855 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585318 5 0.3758 0.647 0.000 0.324 0.000 0.000 0.668 0.008
#> aberrant_ERR2585328 2 0.4521 -0.122 0.000 0.524 0.004 0.000 0.448 0.024
#> aberrant_ERR2585330 2 0.3012 0.717 0.000 0.796 0.000 0.000 0.196 0.008
#> aberrant_ERR2585293 4 0.0837 0.968 0.000 0.000 0.004 0.972 0.004 0.020
#> aberrant_ERR2585342 5 0.3210 0.840 0.000 0.168 0.000 0.000 0.804 0.028
#> aberrant_ERR2585348 2 0.4235 0.505 0.000 0.672 0.004 0.000 0.292 0.032
#> aberrant_ERR2585352 2 0.1913 0.817 0.000 0.908 0.000 0.000 0.080 0.012
#> aberrant_ERR2585308 1 0.4184 -0.968 0.504 0.000 0.000 0.000 0.012 0.484
#> aberrant_ERR2585349 2 0.0363 0.849 0.000 0.988 0.000 0.000 0.012 0.000
#> aberrant_ERR2585316 5 0.4313 0.850 0.000 0.148 0.000 0.000 0.728 0.124
#> aberrant_ERR2585306 5 0.4321 0.606 0.020 0.004 0.000 0.024 0.712 0.240
#> aberrant_ERR2585324 2 0.4487 0.505 0.000 0.668 0.000 0.000 0.264 0.068
#> aberrant_ERR2585310 3 0.4912 0.564 0.000 0.024 0.672 0.000 0.236 0.068
#> aberrant_ERR2585296 3 0.3228 0.819 0.096 0.000 0.844 0.000 0.028 0.032
#> aberrant_ERR2585275 4 0.0000 0.971 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585311 5 0.4313 0.854 0.000 0.148 0.000 0.000 0.728 0.124
#> aberrant_ERR2585292 4 0.0837 0.968 0.000 0.000 0.004 0.972 0.004 0.020
#> aberrant_ERR2585282 5 0.4047 0.836 0.000 0.152 0.004 0.000 0.760 0.084
#> aberrant_ERR2585305 5 0.4745 0.822 0.000 0.136 0.000 0.000 0.676 0.188
#> aberrant_ERR2585278 2 0.3156 0.693 0.000 0.800 0.000 0.000 0.180 0.020
#> aberrant_ERR2585347 5 0.5548 0.640 0.000 0.268 0.000 0.000 0.548 0.184
#> aberrant_ERR2585332 5 0.3493 0.861 0.000 0.148 0.000 0.000 0.796 0.056
#> aberrant_ERR2585280 5 0.5775 0.498 0.000 0.328 0.000 0.000 0.480 0.192
#> aberrant_ERR2585304 3 0.1974 0.828 0.000 0.048 0.920 0.000 0.012 0.020
#> aberrant_ERR2585322 2 0.0146 0.854 0.000 0.996 0.000 0.000 0.000 0.004
#> aberrant_ERR2585279 2 0.2056 0.768 0.000 0.904 0.080 0.000 0.012 0.004
#> aberrant_ERR2585277 2 0.0146 0.854 0.000 0.996 0.000 0.000 0.000 0.004
#> aberrant_ERR2585295 2 0.5270 0.440 0.000 0.604 0.000 0.000 0.216 0.180
#> aberrant_ERR2585333 5 0.4183 0.846 0.000 0.152 0.000 0.000 0.740 0.108
#> aberrant_ERR2585285 2 0.3967 0.384 0.000 0.632 0.000 0.000 0.356 0.012
#> aberrant_ERR2585286 2 0.0146 0.854 0.000 0.996 0.000 0.000 0.000 0.004
#> aberrant_ERR2585294 5 0.4849 0.832 0.000 0.148 0.000 0.000 0.664 0.188
#> aberrant_ERR2585300 5 0.4813 0.840 0.000 0.144 0.000 0.008 0.692 0.156
#> aberrant_ERR2585334 2 0.0653 0.845 0.000 0.980 0.004 0.000 0.012 0.004
#> aberrant_ERR2585361 2 0.3245 0.736 0.000 0.800 0.000 0.000 0.172 0.028
#> aberrant_ERR2585372 5 0.3025 0.847 0.000 0.156 0.000 0.000 0.820 0.024
#> round_ERR2585217 3 0.0909 0.866 0.000 0.020 0.968 0.000 0.012 0.000
#> round_ERR2585205 1 0.1967 0.468 0.904 0.000 0.000 0.000 0.012 0.084
#> round_ERR2585214 3 0.0909 0.866 0.000 0.020 0.968 0.000 0.012 0.000
#> round_ERR2585202 3 0.0909 0.866 0.000 0.020 0.968 0.000 0.012 0.000
#> aberrant_ERR2585367 2 0.2776 0.791 0.000 0.860 0.004 0.000 0.104 0.032
#> round_ERR2585220 1 0.2177 0.501 0.908 0.000 0.052 0.000 0.008 0.032
#> round_ERR2585238 1 0.2094 0.464 0.900 0.000 0.000 0.000 0.020 0.080
#> aberrant_ERR2585276 5 0.4849 0.832 0.000 0.148 0.000 0.000 0.664 0.188
#> round_ERR2585218 1 0.2070 0.456 0.896 0.000 0.000 0.000 0.012 0.092
#> aberrant_ERR2585363 2 0.1434 0.833 0.000 0.940 0.000 0.000 0.048 0.012
#> round_ERR2585201 3 0.0692 0.867 0.000 0.020 0.976 0.000 0.004 0.000
#> round_ERR2585210 1 0.0820 0.533 0.972 0.000 0.000 0.000 0.012 0.016
#> aberrant_ERR2585362 5 0.4657 0.252 0.000 0.456 0.004 0.000 0.508 0.032
#> aberrant_ERR2585360 5 0.3307 0.856 0.000 0.148 0.000 0.000 0.808 0.044
#> round_ERR2585209 3 0.2172 0.855 0.044 0.000 0.912 0.000 0.024 0.020
#> round_ERR2585242 3 0.0806 0.868 0.020 0.000 0.972 0.000 0.000 0.008
#> round_ERR2585216 1 0.5168 -0.105 0.516 0.000 0.420 0.000 0.032 0.032
#> round_ERR2585219 1 0.2459 0.493 0.896 0.000 0.052 0.000 0.020 0.032
#> round_ERR2585237 3 0.0909 0.866 0.000 0.020 0.968 0.000 0.012 0.000
#> round_ERR2585198 3 0.0692 0.867 0.000 0.020 0.976 0.000 0.004 0.000
#> round_ERR2585211 1 0.1967 0.468 0.904 0.000 0.000 0.000 0.012 0.084
#> round_ERR2585206 1 0.1745 0.487 0.920 0.000 0.000 0.000 0.012 0.068
#> aberrant_ERR2585281 2 0.4125 0.664 0.000 0.748 0.000 0.000 0.128 0.124
#> round_ERR2585212 1 0.5058 -0.171 0.504 0.000 0.440 0.000 0.024 0.032
#> round_ERR2585221 1 0.4332 -0.486 0.644 0.000 0.000 0.000 0.040 0.316
#> round_ERR2585243 1 0.1633 0.530 0.932 0.000 0.000 0.000 0.044 0.024
#> round_ERR2585204 3 0.0909 0.866 0.000 0.020 0.968 0.000 0.012 0.000
#> round_ERR2585213 3 0.0909 0.866 0.000 0.020 0.968 0.000 0.012 0.000
#> aberrant_ERR2585373 5 0.3608 0.861 0.000 0.148 0.000 0.000 0.788 0.064
#> aberrant_ERR2585358 5 0.3284 0.839 0.000 0.168 0.000 0.000 0.800 0.032
#> aberrant_ERR2585365 2 0.3253 0.719 0.000 0.788 0.000 0.000 0.192 0.020
#> aberrant_ERR2585359 5 0.3871 0.857 0.000 0.148 0.000 0.000 0.768 0.084
#> aberrant_ERR2585370 2 0.0146 0.854 0.000 0.996 0.000 0.000 0.000 0.004
#> round_ERR2585215 1 0.1367 0.511 0.944 0.000 0.000 0.000 0.012 0.044
#> round_ERR2585262 3 0.0964 0.865 0.000 0.016 0.968 0.000 0.012 0.004
#> round_ERR2585199 3 0.0909 0.866 0.000 0.020 0.968 0.000 0.012 0.000
#> aberrant_ERR2585369 5 0.2814 0.843 0.000 0.172 0.000 0.000 0.820 0.008
#> round_ERR2585208 1 0.3641 -0.115 0.732 0.000 0.000 0.000 0.020 0.248
#> round_ERR2585252 6 0.4098 0.987 0.496 0.000 0.000 0.000 0.008 0.496
#> round_ERR2585236 3 0.5252 0.462 0.364 0.000 0.560 0.000 0.036 0.040
#> aberrant_ERR2585284 4 0.3219 0.919 0.000 0.000 0.012 0.828 0.028 0.132
#> round_ERR2585224 6 0.3999 0.993 0.496 0.000 0.000 0.000 0.004 0.500
#> round_ERR2585260 1 0.0972 0.524 0.964 0.000 0.000 0.000 0.008 0.028
#> round_ERR2585229 1 0.3865 -0.174 0.720 0.000 0.000 0.000 0.032 0.248
#> aberrant_ERR2585364 5 0.4190 0.851 0.000 0.148 0.000 0.000 0.740 0.112
#> round_ERR2585253 1 0.4185 -0.983 0.496 0.000 0.000 0.000 0.012 0.492
#> aberrant_ERR2585368 2 0.0000 0.855 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585371 2 0.0000 0.855 0.000 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585239 1 0.1492 0.531 0.940 0.000 0.000 0.000 0.036 0.024
#> round_ERR2585273 1 0.1245 0.535 0.952 0.000 0.000 0.000 0.016 0.032
#> round_ERR2585256 3 0.2604 0.845 0.056 0.000 0.888 0.000 0.024 0.032
#> round_ERR2585272 1 0.1151 0.533 0.956 0.000 0.000 0.000 0.012 0.032
#> round_ERR2585246 1 0.4316 -0.478 0.648 0.000 0.000 0.000 0.040 0.312
#> round_ERR2585261 3 0.1622 0.864 0.028 0.000 0.940 0.000 0.016 0.016
#> round_ERR2585254 3 0.2665 0.843 0.060 0.000 0.884 0.000 0.024 0.032
#> round_ERR2585225 3 0.1232 0.868 0.024 0.000 0.956 0.000 0.004 0.016
#> round_ERR2585235 3 0.5279 0.436 0.376 0.000 0.548 0.000 0.040 0.036
#> round_ERR2585271 1 0.0717 0.535 0.976 0.000 0.000 0.000 0.008 0.016
#> round_ERR2585251 1 0.4777 0.257 0.628 0.000 0.316 0.000 0.024 0.032
#> round_ERR2585255 3 0.0837 0.866 0.000 0.020 0.972 0.000 0.004 0.004
#> round_ERR2585257 3 0.0692 0.868 0.020 0.000 0.976 0.000 0.000 0.004
#> round_ERR2585226 1 0.3255 0.466 0.848 0.000 0.076 0.000 0.044 0.032
#> round_ERR2585265 1 0.0935 0.529 0.964 0.000 0.000 0.000 0.004 0.032
#> round_ERR2585259 3 0.2854 0.838 0.068 0.000 0.872 0.000 0.024 0.036
#> round_ERR2585247 1 0.2250 0.475 0.896 0.000 0.000 0.000 0.040 0.064
#> round_ERR2585241 1 0.2019 0.462 0.900 0.000 0.000 0.000 0.012 0.088
#> round_ERR2585263 3 0.4353 0.716 0.220 0.000 0.720 0.000 0.028 0.032
#> round_ERR2585264 6 0.3868 0.991 0.496 0.000 0.000 0.000 0.000 0.504
#> round_ERR2585233 3 0.1616 0.865 0.028 0.000 0.940 0.000 0.012 0.020
#> round_ERR2585223 1 0.2333 0.447 0.884 0.000 0.000 0.000 0.024 0.092
#> round_ERR2585234 3 0.0692 0.867 0.000 0.020 0.976 0.000 0.004 0.000
#> round_ERR2585222 1 0.1575 0.529 0.936 0.000 0.000 0.000 0.032 0.032
#> round_ERR2585228 1 0.1196 0.516 0.952 0.000 0.000 0.000 0.008 0.040
#> round_ERR2585248 1 0.4098 -0.984 0.496 0.000 0.000 0.000 0.008 0.496
#> round_ERR2585240 3 0.4608 0.695 0.228 0.000 0.700 0.000 0.032 0.040
#> round_ERR2585270 1 0.5051 -0.144 0.512 0.000 0.432 0.000 0.024 0.032
#> round_ERR2585232 3 0.5221 0.488 0.352 0.000 0.572 0.000 0.036 0.040
#> aberrant_ERR2585341 2 0.2679 0.796 0.000 0.868 0.004 0.000 0.032 0.096
#> aberrant_ERR2585355 2 0.0146 0.854 0.000 0.996 0.000 0.000 0.000 0.004
#> round_ERR2585227 1 0.4856 0.325 0.656 0.000 0.272 0.000 0.036 0.036
#> aberrant_ERR2585351 5 0.4076 0.472 0.000 0.396 0.000 0.000 0.592 0.012
#> round_ERR2585269 6 0.3999 0.990 0.496 0.000 0.000 0.000 0.004 0.500
#> aberrant_ERR2585357 2 0.0000 0.855 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585350 2 0.0291 0.854 0.000 0.992 0.000 0.000 0.004 0.004
#> round_ERR2585250 3 0.5152 0.559 0.312 0.000 0.608 0.000 0.044 0.036
#> round_ERR2585245 6 0.3999 0.993 0.496 0.000 0.000 0.000 0.004 0.500
#> aberrant_ERR2585353 5 0.3934 0.743 0.000 0.260 0.000 0.000 0.708 0.032
#> round_ERR2585258 1 0.2046 0.498 0.908 0.000 0.000 0.000 0.032 0.060
#> aberrant_ERR2585354 5 0.3101 0.855 0.000 0.148 0.000 0.000 0.820 0.032
#> round_ERR2585249 6 0.3999 0.993 0.496 0.000 0.000 0.000 0.004 0.500
#> round_ERR2585268 1 0.5364 -0.244 0.464 0.000 0.460 0.000 0.044 0.032
#> aberrant_ERR2585356 5 0.4190 0.851 0.000 0.148 0.000 0.000 0.740 0.112
#> round_ERR2585266 3 0.1346 0.867 0.024 0.000 0.952 0.000 0.008 0.016
#> round_ERR2585231 1 0.4264 -0.976 0.496 0.000 0.000 0.000 0.016 0.488
#> round_ERR2585230 1 0.1418 0.531 0.944 0.000 0.000 0.000 0.032 0.024
#> round_ERR2585267 1 0.4260 -0.947 0.512 0.000 0.000 0.000 0.016 0.472
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> ATC:kmeans 160 4.49e-20 2
#> ATC:kmeans 155 1.79e-25 3
#> ATC:kmeans 156 4.70e-28 4
#> ATC:kmeans 146 2.06e-24 5
#> ATC:kmeans 121 1.92e-20 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "skmeans"]
# you can also extract it by
# res = res_list["ATC:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.983 0.992 0.5030 0.498 0.498
#> 3 3 0.957 0.943 0.967 0.2500 0.848 0.702
#> 4 4 0.927 0.895 0.958 0.1225 0.883 0.704
#> 5 5 0.804 0.790 0.893 0.0717 0.921 0.750
#> 6 6 0.765 0.675 0.834 0.0378 0.985 0.941
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585338 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585325 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585283 1 0.6531 0.805 0.832 0.168
#> aberrant_ERR2585343 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585329 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585317 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585339 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585335 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585287 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585321 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585297 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585319 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585315 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585336 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585307 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585301 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585326 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585331 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585346 1 0.1414 0.974 0.980 0.020
#> aberrant_ERR2585314 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585298 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585345 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585299 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585303 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585313 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585318 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585328 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585330 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585293 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585342 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585348 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585352 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585308 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585349 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585316 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585306 1 0.3431 0.930 0.936 0.064
#> aberrant_ERR2585324 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585310 2 0.2236 0.958 0.036 0.964
#> aberrant_ERR2585296 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585275 1 0.5408 0.861 0.876 0.124
#> aberrant_ERR2585311 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585292 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585282 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585305 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585278 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585347 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585332 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585280 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585304 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585322 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585279 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585277 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585295 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585333 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585285 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585286 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585294 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585300 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585334 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585361 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585372 2 0.0000 0.991 0.000 1.000
#> round_ERR2585217 2 0.6048 0.833 0.148 0.852
#> round_ERR2585205 1 0.0000 0.993 1.000 0.000
#> round_ERR2585214 2 0.6712 0.796 0.176 0.824
#> round_ERR2585202 2 0.1414 0.973 0.020 0.980
#> aberrant_ERR2585367 2 0.0000 0.991 0.000 1.000
#> round_ERR2585220 1 0.0000 0.993 1.000 0.000
#> round_ERR2585238 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585276 2 0.0000 0.991 0.000 1.000
#> round_ERR2585218 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.991 0.000 1.000
#> round_ERR2585201 1 0.0000 0.993 1.000 0.000
#> round_ERR2585210 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585362 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585360 2 0.0000 0.991 0.000 1.000
#> round_ERR2585209 1 0.0000 0.993 1.000 0.000
#> round_ERR2585242 1 0.0000 0.993 1.000 0.000
#> round_ERR2585216 1 0.0000 0.993 1.000 0.000
#> round_ERR2585219 1 0.0000 0.993 1.000 0.000
#> round_ERR2585237 2 0.6887 0.784 0.184 0.816
#> round_ERR2585198 1 0.0000 0.993 1.000 0.000
#> round_ERR2585211 1 0.0000 0.993 1.000 0.000
#> round_ERR2585206 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585281 2 0.0000 0.991 0.000 1.000
#> round_ERR2585212 1 0.0000 0.993 1.000 0.000
#> round_ERR2585221 1 0.0000 0.993 1.000 0.000
#> round_ERR2585243 1 0.0000 0.993 1.000 0.000
#> round_ERR2585204 2 0.6148 0.828 0.152 0.848
#> round_ERR2585213 2 0.0376 0.988 0.004 0.996
#> aberrant_ERR2585373 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585358 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585365 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585359 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585370 2 0.0000 0.991 0.000 1.000
#> round_ERR2585215 1 0.0000 0.993 1.000 0.000
#> round_ERR2585262 2 0.0000 0.991 0.000 1.000
#> round_ERR2585199 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585369 2 0.0000 0.991 0.000 1.000
#> round_ERR2585208 1 0.0000 0.993 1.000 0.000
#> round_ERR2585252 1 0.0000 0.993 1.000 0.000
#> round_ERR2585236 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585284 1 0.6531 0.805 0.832 0.168
#> round_ERR2585224 1 0.0000 0.993 1.000 0.000
#> round_ERR2585260 1 0.0000 0.993 1.000 0.000
#> round_ERR2585229 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585364 2 0.0000 0.991 0.000 1.000
#> round_ERR2585253 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585371 2 0.0000 0.991 0.000 1.000
#> round_ERR2585239 1 0.0000 0.993 1.000 0.000
#> round_ERR2585273 1 0.0000 0.993 1.000 0.000
#> round_ERR2585256 1 0.0000 0.993 1.000 0.000
#> round_ERR2585272 1 0.0000 0.993 1.000 0.000
#> round_ERR2585246 1 0.0000 0.993 1.000 0.000
#> round_ERR2585261 1 0.0000 0.993 1.000 0.000
#> round_ERR2585254 1 0.0000 0.993 1.000 0.000
#> round_ERR2585225 1 0.0000 0.993 1.000 0.000
#> round_ERR2585235 1 0.0000 0.993 1.000 0.000
#> round_ERR2585271 1 0.0000 0.993 1.000 0.000
#> round_ERR2585251 1 0.0000 0.993 1.000 0.000
#> round_ERR2585255 1 0.0000 0.993 1.000 0.000
#> round_ERR2585257 1 0.0000 0.993 1.000 0.000
#> round_ERR2585226 1 0.0000 0.993 1.000 0.000
#> round_ERR2585265 1 0.0000 0.993 1.000 0.000
#> round_ERR2585259 1 0.0000 0.993 1.000 0.000
#> round_ERR2585247 1 0.0000 0.993 1.000 0.000
#> round_ERR2585241 1 0.0000 0.993 1.000 0.000
#> round_ERR2585263 1 0.0000 0.993 1.000 0.000
#> round_ERR2585264 1 0.0000 0.993 1.000 0.000
#> round_ERR2585233 1 0.0000 0.993 1.000 0.000
#> round_ERR2585223 1 0.0000 0.993 1.000 0.000
#> round_ERR2585234 1 0.0000 0.993 1.000 0.000
#> round_ERR2585222 1 0.0000 0.993 1.000 0.000
#> round_ERR2585228 1 0.0000 0.993 1.000 0.000
#> round_ERR2585248 1 0.0000 0.993 1.000 0.000
#> round_ERR2585240 1 0.0000 0.993 1.000 0.000
#> round_ERR2585270 1 0.0000 0.993 1.000 0.000
#> round_ERR2585232 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585341 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585355 2 0.0000 0.991 0.000 1.000
#> round_ERR2585227 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585351 2 0.0000 0.991 0.000 1.000
#> round_ERR2585269 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.991 0.000 1.000
#> aberrant_ERR2585350 2 0.0000 0.991 0.000 1.000
#> round_ERR2585250 1 0.0000 0.993 1.000 0.000
#> round_ERR2585245 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.991 0.000 1.000
#> round_ERR2585258 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.991 0.000 1.000
#> round_ERR2585249 1 0.0000 0.993 1.000 0.000
#> round_ERR2585268 1 0.0000 0.993 1.000 0.000
#> aberrant_ERR2585356 2 0.0000 0.991 0.000 1.000
#> round_ERR2585266 1 0.0000 0.993 1.000 0.000
#> round_ERR2585231 1 0.0000 0.993 1.000 0.000
#> round_ERR2585230 1 0.0000 0.993 1.000 0.000
#> round_ERR2585267 1 0.0000 0.993 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.0237 0.980 0.000 0.996 0.004
#> aberrant_ERR2585338 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585325 2 0.0237 0.980 0.000 0.996 0.004
#> aberrant_ERR2585283 1 0.3263 0.889 0.912 0.048 0.040
#> aberrant_ERR2585343 2 0.1411 0.968 0.000 0.964 0.036
#> aberrant_ERR2585329 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585317 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585339 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585335 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585287 2 0.2681 0.942 0.028 0.932 0.040
#> aberrant_ERR2585321 2 0.1411 0.968 0.000 0.964 0.036
#> aberrant_ERR2585297 1 0.0000 0.972 1.000 0.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585319 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585315 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585336 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585307 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585301 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585326 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585331 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585346 1 0.1529 0.940 0.960 0.000 0.040
#> aberrant_ERR2585314 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585298 3 0.1529 0.931 0.040 0.000 0.960
#> aberrant_ERR2585345 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585299 1 0.0000 0.972 1.000 0.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.972 1.000 0.000 0.000
#> aberrant_ERR2585303 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585313 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585318 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585328 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585330 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585293 1 0.1529 0.940 0.960 0.000 0.040
#> aberrant_ERR2585342 2 0.0424 0.979 0.000 0.992 0.008
#> aberrant_ERR2585348 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585352 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585308 1 0.0000 0.972 1.000 0.000 0.000
#> aberrant_ERR2585349 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585316 2 0.1529 0.966 0.000 0.960 0.040
#> aberrant_ERR2585306 1 0.1529 0.940 0.960 0.000 0.040
#> aberrant_ERR2585324 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585310 2 0.5730 0.753 0.060 0.796 0.144
#> aberrant_ERR2585296 3 0.5016 0.728 0.240 0.000 0.760
#> aberrant_ERR2585275 1 0.2269 0.926 0.944 0.016 0.040
#> aberrant_ERR2585311 2 0.1411 0.968 0.000 0.964 0.036
#> aberrant_ERR2585292 1 0.1529 0.940 0.960 0.000 0.040
#> aberrant_ERR2585282 2 0.1411 0.968 0.000 0.964 0.036
#> aberrant_ERR2585305 2 0.1950 0.960 0.008 0.952 0.040
#> aberrant_ERR2585278 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585347 2 0.1289 0.970 0.000 0.968 0.032
#> aberrant_ERR2585332 2 0.1411 0.968 0.000 0.964 0.036
#> aberrant_ERR2585280 2 0.1411 0.968 0.000 0.964 0.036
#> aberrant_ERR2585304 3 0.3816 0.820 0.000 0.148 0.852
#> aberrant_ERR2585322 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585279 2 0.5810 0.477 0.000 0.664 0.336
#> aberrant_ERR2585277 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585295 2 0.0747 0.976 0.000 0.984 0.016
#> aberrant_ERR2585333 2 0.1411 0.968 0.000 0.964 0.036
#> aberrant_ERR2585285 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585286 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585294 2 0.1529 0.966 0.000 0.960 0.040
#> aberrant_ERR2585300 2 0.1529 0.966 0.000 0.960 0.040
#> aberrant_ERR2585334 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585361 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585372 2 0.1411 0.968 0.000 0.964 0.036
#> round_ERR2585217 3 0.1647 0.921 0.004 0.036 0.960
#> round_ERR2585205 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585214 3 0.1647 0.921 0.004 0.036 0.960
#> round_ERR2585202 3 0.1529 0.918 0.000 0.040 0.960
#> aberrant_ERR2585367 2 0.0000 0.981 0.000 1.000 0.000
#> round_ERR2585220 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585238 1 0.0000 0.972 1.000 0.000 0.000
#> aberrant_ERR2585276 2 0.1529 0.966 0.000 0.960 0.040
#> round_ERR2585218 1 0.0000 0.972 1.000 0.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.981 0.000 1.000 0.000
#> round_ERR2585201 3 0.1529 0.931 0.040 0.000 0.960
#> round_ERR2585210 1 0.0000 0.972 1.000 0.000 0.000
#> aberrant_ERR2585362 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585360 2 0.0592 0.977 0.000 0.988 0.012
#> round_ERR2585209 3 0.1529 0.931 0.040 0.000 0.960
#> round_ERR2585242 3 0.1529 0.931 0.040 0.000 0.960
#> round_ERR2585216 1 0.3686 0.837 0.860 0.000 0.140
#> round_ERR2585219 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585237 3 0.1647 0.921 0.004 0.036 0.960
#> round_ERR2585198 3 0.1711 0.923 0.008 0.032 0.960
#> round_ERR2585211 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.972 1.000 0.000 0.000
#> aberrant_ERR2585281 2 0.0000 0.981 0.000 1.000 0.000
#> round_ERR2585212 1 0.3482 0.850 0.872 0.000 0.128
#> round_ERR2585221 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585243 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585204 3 0.1647 0.921 0.004 0.036 0.960
#> round_ERR2585213 3 0.1529 0.918 0.000 0.040 0.960
#> aberrant_ERR2585373 2 0.1529 0.966 0.000 0.960 0.040
#> aberrant_ERR2585358 2 0.1289 0.970 0.000 0.968 0.032
#> aberrant_ERR2585365 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585359 2 0.1529 0.966 0.000 0.960 0.040
#> aberrant_ERR2585370 2 0.0000 0.981 0.000 1.000 0.000
#> round_ERR2585215 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585262 3 0.1529 0.918 0.000 0.040 0.960
#> round_ERR2585199 3 0.1529 0.918 0.000 0.040 0.960
#> aberrant_ERR2585369 2 0.0000 0.981 0.000 1.000 0.000
#> round_ERR2585208 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585236 1 0.2066 0.924 0.940 0.000 0.060
#> aberrant_ERR2585284 1 0.5222 0.750 0.816 0.144 0.040
#> round_ERR2585224 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585260 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585229 1 0.0000 0.972 1.000 0.000 0.000
#> aberrant_ERR2585364 2 0.1529 0.966 0.000 0.960 0.040
#> round_ERR2585253 1 0.0000 0.972 1.000 0.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585371 2 0.0000 0.981 0.000 1.000 0.000
#> round_ERR2585239 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585273 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585256 3 0.1643 0.929 0.044 0.000 0.956
#> round_ERR2585272 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585246 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585261 3 0.1529 0.931 0.040 0.000 0.960
#> round_ERR2585254 3 0.1529 0.931 0.040 0.000 0.960
#> round_ERR2585225 3 0.1529 0.931 0.040 0.000 0.960
#> round_ERR2585235 1 0.3116 0.874 0.892 0.000 0.108
#> round_ERR2585271 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585251 1 0.3879 0.818 0.848 0.000 0.152
#> round_ERR2585255 3 0.1529 0.931 0.040 0.000 0.960
#> round_ERR2585257 3 0.1529 0.931 0.040 0.000 0.960
#> round_ERR2585226 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585265 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585259 3 0.1753 0.927 0.048 0.000 0.952
#> round_ERR2585247 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585241 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585263 3 0.5098 0.717 0.248 0.000 0.752
#> round_ERR2585264 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585233 3 0.1529 0.931 0.040 0.000 0.960
#> round_ERR2585223 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585234 3 0.1529 0.931 0.040 0.000 0.960
#> round_ERR2585222 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585228 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585248 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585240 3 0.6111 0.423 0.396 0.000 0.604
#> round_ERR2585270 1 0.4002 0.807 0.840 0.000 0.160
#> round_ERR2585232 3 0.6140 0.384 0.404 0.000 0.596
#> aberrant_ERR2585341 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585355 2 0.0000 0.981 0.000 1.000 0.000
#> round_ERR2585227 1 0.0424 0.966 0.992 0.000 0.008
#> aberrant_ERR2585351 2 0.0000 0.981 0.000 1.000 0.000
#> round_ERR2585269 1 0.0000 0.972 1.000 0.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.981 0.000 1.000 0.000
#> aberrant_ERR2585350 2 0.0000 0.981 0.000 1.000 0.000
#> round_ERR2585250 1 0.3879 0.820 0.848 0.000 0.152
#> round_ERR2585245 1 0.0000 0.972 1.000 0.000 0.000
#> aberrant_ERR2585353 2 0.1289 0.970 0.000 0.968 0.032
#> round_ERR2585258 1 0.0000 0.972 1.000 0.000 0.000
#> aberrant_ERR2585354 2 0.1031 0.973 0.000 0.976 0.024
#> round_ERR2585249 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585268 1 0.1411 0.945 0.964 0.000 0.036
#> aberrant_ERR2585356 2 0.1529 0.966 0.000 0.960 0.040
#> round_ERR2585266 3 0.1643 0.929 0.044 0.000 0.956
#> round_ERR2585231 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585230 1 0.0000 0.972 1.000 0.000 0.000
#> round_ERR2585267 1 0.0000 0.972 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585338 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585325 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585283 4 0.0188 0.8937 0.004 0.000 0.000 0.996
#> aberrant_ERR2585343 2 0.4790 0.3797 0.000 0.620 0.000 0.380
#> aberrant_ERR2585329 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585317 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585339 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585335 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585287 4 0.0188 0.8956 0.000 0.004 0.000 0.996
#> aberrant_ERR2585321 4 0.4790 0.4098 0.000 0.380 0.000 0.620
#> aberrant_ERR2585297 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585319 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585315 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585336 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585307 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585301 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585326 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585331 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585346 4 0.0188 0.8937 0.004 0.000 0.000 0.996
#> aberrant_ERR2585314 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585298 3 0.0000 0.9633 0.000 0.000 1.000 0.000
#> aberrant_ERR2585345 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585299 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> aberrant_ERR2585303 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585313 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585318 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585328 2 0.0188 0.9406 0.000 0.996 0.000 0.004
#> aberrant_ERR2585330 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585293 4 0.0188 0.8937 0.004 0.000 0.000 0.996
#> aberrant_ERR2585342 2 0.0592 0.9327 0.000 0.984 0.000 0.016
#> aberrant_ERR2585348 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585352 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585308 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> aberrant_ERR2585349 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585316 4 0.0469 0.8919 0.000 0.012 0.000 0.988
#> aberrant_ERR2585306 4 0.0188 0.8937 0.004 0.000 0.000 0.996
#> aberrant_ERR2585324 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585310 2 0.6458 0.6050 0.048 0.712 0.108 0.132
#> aberrant_ERR2585296 1 0.5158 0.1228 0.524 0.000 0.472 0.004
#> aberrant_ERR2585275 4 0.0188 0.8937 0.004 0.000 0.000 0.996
#> aberrant_ERR2585311 4 0.4877 0.3405 0.000 0.408 0.000 0.592
#> aberrant_ERR2585292 4 0.0188 0.8937 0.004 0.000 0.000 0.996
#> aberrant_ERR2585282 2 0.3486 0.7573 0.000 0.812 0.000 0.188
#> aberrant_ERR2585305 4 0.0336 0.8943 0.000 0.008 0.000 0.992
#> aberrant_ERR2585278 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585347 2 0.4830 0.3580 0.000 0.608 0.000 0.392
#> aberrant_ERR2585332 2 0.4304 0.5977 0.000 0.716 0.000 0.284
#> aberrant_ERR2585280 2 0.4817 0.3645 0.000 0.612 0.000 0.388
#> aberrant_ERR2585304 3 0.4543 0.4775 0.000 0.324 0.676 0.000
#> aberrant_ERR2585322 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585279 2 0.2469 0.8415 0.000 0.892 0.108 0.000
#> aberrant_ERR2585277 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585295 2 0.3486 0.7585 0.000 0.812 0.000 0.188
#> aberrant_ERR2585333 2 0.4994 0.0397 0.000 0.520 0.000 0.480
#> aberrant_ERR2585285 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585286 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585294 4 0.0188 0.8956 0.000 0.004 0.000 0.996
#> aberrant_ERR2585300 4 0.0188 0.8956 0.000 0.004 0.000 0.996
#> aberrant_ERR2585334 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585361 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585372 2 0.2704 0.8369 0.000 0.876 0.000 0.124
#> round_ERR2585217 3 0.0000 0.9633 0.000 0.000 1.000 0.000
#> round_ERR2585205 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585214 3 0.0000 0.9633 0.000 0.000 1.000 0.000
#> round_ERR2585202 3 0.0000 0.9633 0.000 0.000 1.000 0.000
#> aberrant_ERR2585367 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> round_ERR2585220 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585238 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> aberrant_ERR2585276 4 0.0336 0.8945 0.000 0.008 0.000 0.992
#> round_ERR2585218 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> round_ERR2585201 3 0.0000 0.9633 0.000 0.000 1.000 0.000
#> round_ERR2585210 1 0.0188 0.9686 0.996 0.000 0.000 0.004
#> aberrant_ERR2585362 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585360 2 0.1118 0.9190 0.000 0.964 0.000 0.036
#> round_ERR2585209 3 0.0376 0.9600 0.004 0.000 0.992 0.004
#> round_ERR2585242 3 0.0188 0.9619 0.000 0.000 0.996 0.004
#> round_ERR2585216 1 0.0188 0.9686 0.996 0.000 0.000 0.004
#> round_ERR2585219 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585237 3 0.0000 0.9633 0.000 0.000 1.000 0.000
#> round_ERR2585198 3 0.0000 0.9633 0.000 0.000 1.000 0.000
#> round_ERR2585211 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> aberrant_ERR2585281 2 0.1867 0.8893 0.000 0.928 0.000 0.072
#> round_ERR2585212 1 0.0657 0.9612 0.984 0.000 0.012 0.004
#> round_ERR2585221 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585243 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585204 3 0.0000 0.9633 0.000 0.000 1.000 0.000
#> round_ERR2585213 3 0.0000 0.9633 0.000 0.000 1.000 0.000
#> aberrant_ERR2585373 4 0.4564 0.5369 0.000 0.328 0.000 0.672
#> aberrant_ERR2585358 2 0.1792 0.8925 0.000 0.932 0.000 0.068
#> aberrant_ERR2585365 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585359 4 0.3942 0.6860 0.000 0.236 0.000 0.764
#> aberrant_ERR2585370 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> round_ERR2585215 1 0.0188 0.9686 0.996 0.000 0.000 0.004
#> round_ERR2585262 3 0.0000 0.9633 0.000 0.000 1.000 0.000
#> round_ERR2585199 3 0.0000 0.9633 0.000 0.000 1.000 0.000
#> aberrant_ERR2585369 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> round_ERR2585208 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585236 1 0.1305 0.9414 0.960 0.000 0.036 0.004
#> aberrant_ERR2585284 4 0.0188 0.8956 0.000 0.004 0.000 0.996
#> round_ERR2585224 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585260 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585229 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> aberrant_ERR2585364 4 0.0188 0.8956 0.000 0.004 0.000 0.996
#> round_ERR2585253 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585371 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> round_ERR2585239 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585273 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585256 3 0.1978 0.8953 0.068 0.000 0.928 0.004
#> round_ERR2585272 1 0.0188 0.9686 0.996 0.000 0.000 0.004
#> round_ERR2585246 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585261 3 0.0188 0.9619 0.000 0.000 0.996 0.004
#> round_ERR2585254 3 0.1004 0.9429 0.024 0.000 0.972 0.004
#> round_ERR2585225 3 0.0188 0.9619 0.000 0.000 0.996 0.004
#> round_ERR2585235 1 0.0779 0.9581 0.980 0.000 0.016 0.004
#> round_ERR2585271 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585251 1 0.0707 0.9566 0.980 0.000 0.020 0.000
#> round_ERR2585255 3 0.0000 0.9633 0.000 0.000 1.000 0.000
#> round_ERR2585257 3 0.0000 0.9633 0.000 0.000 1.000 0.000
#> round_ERR2585226 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585265 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585259 3 0.2401 0.8652 0.092 0.000 0.904 0.004
#> round_ERR2585247 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585241 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585263 1 0.5143 0.1786 0.540 0.000 0.456 0.004
#> round_ERR2585264 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585233 3 0.1978 0.8954 0.068 0.000 0.928 0.004
#> round_ERR2585223 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585234 3 0.0000 0.9633 0.000 0.000 1.000 0.000
#> round_ERR2585222 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585228 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585248 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585240 1 0.4252 0.6656 0.744 0.000 0.252 0.004
#> round_ERR2585270 1 0.0524 0.9639 0.988 0.000 0.008 0.004
#> round_ERR2585232 1 0.3355 0.8033 0.836 0.000 0.160 0.004
#> aberrant_ERR2585341 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585355 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> round_ERR2585227 1 0.0188 0.9686 0.996 0.000 0.000 0.004
#> aberrant_ERR2585351 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> round_ERR2585269 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> aberrant_ERR2585350 2 0.0000 0.9432 0.000 1.000 0.000 0.000
#> round_ERR2585250 1 0.1576 0.9298 0.948 0.000 0.048 0.004
#> round_ERR2585245 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> aberrant_ERR2585353 2 0.2011 0.8813 0.000 0.920 0.000 0.080
#> round_ERR2585258 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> aberrant_ERR2585354 2 0.1792 0.8928 0.000 0.932 0.000 0.068
#> round_ERR2585249 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585268 1 0.0779 0.9581 0.980 0.000 0.016 0.004
#> aberrant_ERR2585356 4 0.0336 0.8945 0.000 0.008 0.000 0.992
#> round_ERR2585266 3 0.0779 0.9508 0.016 0.000 0.980 0.004
#> round_ERR2585231 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585230 1 0.0000 0.9707 1.000 0.000 0.000 0.000
#> round_ERR2585267 1 0.0000 0.9707 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 2 0.3906 0.675625 0.000 0.744 0.000 0.016 0.240
#> aberrant_ERR2585338 2 0.0510 0.835100 0.000 0.984 0.000 0.000 0.016
#> aberrant_ERR2585325 2 0.3690 0.718133 0.000 0.780 0.000 0.020 0.200
#> aberrant_ERR2585283 4 0.0162 0.893260 0.000 0.000 0.000 0.996 0.004
#> aberrant_ERR2585343 5 0.3427 0.719742 0.000 0.108 0.000 0.056 0.836
#> aberrant_ERR2585329 2 0.0000 0.839551 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585317 2 0.0000 0.839551 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585339 2 0.0000 0.839551 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585335 2 0.4030 0.387330 0.000 0.648 0.000 0.000 0.352
#> aberrant_ERR2585287 4 0.0404 0.888057 0.000 0.000 0.000 0.988 0.012
#> aberrant_ERR2585321 5 0.2983 0.671356 0.000 0.040 0.000 0.096 0.864
#> aberrant_ERR2585297 1 0.0162 0.956276 0.996 0.000 0.000 0.004 0.000
#> aberrant_ERR2585337 2 0.0000 0.839551 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585319 2 0.3966 0.472296 0.000 0.664 0.000 0.000 0.336
#> aberrant_ERR2585315 2 0.0404 0.835714 0.000 0.988 0.000 0.000 0.012
#> aberrant_ERR2585336 2 0.0000 0.839551 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585307 2 0.0162 0.838609 0.000 0.996 0.000 0.004 0.000
#> aberrant_ERR2585301 5 0.4283 0.253497 0.000 0.456 0.000 0.000 0.544
#> aberrant_ERR2585326 2 0.0000 0.839551 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585331 2 0.0000 0.839551 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585346 4 0.0162 0.893260 0.000 0.000 0.000 0.996 0.004
#> aberrant_ERR2585314 2 0.0000 0.839551 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585298 3 0.0703 0.952297 0.000 0.000 0.976 0.000 0.024
#> aberrant_ERR2585345 2 0.0000 0.839551 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585299 1 0.0324 0.955924 0.992 0.000 0.000 0.004 0.004
#> aberrant_ERR2585309 1 0.0162 0.956276 0.996 0.000 0.000 0.004 0.000
#> aberrant_ERR2585303 2 0.2488 0.783651 0.000 0.872 0.000 0.004 0.124
#> aberrant_ERR2585313 2 0.0000 0.839551 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585318 5 0.4030 0.545161 0.000 0.352 0.000 0.000 0.648
#> aberrant_ERR2585328 2 0.3519 0.695007 0.000 0.776 0.000 0.008 0.216
#> aberrant_ERR2585330 2 0.3966 0.442461 0.000 0.664 0.000 0.000 0.336
#> aberrant_ERR2585293 4 0.0162 0.893260 0.000 0.000 0.000 0.996 0.004
#> aberrant_ERR2585342 5 0.3715 0.681820 0.000 0.260 0.000 0.004 0.736
#> aberrant_ERR2585348 2 0.3928 0.571617 0.000 0.700 0.000 0.004 0.296
#> aberrant_ERR2585352 2 0.2813 0.740858 0.000 0.832 0.000 0.000 0.168
#> aberrant_ERR2585308 1 0.0000 0.956805 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585349 2 0.0000 0.839551 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585316 5 0.4273 0.040602 0.000 0.000 0.000 0.448 0.552
#> aberrant_ERR2585306 4 0.1544 0.853449 0.000 0.000 0.000 0.932 0.068
#> aberrant_ERR2585324 2 0.3366 0.649217 0.000 0.768 0.000 0.000 0.232
#> aberrant_ERR2585310 5 0.6696 0.231029 0.020 0.428 0.060 0.032 0.460
#> aberrant_ERR2585296 1 0.5574 0.162961 0.512 0.000 0.416 0.000 0.072
#> aberrant_ERR2585275 4 0.0162 0.893260 0.000 0.000 0.000 0.996 0.004
#> aberrant_ERR2585311 5 0.3058 0.670275 0.000 0.044 0.000 0.096 0.860
#> aberrant_ERR2585292 4 0.0162 0.893260 0.000 0.000 0.000 0.996 0.004
#> aberrant_ERR2585282 5 0.3764 0.723034 0.000 0.156 0.000 0.044 0.800
#> aberrant_ERR2585305 5 0.4547 0.146984 0.000 0.012 0.000 0.400 0.588
#> aberrant_ERR2585278 2 0.3143 0.654636 0.000 0.796 0.000 0.000 0.204
#> aberrant_ERR2585347 2 0.6103 0.336573 0.000 0.548 0.000 0.292 0.160
#> aberrant_ERR2585332 5 0.3051 0.725083 0.000 0.120 0.000 0.028 0.852
#> aberrant_ERR2585280 2 0.6163 0.286806 0.000 0.536 0.000 0.300 0.164
#> aberrant_ERR2585304 2 0.3659 0.607388 0.000 0.768 0.220 0.000 0.012
#> aberrant_ERR2585322 2 0.0000 0.839551 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585279 2 0.1628 0.796005 0.000 0.936 0.056 0.000 0.008
#> aberrant_ERR2585277 2 0.0162 0.838160 0.000 0.996 0.000 0.000 0.004
#> aberrant_ERR2585295 2 0.4732 0.609558 0.000 0.716 0.000 0.208 0.076
#> aberrant_ERR2585333 5 0.3810 0.703847 0.000 0.100 0.000 0.088 0.812
#> aberrant_ERR2585285 2 0.4227 0.161605 0.000 0.580 0.000 0.000 0.420
#> aberrant_ERR2585286 2 0.0162 0.838160 0.000 0.996 0.000 0.000 0.004
#> aberrant_ERR2585294 5 0.4957 -0.000927 0.000 0.028 0.000 0.444 0.528
#> aberrant_ERR2585300 4 0.3913 0.513575 0.000 0.000 0.000 0.676 0.324
#> aberrant_ERR2585334 2 0.0324 0.836595 0.000 0.992 0.004 0.000 0.004
#> aberrant_ERR2585361 2 0.3837 0.540487 0.000 0.692 0.000 0.000 0.308
#> aberrant_ERR2585372 5 0.3456 0.731787 0.000 0.184 0.000 0.016 0.800
#> round_ERR2585217 3 0.0794 0.951115 0.000 0.000 0.972 0.000 0.028
#> round_ERR2585205 1 0.0404 0.954458 0.988 0.000 0.000 0.000 0.012
#> round_ERR2585214 3 0.0290 0.953180 0.000 0.000 0.992 0.000 0.008
#> round_ERR2585202 3 0.0451 0.952828 0.000 0.000 0.988 0.004 0.008
#> aberrant_ERR2585367 2 0.2648 0.760038 0.000 0.848 0.000 0.000 0.152
#> round_ERR2585220 1 0.0880 0.945508 0.968 0.000 0.000 0.000 0.032
#> round_ERR2585238 1 0.0000 0.956805 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585276 4 0.4718 0.205386 0.000 0.016 0.000 0.540 0.444
#> round_ERR2585218 1 0.0000 0.956805 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585363 2 0.1608 0.810454 0.000 0.928 0.000 0.000 0.072
#> round_ERR2585201 3 0.0290 0.953239 0.000 0.000 0.992 0.000 0.008
#> round_ERR2585210 1 0.0510 0.953545 0.984 0.000 0.000 0.000 0.016
#> aberrant_ERR2585362 2 0.4138 0.373716 0.000 0.616 0.000 0.000 0.384
#> aberrant_ERR2585360 5 0.3582 0.720944 0.000 0.224 0.000 0.008 0.768
#> round_ERR2585209 3 0.1478 0.935671 0.000 0.000 0.936 0.000 0.064
#> round_ERR2585242 3 0.0703 0.950813 0.000 0.000 0.976 0.000 0.024
#> round_ERR2585216 1 0.2446 0.898780 0.900 0.000 0.044 0.000 0.056
#> round_ERR2585219 1 0.0794 0.948148 0.972 0.000 0.000 0.000 0.028
#> round_ERR2585237 3 0.0880 0.950289 0.000 0.000 0.968 0.000 0.032
#> round_ERR2585198 3 0.0162 0.953450 0.000 0.000 0.996 0.000 0.004
#> round_ERR2585211 1 0.0404 0.954512 0.988 0.000 0.000 0.000 0.012
#> round_ERR2585206 1 0.0290 0.955688 0.992 0.000 0.000 0.000 0.008
#> aberrant_ERR2585281 2 0.2740 0.776177 0.000 0.876 0.000 0.096 0.028
#> round_ERR2585212 1 0.2359 0.903089 0.904 0.000 0.036 0.000 0.060
#> round_ERR2585221 1 0.0000 0.956805 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585243 1 0.0324 0.955725 0.992 0.000 0.000 0.004 0.004
#> round_ERR2585204 3 0.0290 0.953180 0.000 0.000 0.992 0.000 0.008
#> round_ERR2585213 3 0.0798 0.944922 0.000 0.016 0.976 0.000 0.008
#> aberrant_ERR2585373 5 0.2922 0.687050 0.000 0.056 0.000 0.072 0.872
#> aberrant_ERR2585358 5 0.3318 0.731469 0.000 0.180 0.000 0.012 0.808
#> aberrant_ERR2585365 2 0.4045 0.431189 0.000 0.644 0.000 0.000 0.356
#> aberrant_ERR2585359 5 0.2921 0.636397 0.000 0.020 0.000 0.124 0.856
#> aberrant_ERR2585370 2 0.0000 0.839551 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585215 1 0.0162 0.956403 0.996 0.000 0.000 0.000 0.004
#> round_ERR2585262 3 0.1124 0.952661 0.000 0.000 0.960 0.004 0.036
#> round_ERR2585199 3 0.0451 0.952099 0.000 0.004 0.988 0.000 0.008
#> aberrant_ERR2585369 5 0.3752 0.652020 0.000 0.292 0.000 0.000 0.708
#> round_ERR2585208 1 0.0000 0.956805 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.956805 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585236 1 0.1648 0.926126 0.940 0.000 0.040 0.000 0.020
#> aberrant_ERR2585284 4 0.0162 0.893260 0.000 0.000 0.000 0.996 0.004
#> round_ERR2585224 1 0.0162 0.956276 0.996 0.000 0.000 0.004 0.000
#> round_ERR2585260 1 0.0162 0.956420 0.996 0.000 0.000 0.000 0.004
#> round_ERR2585229 1 0.0162 0.956457 0.996 0.000 0.000 0.000 0.004
#> aberrant_ERR2585364 5 0.4161 0.238778 0.000 0.000 0.000 0.392 0.608
#> round_ERR2585253 1 0.0162 0.956276 0.996 0.000 0.000 0.004 0.000
#> aberrant_ERR2585368 2 0.0000 0.839551 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585371 2 0.0000 0.839551 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585239 1 0.0000 0.956805 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585273 1 0.0290 0.955527 0.992 0.000 0.000 0.000 0.008
#> round_ERR2585256 3 0.3180 0.856972 0.076 0.000 0.856 0.000 0.068
#> round_ERR2585272 1 0.0162 0.956420 0.996 0.000 0.000 0.000 0.004
#> round_ERR2585246 1 0.0162 0.956276 0.996 0.000 0.000 0.004 0.000
#> round_ERR2585261 3 0.1270 0.942042 0.000 0.000 0.948 0.000 0.052
#> round_ERR2585254 3 0.2054 0.920176 0.028 0.000 0.920 0.000 0.052
#> round_ERR2585225 3 0.0955 0.949379 0.004 0.000 0.968 0.000 0.028
#> round_ERR2585235 1 0.2514 0.891566 0.896 0.000 0.060 0.000 0.044
#> round_ERR2585271 1 0.0162 0.956506 0.996 0.000 0.000 0.000 0.004
#> round_ERR2585251 1 0.1493 0.932879 0.948 0.000 0.028 0.000 0.024
#> round_ERR2585255 3 0.0510 0.953162 0.000 0.000 0.984 0.000 0.016
#> round_ERR2585257 3 0.0609 0.952593 0.000 0.000 0.980 0.000 0.020
#> round_ERR2585226 1 0.0290 0.955527 0.992 0.000 0.000 0.000 0.008
#> round_ERR2585265 1 0.0510 0.953085 0.984 0.000 0.000 0.000 0.016
#> round_ERR2585259 3 0.3631 0.816372 0.104 0.000 0.824 0.000 0.072
#> round_ERR2585247 1 0.0000 0.956805 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585241 1 0.0000 0.956805 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585263 1 0.5650 -0.019754 0.464 0.000 0.460 0.000 0.076
#> round_ERR2585264 1 0.0162 0.956276 0.996 0.000 0.000 0.004 0.000
#> round_ERR2585233 3 0.3532 0.793885 0.128 0.000 0.824 0.000 0.048
#> round_ERR2585223 1 0.0000 0.956805 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585234 3 0.0162 0.953450 0.000 0.000 0.996 0.000 0.004
#> round_ERR2585222 1 0.0000 0.956805 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585228 1 0.0000 0.956805 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585248 1 0.0162 0.956276 0.996 0.000 0.000 0.004 0.000
#> round_ERR2585240 1 0.4264 0.694705 0.744 0.000 0.212 0.000 0.044
#> round_ERR2585270 1 0.2291 0.906248 0.908 0.000 0.036 0.000 0.056
#> round_ERR2585232 1 0.3595 0.800735 0.816 0.000 0.140 0.000 0.044
#> aberrant_ERR2585341 2 0.1410 0.822053 0.000 0.940 0.000 0.000 0.060
#> aberrant_ERR2585355 2 0.0000 0.839551 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585227 1 0.0609 0.951230 0.980 0.000 0.000 0.000 0.020
#> aberrant_ERR2585351 5 0.4150 0.468088 0.000 0.388 0.000 0.000 0.612
#> round_ERR2585269 1 0.0000 0.956805 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.839551 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585350 2 0.0162 0.838557 0.000 0.996 0.000 0.000 0.004
#> round_ERR2585250 1 0.2735 0.874822 0.880 0.000 0.084 0.000 0.036
#> round_ERR2585245 1 0.0162 0.956276 0.996 0.000 0.000 0.004 0.000
#> aberrant_ERR2585353 5 0.4275 0.635132 0.000 0.284 0.000 0.020 0.696
#> round_ERR2585258 1 0.0000 0.956805 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585354 5 0.3132 0.732512 0.000 0.172 0.000 0.008 0.820
#> round_ERR2585249 1 0.0000 0.956805 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585268 1 0.1818 0.924315 0.932 0.000 0.024 0.000 0.044
#> aberrant_ERR2585356 5 0.3177 0.544224 0.000 0.000 0.000 0.208 0.792
#> round_ERR2585266 3 0.1818 0.916832 0.044 0.000 0.932 0.000 0.024
#> round_ERR2585231 1 0.0000 0.956805 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585230 1 0.0162 0.956506 0.996 0.000 0.000 0.000 0.004
#> round_ERR2585267 1 0.0162 0.956276 0.996 0.000 0.000 0.004 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 2 0.6147 0.31602 0.000 0.492 0.000 0.016 0.276 0.216
#> aberrant_ERR2585338 2 0.1320 0.77892 0.000 0.948 0.000 0.000 0.036 0.016
#> aberrant_ERR2585325 2 0.5935 0.40659 0.000 0.544 0.000 0.016 0.216 0.224
#> aberrant_ERR2585283 4 0.0146 0.81901 0.000 0.000 0.000 0.996 0.004 0.000
#> aberrant_ERR2585343 5 0.3238 0.55710 0.000 0.044 0.000 0.044 0.852 0.060
#> aberrant_ERR2585329 2 0.0603 0.78056 0.000 0.980 0.000 0.000 0.016 0.004
#> aberrant_ERR2585317 2 0.0692 0.78030 0.000 0.976 0.000 0.000 0.020 0.004
#> aberrant_ERR2585339 2 0.1049 0.78037 0.000 0.960 0.000 0.000 0.032 0.008
#> aberrant_ERR2585335 2 0.4419 0.33771 0.000 0.584 0.000 0.000 0.384 0.032
#> aberrant_ERR2585287 4 0.0363 0.80508 0.000 0.000 0.000 0.988 0.000 0.012
#> aberrant_ERR2585321 5 0.2159 0.52908 0.000 0.012 0.000 0.012 0.904 0.072
#> aberrant_ERR2585297 1 0.0146 0.91236 0.996 0.000 0.000 0.000 0.000 0.004
#> aberrant_ERR2585337 2 0.0405 0.78057 0.000 0.988 0.000 0.000 0.008 0.004
#> aberrant_ERR2585319 2 0.5599 0.26387 0.000 0.520 0.000 0.000 0.312 0.168
#> aberrant_ERR2585315 2 0.1984 0.76387 0.000 0.912 0.000 0.000 0.032 0.056
#> aberrant_ERR2585336 2 0.0692 0.78083 0.000 0.976 0.000 0.000 0.020 0.004
#> aberrant_ERR2585307 2 0.0909 0.78229 0.000 0.968 0.000 0.000 0.020 0.012
#> aberrant_ERR2585301 5 0.6016 0.04967 0.000 0.320 0.000 0.004 0.456 0.220
#> aberrant_ERR2585326 2 0.0520 0.78069 0.000 0.984 0.000 0.000 0.008 0.008
#> aberrant_ERR2585331 2 0.0260 0.77805 0.000 0.992 0.000 0.000 0.000 0.008
#> aberrant_ERR2585346 4 0.0146 0.81901 0.000 0.000 0.000 0.996 0.004 0.000
#> aberrant_ERR2585314 2 0.0972 0.78063 0.000 0.964 0.000 0.000 0.028 0.008
#> aberrant_ERR2585298 3 0.1765 0.84031 0.000 0.000 0.904 0.000 0.000 0.096
#> aberrant_ERR2585345 2 0.0363 0.78031 0.000 0.988 0.000 0.000 0.012 0.000
#> aberrant_ERR2585299 1 0.0260 0.91260 0.992 0.000 0.000 0.000 0.000 0.008
#> aberrant_ERR2585309 1 0.0146 0.91236 0.996 0.000 0.000 0.000 0.000 0.004
#> aberrant_ERR2585303 2 0.2913 0.73399 0.000 0.848 0.000 0.004 0.116 0.032
#> aberrant_ERR2585313 2 0.0806 0.78016 0.000 0.972 0.000 0.000 0.020 0.008
#> aberrant_ERR2585318 5 0.3859 0.39991 0.000 0.288 0.000 0.000 0.692 0.020
#> aberrant_ERR2585328 2 0.3969 0.65155 0.000 0.740 0.000 0.004 0.212 0.044
#> aberrant_ERR2585330 2 0.4319 0.33046 0.000 0.576 0.000 0.000 0.400 0.024
#> aberrant_ERR2585293 4 0.0146 0.81901 0.000 0.000 0.000 0.996 0.004 0.000
#> aberrant_ERR2585342 5 0.3091 0.54923 0.000 0.148 0.000 0.004 0.824 0.024
#> aberrant_ERR2585348 2 0.4718 0.38671 0.000 0.572 0.000 0.008 0.384 0.036
#> aberrant_ERR2585352 2 0.3799 0.60070 0.000 0.704 0.000 0.000 0.276 0.020
#> aberrant_ERR2585308 1 0.0146 0.91236 0.996 0.000 0.000 0.000 0.000 0.004
#> aberrant_ERR2585349 2 0.1418 0.77551 0.000 0.944 0.000 0.000 0.032 0.024
#> aberrant_ERR2585316 5 0.5605 -0.20611 0.000 0.000 0.000 0.360 0.488 0.152
#> aberrant_ERR2585306 4 0.3860 0.62007 0.000 0.000 0.000 0.764 0.072 0.164
#> aberrant_ERR2585324 2 0.5278 0.42703 0.000 0.604 0.000 0.000 0.204 0.192
#> aberrant_ERR2585310 6 0.7829 0.19874 0.004 0.264 0.128 0.012 0.248 0.344
#> aberrant_ERR2585296 1 0.6129 -0.13703 0.348 0.000 0.324 0.000 0.000 0.328
#> aberrant_ERR2585275 4 0.0146 0.81901 0.000 0.000 0.000 0.996 0.004 0.000
#> aberrant_ERR2585311 5 0.3761 0.39373 0.000 0.008 0.000 0.032 0.764 0.196
#> aberrant_ERR2585292 4 0.0146 0.81901 0.000 0.000 0.000 0.996 0.004 0.000
#> aberrant_ERR2585282 5 0.4663 0.48441 0.000 0.080 0.000 0.040 0.736 0.144
#> aberrant_ERR2585305 5 0.6405 -0.44351 0.000 0.020 0.000 0.224 0.400 0.356
#> aberrant_ERR2585278 2 0.3746 0.62056 0.000 0.760 0.000 0.000 0.192 0.048
#> aberrant_ERR2585347 2 0.7206 0.03496 0.000 0.436 0.000 0.220 0.132 0.212
#> aberrant_ERR2585332 5 0.3256 0.53002 0.000 0.032 0.000 0.020 0.836 0.112
#> aberrant_ERR2585280 2 0.6982 0.06227 0.000 0.460 0.000 0.196 0.100 0.244
#> aberrant_ERR2585304 2 0.4710 0.41252 0.000 0.684 0.208 0.000 0.004 0.104
#> aberrant_ERR2585322 2 0.0717 0.78135 0.000 0.976 0.000 0.000 0.008 0.016
#> aberrant_ERR2585279 2 0.1643 0.73405 0.000 0.924 0.068 0.000 0.000 0.008
#> aberrant_ERR2585277 2 0.0146 0.77800 0.000 0.996 0.000 0.000 0.000 0.004
#> aberrant_ERR2585295 2 0.6133 0.30802 0.000 0.560 0.000 0.168 0.044 0.228
#> aberrant_ERR2585333 5 0.4889 0.43770 0.000 0.056 0.000 0.056 0.708 0.180
#> aberrant_ERR2585285 2 0.4737 0.32024 0.000 0.572 0.000 0.000 0.372 0.056
#> aberrant_ERR2585286 2 0.0363 0.77815 0.000 0.988 0.000 0.000 0.000 0.012
#> aberrant_ERR2585294 6 0.6796 0.06836 0.000 0.040 0.000 0.280 0.320 0.360
#> aberrant_ERR2585300 4 0.5536 0.11697 0.000 0.000 0.000 0.540 0.292 0.168
#> aberrant_ERR2585334 2 0.0363 0.77730 0.000 0.988 0.000 0.000 0.000 0.012
#> aberrant_ERR2585361 2 0.4421 0.32670 0.000 0.552 0.000 0.004 0.424 0.020
#> aberrant_ERR2585372 5 0.2937 0.57098 0.000 0.100 0.000 0.004 0.852 0.044
#> round_ERR2585217 3 0.1327 0.83370 0.000 0.000 0.936 0.000 0.000 0.064
#> round_ERR2585205 1 0.1267 0.90571 0.940 0.000 0.000 0.000 0.000 0.060
#> round_ERR2585214 3 0.0713 0.83497 0.000 0.000 0.972 0.000 0.000 0.028
#> round_ERR2585202 3 0.1010 0.83246 0.000 0.004 0.960 0.000 0.000 0.036
#> aberrant_ERR2585367 2 0.3905 0.63048 0.000 0.716 0.000 0.004 0.256 0.024
#> round_ERR2585220 1 0.2340 0.86444 0.852 0.000 0.000 0.000 0.000 0.148
#> round_ERR2585238 1 0.0000 0.91242 1.000 0.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585276 4 0.6418 -0.30426 0.000 0.016 0.000 0.404 0.280 0.300
#> round_ERR2585218 1 0.0865 0.91083 0.964 0.000 0.000 0.000 0.000 0.036
#> aberrant_ERR2585363 2 0.3003 0.70631 0.000 0.812 0.000 0.000 0.172 0.016
#> round_ERR2585201 3 0.0865 0.84392 0.000 0.000 0.964 0.000 0.000 0.036
#> round_ERR2585210 1 0.1765 0.89108 0.904 0.000 0.000 0.000 0.000 0.096
#> aberrant_ERR2585362 5 0.5021 -0.00554 0.000 0.436 0.000 0.008 0.504 0.052
#> aberrant_ERR2585360 5 0.2793 0.56865 0.000 0.112 0.000 0.004 0.856 0.028
#> round_ERR2585209 3 0.2969 0.78706 0.000 0.000 0.776 0.000 0.000 0.224
#> round_ERR2585242 3 0.2340 0.82634 0.000 0.000 0.852 0.000 0.000 0.148
#> round_ERR2585216 1 0.4172 0.71827 0.680 0.000 0.040 0.000 0.000 0.280
#> round_ERR2585219 1 0.2300 0.86766 0.856 0.000 0.000 0.000 0.000 0.144
#> round_ERR2585237 3 0.1075 0.83723 0.000 0.000 0.952 0.000 0.000 0.048
#> round_ERR2585198 3 0.0363 0.84154 0.000 0.000 0.988 0.000 0.000 0.012
#> round_ERR2585211 1 0.1204 0.90598 0.944 0.000 0.000 0.000 0.000 0.056
#> round_ERR2585206 1 0.1007 0.90908 0.956 0.000 0.000 0.000 0.000 0.044
#> aberrant_ERR2585281 2 0.3832 0.67253 0.000 0.808 0.000 0.080 0.032 0.080
#> round_ERR2585212 1 0.4291 0.71942 0.680 0.000 0.052 0.000 0.000 0.268
#> round_ERR2585221 1 0.0146 0.91236 0.996 0.000 0.000 0.000 0.000 0.004
#> round_ERR2585243 1 0.0632 0.91295 0.976 0.000 0.000 0.000 0.000 0.024
#> round_ERR2585204 3 0.0632 0.83480 0.000 0.000 0.976 0.000 0.000 0.024
#> round_ERR2585213 3 0.1713 0.80583 0.000 0.044 0.928 0.000 0.000 0.028
#> aberrant_ERR2585373 5 0.3105 0.50729 0.000 0.012 0.000 0.036 0.844 0.108
#> aberrant_ERR2585358 5 0.2094 0.57899 0.000 0.080 0.000 0.000 0.900 0.020
#> aberrant_ERR2585365 2 0.4242 0.23652 0.000 0.536 0.000 0.000 0.448 0.016
#> aberrant_ERR2585359 5 0.2658 0.50461 0.000 0.008 0.000 0.036 0.876 0.080
#> aberrant_ERR2585370 2 0.0291 0.77904 0.000 0.992 0.000 0.000 0.004 0.004
#> round_ERR2585215 1 0.0937 0.91069 0.960 0.000 0.000 0.000 0.000 0.040
#> round_ERR2585262 3 0.2218 0.82500 0.000 0.012 0.884 0.000 0.000 0.104
#> round_ERR2585199 3 0.0777 0.83373 0.000 0.004 0.972 0.000 0.000 0.024
#> aberrant_ERR2585369 5 0.3956 0.43975 0.000 0.252 0.000 0.000 0.712 0.036
#> round_ERR2585208 1 0.0260 0.91263 0.992 0.000 0.000 0.000 0.000 0.008
#> round_ERR2585252 1 0.0146 0.91236 0.996 0.000 0.000 0.000 0.000 0.004
#> round_ERR2585236 1 0.4229 0.74302 0.712 0.000 0.068 0.000 0.000 0.220
#> aberrant_ERR2585284 4 0.0146 0.81901 0.000 0.000 0.000 0.996 0.004 0.000
#> round_ERR2585224 1 0.0146 0.91236 0.996 0.000 0.000 0.000 0.000 0.004
#> round_ERR2585260 1 0.0547 0.91321 0.980 0.000 0.000 0.000 0.000 0.020
#> round_ERR2585229 1 0.0146 0.91275 0.996 0.000 0.000 0.000 0.000 0.004
#> aberrant_ERR2585364 5 0.5003 0.05705 0.000 0.000 0.000 0.288 0.608 0.104
#> round_ERR2585253 1 0.0146 0.91236 0.996 0.000 0.000 0.000 0.000 0.004
#> aberrant_ERR2585368 2 0.0146 0.77800 0.000 0.996 0.000 0.000 0.000 0.004
#> aberrant_ERR2585371 2 0.0146 0.77800 0.000 0.996 0.000 0.000 0.000 0.004
#> round_ERR2585239 1 0.0632 0.91313 0.976 0.000 0.000 0.000 0.000 0.024
#> round_ERR2585273 1 0.0937 0.90962 0.960 0.000 0.000 0.000 0.000 0.040
#> round_ERR2585256 3 0.4386 0.69714 0.048 0.000 0.652 0.000 0.000 0.300
#> round_ERR2585272 1 0.1910 0.88483 0.892 0.000 0.000 0.000 0.000 0.108
#> round_ERR2585246 1 0.0146 0.91236 0.996 0.000 0.000 0.000 0.000 0.004
#> round_ERR2585261 3 0.2941 0.80224 0.000 0.000 0.780 0.000 0.000 0.220
#> round_ERR2585254 3 0.3665 0.75630 0.020 0.000 0.728 0.000 0.000 0.252
#> round_ERR2585225 3 0.3078 0.80734 0.012 0.000 0.796 0.000 0.000 0.192
#> round_ERR2585235 1 0.4616 0.66232 0.648 0.000 0.072 0.000 0.000 0.280
#> round_ERR2585271 1 0.1204 0.90723 0.944 0.000 0.000 0.000 0.000 0.056
#> round_ERR2585251 1 0.3221 0.82165 0.792 0.000 0.020 0.000 0.000 0.188
#> round_ERR2585255 3 0.1267 0.84424 0.000 0.000 0.940 0.000 0.000 0.060
#> round_ERR2585257 3 0.2178 0.83699 0.000 0.000 0.868 0.000 0.000 0.132
#> round_ERR2585226 1 0.1556 0.89322 0.920 0.000 0.000 0.000 0.000 0.080
#> round_ERR2585265 1 0.0713 0.91282 0.972 0.000 0.000 0.000 0.000 0.028
#> round_ERR2585259 3 0.4847 0.63720 0.072 0.000 0.588 0.000 0.000 0.340
#> round_ERR2585247 1 0.0000 0.91242 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585241 1 0.0790 0.91105 0.968 0.000 0.000 0.000 0.000 0.032
#> round_ERR2585263 3 0.6119 0.19040 0.324 0.000 0.364 0.000 0.000 0.312
#> round_ERR2585264 1 0.0146 0.91236 0.996 0.000 0.000 0.000 0.000 0.004
#> round_ERR2585233 3 0.4479 0.72762 0.080 0.000 0.684 0.000 0.000 0.236
#> round_ERR2585223 1 0.0260 0.91275 0.992 0.000 0.000 0.000 0.000 0.008
#> round_ERR2585234 3 0.0937 0.84426 0.000 0.000 0.960 0.000 0.000 0.040
#> round_ERR2585222 1 0.0713 0.91276 0.972 0.000 0.000 0.000 0.000 0.028
#> round_ERR2585228 1 0.1141 0.90802 0.948 0.000 0.000 0.000 0.000 0.052
#> round_ERR2585248 1 0.0146 0.91236 0.996 0.000 0.000 0.000 0.000 0.004
#> round_ERR2585240 1 0.5486 0.44652 0.568 0.000 0.208 0.000 0.000 0.224
#> round_ERR2585270 1 0.3920 0.77240 0.736 0.000 0.048 0.000 0.000 0.216
#> round_ERR2585232 1 0.5539 0.46046 0.552 0.000 0.188 0.000 0.000 0.260
#> aberrant_ERR2585341 2 0.3065 0.72484 0.000 0.844 0.000 0.004 0.052 0.100
#> aberrant_ERR2585355 2 0.0520 0.77988 0.000 0.984 0.000 0.000 0.008 0.008
#> round_ERR2585227 1 0.2697 0.83312 0.812 0.000 0.000 0.000 0.000 0.188
#> aberrant_ERR2585351 5 0.4170 0.34046 0.000 0.328 0.000 0.004 0.648 0.020
#> round_ERR2585269 1 0.0146 0.91236 0.996 0.000 0.000 0.000 0.000 0.004
#> aberrant_ERR2585357 2 0.0363 0.78031 0.000 0.988 0.000 0.000 0.012 0.000
#> aberrant_ERR2585350 2 0.0935 0.78080 0.000 0.964 0.000 0.000 0.032 0.004
#> round_ERR2585250 1 0.3896 0.78206 0.748 0.000 0.056 0.000 0.000 0.196
#> round_ERR2585245 1 0.0146 0.91236 0.996 0.000 0.000 0.000 0.000 0.004
#> aberrant_ERR2585353 5 0.3187 0.49598 0.000 0.188 0.000 0.004 0.796 0.012
#> round_ERR2585258 1 0.0363 0.91329 0.988 0.000 0.000 0.000 0.000 0.012
#> aberrant_ERR2585354 5 0.1867 0.57502 0.000 0.064 0.000 0.000 0.916 0.020
#> round_ERR2585249 1 0.0146 0.91236 0.996 0.000 0.000 0.000 0.000 0.004
#> round_ERR2585268 1 0.3541 0.77163 0.728 0.000 0.012 0.000 0.000 0.260
#> aberrant_ERR2585356 5 0.4336 0.25405 0.000 0.000 0.000 0.116 0.724 0.160
#> round_ERR2585266 3 0.4032 0.76188 0.068 0.000 0.740 0.000 0.000 0.192
#> round_ERR2585231 1 0.0146 0.91236 0.996 0.000 0.000 0.000 0.000 0.004
#> round_ERR2585230 1 0.1075 0.90944 0.952 0.000 0.000 0.000 0.000 0.048
#> round_ERR2585267 1 0.0146 0.91236 0.996 0.000 0.000 0.000 0.000 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> ATC:skmeans 160 4.49e-20 2
#> ATC:skmeans 157 1.19e-24 3
#> ATC:skmeans 151 1.71e-28 4
#> ATC:skmeans 142 1.01e-25 5
#> ATC:skmeans 125 4.64e-22 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "pam"]
# you can also extract it by
# res = res_list["ATC:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'ATC' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.556 0.869 0.927 0.4788 0.502 0.502
#> 3 3 0.734 0.765 0.829 0.2454 0.831 0.668
#> 4 4 0.869 0.869 0.949 0.1211 0.891 0.723
#> 5 5 0.800 0.784 0.914 0.0669 0.950 0.850
#> 6 6 0.769 0.729 0.889 0.0381 0.972 0.902
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585338 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585325 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585283 1 0.9129 0.607 0.672 0.328
#> aberrant_ERR2585343 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585329 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585317 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585339 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585335 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585287 2 0.9866 0.109 0.432 0.568
#> aberrant_ERR2585321 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585297 1 0.0000 0.892 1.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585319 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585315 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585336 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585307 2 0.4161 0.867 0.084 0.916
#> aberrant_ERR2585301 2 0.2603 0.908 0.044 0.956
#> aberrant_ERR2585326 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585331 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585346 1 0.4298 0.888 0.912 0.088
#> aberrant_ERR2585314 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585298 1 0.6148 0.874 0.848 0.152
#> aberrant_ERR2585345 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585299 1 0.0672 0.893 0.992 0.008
#> aberrant_ERR2585309 1 0.0000 0.892 1.000 0.000
#> aberrant_ERR2585303 2 0.0938 0.937 0.012 0.988
#> aberrant_ERR2585313 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585318 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585328 2 0.7139 0.717 0.196 0.804
#> aberrant_ERR2585330 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585293 1 0.0000 0.892 1.000 0.000
#> aberrant_ERR2585342 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585348 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585352 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585308 1 0.0000 0.892 1.000 0.000
#> aberrant_ERR2585349 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585316 2 0.5737 0.804 0.136 0.864
#> aberrant_ERR2585306 1 0.6247 0.872 0.844 0.156
#> aberrant_ERR2585324 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585310 1 0.7883 0.797 0.764 0.236
#> aberrant_ERR2585296 1 0.6247 0.872 0.844 0.156
#> aberrant_ERR2585275 1 0.9775 0.440 0.588 0.412
#> aberrant_ERR2585311 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585292 1 0.0000 0.892 1.000 0.000
#> aberrant_ERR2585282 2 0.0376 0.943 0.004 0.996
#> aberrant_ERR2585305 1 0.9710 0.494 0.600 0.400
#> aberrant_ERR2585278 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585347 2 0.0376 0.943 0.004 0.996
#> aberrant_ERR2585332 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585280 2 0.7376 0.699 0.208 0.792
#> aberrant_ERR2585304 1 0.8661 0.723 0.712 0.288
#> aberrant_ERR2585322 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585279 2 0.9358 0.389 0.352 0.648
#> aberrant_ERR2585277 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585295 2 0.9393 0.377 0.356 0.644
#> aberrant_ERR2585333 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585285 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585286 2 0.0376 0.943 0.004 0.996
#> aberrant_ERR2585294 2 0.8955 0.492 0.312 0.688
#> aberrant_ERR2585300 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585334 2 0.9248 0.422 0.340 0.660
#> aberrant_ERR2585361 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585372 2 0.0000 0.946 0.000 1.000
#> round_ERR2585217 1 0.7883 0.797 0.764 0.236
#> round_ERR2585205 1 0.0000 0.892 1.000 0.000
#> round_ERR2585214 1 0.7883 0.797 0.764 0.236
#> round_ERR2585202 1 0.7883 0.797 0.764 0.236
#> aberrant_ERR2585367 2 0.0000 0.946 0.000 1.000
#> round_ERR2585220 1 0.0672 0.893 0.992 0.008
#> round_ERR2585238 1 0.0000 0.892 1.000 0.000
#> aberrant_ERR2585276 2 0.8813 0.520 0.300 0.700
#> round_ERR2585218 1 0.0000 0.892 1.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.946 0.000 1.000
#> round_ERR2585201 1 0.6623 0.860 0.828 0.172
#> round_ERR2585210 1 0.0000 0.892 1.000 0.000
#> aberrant_ERR2585362 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585360 2 0.0000 0.946 0.000 1.000
#> round_ERR2585209 1 0.6048 0.876 0.852 0.148
#> round_ERR2585242 1 0.6148 0.874 0.848 0.152
#> round_ERR2585216 1 0.0938 0.893 0.988 0.012
#> round_ERR2585219 1 0.0938 0.893 0.988 0.012
#> round_ERR2585237 1 0.7883 0.797 0.764 0.236
#> round_ERR2585198 1 0.6712 0.857 0.824 0.176
#> round_ERR2585211 1 0.0000 0.892 1.000 0.000
#> round_ERR2585206 1 0.0000 0.892 1.000 0.000
#> aberrant_ERR2585281 2 0.9393 0.377 0.356 0.644
#> round_ERR2585212 1 0.3879 0.889 0.924 0.076
#> round_ERR2585221 1 0.0000 0.892 1.000 0.000
#> round_ERR2585243 1 0.0000 0.892 1.000 0.000
#> round_ERR2585204 1 0.7883 0.797 0.764 0.236
#> round_ERR2585213 1 0.8207 0.771 0.744 0.256
#> aberrant_ERR2585373 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585358 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585365 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585359 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585370 2 0.0000 0.946 0.000 1.000
#> round_ERR2585215 1 0.0000 0.892 1.000 0.000
#> round_ERR2585262 1 0.8016 0.787 0.756 0.244
#> round_ERR2585199 1 0.7883 0.797 0.764 0.236
#> aberrant_ERR2585369 2 0.0000 0.946 0.000 1.000
#> round_ERR2585208 1 0.0000 0.892 1.000 0.000
#> round_ERR2585252 1 0.0000 0.892 1.000 0.000
#> round_ERR2585236 1 0.5059 0.885 0.888 0.112
#> aberrant_ERR2585284 1 0.8499 0.742 0.724 0.276
#> round_ERR2585224 1 0.0000 0.892 1.000 0.000
#> round_ERR2585260 1 0.0000 0.892 1.000 0.000
#> round_ERR2585229 1 0.0000 0.892 1.000 0.000
#> aberrant_ERR2585364 2 0.0000 0.946 0.000 1.000
#> round_ERR2585253 1 0.0000 0.892 1.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585371 2 0.0000 0.946 0.000 1.000
#> round_ERR2585239 1 0.3274 0.891 0.940 0.060
#> round_ERR2585273 1 0.0376 0.892 0.996 0.004
#> round_ERR2585256 1 0.6148 0.874 0.848 0.152
#> round_ERR2585272 1 0.0000 0.892 1.000 0.000
#> round_ERR2585246 1 0.0000 0.892 1.000 0.000
#> round_ERR2585261 1 0.6148 0.874 0.848 0.152
#> round_ERR2585254 1 0.6148 0.874 0.848 0.152
#> round_ERR2585225 1 0.6148 0.874 0.848 0.152
#> round_ERR2585235 1 0.5946 0.877 0.856 0.144
#> round_ERR2585271 1 0.0000 0.892 1.000 0.000
#> round_ERR2585251 1 0.5059 0.885 0.888 0.112
#> round_ERR2585255 1 0.7453 0.823 0.788 0.212
#> round_ERR2585257 1 0.6531 0.863 0.832 0.168
#> round_ERR2585226 1 0.6048 0.876 0.852 0.148
#> round_ERR2585265 1 0.0000 0.892 1.000 0.000
#> round_ERR2585259 1 0.5737 0.880 0.864 0.136
#> round_ERR2585247 1 0.0000 0.892 1.000 0.000
#> round_ERR2585241 1 0.0000 0.892 1.000 0.000
#> round_ERR2585263 1 0.6148 0.874 0.848 0.152
#> round_ERR2585264 1 0.0000 0.892 1.000 0.000
#> round_ERR2585233 1 0.6148 0.874 0.848 0.152
#> round_ERR2585223 1 0.0000 0.892 1.000 0.000
#> round_ERR2585234 1 0.6438 0.866 0.836 0.164
#> round_ERR2585222 1 0.6048 0.876 0.852 0.148
#> round_ERR2585228 1 0.0000 0.892 1.000 0.000
#> round_ERR2585248 1 0.0000 0.892 1.000 0.000
#> round_ERR2585240 1 0.6148 0.874 0.848 0.152
#> round_ERR2585270 1 0.5629 0.881 0.868 0.132
#> round_ERR2585232 1 0.6148 0.874 0.848 0.152
#> aberrant_ERR2585341 2 0.2948 0.900 0.052 0.948
#> aberrant_ERR2585355 2 0.0000 0.946 0.000 1.000
#> round_ERR2585227 1 0.5178 0.884 0.884 0.116
#> aberrant_ERR2585351 2 0.0000 0.946 0.000 1.000
#> round_ERR2585269 1 0.0000 0.892 1.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.946 0.000 1.000
#> aberrant_ERR2585350 2 0.0000 0.946 0.000 1.000
#> round_ERR2585250 1 0.6148 0.874 0.848 0.152
#> round_ERR2585245 1 0.0000 0.892 1.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.946 0.000 1.000
#> round_ERR2585258 1 0.0000 0.892 1.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.946 0.000 1.000
#> round_ERR2585249 1 0.0000 0.892 1.000 0.000
#> round_ERR2585268 1 0.6148 0.874 0.848 0.152
#> aberrant_ERR2585356 2 0.0000 0.946 0.000 1.000
#> round_ERR2585266 1 0.6148 0.874 0.848 0.152
#> round_ERR2585231 1 0.0000 0.892 1.000 0.000
#> round_ERR2585230 1 0.2948 0.891 0.948 0.052
#> round_ERR2585267 1 0.0000 0.892 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585338 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585325 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585283 1 0.7708 0.366 0.528 0.048 0.424
#> aberrant_ERR2585343 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585329 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585317 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585339 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585335 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585287 2 0.9576 -0.310 0.396 0.408 0.196
#> aberrant_ERR2585321 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585297 1 0.0000 0.806 1.000 0.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585319 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585315 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585336 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585307 2 0.2711 0.843 0.088 0.912 0.000
#> aberrant_ERR2585301 2 0.2173 0.881 0.048 0.944 0.008
#> aberrant_ERR2585326 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585331 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585346 1 0.6516 0.362 0.516 0.004 0.480
#> aberrant_ERR2585314 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585298 3 0.6688 0.942 0.408 0.012 0.580
#> aberrant_ERR2585345 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585299 1 0.0000 0.806 1.000 0.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.806 1.000 0.000 0.000
#> aberrant_ERR2585303 2 0.0592 0.919 0.012 0.988 0.000
#> aberrant_ERR2585313 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585318 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585328 2 0.5318 0.669 0.204 0.780 0.016
#> aberrant_ERR2585330 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585293 1 0.6192 0.396 0.580 0.000 0.420
#> aberrant_ERR2585342 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585348 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585352 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585308 1 0.0000 0.806 1.000 0.000 0.000
#> aberrant_ERR2585349 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585316 2 0.4485 0.766 0.136 0.844 0.020
#> aberrant_ERR2585306 1 0.1337 0.774 0.972 0.016 0.012
#> aberrant_ERR2585324 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585310 3 0.6950 0.930 0.408 0.020 0.572
#> aberrant_ERR2585296 3 0.6688 0.942 0.408 0.012 0.580
#> aberrant_ERR2585275 3 0.8646 -0.227 0.320 0.124 0.556
#> aberrant_ERR2585311 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585292 1 0.6192 0.396 0.580 0.000 0.420
#> aberrant_ERR2585282 2 0.0237 0.926 0.004 0.996 0.000
#> aberrant_ERR2585305 2 0.9956 -0.427 0.352 0.360 0.288
#> aberrant_ERR2585278 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585347 2 0.0424 0.922 0.008 0.992 0.000
#> aberrant_ERR2585332 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585280 2 0.5461 0.650 0.216 0.768 0.016
#> aberrant_ERR2585304 3 0.6811 0.935 0.404 0.016 0.580
#> aberrant_ERR2585322 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585279 1 0.9967 -0.302 0.364 0.340 0.296
#> aberrant_ERR2585277 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585295 2 0.9724 -0.292 0.364 0.412 0.224
#> aberrant_ERR2585333 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585285 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585286 2 0.0237 0.926 0.004 0.996 0.000
#> aberrant_ERR2585294 2 0.7918 0.287 0.328 0.596 0.076
#> aberrant_ERR2585300 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585334 2 0.8533 0.104 0.360 0.536 0.104
#> aberrant_ERR2585361 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585372 2 0.0000 0.929 0.000 1.000 0.000
#> round_ERR2585217 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585205 1 0.0000 0.806 1.000 0.000 0.000
#> round_ERR2585214 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585202 3 0.6688 0.942 0.408 0.012 0.580
#> aberrant_ERR2585367 2 0.0000 0.929 0.000 1.000 0.000
#> round_ERR2585220 1 0.4750 0.339 0.784 0.000 0.216
#> round_ERR2585238 1 0.0000 0.806 1.000 0.000 0.000
#> aberrant_ERR2585276 2 0.8125 0.229 0.340 0.576 0.084
#> round_ERR2585218 1 0.0000 0.806 1.000 0.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.929 0.000 1.000 0.000
#> round_ERR2585201 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585210 1 0.1529 0.757 0.960 0.000 0.040
#> aberrant_ERR2585362 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585360 2 0.0000 0.929 0.000 1.000 0.000
#> round_ERR2585209 3 0.6553 0.936 0.412 0.008 0.580
#> round_ERR2585242 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585216 3 0.6280 0.861 0.460 0.000 0.540
#> round_ERR2585219 1 0.5058 0.231 0.756 0.000 0.244
#> round_ERR2585237 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585198 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585211 1 0.0000 0.806 1.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.806 1.000 0.000 0.000
#> aberrant_ERR2585281 2 0.9678 -0.270 0.364 0.420 0.216
#> round_ERR2585212 1 0.6309 -0.774 0.504 0.000 0.496
#> round_ERR2585221 1 0.0000 0.806 1.000 0.000 0.000
#> round_ERR2585243 1 0.3116 0.646 0.892 0.000 0.108
#> round_ERR2585204 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585213 3 0.6688 0.942 0.408 0.012 0.580
#> aberrant_ERR2585373 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585358 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585365 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585359 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585370 2 0.0000 0.929 0.000 1.000 0.000
#> round_ERR2585215 1 0.0747 0.789 0.984 0.000 0.016
#> round_ERR2585262 3 0.7389 0.902 0.408 0.036 0.556
#> round_ERR2585199 3 0.6688 0.942 0.408 0.012 0.580
#> aberrant_ERR2585369 2 0.0000 0.929 0.000 1.000 0.000
#> round_ERR2585208 1 0.0000 0.806 1.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.806 1.000 0.000 0.000
#> round_ERR2585236 3 0.6398 0.928 0.416 0.004 0.580
#> aberrant_ERR2585284 3 0.5536 0.612 0.236 0.012 0.752
#> round_ERR2585224 1 0.0000 0.806 1.000 0.000 0.000
#> round_ERR2585260 1 0.0892 0.784 0.980 0.000 0.020
#> round_ERR2585229 1 0.0000 0.806 1.000 0.000 0.000
#> aberrant_ERR2585364 2 0.0000 0.929 0.000 1.000 0.000
#> round_ERR2585253 1 0.0000 0.806 1.000 0.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585371 2 0.0000 0.929 0.000 1.000 0.000
#> round_ERR2585239 1 0.6713 -0.565 0.572 0.012 0.416
#> round_ERR2585273 1 0.2625 0.683 0.916 0.000 0.084
#> round_ERR2585256 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585272 1 0.6291 -0.697 0.532 0.000 0.468
#> round_ERR2585246 1 0.0000 0.806 1.000 0.000 0.000
#> round_ERR2585261 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585254 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585225 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585235 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585271 1 0.0237 0.802 0.996 0.000 0.004
#> round_ERR2585251 3 0.6625 0.900 0.440 0.008 0.552
#> round_ERR2585255 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585257 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585226 3 0.6813 0.847 0.468 0.012 0.520
#> round_ERR2585265 1 0.0237 0.802 0.996 0.000 0.004
#> round_ERR2585259 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585247 1 0.0000 0.806 1.000 0.000 0.000
#> round_ERR2585241 1 0.0000 0.806 1.000 0.000 0.000
#> round_ERR2585263 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585264 1 0.0000 0.806 1.000 0.000 0.000
#> round_ERR2585233 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585223 1 0.0000 0.806 1.000 0.000 0.000
#> round_ERR2585234 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585222 1 0.6675 -0.539 0.584 0.012 0.404
#> round_ERR2585228 1 0.0000 0.806 1.000 0.000 0.000
#> round_ERR2585248 1 0.0000 0.806 1.000 0.000 0.000
#> round_ERR2585240 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585270 3 0.6822 0.819 0.480 0.012 0.508
#> round_ERR2585232 3 0.6688 0.942 0.408 0.012 0.580
#> aberrant_ERR2585341 2 0.1860 0.881 0.052 0.948 0.000
#> aberrant_ERR2585355 2 0.0000 0.929 0.000 1.000 0.000
#> round_ERR2585227 3 0.6625 0.900 0.440 0.008 0.552
#> aberrant_ERR2585351 2 0.0000 0.929 0.000 1.000 0.000
#> round_ERR2585269 1 0.0000 0.806 1.000 0.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.929 0.000 1.000 0.000
#> aberrant_ERR2585350 2 0.0000 0.929 0.000 1.000 0.000
#> round_ERR2585250 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585245 1 0.0000 0.806 1.000 0.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.929 0.000 1.000 0.000
#> round_ERR2585258 1 0.0000 0.806 1.000 0.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.929 0.000 1.000 0.000
#> round_ERR2585249 1 0.0000 0.806 1.000 0.000 0.000
#> round_ERR2585268 3 0.6763 0.905 0.436 0.012 0.552
#> aberrant_ERR2585356 2 0.0000 0.929 0.000 1.000 0.000
#> round_ERR2585266 3 0.6688 0.942 0.408 0.012 0.580
#> round_ERR2585231 1 0.0000 0.806 1.000 0.000 0.000
#> round_ERR2585230 1 0.5659 0.195 0.740 0.012 0.248
#> round_ERR2585267 1 0.0000 0.806 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585338 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585325 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585283 4 0.0000 0.9385 0.000 0.000 0.000 1.000
#> aberrant_ERR2585343 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585329 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585317 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585339 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585335 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585287 3 0.4817 0.3980 0.000 0.388 0.612 0.000
#> aberrant_ERR2585321 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585297 1 0.0817 0.9266 0.976 0.000 0.024 0.000
#> aberrant_ERR2585337 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585319 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585315 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585336 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585307 2 0.2149 0.8683 0.000 0.912 0.088 0.000
#> aberrant_ERR2585301 2 0.1557 0.9082 0.000 0.944 0.056 0.000
#> aberrant_ERR2585326 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585331 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585346 4 0.4040 0.6820 0.248 0.000 0.000 0.752
#> aberrant_ERR2585314 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585298 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> aberrant_ERR2585345 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585299 1 0.0921 0.9254 0.972 0.000 0.028 0.000
#> aberrant_ERR2585309 1 0.0000 0.9307 1.000 0.000 0.000 0.000
#> aberrant_ERR2585303 2 0.0469 0.9548 0.000 0.988 0.012 0.000
#> aberrant_ERR2585313 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585318 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585328 2 0.3801 0.6811 0.000 0.780 0.220 0.000
#> aberrant_ERR2585330 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585293 4 0.0000 0.9385 0.000 0.000 0.000 1.000
#> aberrant_ERR2585342 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585348 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585352 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585308 1 0.0000 0.9307 1.000 0.000 0.000 0.000
#> aberrant_ERR2585349 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585316 2 0.3123 0.7777 0.000 0.844 0.156 0.000
#> aberrant_ERR2585306 1 0.0779 0.9251 0.980 0.004 0.016 0.000
#> aberrant_ERR2585324 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585310 3 0.0336 0.8756 0.000 0.008 0.992 0.000
#> aberrant_ERR2585296 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> aberrant_ERR2585275 4 0.0000 0.9385 0.000 0.000 0.000 1.000
#> aberrant_ERR2585311 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585292 4 0.0000 0.9385 0.000 0.000 0.000 1.000
#> aberrant_ERR2585282 2 0.0188 0.9627 0.000 0.996 0.004 0.000
#> aberrant_ERR2585305 3 0.4730 0.4131 0.000 0.364 0.636 0.000
#> aberrant_ERR2585278 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585347 2 0.0336 0.9591 0.000 0.992 0.008 0.000
#> aberrant_ERR2585332 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585280 2 0.3975 0.6502 0.000 0.760 0.240 0.000
#> aberrant_ERR2585304 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> aberrant_ERR2585322 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585279 3 0.4304 0.5437 0.000 0.284 0.716 0.000
#> aberrant_ERR2585277 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585295 3 0.4697 0.4442 0.000 0.356 0.644 0.000
#> aberrant_ERR2585333 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585285 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585286 2 0.0188 0.9627 0.000 0.996 0.004 0.000
#> aberrant_ERR2585294 2 0.4948 0.1576 0.000 0.560 0.440 0.000
#> aberrant_ERR2585300 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585334 3 0.4999 0.0766 0.000 0.492 0.508 0.000
#> aberrant_ERR2585361 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585372 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> round_ERR2585217 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585205 1 0.2868 0.8199 0.864 0.000 0.136 0.000
#> round_ERR2585214 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585202 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> aberrant_ERR2585367 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> round_ERR2585220 3 0.4888 0.2995 0.412 0.000 0.588 0.000
#> round_ERR2585238 1 0.0000 0.9307 1.000 0.000 0.000 0.000
#> aberrant_ERR2585276 2 0.4972 0.0977 0.000 0.544 0.456 0.000
#> round_ERR2585218 1 0.0188 0.9310 0.996 0.000 0.004 0.000
#> aberrant_ERR2585363 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> round_ERR2585201 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585210 1 0.3123 0.7934 0.844 0.000 0.156 0.000
#> aberrant_ERR2585362 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585360 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> round_ERR2585209 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585242 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585216 3 0.1022 0.8644 0.032 0.000 0.968 0.000
#> round_ERR2585219 3 0.4933 0.2325 0.432 0.000 0.568 0.000
#> round_ERR2585237 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585198 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585211 1 0.1022 0.9232 0.968 0.000 0.032 0.000
#> round_ERR2585206 1 0.0469 0.9301 0.988 0.000 0.012 0.000
#> aberrant_ERR2585281 3 0.4730 0.4331 0.000 0.364 0.636 0.000
#> round_ERR2585212 3 0.1867 0.8352 0.072 0.000 0.928 0.000
#> round_ERR2585221 1 0.3311 0.7420 0.828 0.000 0.172 0.000
#> round_ERR2585243 1 0.4134 0.6138 0.740 0.000 0.260 0.000
#> round_ERR2585204 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585213 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> aberrant_ERR2585373 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585358 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585365 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585359 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585370 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> round_ERR2585215 1 0.2345 0.8687 0.900 0.000 0.100 0.000
#> round_ERR2585262 3 0.0817 0.8633 0.000 0.024 0.976 0.000
#> round_ERR2585199 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> aberrant_ERR2585369 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> round_ERR2585208 1 0.0000 0.9307 1.000 0.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.9307 1.000 0.000 0.000 0.000
#> round_ERR2585236 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> aberrant_ERR2585284 3 0.4511 0.6517 0.040 0.000 0.784 0.176
#> round_ERR2585224 1 0.0000 0.9307 1.000 0.000 0.000 0.000
#> round_ERR2585260 1 0.2647 0.8428 0.880 0.000 0.120 0.000
#> round_ERR2585229 1 0.0000 0.9307 1.000 0.000 0.000 0.000
#> aberrant_ERR2585364 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> round_ERR2585253 1 0.0000 0.9307 1.000 0.000 0.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585371 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> round_ERR2585239 3 0.2868 0.7688 0.136 0.000 0.864 0.000
#> round_ERR2585273 1 0.3172 0.7874 0.840 0.000 0.160 0.000
#> round_ERR2585256 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585272 3 0.2281 0.8121 0.096 0.000 0.904 0.000
#> round_ERR2585246 1 0.0817 0.9252 0.976 0.000 0.024 0.000
#> round_ERR2585261 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585254 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585225 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585235 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585271 1 0.1637 0.9058 0.940 0.000 0.060 0.000
#> round_ERR2585251 3 0.0921 0.8669 0.028 0.000 0.972 0.000
#> round_ERR2585255 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585257 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585226 3 0.1557 0.8473 0.056 0.000 0.944 0.000
#> round_ERR2585265 1 0.2011 0.8878 0.920 0.000 0.080 0.000
#> round_ERR2585259 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585247 1 0.1867 0.8948 0.928 0.000 0.072 0.000
#> round_ERR2585241 1 0.1637 0.8950 0.940 0.000 0.060 0.000
#> round_ERR2585263 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585264 1 0.0000 0.9307 1.000 0.000 0.000 0.000
#> round_ERR2585233 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585223 1 0.0336 0.9301 0.992 0.000 0.008 0.000
#> round_ERR2585234 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585222 3 0.3400 0.7214 0.180 0.000 0.820 0.000
#> round_ERR2585228 1 0.1716 0.9011 0.936 0.000 0.064 0.000
#> round_ERR2585248 1 0.0000 0.9307 1.000 0.000 0.000 0.000
#> round_ERR2585240 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585270 3 0.2345 0.8057 0.100 0.000 0.900 0.000
#> round_ERR2585232 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> aberrant_ERR2585341 2 0.1557 0.9069 0.000 0.944 0.056 0.000
#> aberrant_ERR2585355 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> round_ERR2585227 3 0.1022 0.8647 0.032 0.000 0.968 0.000
#> aberrant_ERR2585351 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> round_ERR2585269 1 0.0000 0.9307 1.000 0.000 0.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> aberrant_ERR2585350 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> round_ERR2585250 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585245 1 0.0000 0.9307 1.000 0.000 0.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> round_ERR2585258 1 0.0188 0.9310 0.996 0.000 0.004 0.000
#> aberrant_ERR2585354 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> round_ERR2585249 1 0.0000 0.9307 1.000 0.000 0.000 0.000
#> round_ERR2585268 3 0.1302 0.8557 0.044 0.000 0.956 0.000
#> aberrant_ERR2585356 2 0.0000 0.9664 0.000 1.000 0.000 0.000
#> round_ERR2585266 3 0.0000 0.8805 0.000 0.000 1.000 0.000
#> round_ERR2585231 1 0.0000 0.9307 1.000 0.000 0.000 0.000
#> round_ERR2585230 3 0.4804 0.3844 0.384 0.000 0.616 0.000
#> round_ERR2585267 1 0.0000 0.9307 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 2 0.4242 0.23126 0.000 0.572 0.000 0.000 0.428
#> aberrant_ERR2585338 2 0.0609 0.90239 0.000 0.980 0.000 0.000 0.020
#> aberrant_ERR2585325 5 0.3305 0.12254 0.000 0.224 0.000 0.000 0.776
#> aberrant_ERR2585283 4 0.0000 0.89197 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585343 2 0.1792 0.84363 0.000 0.916 0.000 0.000 0.084
#> aberrant_ERR2585329 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585317 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585339 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585335 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585287 5 0.0451 0.06076 0.000 0.008 0.004 0.000 0.988
#> aberrant_ERR2585321 2 0.0162 0.91053 0.000 0.996 0.000 0.000 0.004
#> aberrant_ERR2585297 1 0.0703 0.92581 0.976 0.000 0.024 0.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585319 5 0.4278 0.22324 0.000 0.452 0.000 0.000 0.548
#> aberrant_ERR2585315 2 0.0404 0.90606 0.000 0.988 0.000 0.000 0.012
#> aberrant_ERR2585336 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585307 2 0.1671 0.82327 0.000 0.924 0.076 0.000 0.000
#> aberrant_ERR2585301 2 0.4481 0.55554 0.000 0.720 0.048 0.000 0.232
#> aberrant_ERR2585326 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585331 2 0.0609 0.90251 0.000 0.980 0.000 0.000 0.020
#> aberrant_ERR2585346 4 0.5900 0.55618 0.212 0.000 0.000 0.600 0.188
#> aberrant_ERR2585314 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585298 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585345 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585299 1 0.0794 0.92466 0.972 0.000 0.028 0.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.92995 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585303 2 0.0404 0.90305 0.000 0.988 0.012 0.000 0.000
#> aberrant_ERR2585313 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585318 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585328 2 0.3143 0.58634 0.000 0.796 0.204 0.000 0.000
#> aberrant_ERR2585330 2 0.0162 0.91034 0.000 0.996 0.000 0.000 0.004
#> aberrant_ERR2585293 4 0.0000 0.89197 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585342 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585348 2 0.0510 0.90485 0.000 0.984 0.000 0.000 0.016
#> aberrant_ERR2585352 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585308 1 0.0000 0.92995 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585349 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585316 2 0.5296 0.42467 0.000 0.676 0.144 0.000 0.180
#> aberrant_ERR2585306 5 0.4440 -0.03980 0.468 0.000 0.004 0.000 0.528
#> aberrant_ERR2585324 5 0.4278 0.22324 0.000 0.452 0.000 0.000 0.548
#> aberrant_ERR2585310 3 0.0290 0.89032 0.000 0.008 0.992 0.000 0.000
#> aberrant_ERR2585296 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585275 4 0.1121 0.87201 0.000 0.000 0.000 0.956 0.044
#> aberrant_ERR2585311 2 0.3796 0.49919 0.000 0.700 0.000 0.000 0.300
#> aberrant_ERR2585292 4 0.0000 0.89197 0.000 0.000 0.000 1.000 0.000
#> aberrant_ERR2585282 2 0.4449 -0.11616 0.000 0.512 0.004 0.000 0.484
#> aberrant_ERR2585305 5 0.5773 0.43729 0.000 0.100 0.356 0.000 0.544
#> aberrant_ERR2585278 2 0.0162 0.91034 0.000 0.996 0.000 0.000 0.004
#> aberrant_ERR2585347 2 0.2077 0.83732 0.000 0.908 0.008 0.000 0.084
#> aberrant_ERR2585332 2 0.2773 0.74498 0.000 0.836 0.000 0.000 0.164
#> aberrant_ERR2585280 5 0.3991 0.31572 0.000 0.048 0.172 0.000 0.780
#> aberrant_ERR2585304 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585322 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585279 3 0.3969 0.38103 0.000 0.304 0.692 0.000 0.004
#> aberrant_ERR2585277 2 0.0609 0.90251 0.000 0.980 0.000 0.000 0.020
#> aberrant_ERR2585295 5 0.4708 0.27187 0.000 0.016 0.436 0.000 0.548
#> aberrant_ERR2585333 2 0.4304 -0.11304 0.000 0.516 0.000 0.000 0.484
#> aberrant_ERR2585285 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585286 2 0.0865 0.89723 0.000 0.972 0.004 0.000 0.024
#> aberrant_ERR2585294 5 0.5663 0.43280 0.000 0.088 0.364 0.000 0.548
#> aberrant_ERR2585300 5 0.4304 0.13568 0.000 0.484 0.000 0.000 0.516
#> aberrant_ERR2585334 3 0.4894 -0.00408 0.000 0.456 0.520 0.000 0.024
#> aberrant_ERR2585361 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585372 2 0.0404 0.90650 0.000 0.988 0.000 0.000 0.012
#> round_ERR2585217 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585205 1 0.2471 0.82039 0.864 0.000 0.136 0.000 0.000
#> round_ERR2585214 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585202 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585367 2 0.0290 0.90884 0.000 0.992 0.000 0.000 0.008
#> round_ERR2585220 3 0.4227 0.28390 0.420 0.000 0.580 0.000 0.000
#> round_ERR2585238 1 0.0000 0.92995 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585276 5 0.5559 0.42028 0.000 0.076 0.380 0.000 0.544
#> round_ERR2585218 1 0.0162 0.93018 0.996 0.000 0.004 0.000 0.000
#> aberrant_ERR2585363 2 0.0510 0.90472 0.000 0.984 0.000 0.000 0.016
#> round_ERR2585201 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585210 1 0.2648 0.80106 0.848 0.000 0.152 0.000 0.000
#> aberrant_ERR2585362 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585360 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585209 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585242 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585216 3 0.0880 0.87748 0.032 0.000 0.968 0.000 0.000
#> round_ERR2585219 3 0.4262 0.21546 0.440 0.000 0.560 0.000 0.000
#> round_ERR2585237 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585198 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585211 1 0.0880 0.92249 0.968 0.000 0.032 0.000 0.000
#> round_ERR2585206 1 0.0404 0.92918 0.988 0.000 0.012 0.000 0.000
#> aberrant_ERR2585281 3 0.6380 -0.04023 0.000 0.260 0.516 0.000 0.224
#> round_ERR2585212 3 0.1608 0.84288 0.072 0.000 0.928 0.000 0.000
#> round_ERR2585221 1 0.2852 0.74285 0.828 0.000 0.172 0.000 0.000
#> round_ERR2585243 1 0.3586 0.60819 0.736 0.000 0.264 0.000 0.000
#> round_ERR2585204 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585213 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585373 2 0.3109 0.67798 0.000 0.800 0.000 0.000 0.200
#> aberrant_ERR2585358 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585365 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585359 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585370 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585215 1 0.2020 0.86951 0.900 0.000 0.100 0.000 0.000
#> round_ERR2585262 3 0.0703 0.87526 0.000 0.024 0.976 0.000 0.000
#> round_ERR2585199 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585369 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585208 1 0.0000 0.92995 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.92995 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585236 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585284 3 0.5748 0.50340 0.032 0.000 0.680 0.172 0.116
#> round_ERR2585224 1 0.0000 0.92995 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585260 1 0.2280 0.84435 0.880 0.000 0.120 0.000 0.000
#> round_ERR2585229 1 0.0000 0.92995 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585364 2 0.3366 0.63716 0.000 0.768 0.000 0.000 0.232
#> round_ERR2585253 1 0.0000 0.92995 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585368 2 0.0703 0.89965 0.000 0.976 0.000 0.000 0.024
#> aberrant_ERR2585371 2 0.0703 0.89965 0.000 0.976 0.000 0.000 0.024
#> round_ERR2585239 3 0.2377 0.77494 0.128 0.000 0.872 0.000 0.000
#> round_ERR2585273 1 0.2690 0.79536 0.844 0.000 0.156 0.000 0.000
#> round_ERR2585256 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585272 3 0.1908 0.82058 0.092 0.000 0.908 0.000 0.000
#> round_ERR2585246 1 0.0703 0.92407 0.976 0.000 0.024 0.000 0.000
#> round_ERR2585261 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585254 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585225 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585235 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585271 1 0.1410 0.90576 0.940 0.000 0.060 0.000 0.000
#> round_ERR2585251 3 0.0794 0.88044 0.028 0.000 0.972 0.000 0.000
#> round_ERR2585255 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585257 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585226 3 0.1341 0.85708 0.056 0.000 0.944 0.000 0.000
#> round_ERR2585265 1 0.1732 0.88834 0.920 0.000 0.080 0.000 0.000
#> round_ERR2585259 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585247 1 0.1608 0.89501 0.928 0.000 0.072 0.000 0.000
#> round_ERR2585241 1 0.1410 0.89444 0.940 0.000 0.060 0.000 0.000
#> round_ERR2585263 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585264 1 0.0000 0.92995 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585233 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585223 1 0.0290 0.92897 0.992 0.000 0.008 0.000 0.000
#> round_ERR2585234 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585222 3 0.2891 0.71580 0.176 0.000 0.824 0.000 0.000
#> round_ERR2585228 1 0.1478 0.90116 0.936 0.000 0.064 0.000 0.000
#> round_ERR2585248 1 0.0000 0.92995 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585240 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585270 3 0.2074 0.80494 0.104 0.000 0.896 0.000 0.000
#> round_ERR2585232 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585341 2 0.2473 0.80645 0.000 0.896 0.072 0.000 0.032
#> aberrant_ERR2585355 2 0.0703 0.89965 0.000 0.976 0.000 0.000 0.024
#> round_ERR2585227 3 0.0880 0.87781 0.032 0.000 0.968 0.000 0.000
#> aberrant_ERR2585351 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585269 1 0.0000 0.92995 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585350 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585250 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585245 1 0.0000 0.92995 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585258 1 0.0162 0.93014 0.996 0.000 0.004 0.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.91197 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585249 1 0.0000 0.92995 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585268 3 0.1121 0.86715 0.044 0.000 0.956 0.000 0.000
#> aberrant_ERR2585356 2 0.3983 0.39437 0.000 0.660 0.000 0.000 0.340
#> round_ERR2585266 3 0.0000 0.89660 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585231 1 0.0000 0.92995 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585230 3 0.4088 0.40478 0.368 0.000 0.632 0.000 0.000
#> round_ERR2585267 1 0.0000 0.92995 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 6 0.5919 1.05e-01 0.000 0.328 0.000 0.000 0.224 0.448
#> aberrant_ERR2585338 2 0.3464 1.21e-01 0.000 0.688 0.000 0.000 0.000 0.312
#> aberrant_ERR2585325 5 0.4986 6.94e-05 0.000 0.068 0.000 0.000 0.488 0.444
#> aberrant_ERR2585283 4 0.0000 8.83e-01 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585343 2 0.2793 5.37e-01 0.000 0.800 0.000 0.000 0.200 0.000
#> aberrant_ERR2585329 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585317 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585339 2 0.0632 8.01e-01 0.000 0.976 0.000 0.000 0.000 0.024
#> aberrant_ERR2585335 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585287 5 0.3101 2.54e-01 0.000 0.000 0.000 0.000 0.756 0.244
#> aberrant_ERR2585321 2 0.0547 8.05e-01 0.000 0.980 0.000 0.000 0.020 0.000
#> aberrant_ERR2585297 1 0.0632 9.24e-01 0.976 0.000 0.024 0.000 0.000 0.000
#> aberrant_ERR2585337 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585319 5 0.2969 5.36e-01 0.000 0.224 0.000 0.000 0.776 0.000
#> aberrant_ERR2585315 2 0.0363 8.12e-01 0.000 0.988 0.000 0.000 0.012 0.000
#> aberrant_ERR2585336 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585307 2 0.1951 6.84e-01 0.000 0.908 0.076 0.000 0.000 0.016
#> aberrant_ERR2585301 2 0.4151 3.19e-01 0.000 0.692 0.044 0.000 0.264 0.000
#> aberrant_ERR2585326 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585331 2 0.3050 4.10e-01 0.000 0.764 0.000 0.000 0.000 0.236
#> aberrant_ERR2585346 4 0.5276 5.10e-01 0.208 0.000 0.000 0.604 0.188 0.000
#> aberrant_ERR2585314 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585298 3 0.0363 9.01e-01 0.000 0.000 0.988 0.000 0.000 0.012
#> aberrant_ERR2585345 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585299 1 0.0713 9.23e-01 0.972 0.000 0.028 0.000 0.000 0.000
#> aberrant_ERR2585309 1 0.0146 9.28e-01 0.996 0.000 0.004 0.000 0.000 0.000
#> aberrant_ERR2585303 2 0.0622 8.04e-01 0.000 0.980 0.012 0.000 0.000 0.008
#> aberrant_ERR2585313 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585318 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585328 2 0.3043 3.83e-01 0.000 0.792 0.200 0.000 0.000 0.008
#> aberrant_ERR2585330 2 0.0146 8.19e-01 0.000 0.996 0.000 0.000 0.004 0.000
#> aberrant_ERR2585293 4 0.0000 8.83e-01 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585342 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585348 2 0.2854 5.00e-01 0.000 0.792 0.000 0.000 0.000 0.208
#> aberrant_ERR2585352 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585308 1 0.0146 9.28e-01 0.996 0.000 0.004 0.000 0.000 0.000
#> aberrant_ERR2585349 2 0.0260 8.15e-01 0.000 0.992 0.000 0.000 0.000 0.008
#> aberrant_ERR2585316 2 0.4924 1.60e-01 0.000 0.652 0.144 0.000 0.204 0.000
#> aberrant_ERR2585306 5 0.3288 2.80e-01 0.276 0.000 0.000 0.000 0.724 0.000
#> aberrant_ERR2585324 5 0.2969 5.36e-01 0.000 0.224 0.000 0.000 0.776 0.000
#> aberrant_ERR2585310 3 0.0260 8.97e-01 0.000 0.008 0.992 0.000 0.000 0.000
#> aberrant_ERR2585296 3 0.0000 9.00e-01 0.000 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585275 4 0.1007 8.60e-01 0.000 0.000 0.000 0.956 0.044 0.000
#> aberrant_ERR2585311 2 0.3695 1.62e-01 0.000 0.624 0.000 0.000 0.376 0.000
#> aberrant_ERR2585292 4 0.0000 8.83e-01 0.000 0.000 0.000 1.000 0.000 0.000
#> aberrant_ERR2585282 5 0.3703 4.38e-01 0.000 0.304 0.004 0.000 0.688 0.004
#> aberrant_ERR2585305 5 0.4149 5.41e-01 0.000 0.064 0.216 0.000 0.720 0.000
#> aberrant_ERR2585278 2 0.0146 8.19e-01 0.000 0.996 0.000 0.000 0.004 0.000
#> aberrant_ERR2585347 2 0.2473 6.43e-01 0.000 0.856 0.008 0.000 0.136 0.000
#> aberrant_ERR2585332 2 0.2378 6.33e-01 0.000 0.848 0.000 0.000 0.152 0.000
#> aberrant_ERR2585280 5 0.2615 4.95e-01 0.000 0.004 0.136 0.000 0.852 0.008
#> aberrant_ERR2585304 3 0.0363 9.01e-01 0.000 0.000 0.988 0.000 0.000 0.012
#> aberrant_ERR2585322 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585279 3 0.4291 4.33e-01 0.000 0.292 0.664 0.000 0.000 0.044
#> aberrant_ERR2585277 2 0.2996 4.42e-01 0.000 0.772 0.000 0.000 0.000 0.228
#> aberrant_ERR2585295 5 0.3983 3.93e-01 0.000 0.008 0.348 0.000 0.640 0.004
#> aberrant_ERR2585333 5 0.3515 3.96e-01 0.000 0.324 0.000 0.000 0.676 0.000
#> aberrant_ERR2585285 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585286 2 0.3508 2.00e-01 0.000 0.704 0.004 0.000 0.000 0.292
#> aberrant_ERR2585294 5 0.3837 5.44e-01 0.000 0.052 0.196 0.000 0.752 0.000
#> aberrant_ERR2585300 5 0.3672 2.89e-01 0.000 0.368 0.000 0.000 0.632 0.000
#> aberrant_ERR2585334 3 0.5731 1.02e-01 0.000 0.288 0.508 0.000 0.000 0.204
#> aberrant_ERR2585361 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585372 2 0.0260 8.16e-01 0.000 0.992 0.000 0.000 0.008 0.000
#> round_ERR2585217 3 0.0000 9.00e-01 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585205 1 0.2362 8.22e-01 0.860 0.000 0.136 0.000 0.000 0.004
#> round_ERR2585214 3 0.0363 9.01e-01 0.000 0.000 0.988 0.000 0.000 0.012
#> round_ERR2585202 3 0.0363 9.01e-01 0.000 0.000 0.988 0.000 0.000 0.012
#> aberrant_ERR2585367 2 0.2854 4.96e-01 0.000 0.792 0.000 0.000 0.000 0.208
#> round_ERR2585220 3 0.3782 2.99e-01 0.412 0.000 0.588 0.000 0.000 0.000
#> round_ERR2585238 1 0.0000 9.27e-01 1.000 0.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585276 5 0.4110 4.92e-01 0.000 0.040 0.268 0.000 0.692 0.000
#> round_ERR2585218 1 0.0146 9.29e-01 0.996 0.000 0.004 0.000 0.000 0.000
#> aberrant_ERR2585363 2 0.2730 5.37e-01 0.000 0.808 0.000 0.000 0.000 0.192
#> round_ERR2585201 3 0.0363 9.01e-01 0.000 0.000 0.988 0.000 0.000 0.012
#> round_ERR2585210 1 0.2378 8.05e-01 0.848 0.000 0.152 0.000 0.000 0.000
#> aberrant_ERR2585362 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585360 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585209 3 0.0260 9.01e-01 0.000 0.000 0.992 0.000 0.000 0.008
#> round_ERR2585242 3 0.0363 9.01e-01 0.000 0.000 0.988 0.000 0.000 0.012
#> round_ERR2585216 3 0.0790 8.85e-01 0.032 0.000 0.968 0.000 0.000 0.000
#> round_ERR2585219 3 0.3823 2.23e-01 0.436 0.000 0.564 0.000 0.000 0.000
#> round_ERR2585237 3 0.0000 9.00e-01 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585198 3 0.0363 9.01e-01 0.000 0.000 0.988 0.000 0.000 0.012
#> round_ERR2585211 1 0.0937 9.18e-01 0.960 0.000 0.040 0.000 0.000 0.000
#> round_ERR2585206 1 0.0363 9.28e-01 0.988 0.000 0.012 0.000 0.000 0.000
#> aberrant_ERR2585281 3 0.6267 -3.04e-02 0.000 0.264 0.480 0.000 0.236 0.020
#> round_ERR2585212 3 0.1444 8.54e-01 0.072 0.000 0.928 0.000 0.000 0.000
#> round_ERR2585221 1 0.2703 7.44e-01 0.824 0.000 0.172 0.000 0.000 0.004
#> round_ERR2585243 1 0.3244 6.06e-01 0.732 0.000 0.268 0.000 0.000 0.000
#> round_ERR2585204 3 0.0363 9.01e-01 0.000 0.000 0.988 0.000 0.000 0.012
#> round_ERR2585213 3 0.0363 9.01e-01 0.000 0.000 0.988 0.000 0.000 0.012
#> aberrant_ERR2585373 2 0.2823 5.22e-01 0.000 0.796 0.000 0.000 0.204 0.000
#> aberrant_ERR2585358 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585365 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585359 2 0.0260 8.15e-01 0.000 0.992 0.000 0.000 0.008 0.000
#> aberrant_ERR2585370 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585215 1 0.1863 8.68e-01 0.896 0.000 0.104 0.000 0.000 0.000
#> round_ERR2585262 3 0.0632 8.85e-01 0.000 0.024 0.976 0.000 0.000 0.000
#> round_ERR2585199 3 0.0363 9.01e-01 0.000 0.000 0.988 0.000 0.000 0.012
#> aberrant_ERR2585369 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585208 1 0.0146 9.27e-01 0.996 0.000 0.000 0.000 0.000 0.004
#> round_ERR2585252 1 0.0000 9.27e-01 1.000 0.000 0.000 0.000 0.000 0.000
#> round_ERR2585236 3 0.0000 9.00e-01 0.000 0.000 1.000 0.000 0.000 0.000
#> aberrant_ERR2585284 3 0.5871 5.10e-01 0.032 0.000 0.656 0.172 0.096 0.044
#> round_ERR2585224 1 0.0146 9.27e-01 0.996 0.000 0.000 0.000 0.000 0.004
#> round_ERR2585260 1 0.2092 8.44e-01 0.876 0.000 0.124 0.000 0.000 0.000
#> round_ERR2585229 1 0.0000 9.27e-01 1.000 0.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585364 2 0.3659 1.73e-01 0.000 0.636 0.000 0.000 0.364 0.000
#> round_ERR2585253 1 0.0146 9.27e-01 0.996 0.000 0.000 0.000 0.000 0.004
#> aberrant_ERR2585368 6 0.3851 6.74e-01 0.000 0.460 0.000 0.000 0.000 0.540
#> aberrant_ERR2585371 6 0.3851 6.74e-01 0.000 0.460 0.000 0.000 0.000 0.540
#> round_ERR2585239 3 0.2346 7.95e-01 0.124 0.000 0.868 0.000 0.000 0.008
#> round_ERR2585273 1 0.2454 7.96e-01 0.840 0.000 0.160 0.000 0.000 0.000
#> round_ERR2585256 3 0.0000 9.00e-01 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585272 3 0.1714 8.32e-01 0.092 0.000 0.908 0.000 0.000 0.000
#> round_ERR2585246 1 0.0713 9.23e-01 0.972 0.000 0.028 0.000 0.000 0.000
#> round_ERR2585261 3 0.0000 9.00e-01 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585254 3 0.0000 9.00e-01 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585225 3 0.0363 9.01e-01 0.000 0.000 0.988 0.000 0.000 0.012
#> round_ERR2585235 3 0.0000 9.00e-01 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585271 1 0.1387 9.01e-01 0.932 0.000 0.068 0.000 0.000 0.000
#> round_ERR2585251 3 0.0713 8.88e-01 0.028 0.000 0.972 0.000 0.000 0.000
#> round_ERR2585255 3 0.0363 9.01e-01 0.000 0.000 0.988 0.000 0.000 0.012
#> round_ERR2585257 3 0.0363 9.01e-01 0.000 0.000 0.988 0.000 0.000 0.012
#> round_ERR2585226 3 0.1204 8.67e-01 0.056 0.000 0.944 0.000 0.000 0.000
#> round_ERR2585265 1 0.1610 8.86e-01 0.916 0.000 0.084 0.000 0.000 0.000
#> round_ERR2585259 3 0.0260 9.01e-01 0.000 0.000 0.992 0.000 0.000 0.008
#> round_ERR2585247 1 0.1556 8.90e-01 0.920 0.000 0.080 0.000 0.000 0.000
#> round_ERR2585241 1 0.1411 8.94e-01 0.936 0.000 0.060 0.000 0.000 0.004
#> round_ERR2585263 3 0.0000 9.00e-01 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585264 1 0.0146 9.27e-01 0.996 0.000 0.000 0.000 0.000 0.004
#> round_ERR2585233 3 0.0363 9.01e-01 0.000 0.000 0.988 0.000 0.000 0.012
#> round_ERR2585223 1 0.0405 9.27e-01 0.988 0.000 0.008 0.000 0.000 0.004
#> round_ERR2585234 3 0.0363 9.01e-01 0.000 0.000 0.988 0.000 0.000 0.012
#> round_ERR2585222 3 0.2668 7.40e-01 0.168 0.000 0.828 0.000 0.000 0.004
#> round_ERR2585228 1 0.1531 8.99e-01 0.928 0.000 0.068 0.000 0.000 0.004
#> round_ERR2585248 1 0.0146 9.27e-01 0.996 0.000 0.000 0.000 0.000 0.004
#> round_ERR2585240 3 0.0363 9.01e-01 0.000 0.000 0.988 0.000 0.000 0.012
#> round_ERR2585270 3 0.1863 8.18e-01 0.104 0.000 0.896 0.000 0.000 0.000
#> round_ERR2585232 3 0.0363 9.01e-01 0.000 0.000 0.988 0.000 0.000 0.012
#> aberrant_ERR2585341 6 0.4314 6.63e-01 0.000 0.444 0.020 0.000 0.000 0.536
#> aberrant_ERR2585355 2 0.3866 -6.21e-01 0.000 0.516 0.000 0.000 0.000 0.484
#> round_ERR2585227 3 0.0790 8.86e-01 0.032 0.000 0.968 0.000 0.000 0.000
#> aberrant_ERR2585351 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585269 1 0.0146 9.27e-01 0.996 0.000 0.000 0.000 0.000 0.004
#> aberrant_ERR2585357 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> aberrant_ERR2585350 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585250 3 0.0000 9.00e-01 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585245 1 0.0146 9.27e-01 0.996 0.000 0.000 0.000 0.000 0.004
#> aberrant_ERR2585353 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585258 1 0.0291 9.28e-01 0.992 0.000 0.004 0.000 0.000 0.004
#> aberrant_ERR2585354 2 0.0000 8.21e-01 0.000 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585249 1 0.0146 9.27e-01 0.996 0.000 0.000 0.000 0.000 0.004
#> round_ERR2585268 3 0.1082 8.77e-01 0.040 0.000 0.956 0.000 0.000 0.004
#> aberrant_ERR2585356 2 0.3869 -1.43e-01 0.000 0.500 0.000 0.000 0.500 0.000
#> round_ERR2585266 3 0.0363 9.01e-01 0.000 0.000 0.988 0.000 0.000 0.012
#> round_ERR2585231 1 0.0146 9.27e-01 0.996 0.000 0.000 0.000 0.000 0.004
#> round_ERR2585230 3 0.3795 4.46e-01 0.364 0.000 0.632 0.000 0.000 0.004
#> round_ERR2585267 1 0.0000 9.27e-01 1.000 0.000 0.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> ATC:pam 152 6.61e-24 2
#> ATC:pam 140 1.46e-23 3
#> ATC:pam 150 4.48e-24 4
#> ATC:pam 137 1.21e-22 5
#> ATC:pam 132 5.06e-20 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "mclust"]
# you can also extract it by
# res = res_list["ATC:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'ATC' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.999 0.981 0.992 0.5030 0.497 0.497
#> 3 3 0.931 0.926 0.968 0.1361 0.939 0.878
#> 4 4 0.818 0.823 0.918 0.1237 0.880 0.738
#> 5 5 0.820 0.827 0.919 0.1691 0.846 0.590
#> 6 6 0.718 0.708 0.797 0.0425 0.954 0.825
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585338 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585325 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585283 2 0.327 0.9396 0.060 0.940
#> aberrant_ERR2585343 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585329 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585317 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585339 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585335 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585287 2 0.327 0.9396 0.060 0.940
#> aberrant_ERR2585321 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585297 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585337 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585319 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585315 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585336 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585307 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585301 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585326 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585331 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585346 2 0.327 0.9396 0.060 0.940
#> aberrant_ERR2585314 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585298 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585345 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585299 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585309 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585303 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585313 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585318 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585328 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585330 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585293 2 0.327 0.9396 0.060 0.940
#> aberrant_ERR2585342 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585348 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585352 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585308 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585349 2 0.260 0.9507 0.044 0.956
#> aberrant_ERR2585316 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585306 2 0.204 0.9622 0.032 0.968
#> aberrant_ERR2585324 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585310 2 1.000 0.0324 0.500 0.500
#> aberrant_ERR2585296 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585275 2 0.327 0.9396 0.060 0.940
#> aberrant_ERR2585311 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585292 2 0.327 0.9396 0.060 0.940
#> aberrant_ERR2585282 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585305 2 0.278 0.9501 0.048 0.952
#> aberrant_ERR2585278 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585347 2 0.163 0.9682 0.024 0.976
#> aberrant_ERR2585332 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585280 2 0.184 0.9654 0.028 0.972
#> aberrant_ERR2585304 1 0.327 0.9344 0.940 0.060
#> aberrant_ERR2585322 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585279 2 0.518 0.8783 0.116 0.884
#> aberrant_ERR2585277 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585295 2 0.260 0.9532 0.044 0.956
#> aberrant_ERR2585333 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585285 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585286 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585294 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585300 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585334 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585361 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585372 2 0.000 0.9840 0.000 1.000
#> round_ERR2585217 1 0.000 0.9992 1.000 0.000
#> round_ERR2585205 1 0.000 0.9992 1.000 0.000
#> round_ERR2585214 1 0.000 0.9992 1.000 0.000
#> round_ERR2585202 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585367 2 0.000 0.9840 0.000 1.000
#> round_ERR2585220 1 0.000 0.9992 1.000 0.000
#> round_ERR2585238 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585276 2 0.000 0.9840 0.000 1.000
#> round_ERR2585218 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585363 2 0.000 0.9840 0.000 1.000
#> round_ERR2585201 1 0.000 0.9992 1.000 0.000
#> round_ERR2585210 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585362 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585360 2 0.000 0.9840 0.000 1.000
#> round_ERR2585209 1 0.000 0.9992 1.000 0.000
#> round_ERR2585242 1 0.000 0.9992 1.000 0.000
#> round_ERR2585216 1 0.000 0.9992 1.000 0.000
#> round_ERR2585219 1 0.000 0.9992 1.000 0.000
#> round_ERR2585237 1 0.000 0.9992 1.000 0.000
#> round_ERR2585198 1 0.000 0.9992 1.000 0.000
#> round_ERR2585211 1 0.000 0.9992 1.000 0.000
#> round_ERR2585206 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585281 2 0.184 0.9654 0.028 0.972
#> round_ERR2585212 1 0.000 0.9992 1.000 0.000
#> round_ERR2585221 1 0.000 0.9992 1.000 0.000
#> round_ERR2585243 1 0.000 0.9992 1.000 0.000
#> round_ERR2585204 1 0.000 0.9992 1.000 0.000
#> round_ERR2585213 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585373 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585358 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585365 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585359 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585370 2 0.000 0.9840 0.000 1.000
#> round_ERR2585215 1 0.000 0.9992 1.000 0.000
#> round_ERR2585262 1 0.000 0.9992 1.000 0.000
#> round_ERR2585199 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585369 2 0.000 0.9840 0.000 1.000
#> round_ERR2585208 1 0.000 0.9992 1.000 0.000
#> round_ERR2585252 1 0.000 0.9992 1.000 0.000
#> round_ERR2585236 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585284 2 0.327 0.9396 0.060 0.940
#> round_ERR2585224 1 0.000 0.9992 1.000 0.000
#> round_ERR2585260 1 0.000 0.9992 1.000 0.000
#> round_ERR2585229 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585364 2 0.000 0.9840 0.000 1.000
#> round_ERR2585253 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585368 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585371 2 0.000 0.9840 0.000 1.000
#> round_ERR2585239 1 0.000 0.9992 1.000 0.000
#> round_ERR2585273 1 0.000 0.9992 1.000 0.000
#> round_ERR2585256 1 0.000 0.9992 1.000 0.000
#> round_ERR2585272 1 0.000 0.9992 1.000 0.000
#> round_ERR2585246 1 0.000 0.9992 1.000 0.000
#> round_ERR2585261 1 0.000 0.9992 1.000 0.000
#> round_ERR2585254 1 0.000 0.9992 1.000 0.000
#> round_ERR2585225 1 0.000 0.9992 1.000 0.000
#> round_ERR2585235 1 0.000 0.9992 1.000 0.000
#> round_ERR2585271 1 0.000 0.9992 1.000 0.000
#> round_ERR2585251 1 0.000 0.9992 1.000 0.000
#> round_ERR2585255 1 0.000 0.9992 1.000 0.000
#> round_ERR2585257 1 0.000 0.9992 1.000 0.000
#> round_ERR2585226 1 0.000 0.9992 1.000 0.000
#> round_ERR2585265 1 0.000 0.9992 1.000 0.000
#> round_ERR2585259 1 0.000 0.9992 1.000 0.000
#> round_ERR2585247 1 0.000 0.9992 1.000 0.000
#> round_ERR2585241 1 0.000 0.9992 1.000 0.000
#> round_ERR2585263 1 0.000 0.9992 1.000 0.000
#> round_ERR2585264 1 0.000 0.9992 1.000 0.000
#> round_ERR2585233 1 0.000 0.9992 1.000 0.000
#> round_ERR2585223 1 0.000 0.9992 1.000 0.000
#> round_ERR2585234 1 0.000 0.9992 1.000 0.000
#> round_ERR2585222 1 0.000 0.9992 1.000 0.000
#> round_ERR2585228 1 0.000 0.9992 1.000 0.000
#> round_ERR2585248 1 0.000 0.9992 1.000 0.000
#> round_ERR2585240 1 0.000 0.9992 1.000 0.000
#> round_ERR2585270 1 0.000 0.9992 1.000 0.000
#> round_ERR2585232 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585341 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585355 2 0.000 0.9840 0.000 1.000
#> round_ERR2585227 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585351 2 0.000 0.9840 0.000 1.000
#> round_ERR2585269 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585357 2 0.000 0.9840 0.000 1.000
#> aberrant_ERR2585350 2 0.000 0.9840 0.000 1.000
#> round_ERR2585250 1 0.000 0.9992 1.000 0.000
#> round_ERR2585245 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585353 2 0.000 0.9840 0.000 1.000
#> round_ERR2585258 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585354 2 0.000 0.9840 0.000 1.000
#> round_ERR2585249 1 0.000 0.9992 1.000 0.000
#> round_ERR2585268 1 0.000 0.9992 1.000 0.000
#> aberrant_ERR2585356 2 0.000 0.9840 0.000 1.000
#> round_ERR2585266 1 0.000 0.9992 1.000 0.000
#> round_ERR2585231 1 0.000 0.9992 1.000 0.000
#> round_ERR2585230 1 0.000 0.9992 1.000 0.000
#> round_ERR2585267 1 0.000 0.9992 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585338 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585325 2 0.2959 0.859 0.000 0.900 0.100
#> aberrant_ERR2585283 3 0.0237 0.877 0.000 0.004 0.996
#> aberrant_ERR2585343 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585329 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585317 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585339 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585335 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585287 3 0.0237 0.877 0.000 0.004 0.996
#> aberrant_ERR2585321 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585297 1 0.0000 0.992 1.000 0.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585319 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585315 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585336 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585307 2 0.4291 0.769 0.000 0.820 0.180
#> aberrant_ERR2585301 2 0.1031 0.924 0.000 0.976 0.024
#> aberrant_ERR2585326 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585331 2 0.2356 0.884 0.000 0.928 0.072
#> aberrant_ERR2585346 3 0.0237 0.877 0.000 0.004 0.996
#> aberrant_ERR2585314 2 0.4702 0.726 0.000 0.788 0.212
#> aberrant_ERR2585298 1 0.0237 0.992 0.996 0.000 0.004
#> aberrant_ERR2585345 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585299 1 0.0237 0.992 0.996 0.000 0.004
#> aberrant_ERR2585309 1 0.0000 0.992 1.000 0.000 0.000
#> aberrant_ERR2585303 2 0.0747 0.931 0.000 0.984 0.016
#> aberrant_ERR2585313 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585318 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585328 2 0.4452 0.754 0.000 0.808 0.192
#> aberrant_ERR2585330 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585293 3 0.0237 0.877 0.000 0.004 0.996
#> aberrant_ERR2585342 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585348 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585352 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585308 1 0.0000 0.992 1.000 0.000 0.000
#> aberrant_ERR2585349 2 0.4750 0.722 0.000 0.784 0.216
#> aberrant_ERR2585316 2 0.3412 0.834 0.000 0.876 0.124
#> aberrant_ERR2585306 2 0.4654 0.732 0.000 0.792 0.208
#> aberrant_ERR2585324 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585310 2 0.9690 -0.118 0.376 0.408 0.216
#> aberrant_ERR2585296 1 0.0237 0.992 0.996 0.000 0.004
#> aberrant_ERR2585275 3 0.0237 0.877 0.000 0.004 0.996
#> aberrant_ERR2585311 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585292 3 0.0237 0.877 0.000 0.004 0.996
#> aberrant_ERR2585282 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585305 2 0.4702 0.726 0.000 0.788 0.212
#> aberrant_ERR2585278 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585347 3 0.5810 0.566 0.000 0.336 0.664
#> aberrant_ERR2585332 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585280 3 0.5216 0.711 0.000 0.260 0.740
#> aberrant_ERR2585304 1 0.7960 0.452 0.656 0.136 0.208
#> aberrant_ERR2585322 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585279 2 0.6483 0.313 0.008 0.600 0.392
#> aberrant_ERR2585277 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585295 3 0.5138 0.721 0.000 0.252 0.748
#> aberrant_ERR2585333 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585285 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585286 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585294 2 0.4605 0.737 0.000 0.796 0.204
#> aberrant_ERR2585300 2 0.3412 0.834 0.000 0.876 0.124
#> aberrant_ERR2585334 2 0.5882 0.445 0.000 0.652 0.348
#> aberrant_ERR2585361 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585372 2 0.0000 0.941 0.000 1.000 0.000
#> round_ERR2585217 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585205 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585214 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585202 1 0.0237 0.992 0.996 0.000 0.004
#> aberrant_ERR2585367 2 0.0000 0.941 0.000 1.000 0.000
#> round_ERR2585220 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585238 1 0.0000 0.992 1.000 0.000 0.000
#> aberrant_ERR2585276 2 0.4654 0.732 0.000 0.792 0.208
#> round_ERR2585218 1 0.0000 0.992 1.000 0.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.941 0.000 1.000 0.000
#> round_ERR2585201 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585210 1 0.0000 0.992 1.000 0.000 0.000
#> aberrant_ERR2585362 2 0.1289 0.918 0.000 0.968 0.032
#> aberrant_ERR2585360 2 0.0000 0.941 0.000 1.000 0.000
#> round_ERR2585209 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585242 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585216 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585219 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585237 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585198 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585211 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.992 1.000 0.000 0.000
#> aberrant_ERR2585281 3 0.5178 0.716 0.000 0.256 0.744
#> round_ERR2585212 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585221 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585243 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585204 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585213 1 0.0237 0.992 0.996 0.000 0.004
#> aberrant_ERR2585373 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585358 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585365 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585359 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585370 2 0.0000 0.941 0.000 1.000 0.000
#> round_ERR2585215 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585262 1 0.3192 0.869 0.888 0.000 0.112
#> round_ERR2585199 1 0.0237 0.992 0.996 0.000 0.004
#> aberrant_ERR2585369 2 0.0000 0.941 0.000 1.000 0.000
#> round_ERR2585208 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585236 1 0.0237 0.992 0.996 0.000 0.004
#> aberrant_ERR2585284 3 0.0237 0.877 0.000 0.004 0.996
#> round_ERR2585224 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585260 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585229 1 0.0000 0.992 1.000 0.000 0.000
#> aberrant_ERR2585364 2 0.0000 0.941 0.000 1.000 0.000
#> round_ERR2585253 1 0.0000 0.992 1.000 0.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585371 2 0.0000 0.941 0.000 1.000 0.000
#> round_ERR2585239 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585273 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585256 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585272 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585246 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585261 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585254 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585225 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585235 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585271 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585251 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585255 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585257 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585226 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585265 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585259 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585247 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585241 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585263 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585264 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585233 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585223 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585234 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585222 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585228 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585248 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585240 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585270 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585232 1 0.0237 0.992 0.996 0.000 0.004
#> aberrant_ERR2585341 2 0.0237 0.938 0.000 0.996 0.004
#> aberrant_ERR2585355 2 0.0000 0.941 0.000 1.000 0.000
#> round_ERR2585227 1 0.0000 0.992 1.000 0.000 0.000
#> aberrant_ERR2585351 2 0.0000 0.941 0.000 1.000 0.000
#> round_ERR2585269 1 0.0237 0.992 0.996 0.000 0.004
#> aberrant_ERR2585357 2 0.0000 0.941 0.000 1.000 0.000
#> aberrant_ERR2585350 2 0.0000 0.941 0.000 1.000 0.000
#> round_ERR2585250 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585245 1 0.0000 0.992 1.000 0.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.941 0.000 1.000 0.000
#> round_ERR2585258 1 0.0000 0.992 1.000 0.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.941 0.000 1.000 0.000
#> round_ERR2585249 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585268 1 0.0237 0.992 0.996 0.000 0.004
#> aberrant_ERR2585356 2 0.0000 0.941 0.000 1.000 0.000
#> round_ERR2585266 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585231 1 0.0000 0.992 1.000 0.000 0.000
#> round_ERR2585230 1 0.0237 0.992 0.996 0.000 0.004
#> round_ERR2585267 1 0.0000 0.992 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585338 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585325 2 0.2593 0.8691 0.000 0.892 0.104 0.004
#> aberrant_ERR2585283 4 0.0000 0.9995 0.000 0.000 0.000 1.000
#> aberrant_ERR2585343 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585329 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585317 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585339 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585335 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585287 4 0.0188 0.9972 0.000 0.000 0.004 0.996
#> aberrant_ERR2585321 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585297 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585319 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585315 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585336 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585307 2 0.1474 0.9157 0.000 0.948 0.052 0.000
#> aberrant_ERR2585301 2 0.0188 0.9486 0.000 0.996 0.004 0.000
#> aberrant_ERR2585326 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585331 2 0.2831 0.8542 0.000 0.876 0.120 0.004
#> aberrant_ERR2585346 4 0.0000 0.9995 0.000 0.000 0.000 1.000
#> aberrant_ERR2585314 2 0.4164 0.6890 0.000 0.736 0.264 0.000
#> aberrant_ERR2585298 3 0.4331 0.8403 0.288 0.000 0.712 0.000
#> aberrant_ERR2585345 2 0.0188 0.9484 0.000 0.996 0.004 0.000
#> aberrant_ERR2585299 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> aberrant_ERR2585303 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585313 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585318 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585328 2 0.1867 0.9002 0.000 0.928 0.072 0.000
#> aberrant_ERR2585330 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585293 4 0.0000 0.9995 0.000 0.000 0.000 1.000
#> aberrant_ERR2585342 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585348 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585352 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585308 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> aberrant_ERR2585349 2 0.4907 0.4171 0.000 0.580 0.420 0.000
#> aberrant_ERR2585316 2 0.0188 0.9486 0.000 0.996 0.004 0.000
#> aberrant_ERR2585306 2 0.0817 0.9361 0.000 0.976 0.024 0.000
#> aberrant_ERR2585324 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585310 3 0.2530 0.5314 0.112 0.000 0.888 0.000
#> aberrant_ERR2585296 1 0.4888 -0.1780 0.588 0.000 0.412 0.000
#> aberrant_ERR2585275 4 0.0000 0.9995 0.000 0.000 0.000 1.000
#> aberrant_ERR2585311 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585292 4 0.0000 0.9995 0.000 0.000 0.000 1.000
#> aberrant_ERR2585282 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585305 2 0.4679 0.5721 0.000 0.648 0.352 0.000
#> aberrant_ERR2585278 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585347 2 0.6071 0.6210 0.000 0.684 0.172 0.144
#> aberrant_ERR2585332 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585280 2 0.6475 0.5527 0.000 0.644 0.172 0.184
#> aberrant_ERR2585304 3 0.0921 0.5101 0.028 0.000 0.972 0.000
#> aberrant_ERR2585322 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585279 2 0.6522 0.4869 0.000 0.608 0.280 0.112
#> aberrant_ERR2585277 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585295 2 0.6475 0.5530 0.000 0.644 0.172 0.184
#> aberrant_ERR2585333 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585285 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585286 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585294 2 0.0336 0.9467 0.000 0.992 0.008 0.000
#> aberrant_ERR2585300 2 0.0188 0.9486 0.000 0.996 0.004 0.000
#> aberrant_ERR2585334 2 0.5226 0.7265 0.000 0.756 0.116 0.128
#> aberrant_ERR2585361 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585372 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> round_ERR2585217 3 0.4994 0.5028 0.480 0.000 0.520 0.000
#> round_ERR2585205 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585214 3 0.4072 0.8444 0.252 0.000 0.748 0.000
#> round_ERR2585202 3 0.3726 0.8110 0.212 0.000 0.788 0.000
#> aberrant_ERR2585367 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> round_ERR2585220 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585238 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> aberrant_ERR2585276 2 0.2760 0.8481 0.000 0.872 0.128 0.000
#> round_ERR2585218 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> round_ERR2585201 3 0.4222 0.8460 0.272 0.000 0.728 0.000
#> round_ERR2585210 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> aberrant_ERR2585362 2 0.1022 0.9313 0.000 0.968 0.032 0.000
#> aberrant_ERR2585360 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> round_ERR2585209 1 0.0817 0.8750 0.976 0.000 0.024 0.000
#> round_ERR2585242 3 0.4406 0.8313 0.300 0.000 0.700 0.000
#> round_ERR2585216 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585219 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585237 3 0.4977 0.5634 0.460 0.000 0.540 0.000
#> round_ERR2585198 3 0.4072 0.8444 0.252 0.000 0.748 0.000
#> round_ERR2585211 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> aberrant_ERR2585281 2 0.6475 0.5530 0.000 0.644 0.172 0.184
#> round_ERR2585212 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585221 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585243 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585204 3 0.4072 0.8444 0.252 0.000 0.748 0.000
#> round_ERR2585213 3 0.3688 0.8068 0.208 0.000 0.792 0.000
#> aberrant_ERR2585373 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585358 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585365 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585359 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585370 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> round_ERR2585215 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585262 3 0.1389 0.5513 0.048 0.000 0.952 0.000
#> round_ERR2585199 3 0.4605 0.7918 0.336 0.000 0.664 0.000
#> aberrant_ERR2585369 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> round_ERR2585208 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585236 3 0.4961 0.5982 0.448 0.000 0.552 0.000
#> aberrant_ERR2585284 4 0.0000 0.9995 0.000 0.000 0.000 1.000
#> round_ERR2585224 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585260 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585229 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> aberrant_ERR2585364 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> round_ERR2585253 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585371 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> round_ERR2585239 1 0.1867 0.8126 0.928 0.000 0.072 0.000
#> round_ERR2585273 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585256 1 0.4888 -0.1780 0.588 0.000 0.412 0.000
#> round_ERR2585272 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585246 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585261 1 0.4898 -0.1936 0.584 0.000 0.416 0.000
#> round_ERR2585254 1 0.4817 -0.0747 0.612 0.000 0.388 0.000
#> round_ERR2585225 3 0.4955 0.5896 0.444 0.000 0.556 0.000
#> round_ERR2585235 1 0.1211 0.8558 0.960 0.000 0.040 0.000
#> round_ERR2585271 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585251 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585255 3 0.4277 0.8442 0.280 0.000 0.720 0.000
#> round_ERR2585257 3 0.4304 0.8426 0.284 0.000 0.716 0.000
#> round_ERR2585226 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585265 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585259 1 0.4746 0.0337 0.632 0.000 0.368 0.000
#> round_ERR2585247 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585241 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585263 1 0.0469 0.8871 0.988 0.000 0.012 0.000
#> round_ERR2585264 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585233 1 0.5000 -0.4621 0.500 0.000 0.500 0.000
#> round_ERR2585223 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585234 3 0.4193 0.8462 0.268 0.000 0.732 0.000
#> round_ERR2585222 1 0.1792 0.8190 0.932 0.000 0.068 0.000
#> round_ERR2585228 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585248 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585240 1 0.4250 0.4020 0.724 0.000 0.276 0.000
#> round_ERR2585270 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585232 1 0.4277 0.3903 0.720 0.000 0.280 0.000
#> aberrant_ERR2585341 2 0.0469 0.9439 0.000 0.988 0.012 0.000
#> aberrant_ERR2585355 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> round_ERR2585227 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> aberrant_ERR2585351 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> round_ERR2585269 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> aberrant_ERR2585350 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> round_ERR2585250 1 0.4898 -0.1920 0.584 0.000 0.416 0.000
#> round_ERR2585245 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> round_ERR2585258 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> round_ERR2585249 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585268 1 0.4500 0.2379 0.684 0.000 0.316 0.000
#> aberrant_ERR2585356 2 0.0000 0.9507 0.000 1.000 0.000 0.000
#> round_ERR2585266 3 0.4406 0.8313 0.300 0.000 0.700 0.000
#> round_ERR2585231 1 0.0000 0.8990 1.000 0.000 0.000 0.000
#> round_ERR2585230 1 0.0188 0.8951 0.996 0.000 0.004 0.000
#> round_ERR2585267 1 0.0000 0.8990 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 2 0.4060 0.602 0.000 0.640 0.000 0 0.360
#> aberrant_ERR2585338 2 0.3366 0.777 0.000 0.768 0.000 0 0.232
#> aberrant_ERR2585325 2 0.0162 0.778 0.000 0.996 0.000 0 0.004
#> aberrant_ERR2585283 4 0.0000 1.000 0.000 0.000 0.000 1 0.000
#> aberrant_ERR2585343 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> aberrant_ERR2585329 2 0.4015 0.632 0.000 0.652 0.000 0 0.348
#> aberrant_ERR2585317 5 0.3932 0.422 0.000 0.328 0.000 0 0.672
#> aberrant_ERR2585339 2 0.3039 0.802 0.000 0.808 0.000 0 0.192
#> aberrant_ERR2585335 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> aberrant_ERR2585287 4 0.0000 1.000 0.000 0.000 0.000 1 0.000
#> aberrant_ERR2585321 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> aberrant_ERR2585297 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> aberrant_ERR2585337 2 0.3983 0.646 0.000 0.660 0.000 0 0.340
#> aberrant_ERR2585319 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> aberrant_ERR2585315 2 0.2773 0.812 0.000 0.836 0.000 0 0.164
#> aberrant_ERR2585336 2 0.3274 0.784 0.000 0.780 0.000 0 0.220
#> aberrant_ERR2585307 2 0.4047 0.675 0.000 0.676 0.004 0 0.320
#> aberrant_ERR2585301 5 0.3949 0.481 0.000 0.300 0.004 0 0.696
#> aberrant_ERR2585326 2 0.3074 0.799 0.000 0.804 0.000 0 0.196
#> aberrant_ERR2585331 2 0.1121 0.801 0.000 0.956 0.000 0 0.044
#> aberrant_ERR2585346 4 0.0000 1.000 0.000 0.000 0.000 1 0.000
#> aberrant_ERR2585314 5 0.4403 0.231 0.000 0.384 0.008 0 0.608
#> aberrant_ERR2585298 3 0.0162 0.834 0.004 0.000 0.996 0 0.000
#> aberrant_ERR2585345 2 0.3932 0.665 0.000 0.672 0.000 0 0.328
#> aberrant_ERR2585299 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> aberrant_ERR2585309 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> aberrant_ERR2585303 2 0.1851 0.821 0.000 0.912 0.000 0 0.088
#> aberrant_ERR2585313 2 0.2966 0.805 0.000 0.816 0.000 0 0.184
#> aberrant_ERR2585318 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> aberrant_ERR2585328 2 0.3333 0.790 0.000 0.788 0.004 0 0.208
#> aberrant_ERR2585330 5 0.1270 0.853 0.000 0.052 0.000 0 0.948
#> aberrant_ERR2585293 4 0.0000 1.000 0.000 0.000 0.000 1 0.000
#> aberrant_ERR2585342 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> aberrant_ERR2585348 5 0.4182 0.146 0.000 0.400 0.000 0 0.600
#> aberrant_ERR2585352 5 0.0162 0.887 0.000 0.004 0.000 0 0.996
#> aberrant_ERR2585308 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> aberrant_ERR2585349 2 0.4384 0.661 0.000 0.660 0.016 0 0.324
#> aberrant_ERR2585316 5 0.0324 0.883 0.000 0.004 0.004 0 0.992
#> aberrant_ERR2585306 5 0.0451 0.882 0.000 0.008 0.004 0 0.988
#> aberrant_ERR2585324 2 0.4305 0.266 0.000 0.512 0.000 0 0.488
#> aberrant_ERR2585310 3 0.3236 0.657 0.000 0.020 0.828 0 0.152
#> aberrant_ERR2585296 3 0.4161 0.494 0.392 0.000 0.608 0 0.000
#> aberrant_ERR2585275 4 0.0000 1.000 0.000 0.000 0.000 1 0.000
#> aberrant_ERR2585311 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> aberrant_ERR2585292 4 0.0000 1.000 0.000 0.000 0.000 1 0.000
#> aberrant_ERR2585282 5 0.3177 0.666 0.000 0.208 0.000 0 0.792
#> aberrant_ERR2585305 5 0.3419 0.704 0.000 0.180 0.016 0 0.804
#> aberrant_ERR2585278 5 0.4060 0.309 0.000 0.360 0.000 0 0.640
#> aberrant_ERR2585347 2 0.0000 0.775 0.000 1.000 0.000 0 0.000
#> aberrant_ERR2585332 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> aberrant_ERR2585280 2 0.0000 0.775 0.000 1.000 0.000 0 0.000
#> aberrant_ERR2585304 3 0.0290 0.828 0.000 0.008 0.992 0 0.000
#> aberrant_ERR2585322 2 0.1851 0.820 0.000 0.912 0.000 0 0.088
#> aberrant_ERR2585279 2 0.0794 0.757 0.000 0.972 0.028 0 0.000
#> aberrant_ERR2585277 2 0.0290 0.781 0.000 0.992 0.000 0 0.008
#> aberrant_ERR2585295 2 0.0000 0.775 0.000 1.000 0.000 0 0.000
#> aberrant_ERR2585333 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> aberrant_ERR2585285 5 0.0162 0.887 0.000 0.004 0.000 0 0.996
#> aberrant_ERR2585286 2 0.0290 0.781 0.000 0.992 0.000 0 0.008
#> aberrant_ERR2585294 5 0.4084 0.417 0.000 0.328 0.004 0 0.668
#> aberrant_ERR2585300 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> aberrant_ERR2585334 2 0.0162 0.778 0.000 0.996 0.000 0 0.004
#> aberrant_ERR2585361 5 0.1197 0.856 0.000 0.048 0.000 0 0.952
#> aberrant_ERR2585372 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> round_ERR2585217 3 0.1121 0.818 0.044 0.000 0.956 0 0.000
#> round_ERR2585205 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585214 3 0.0162 0.834 0.004 0.000 0.996 0 0.000
#> round_ERR2585202 3 0.0162 0.834 0.004 0.000 0.996 0 0.000
#> aberrant_ERR2585367 2 0.4268 0.401 0.000 0.556 0.000 0 0.444
#> round_ERR2585220 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585238 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> aberrant_ERR2585276 2 0.4242 0.322 0.000 0.572 0.000 0 0.428
#> round_ERR2585218 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> aberrant_ERR2585363 5 0.2377 0.773 0.000 0.128 0.000 0 0.872
#> round_ERR2585201 3 0.0162 0.834 0.004 0.000 0.996 0 0.000
#> round_ERR2585210 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> aberrant_ERR2585362 5 0.2280 0.791 0.000 0.120 0.000 0 0.880
#> aberrant_ERR2585360 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> round_ERR2585209 1 0.3274 0.693 0.780 0.000 0.220 0 0.000
#> round_ERR2585242 3 0.0162 0.834 0.004 0.000 0.996 0 0.000
#> round_ERR2585216 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585219 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585237 3 0.1121 0.818 0.044 0.000 0.956 0 0.000
#> round_ERR2585198 3 0.0162 0.834 0.004 0.000 0.996 0 0.000
#> round_ERR2585211 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585206 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> aberrant_ERR2585281 2 0.0000 0.775 0.000 1.000 0.000 0 0.000
#> round_ERR2585212 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585221 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585243 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585204 3 0.0162 0.834 0.004 0.000 0.996 0 0.000
#> round_ERR2585213 3 0.0162 0.829 0.000 0.004 0.996 0 0.000
#> aberrant_ERR2585373 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> aberrant_ERR2585358 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> aberrant_ERR2585365 5 0.0162 0.887 0.000 0.004 0.000 0 0.996
#> aberrant_ERR2585359 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> aberrant_ERR2585370 2 0.2127 0.824 0.000 0.892 0.000 0 0.108
#> round_ERR2585215 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585262 3 0.0000 0.829 0.000 0.000 1.000 0 0.000
#> round_ERR2585199 3 0.0162 0.834 0.004 0.000 0.996 0 0.000
#> aberrant_ERR2585369 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> round_ERR2585208 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585252 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585236 3 0.2852 0.727 0.172 0.000 0.828 0 0.000
#> aberrant_ERR2585284 4 0.0000 1.000 0.000 0.000 0.000 1 0.000
#> round_ERR2585224 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585260 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585229 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> aberrant_ERR2585364 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> round_ERR2585253 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> aberrant_ERR2585368 2 0.2074 0.824 0.000 0.896 0.000 0 0.104
#> aberrant_ERR2585371 2 0.1671 0.816 0.000 0.924 0.000 0 0.076
#> round_ERR2585239 1 0.1544 0.911 0.932 0.000 0.068 0 0.000
#> round_ERR2585273 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585256 3 0.3895 0.593 0.320 0.000 0.680 0 0.000
#> round_ERR2585272 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585246 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585261 3 0.3999 0.568 0.344 0.000 0.656 0 0.000
#> round_ERR2585254 3 0.4161 0.494 0.392 0.000 0.608 0 0.000
#> round_ERR2585225 3 0.0290 0.832 0.008 0.000 0.992 0 0.000
#> round_ERR2585235 1 0.2929 0.755 0.820 0.000 0.180 0 0.000
#> round_ERR2585271 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585251 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585255 3 0.0162 0.834 0.004 0.000 0.996 0 0.000
#> round_ERR2585257 3 0.0162 0.834 0.004 0.000 0.996 0 0.000
#> round_ERR2585226 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585265 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585259 3 0.2424 0.759 0.132 0.000 0.868 0 0.000
#> round_ERR2585247 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585241 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585263 1 0.1121 0.938 0.956 0.000 0.044 0 0.000
#> round_ERR2585264 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585233 3 0.0510 0.829 0.016 0.000 0.984 0 0.000
#> round_ERR2585223 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585234 3 0.0162 0.834 0.004 0.000 0.996 0 0.000
#> round_ERR2585222 1 0.1121 0.938 0.956 0.000 0.044 0 0.000
#> round_ERR2585228 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585248 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585240 3 0.3932 0.481 0.328 0.000 0.672 0 0.000
#> round_ERR2585270 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585232 1 0.3612 0.602 0.732 0.000 0.268 0 0.000
#> aberrant_ERR2585341 2 0.0290 0.781 0.000 0.992 0.000 0 0.008
#> aberrant_ERR2585355 2 0.2074 0.823 0.000 0.896 0.000 0 0.104
#> round_ERR2585227 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> aberrant_ERR2585351 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> round_ERR2585269 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> aberrant_ERR2585357 2 0.3366 0.774 0.000 0.768 0.000 0 0.232
#> aberrant_ERR2585350 5 0.3932 0.391 0.000 0.328 0.000 0 0.672
#> round_ERR2585250 3 0.3730 0.624 0.288 0.000 0.712 0 0.000
#> round_ERR2585245 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> aberrant_ERR2585353 5 0.0162 0.887 0.000 0.004 0.000 0 0.996
#> round_ERR2585258 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> aberrant_ERR2585354 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> round_ERR2585249 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585268 3 0.4256 0.388 0.436 0.000 0.564 0 0.000
#> aberrant_ERR2585356 5 0.0000 0.889 0.000 0.000 0.000 0 1.000
#> round_ERR2585266 3 0.0162 0.834 0.004 0.000 0.996 0 0.000
#> round_ERR2585231 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585230 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
#> round_ERR2585267 1 0.0000 0.981 1.000 0.000 0.000 0 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 2 0.3309 0.663 0.000 0.720 0.000 0.000 0.280 NA
#> aberrant_ERR2585338 2 0.3023 0.738 0.000 0.784 0.000 0.000 0.212 NA
#> aberrant_ERR2585325 2 0.2631 0.639 0.000 0.820 0.000 0.000 0.000 NA
#> aberrant_ERR2585283 4 0.0000 0.990 0.000 0.000 0.000 1.000 0.000 NA
#> aberrant_ERR2585343 5 0.0000 0.838 0.000 0.000 0.000 0.000 1.000 NA
#> aberrant_ERR2585329 2 0.3266 0.673 0.000 0.728 0.000 0.000 0.272 NA
#> aberrant_ERR2585317 5 0.3838 0.136 0.000 0.448 0.000 0.000 0.552 NA
#> aberrant_ERR2585339 2 0.2664 0.751 0.000 0.816 0.000 0.000 0.184 NA
#> aberrant_ERR2585335 5 0.0405 0.838 0.000 0.008 0.000 0.000 0.988 NA
#> aberrant_ERR2585287 4 0.1531 0.941 0.000 0.068 0.000 0.928 0.000 NA
#> aberrant_ERR2585321 5 0.0146 0.838 0.000 0.000 0.000 0.000 0.996 NA
#> aberrant_ERR2585297 1 0.0520 0.823 0.984 0.000 0.008 0.000 0.000 NA
#> aberrant_ERR2585337 2 0.3198 0.688 0.000 0.740 0.000 0.000 0.260 NA
#> aberrant_ERR2585319 5 0.0547 0.836 0.000 0.020 0.000 0.000 0.980 NA
#> aberrant_ERR2585315 2 0.2631 0.752 0.000 0.820 0.000 0.000 0.180 NA
#> aberrant_ERR2585336 2 0.2793 0.741 0.000 0.800 0.000 0.000 0.200 NA
#> aberrant_ERR2585307 2 0.3862 0.694 0.000 0.772 0.000 0.000 0.132 NA
#> aberrant_ERR2585301 5 0.5134 0.337 0.000 0.388 0.000 0.000 0.524 NA
#> aberrant_ERR2585326 2 0.2631 0.751 0.000 0.820 0.000 0.000 0.180 NA
#> aberrant_ERR2585331 2 0.2457 0.721 0.000 0.880 0.000 0.000 0.036 NA
#> aberrant_ERR2585346 4 0.0000 0.990 0.000 0.000 0.000 1.000 0.000 NA
#> aberrant_ERR2585314 5 0.5697 0.250 0.000 0.392 0.004 0.000 0.464 NA
#> aberrant_ERR2585298 3 0.0717 0.798 0.008 0.000 0.976 0.000 0.000 NA
#> aberrant_ERR2585345 2 0.3337 0.685 0.000 0.736 0.000 0.000 0.260 NA
#> aberrant_ERR2585299 1 0.2442 0.806 0.884 0.000 0.048 0.000 0.000 NA
#> aberrant_ERR2585309 1 0.1757 0.817 0.916 0.000 0.008 0.000 0.000 NA
#> aberrant_ERR2585303 2 0.3370 0.763 0.000 0.804 0.000 0.000 0.148 NA
#> aberrant_ERR2585313 2 0.2730 0.747 0.000 0.808 0.000 0.000 0.192 NA
#> aberrant_ERR2585318 5 0.0146 0.838 0.000 0.000 0.000 0.000 0.996 NA
#> aberrant_ERR2585328 2 0.3419 0.730 0.000 0.812 0.000 0.000 0.084 NA
#> aberrant_ERR2585330 5 0.2416 0.748 0.000 0.156 0.000 0.000 0.844 NA
#> aberrant_ERR2585293 4 0.0000 0.990 0.000 0.000 0.000 1.000 0.000 NA
#> aberrant_ERR2585342 5 0.0260 0.838 0.000 0.008 0.000 0.000 0.992 NA
#> aberrant_ERR2585348 2 0.3817 0.359 0.000 0.568 0.000 0.000 0.432 NA
#> aberrant_ERR2585352 5 0.1501 0.813 0.000 0.076 0.000 0.000 0.924 NA
#> aberrant_ERR2585308 1 0.2513 0.796 0.852 0.000 0.008 0.000 0.000 NA
#> aberrant_ERR2585349 2 0.4911 0.626 0.000 0.672 0.008 0.000 0.116 NA
#> aberrant_ERR2585316 5 0.2277 0.765 0.000 0.032 0.000 0.000 0.892 NA
#> aberrant_ERR2585306 5 0.2706 0.744 0.000 0.036 0.000 0.000 0.860 NA
#> aberrant_ERR2585324 2 0.3810 0.330 0.000 0.572 0.000 0.000 0.428 NA
#> aberrant_ERR2585310 3 0.6797 0.345 0.004 0.084 0.408 0.000 0.120 NA
#> aberrant_ERR2585296 1 0.5941 0.158 0.448 0.000 0.236 0.000 0.000 NA
#> aberrant_ERR2585275 4 0.0000 0.990 0.000 0.000 0.000 1.000 0.000 NA
#> aberrant_ERR2585311 5 0.0000 0.838 0.000 0.000 0.000 0.000 1.000 NA
#> aberrant_ERR2585292 4 0.0000 0.990 0.000 0.000 0.000 1.000 0.000 NA
#> aberrant_ERR2585282 5 0.4736 0.435 0.000 0.352 0.000 0.000 0.588 NA
#> aberrant_ERR2585305 5 0.4865 0.613 0.000 0.176 0.004 0.000 0.676 NA
#> aberrant_ERR2585278 5 0.3833 0.157 0.000 0.444 0.000 0.000 0.556 NA
#> aberrant_ERR2585347 2 0.2969 0.609 0.000 0.776 0.000 0.000 0.000 NA
#> aberrant_ERR2585332 5 0.0937 0.830 0.000 0.040 0.000 0.000 0.960 NA
#> aberrant_ERR2585280 2 0.2969 0.609 0.000 0.776 0.000 0.000 0.000 NA
#> aberrant_ERR2585304 3 0.3290 0.722 0.000 0.004 0.744 0.000 0.000 NA
#> aberrant_ERR2585322 2 0.3278 0.765 0.000 0.808 0.000 0.000 0.152 NA
#> aberrant_ERR2585279 2 0.3512 0.554 0.000 0.720 0.008 0.000 0.000 NA
#> aberrant_ERR2585277 2 0.4431 0.697 0.000 0.704 0.000 0.000 0.096 NA
#> aberrant_ERR2585295 2 0.2969 0.609 0.000 0.776 0.000 0.000 0.000 NA
#> aberrant_ERR2585333 5 0.0790 0.833 0.000 0.032 0.000 0.000 0.968 NA
#> aberrant_ERR2585285 5 0.2854 0.691 0.000 0.208 0.000 0.000 0.792 NA
#> aberrant_ERR2585286 2 0.4431 0.694 0.000 0.704 0.000 0.000 0.096 NA
#> aberrant_ERR2585294 5 0.5166 0.339 0.000 0.384 0.000 0.000 0.524 NA
#> aberrant_ERR2585300 5 0.0520 0.832 0.000 0.008 0.000 0.000 0.984 NA
#> aberrant_ERR2585334 2 0.2969 0.605 0.000 0.776 0.000 0.000 0.000 NA
#> aberrant_ERR2585361 5 0.3244 0.592 0.000 0.268 0.000 0.000 0.732 NA
#> aberrant_ERR2585372 5 0.1007 0.828 0.000 0.044 0.000 0.000 0.956 NA
#> round_ERR2585217 3 0.4525 0.684 0.088 0.000 0.684 0.000 0.000 NA
#> round_ERR2585205 1 0.1204 0.818 0.944 0.000 0.000 0.000 0.000 NA
#> round_ERR2585214 3 0.0458 0.799 0.000 0.000 0.984 0.000 0.000 NA
#> round_ERR2585202 3 0.2823 0.748 0.000 0.000 0.796 0.000 0.000 NA
#> aberrant_ERR2585367 2 0.3578 0.577 0.000 0.660 0.000 0.000 0.340 NA
#> round_ERR2585220 1 0.1462 0.816 0.936 0.000 0.008 0.000 0.000 NA
#> round_ERR2585238 1 0.1444 0.816 0.928 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585276 2 0.4957 0.260 0.000 0.584 0.000 0.000 0.332 NA
#> round_ERR2585218 1 0.1141 0.819 0.948 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585363 5 0.3482 0.478 0.000 0.316 0.000 0.000 0.684 NA
#> round_ERR2585201 3 0.0146 0.800 0.000 0.000 0.996 0.000 0.000 NA
#> round_ERR2585210 1 0.0260 0.823 0.992 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585362 5 0.4414 0.610 0.000 0.260 0.000 0.000 0.676 NA
#> aberrant_ERR2585360 5 0.0146 0.838 0.000 0.000 0.000 0.000 0.996 NA
#> round_ERR2585209 1 0.5133 0.528 0.624 0.000 0.212 0.000 0.000 NA
#> round_ERR2585242 3 0.0146 0.801 0.000 0.000 0.996 0.000 0.000 NA
#> round_ERR2585216 1 0.1970 0.804 0.900 0.000 0.008 0.000 0.000 NA
#> round_ERR2585219 1 0.2165 0.798 0.884 0.000 0.008 0.000 0.000 NA
#> round_ERR2585237 3 0.4590 0.678 0.096 0.000 0.680 0.000 0.000 NA
#> round_ERR2585198 3 0.0146 0.800 0.000 0.000 0.996 0.000 0.000 NA
#> round_ERR2585211 1 0.1444 0.816 0.928 0.000 0.000 0.000 0.000 NA
#> round_ERR2585206 1 0.1327 0.818 0.936 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585281 2 0.2969 0.609 0.000 0.776 0.000 0.000 0.000 NA
#> round_ERR2585212 1 0.2859 0.769 0.828 0.000 0.016 0.000 0.000 NA
#> round_ERR2585221 1 0.1713 0.820 0.928 0.000 0.028 0.000 0.000 NA
#> round_ERR2585243 1 0.1802 0.812 0.916 0.000 0.012 0.000 0.000 NA
#> round_ERR2585204 3 0.0146 0.799 0.000 0.000 0.996 0.000 0.000 NA
#> round_ERR2585213 3 0.2092 0.776 0.000 0.000 0.876 0.000 0.000 NA
#> aberrant_ERR2585373 5 0.0000 0.838 0.000 0.000 0.000 0.000 1.000 NA
#> aberrant_ERR2585358 5 0.0000 0.838 0.000 0.000 0.000 0.000 1.000 NA
#> aberrant_ERR2585365 5 0.1610 0.806 0.000 0.084 0.000 0.000 0.916 NA
#> aberrant_ERR2585359 5 0.0146 0.838 0.000 0.000 0.000 0.000 0.996 NA
#> aberrant_ERR2585370 2 0.3062 0.762 0.000 0.816 0.000 0.000 0.160 NA
#> round_ERR2585215 1 0.0713 0.822 0.972 0.000 0.000 0.000 0.000 NA
#> round_ERR2585262 3 0.3337 0.717 0.000 0.004 0.736 0.000 0.000 NA
#> round_ERR2585199 3 0.2902 0.750 0.004 0.000 0.800 0.000 0.000 NA
#> aberrant_ERR2585369 5 0.0146 0.838 0.000 0.004 0.000 0.000 0.996 NA
#> round_ERR2585208 1 0.2416 0.786 0.844 0.000 0.000 0.000 0.000 NA
#> round_ERR2585252 1 0.2883 0.752 0.788 0.000 0.000 0.000 0.000 NA
#> round_ERR2585236 3 0.5252 0.601 0.144 0.000 0.592 0.000 0.000 NA
#> aberrant_ERR2585284 4 0.0146 0.988 0.000 0.000 0.000 0.996 0.000 NA
#> round_ERR2585224 1 0.3629 0.711 0.724 0.000 0.016 0.000 0.000 NA
#> round_ERR2585260 1 0.0717 0.824 0.976 0.000 0.008 0.000 0.000 NA
#> round_ERR2585229 1 0.2416 0.784 0.844 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585364 5 0.0291 0.836 0.000 0.004 0.000 0.000 0.992 NA
#> round_ERR2585253 1 0.3175 0.721 0.744 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585368 2 0.3240 0.764 0.000 0.812 0.000 0.000 0.148 NA
#> aberrant_ERR2585371 2 0.3627 0.756 0.000 0.792 0.000 0.000 0.128 NA
#> round_ERR2585239 1 0.5301 0.463 0.584 0.000 0.268 0.000 0.000 NA
#> round_ERR2585273 1 0.0972 0.822 0.964 0.000 0.008 0.000 0.000 NA
#> round_ERR2585256 1 0.5984 0.138 0.444 0.000 0.284 0.000 0.000 NA
#> round_ERR2585272 1 0.1812 0.812 0.912 0.000 0.008 0.000 0.000 NA
#> round_ERR2585246 1 0.0520 0.823 0.984 0.000 0.008 0.000 0.000 NA
#> round_ERR2585261 1 0.5549 0.418 0.556 0.000 0.232 0.000 0.000 NA
#> round_ERR2585254 1 0.5783 0.297 0.500 0.000 0.280 0.000 0.000 NA
#> round_ERR2585225 3 0.1575 0.784 0.032 0.000 0.936 0.000 0.000 NA
#> round_ERR2585235 1 0.5466 0.404 0.556 0.000 0.280 0.000 0.000 NA
#> round_ERR2585271 1 0.0972 0.821 0.964 0.000 0.008 0.000 0.000 NA
#> round_ERR2585251 1 0.2346 0.790 0.868 0.000 0.008 0.000 0.000 NA
#> round_ERR2585255 3 0.0146 0.799 0.000 0.000 0.996 0.000 0.000 NA
#> round_ERR2585257 3 0.0000 0.800 0.000 0.000 1.000 0.000 0.000 NA
#> round_ERR2585226 1 0.2431 0.785 0.860 0.000 0.008 0.000 0.000 NA
#> round_ERR2585265 1 0.1124 0.820 0.956 0.000 0.008 0.000 0.000 NA
#> round_ERR2585259 3 0.5383 0.500 0.248 0.000 0.580 0.000 0.000 NA
#> round_ERR2585247 1 0.0891 0.821 0.968 0.000 0.008 0.000 0.000 NA
#> round_ERR2585241 1 0.2070 0.808 0.892 0.000 0.008 0.000 0.000 NA
#> round_ERR2585263 1 0.3417 0.746 0.796 0.000 0.044 0.000 0.000 NA
#> round_ERR2585264 1 0.3652 0.709 0.720 0.000 0.016 0.000 0.000 NA
#> round_ERR2585233 3 0.2263 0.765 0.048 0.000 0.896 0.000 0.000 NA
#> round_ERR2585223 1 0.1418 0.822 0.944 0.000 0.024 0.000 0.000 NA
#> round_ERR2585234 3 0.0146 0.800 0.000 0.000 0.996 0.000 0.000 NA
#> round_ERR2585222 1 0.5143 0.515 0.612 0.000 0.248 0.000 0.000 NA
#> round_ERR2585228 1 0.0363 0.823 0.988 0.000 0.000 0.000 0.000 NA
#> round_ERR2585248 1 0.3652 0.709 0.720 0.000 0.016 0.000 0.000 NA
#> round_ERR2585240 3 0.4319 0.627 0.168 0.000 0.724 0.000 0.000 NA
#> round_ERR2585270 1 0.2513 0.781 0.852 0.000 0.008 0.000 0.000 NA
#> round_ERR2585232 3 0.5143 0.524 0.248 0.000 0.612 0.000 0.000 NA
#> aberrant_ERR2585341 2 0.3543 0.648 0.000 0.768 0.000 0.000 0.032 NA
#> aberrant_ERR2585355 2 0.3139 0.762 0.000 0.812 0.000 0.000 0.160 NA
#> round_ERR2585227 1 0.2431 0.785 0.860 0.000 0.008 0.000 0.000 NA
#> aberrant_ERR2585351 5 0.0146 0.838 0.000 0.000 0.000 0.000 0.996 NA
#> round_ERR2585269 1 0.2664 0.769 0.816 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585357 2 0.2823 0.739 0.000 0.796 0.000 0.000 0.204 NA
#> aberrant_ERR2585350 2 0.3833 0.318 0.000 0.556 0.000 0.000 0.444 NA
#> round_ERR2585250 3 0.5673 0.211 0.356 0.000 0.480 0.000 0.000 NA
#> round_ERR2585245 1 0.3652 0.709 0.720 0.000 0.016 0.000 0.000 NA
#> aberrant_ERR2585353 5 0.0713 0.833 0.000 0.028 0.000 0.000 0.972 NA
#> round_ERR2585258 1 0.0632 0.824 0.976 0.000 0.000 0.000 0.000 NA
#> aberrant_ERR2585354 5 0.0146 0.838 0.000 0.000 0.000 0.000 0.996 NA
#> round_ERR2585249 1 0.3652 0.709 0.720 0.000 0.016 0.000 0.000 NA
#> round_ERR2585268 1 0.5682 0.308 0.512 0.000 0.300 0.000 0.000 NA
#> aberrant_ERR2585356 5 0.0146 0.838 0.000 0.000 0.000 0.000 0.996 NA
#> round_ERR2585266 3 0.0458 0.799 0.000 0.000 0.984 0.000 0.000 NA
#> round_ERR2585231 1 0.4814 0.648 0.644 0.000 0.100 0.000 0.000 NA
#> round_ERR2585230 1 0.3520 0.750 0.804 0.000 0.100 0.000 0.000 NA
#> round_ERR2585267 1 0.3532 0.769 0.796 0.000 0.064 0.000 0.000 NA
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> ATC:mclust 159 5.02e-30 2
#> ATC:mclust 156 9.21e-30 3
#> ATC:mclust 148 2.21e-26 4
#> ATC:mclust 146 3.94e-25 5
#> ATC:mclust 139 2.43e-24 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "NMF"]
# you can also extract it by
# res = res_list["ATC:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 5576 rows and 160 columns.
#> Top rows (558, 1116, 1673, 2230, 2788) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.976 0.990 0.5015 0.499 0.499
#> 3 3 0.806 0.828 0.921 0.2964 0.809 0.633
#> 4 4 0.765 0.791 0.889 0.0870 0.872 0.667
#> 5 5 0.699 0.707 0.832 0.0364 0.977 0.922
#> 6 6 0.652 0.548 0.766 0.0478 0.959 0.861
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> aberrant_ERR2585320 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585338 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585325 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585283 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585343 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585329 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585317 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585339 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585335 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585287 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585321 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585297 1 0.0000 0.9896 1.000 0.000
#> aberrant_ERR2585337 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585319 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585315 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585336 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585307 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585301 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585326 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585331 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585346 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585314 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585298 1 0.0000 0.9896 1.000 0.000
#> aberrant_ERR2585345 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585299 1 0.0000 0.9896 1.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.9896 1.000 0.000
#> aberrant_ERR2585303 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585313 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585318 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585328 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585330 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585293 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585342 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585348 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585352 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585308 1 0.0000 0.9896 1.000 0.000
#> aberrant_ERR2585349 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585316 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585306 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585324 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585310 2 0.6048 0.8224 0.148 0.852
#> aberrant_ERR2585296 1 0.0000 0.9896 1.000 0.000
#> aberrant_ERR2585275 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585311 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585292 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585282 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585305 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585278 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585347 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585332 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585280 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585304 2 0.0672 0.9824 0.008 0.992
#> aberrant_ERR2585322 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585279 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585277 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585295 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585333 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585285 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585286 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585294 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585300 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585334 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585361 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585372 2 0.0000 0.9901 0.000 1.000
#> round_ERR2585217 1 0.0376 0.9859 0.996 0.004
#> round_ERR2585205 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585214 1 0.7815 0.7001 0.768 0.232
#> round_ERR2585202 1 0.8861 0.5651 0.696 0.304
#> aberrant_ERR2585367 2 0.0000 0.9901 0.000 1.000
#> round_ERR2585220 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585238 1 0.0000 0.9896 1.000 0.000
#> aberrant_ERR2585276 2 0.0000 0.9901 0.000 1.000
#> round_ERR2585218 1 0.0000 0.9896 1.000 0.000
#> aberrant_ERR2585363 2 0.0000 0.9901 0.000 1.000
#> round_ERR2585201 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585210 1 0.0000 0.9896 1.000 0.000
#> aberrant_ERR2585362 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585360 2 0.0000 0.9901 0.000 1.000
#> round_ERR2585209 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585242 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585216 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585219 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585237 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585198 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585211 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585206 1 0.0000 0.9896 1.000 0.000
#> aberrant_ERR2585281 2 0.0000 0.9901 0.000 1.000
#> round_ERR2585212 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585221 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585243 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585204 1 0.7299 0.7447 0.796 0.204
#> round_ERR2585213 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585373 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585358 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585365 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585359 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585370 2 0.0000 0.9901 0.000 1.000
#> round_ERR2585215 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585262 2 0.7056 0.7587 0.192 0.808
#> round_ERR2585199 2 0.9977 0.0928 0.472 0.528
#> aberrant_ERR2585369 2 0.0000 0.9901 0.000 1.000
#> round_ERR2585208 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585252 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585236 1 0.0000 0.9896 1.000 0.000
#> aberrant_ERR2585284 2 0.0000 0.9901 0.000 1.000
#> round_ERR2585224 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585260 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585229 1 0.0000 0.9896 1.000 0.000
#> aberrant_ERR2585364 2 0.0000 0.9901 0.000 1.000
#> round_ERR2585253 1 0.0000 0.9896 1.000 0.000
#> aberrant_ERR2585368 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585371 2 0.0000 0.9901 0.000 1.000
#> round_ERR2585239 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585273 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585256 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585272 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585246 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585261 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585254 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585225 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585235 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585271 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585251 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585255 1 0.1184 0.9745 0.984 0.016
#> round_ERR2585257 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585226 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585265 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585259 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585247 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585241 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585263 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585264 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585233 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585223 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585234 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585222 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585228 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585248 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585240 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585270 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585232 1 0.0000 0.9896 1.000 0.000
#> aberrant_ERR2585341 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585355 2 0.0000 0.9901 0.000 1.000
#> round_ERR2585227 1 0.0000 0.9896 1.000 0.000
#> aberrant_ERR2585351 2 0.0000 0.9901 0.000 1.000
#> round_ERR2585269 1 0.0000 0.9896 1.000 0.000
#> aberrant_ERR2585357 2 0.0000 0.9901 0.000 1.000
#> aberrant_ERR2585350 2 0.0000 0.9901 0.000 1.000
#> round_ERR2585250 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585245 1 0.0000 0.9896 1.000 0.000
#> aberrant_ERR2585353 2 0.0000 0.9901 0.000 1.000
#> round_ERR2585258 1 0.0000 0.9896 1.000 0.000
#> aberrant_ERR2585354 2 0.0000 0.9901 0.000 1.000
#> round_ERR2585249 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585268 1 0.0000 0.9896 1.000 0.000
#> aberrant_ERR2585356 2 0.0000 0.9901 0.000 1.000
#> round_ERR2585266 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585231 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585230 1 0.0000 0.9896 1.000 0.000
#> round_ERR2585267 1 0.0000 0.9896 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> aberrant_ERR2585320 2 0.3267 0.8021 0.000 0.884 0.116
#> aberrant_ERR2585338 3 0.3879 0.7727 0.000 0.152 0.848
#> aberrant_ERR2585325 2 0.3619 0.7896 0.000 0.864 0.136
#> aberrant_ERR2585283 2 0.0000 0.8382 0.000 1.000 0.000
#> aberrant_ERR2585343 2 0.0000 0.8382 0.000 1.000 0.000
#> aberrant_ERR2585329 2 0.6045 0.5133 0.000 0.620 0.380
#> aberrant_ERR2585317 2 0.4605 0.7367 0.000 0.796 0.204
#> aberrant_ERR2585339 2 0.6095 0.4908 0.000 0.608 0.392
#> aberrant_ERR2585335 2 0.1529 0.8362 0.000 0.960 0.040
#> aberrant_ERR2585287 2 0.1163 0.8391 0.000 0.972 0.028
#> aberrant_ERR2585321 2 0.0000 0.8382 0.000 1.000 0.000
#> aberrant_ERR2585297 1 0.0000 0.9824 1.000 0.000 0.000
#> aberrant_ERR2585337 2 0.5678 0.6147 0.000 0.684 0.316
#> aberrant_ERR2585319 2 0.0237 0.8392 0.000 0.996 0.004
#> aberrant_ERR2585315 2 0.0237 0.8392 0.000 0.996 0.004
#> aberrant_ERR2585336 2 0.6302 0.2645 0.000 0.520 0.480
#> aberrant_ERR2585307 2 0.6280 0.3260 0.000 0.540 0.460
#> aberrant_ERR2585301 2 0.1529 0.8363 0.000 0.960 0.040
#> aberrant_ERR2585326 2 0.5785 0.5943 0.000 0.668 0.332
#> aberrant_ERR2585331 3 0.0000 0.9045 0.000 0.000 1.000
#> aberrant_ERR2585346 2 0.0000 0.8382 0.000 1.000 0.000
#> aberrant_ERR2585314 2 0.6244 0.3815 0.000 0.560 0.440
#> aberrant_ERR2585298 3 0.0424 0.9058 0.008 0.000 0.992
#> aberrant_ERR2585345 2 0.6252 0.3685 0.000 0.556 0.444
#> aberrant_ERR2585299 1 0.0000 0.9824 1.000 0.000 0.000
#> aberrant_ERR2585309 1 0.0000 0.9824 1.000 0.000 0.000
#> aberrant_ERR2585303 2 0.6274 0.3333 0.000 0.544 0.456
#> aberrant_ERR2585313 2 0.5216 0.6826 0.000 0.740 0.260
#> aberrant_ERR2585318 2 0.0747 0.8402 0.000 0.984 0.016
#> aberrant_ERR2585328 3 0.2537 0.8552 0.000 0.080 0.920
#> aberrant_ERR2585330 2 0.0237 0.8392 0.000 0.996 0.004
#> aberrant_ERR2585293 2 0.0424 0.8354 0.000 0.992 0.008
#> aberrant_ERR2585342 2 0.0747 0.8402 0.000 0.984 0.016
#> aberrant_ERR2585348 2 0.6260 0.3632 0.000 0.552 0.448
#> aberrant_ERR2585352 2 0.2878 0.8144 0.000 0.904 0.096
#> aberrant_ERR2585308 1 0.0000 0.9824 1.000 0.000 0.000
#> aberrant_ERR2585349 3 0.0000 0.9045 0.000 0.000 1.000
#> aberrant_ERR2585316 2 0.0000 0.8382 0.000 1.000 0.000
#> aberrant_ERR2585306 2 0.0424 0.8366 0.008 0.992 0.000
#> aberrant_ERR2585324 2 0.0237 0.8392 0.000 0.996 0.004
#> aberrant_ERR2585310 2 0.6518 0.1348 0.484 0.512 0.004
#> aberrant_ERR2585296 1 0.0000 0.9824 1.000 0.000 0.000
#> aberrant_ERR2585275 2 0.0000 0.8382 0.000 1.000 0.000
#> aberrant_ERR2585311 2 0.0237 0.8392 0.000 0.996 0.004
#> aberrant_ERR2585292 2 0.0424 0.8354 0.000 0.992 0.008
#> aberrant_ERR2585282 2 0.2878 0.8139 0.000 0.904 0.096
#> aberrant_ERR2585305 2 0.2625 0.7887 0.084 0.916 0.000
#> aberrant_ERR2585278 2 0.0892 0.8402 0.000 0.980 0.020
#> aberrant_ERR2585347 2 0.5098 0.6971 0.000 0.752 0.248
#> aberrant_ERR2585332 2 0.1964 0.8310 0.000 0.944 0.056
#> aberrant_ERR2585280 2 0.1289 0.8387 0.000 0.968 0.032
#> aberrant_ERR2585304 3 0.6062 0.7352 0.072 0.148 0.780
#> aberrant_ERR2585322 2 0.6307 0.2332 0.000 0.512 0.488
#> aberrant_ERR2585279 3 0.0000 0.9045 0.000 0.000 1.000
#> aberrant_ERR2585277 3 0.0892 0.8970 0.000 0.020 0.980
#> aberrant_ERR2585295 3 0.5882 0.3562 0.000 0.348 0.652
#> aberrant_ERR2585333 2 0.0000 0.8382 0.000 1.000 0.000
#> aberrant_ERR2585285 2 0.0892 0.8402 0.000 0.980 0.020
#> aberrant_ERR2585286 3 0.2356 0.8618 0.000 0.072 0.928
#> aberrant_ERR2585294 2 0.0000 0.8382 0.000 1.000 0.000
#> aberrant_ERR2585300 2 0.0000 0.8382 0.000 1.000 0.000
#> aberrant_ERR2585334 3 0.0000 0.9045 0.000 0.000 1.000
#> aberrant_ERR2585361 2 0.2448 0.8233 0.000 0.924 0.076
#> aberrant_ERR2585372 2 0.1163 0.8390 0.000 0.972 0.028
#> round_ERR2585217 3 0.0424 0.9058 0.008 0.000 0.992
#> round_ERR2585205 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585214 3 0.0237 0.9053 0.004 0.000 0.996
#> round_ERR2585202 3 0.0747 0.9019 0.016 0.000 0.984
#> aberrant_ERR2585367 2 0.5098 0.6956 0.000 0.752 0.248
#> round_ERR2585220 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585238 1 0.0000 0.9824 1.000 0.000 0.000
#> aberrant_ERR2585276 2 0.0000 0.8382 0.000 1.000 0.000
#> round_ERR2585218 1 0.0000 0.9824 1.000 0.000 0.000
#> aberrant_ERR2585363 2 0.5560 0.6393 0.000 0.700 0.300
#> round_ERR2585201 3 0.0424 0.9058 0.008 0.000 0.992
#> round_ERR2585210 1 0.0000 0.9824 1.000 0.000 0.000
#> aberrant_ERR2585362 2 0.6225 0.4059 0.000 0.568 0.432
#> aberrant_ERR2585360 2 0.0747 0.8404 0.000 0.984 0.016
#> round_ERR2585209 1 0.3340 0.8597 0.880 0.000 0.120
#> round_ERR2585242 3 0.2711 0.8410 0.088 0.000 0.912
#> round_ERR2585216 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585219 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585237 3 0.0424 0.9058 0.008 0.000 0.992
#> round_ERR2585198 3 0.0592 0.9038 0.012 0.000 0.988
#> round_ERR2585211 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585206 1 0.0000 0.9824 1.000 0.000 0.000
#> aberrant_ERR2585281 3 0.6308 -0.1708 0.000 0.492 0.508
#> round_ERR2585212 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585221 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585243 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585204 3 0.0000 0.9045 0.000 0.000 1.000
#> round_ERR2585213 3 0.0000 0.9045 0.000 0.000 1.000
#> aberrant_ERR2585373 2 0.0000 0.8382 0.000 1.000 0.000
#> aberrant_ERR2585358 2 0.0424 0.8399 0.000 0.992 0.008
#> aberrant_ERR2585365 2 0.5733 0.6031 0.000 0.676 0.324
#> aberrant_ERR2585359 2 0.0000 0.8382 0.000 1.000 0.000
#> aberrant_ERR2585370 3 0.4504 0.7033 0.000 0.196 0.804
#> round_ERR2585215 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585262 3 0.0424 0.9058 0.008 0.000 0.992
#> round_ERR2585199 3 0.0424 0.9058 0.008 0.000 0.992
#> aberrant_ERR2585369 2 0.1411 0.8372 0.000 0.964 0.036
#> round_ERR2585208 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585252 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585236 1 0.0000 0.9824 1.000 0.000 0.000
#> aberrant_ERR2585284 3 0.6235 0.0475 0.000 0.436 0.564
#> round_ERR2585224 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585260 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585229 1 0.0000 0.9824 1.000 0.000 0.000
#> aberrant_ERR2585364 2 0.0000 0.8382 0.000 1.000 0.000
#> round_ERR2585253 1 0.0000 0.9824 1.000 0.000 0.000
#> aberrant_ERR2585368 3 0.0237 0.9038 0.000 0.004 0.996
#> aberrant_ERR2585371 3 0.0000 0.9045 0.000 0.000 1.000
#> round_ERR2585239 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585273 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585256 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585272 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585246 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585261 1 0.4605 0.7355 0.796 0.000 0.204
#> round_ERR2585254 1 0.0237 0.9790 0.996 0.000 0.004
#> round_ERR2585225 3 0.0592 0.9035 0.012 0.000 0.988
#> round_ERR2585235 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585271 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585251 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585255 3 0.0424 0.9058 0.008 0.000 0.992
#> round_ERR2585257 3 0.0424 0.9058 0.008 0.000 0.992
#> round_ERR2585226 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585265 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585259 1 0.6008 0.3856 0.628 0.000 0.372
#> round_ERR2585247 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585241 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585263 1 0.0237 0.9790 0.996 0.000 0.004
#> round_ERR2585264 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585233 3 0.3551 0.7902 0.132 0.000 0.868
#> round_ERR2585223 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585234 3 0.0424 0.9058 0.008 0.000 0.992
#> round_ERR2585222 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585228 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585248 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585240 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585270 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585232 1 0.0000 0.9824 1.000 0.000 0.000
#> aberrant_ERR2585341 3 0.3412 0.8081 0.000 0.124 0.876
#> aberrant_ERR2585355 3 0.2537 0.8557 0.000 0.080 0.920
#> round_ERR2585227 1 0.0000 0.9824 1.000 0.000 0.000
#> aberrant_ERR2585351 2 0.2625 0.8203 0.000 0.916 0.084
#> round_ERR2585269 1 0.0000 0.9824 1.000 0.000 0.000
#> aberrant_ERR2585357 2 0.6291 0.3052 0.000 0.532 0.468
#> aberrant_ERR2585350 2 0.5968 0.5401 0.000 0.636 0.364
#> round_ERR2585250 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585245 1 0.0000 0.9824 1.000 0.000 0.000
#> aberrant_ERR2585353 2 0.0892 0.8405 0.000 0.980 0.020
#> round_ERR2585258 1 0.0000 0.9824 1.000 0.000 0.000
#> aberrant_ERR2585354 2 0.0237 0.8392 0.000 0.996 0.004
#> round_ERR2585249 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585268 1 0.0000 0.9824 1.000 0.000 0.000
#> aberrant_ERR2585356 2 0.0000 0.8382 0.000 1.000 0.000
#> round_ERR2585266 1 0.5733 0.5430 0.676 0.000 0.324
#> round_ERR2585231 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585230 1 0.0000 0.9824 1.000 0.000 0.000
#> round_ERR2585267 1 0.0000 0.9824 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> aberrant_ERR2585320 2 0.5464 0.56711 0.000 0.708 0.064 0.228
#> aberrant_ERR2585338 3 0.4356 0.58237 0.000 0.292 0.708 0.000
#> aberrant_ERR2585325 4 0.7714 0.39621 0.000 0.244 0.316 0.440
#> aberrant_ERR2585283 4 0.2704 0.77179 0.000 0.124 0.000 0.876
#> aberrant_ERR2585343 2 0.1302 0.78064 0.000 0.956 0.000 0.044
#> aberrant_ERR2585329 2 0.5128 0.65579 0.000 0.760 0.148 0.092
#> aberrant_ERR2585317 2 0.3081 0.77367 0.000 0.888 0.048 0.064
#> aberrant_ERR2585339 3 0.6634 0.38240 0.000 0.312 0.580 0.108
#> aberrant_ERR2585335 2 0.0779 0.78720 0.000 0.980 0.004 0.016
#> aberrant_ERR2585287 3 0.6570 0.38376 0.000 0.100 0.580 0.320
#> aberrant_ERR2585321 2 0.1211 0.78575 0.000 0.960 0.000 0.040
#> aberrant_ERR2585297 1 0.0336 0.96748 0.992 0.000 0.000 0.008
#> aberrant_ERR2585337 2 0.7666 0.00373 0.000 0.396 0.392 0.212
#> aberrant_ERR2585319 2 0.3486 0.68978 0.000 0.812 0.000 0.188
#> aberrant_ERR2585315 4 0.4103 0.78415 0.000 0.256 0.000 0.744
#> aberrant_ERR2585336 2 0.7081 0.21077 0.000 0.484 0.388 0.128
#> aberrant_ERR2585307 3 0.7083 0.26633 0.000 0.328 0.528 0.144
#> aberrant_ERR2585301 2 0.3052 0.74985 0.000 0.860 0.004 0.136
#> aberrant_ERR2585326 3 0.6393 0.44259 0.000 0.284 0.616 0.100
#> aberrant_ERR2585331 3 0.0707 0.84993 0.000 0.020 0.980 0.000
#> aberrant_ERR2585346 4 0.3401 0.78328 0.000 0.152 0.008 0.840
#> aberrant_ERR2585314 2 0.2796 0.74778 0.000 0.892 0.092 0.016
#> aberrant_ERR2585298 3 0.0000 0.84881 0.000 0.000 1.000 0.000
#> aberrant_ERR2585345 2 0.7108 0.22593 0.000 0.512 0.348 0.140
#> aberrant_ERR2585299 1 0.0524 0.96829 0.988 0.000 0.008 0.004
#> aberrant_ERR2585309 1 0.0779 0.96271 0.980 0.016 0.000 0.004
#> aberrant_ERR2585303 3 0.4453 0.64180 0.000 0.244 0.744 0.012
#> aberrant_ERR2585313 3 0.7893 -0.20441 0.000 0.324 0.376 0.300
#> aberrant_ERR2585318 2 0.0817 0.78393 0.000 0.976 0.000 0.024
#> aberrant_ERR2585328 3 0.4961 0.16638 0.000 0.448 0.552 0.000
#> aberrant_ERR2585330 2 0.3649 0.66762 0.000 0.796 0.000 0.204
#> aberrant_ERR2585293 4 0.2081 0.73008 0.000 0.084 0.000 0.916
#> aberrant_ERR2585342 2 0.0592 0.79178 0.000 0.984 0.000 0.016
#> aberrant_ERR2585348 2 0.3787 0.72863 0.000 0.840 0.124 0.036
#> aberrant_ERR2585352 2 0.1059 0.79414 0.000 0.972 0.012 0.016
#> aberrant_ERR2585308 1 0.0336 0.96742 0.992 0.000 0.000 0.008
#> aberrant_ERR2585349 3 0.4635 0.62362 0.000 0.268 0.720 0.012
#> aberrant_ERR2585316 2 0.1474 0.78588 0.000 0.948 0.000 0.052
#> aberrant_ERR2585306 4 0.4744 0.76473 0.012 0.284 0.000 0.704
#> aberrant_ERR2585324 4 0.4222 0.77517 0.000 0.272 0.000 0.728
#> aberrant_ERR2585310 1 0.4839 0.66270 0.756 0.200 0.000 0.044
#> aberrant_ERR2585296 1 0.4631 0.75789 0.784 0.180 0.012 0.024
#> aberrant_ERR2585275 4 0.2589 0.76742 0.000 0.116 0.000 0.884
#> aberrant_ERR2585311 2 0.2704 0.74896 0.000 0.876 0.000 0.124
#> aberrant_ERR2585292 4 0.2081 0.73008 0.000 0.084 0.000 0.916
#> aberrant_ERR2585282 2 0.2089 0.79370 0.000 0.932 0.020 0.048
#> aberrant_ERR2585305 2 0.2924 0.75845 0.016 0.884 0.000 0.100
#> aberrant_ERR2585278 4 0.4998 0.33898 0.000 0.488 0.000 0.512
#> aberrant_ERR2585347 2 0.7914 -0.29097 0.000 0.348 0.308 0.344
#> aberrant_ERR2585332 2 0.0779 0.79090 0.000 0.980 0.004 0.016
#> aberrant_ERR2585280 4 0.5719 0.71183 0.000 0.152 0.132 0.716
#> aberrant_ERR2585304 3 0.1878 0.83440 0.008 0.008 0.944 0.040
#> aberrant_ERR2585322 3 0.6401 0.50917 0.000 0.172 0.652 0.176
#> aberrant_ERR2585279 3 0.0188 0.84741 0.000 0.000 0.996 0.004
#> aberrant_ERR2585277 3 0.0707 0.84981 0.000 0.020 0.980 0.000
#> aberrant_ERR2585295 3 0.1833 0.83780 0.000 0.032 0.944 0.024
#> aberrant_ERR2585333 4 0.4605 0.70889 0.000 0.336 0.000 0.664
#> aberrant_ERR2585285 2 0.3942 0.61690 0.000 0.764 0.000 0.236
#> aberrant_ERR2585286 3 0.0817 0.84906 0.000 0.024 0.976 0.000
#> aberrant_ERR2585294 4 0.4356 0.75681 0.000 0.292 0.000 0.708
#> aberrant_ERR2585300 4 0.4585 0.70756 0.000 0.332 0.000 0.668
#> aberrant_ERR2585334 3 0.0336 0.84972 0.000 0.008 0.992 0.000
#> aberrant_ERR2585361 2 0.2730 0.77668 0.000 0.896 0.016 0.088
#> aberrant_ERR2585372 2 0.2647 0.75366 0.000 0.880 0.000 0.120
#> round_ERR2585217 3 0.1853 0.84391 0.012 0.028 0.948 0.012
#> round_ERR2585205 1 0.0804 0.96656 0.980 0.000 0.008 0.012
#> round_ERR2585214 3 0.0000 0.84881 0.000 0.000 1.000 0.000
#> round_ERR2585202 3 0.1174 0.84400 0.020 0.012 0.968 0.000
#> aberrant_ERR2585367 2 0.5432 0.64777 0.000 0.740 0.124 0.136
#> round_ERR2585220 1 0.0336 0.96850 0.992 0.000 0.008 0.000
#> round_ERR2585238 1 0.0188 0.96879 0.996 0.000 0.004 0.000
#> aberrant_ERR2585276 4 0.4088 0.79055 0.000 0.232 0.004 0.764
#> round_ERR2585218 1 0.0336 0.96850 0.992 0.000 0.008 0.000
#> aberrant_ERR2585363 2 0.2222 0.77727 0.000 0.924 0.060 0.016
#> round_ERR2585201 3 0.0000 0.84881 0.000 0.000 1.000 0.000
#> round_ERR2585210 1 0.0927 0.96499 0.976 0.000 0.008 0.016
#> aberrant_ERR2585362 2 0.2466 0.76265 0.000 0.916 0.056 0.028
#> aberrant_ERR2585360 2 0.0592 0.78788 0.000 0.984 0.000 0.016
#> round_ERR2585209 1 0.2868 0.84913 0.864 0.000 0.136 0.000
#> round_ERR2585242 3 0.0895 0.83880 0.020 0.000 0.976 0.004
#> round_ERR2585216 1 0.1762 0.95129 0.952 0.016 0.012 0.020
#> round_ERR2585219 1 0.0804 0.96653 0.980 0.000 0.008 0.012
#> round_ERR2585237 3 0.1406 0.84607 0.016 0.024 0.960 0.000
#> round_ERR2585198 3 0.0376 0.84668 0.004 0.000 0.992 0.004
#> round_ERR2585211 1 0.0524 0.96823 0.988 0.000 0.008 0.004
#> round_ERR2585206 1 0.0524 0.96823 0.988 0.000 0.008 0.004
#> aberrant_ERR2585281 3 0.2739 0.81162 0.000 0.060 0.904 0.036
#> round_ERR2585212 1 0.0927 0.96499 0.976 0.000 0.008 0.016
#> round_ERR2585221 1 0.0707 0.96379 0.980 0.000 0.000 0.020
#> round_ERR2585243 1 0.0469 0.96756 0.988 0.000 0.000 0.012
#> round_ERR2585204 3 0.0000 0.84881 0.000 0.000 1.000 0.000
#> round_ERR2585213 3 0.0000 0.84881 0.000 0.000 1.000 0.000
#> aberrant_ERR2585373 2 0.2345 0.76621 0.000 0.900 0.000 0.100
#> aberrant_ERR2585358 2 0.1211 0.79232 0.000 0.960 0.000 0.040
#> aberrant_ERR2585365 2 0.2089 0.77808 0.000 0.932 0.048 0.020
#> aberrant_ERR2585359 2 0.0817 0.78778 0.000 0.976 0.000 0.024
#> aberrant_ERR2585370 3 0.1661 0.83646 0.000 0.052 0.944 0.004
#> round_ERR2585215 1 0.0672 0.96747 0.984 0.000 0.008 0.008
#> round_ERR2585262 3 0.1302 0.84349 0.000 0.044 0.956 0.000
#> round_ERR2585199 3 0.0336 0.84972 0.000 0.008 0.992 0.000
#> aberrant_ERR2585369 2 0.0469 0.79053 0.000 0.988 0.000 0.012
#> round_ERR2585208 1 0.0188 0.96793 0.996 0.000 0.000 0.004
#> round_ERR2585252 1 0.0336 0.96742 0.992 0.000 0.000 0.008
#> round_ERR2585236 1 0.0804 0.96653 0.980 0.000 0.008 0.012
#> aberrant_ERR2585284 2 0.5993 0.43806 0.000 0.628 0.308 0.064
#> round_ERR2585224 1 0.0817 0.96201 0.976 0.000 0.000 0.024
#> round_ERR2585260 1 0.0188 0.96879 0.996 0.000 0.004 0.000
#> round_ERR2585229 1 0.0188 0.96855 0.996 0.000 0.000 0.004
#> aberrant_ERR2585364 2 0.3837 0.63490 0.000 0.776 0.000 0.224
#> round_ERR2585253 1 0.0188 0.96855 0.996 0.000 0.000 0.004
#> aberrant_ERR2585368 3 0.0707 0.84993 0.000 0.020 0.980 0.000
#> aberrant_ERR2585371 3 0.0707 0.84993 0.000 0.020 0.980 0.000
#> round_ERR2585239 1 0.0672 0.96826 0.984 0.000 0.008 0.008
#> round_ERR2585273 1 0.0804 0.96906 0.980 0.000 0.008 0.012
#> round_ERR2585256 1 0.0469 0.96793 0.988 0.000 0.012 0.000
#> round_ERR2585272 1 0.0336 0.96850 0.992 0.000 0.008 0.000
#> round_ERR2585246 1 0.0592 0.96535 0.984 0.000 0.000 0.016
#> round_ERR2585261 1 0.3172 0.81519 0.840 0.000 0.160 0.000
#> round_ERR2585254 1 0.0817 0.96220 0.976 0.000 0.024 0.000
#> round_ERR2585225 3 0.0657 0.84386 0.012 0.000 0.984 0.004
#> round_ERR2585235 1 0.0657 0.96838 0.984 0.000 0.012 0.004
#> round_ERR2585271 1 0.0672 0.96747 0.984 0.000 0.008 0.008
#> round_ERR2585251 1 0.0336 0.96850 0.992 0.000 0.008 0.000
#> round_ERR2585255 3 0.0188 0.84947 0.000 0.004 0.996 0.000
#> round_ERR2585257 3 0.0376 0.84668 0.004 0.000 0.992 0.004
#> round_ERR2585226 1 0.0469 0.96652 0.988 0.000 0.000 0.012
#> round_ERR2585265 1 0.0336 0.96850 0.992 0.000 0.008 0.000
#> round_ERR2585259 1 0.4492 0.65438 0.732 0.004 0.260 0.004
#> round_ERR2585247 1 0.0000 0.96829 1.000 0.000 0.000 0.000
#> round_ERR2585241 1 0.0336 0.96850 0.992 0.000 0.008 0.000
#> round_ERR2585263 1 0.3874 0.85831 0.856 0.096 0.024 0.024
#> round_ERR2585264 1 0.0336 0.96742 0.992 0.000 0.000 0.008
#> round_ERR2585233 3 0.1489 0.81870 0.044 0.000 0.952 0.004
#> round_ERR2585223 1 0.0336 0.96748 0.992 0.000 0.000 0.008
#> round_ERR2585234 3 0.0188 0.84741 0.000 0.000 0.996 0.004
#> round_ERR2585222 1 0.0524 0.96893 0.988 0.000 0.008 0.004
#> round_ERR2585228 1 0.0188 0.96879 0.996 0.000 0.004 0.000
#> round_ERR2585248 1 0.0188 0.96793 0.996 0.000 0.000 0.004
#> round_ERR2585240 1 0.2924 0.88464 0.884 0.000 0.100 0.016
#> round_ERR2585270 1 0.0804 0.96643 0.980 0.000 0.012 0.008
#> round_ERR2585232 1 0.1004 0.96347 0.972 0.000 0.024 0.004
#> aberrant_ERR2585341 3 0.1389 0.84084 0.000 0.048 0.952 0.000
#> aberrant_ERR2585355 3 0.1557 0.83706 0.000 0.056 0.944 0.000
#> round_ERR2585227 1 0.0672 0.96879 0.984 0.000 0.008 0.008
#> aberrant_ERR2585351 2 0.0895 0.78506 0.000 0.976 0.004 0.020
#> round_ERR2585269 1 0.0524 0.96710 0.988 0.004 0.000 0.008
#> aberrant_ERR2585357 3 0.5397 0.61337 0.000 0.212 0.720 0.068
#> aberrant_ERR2585350 2 0.6195 0.48773 0.000 0.648 0.252 0.100
#> round_ERR2585250 1 0.0336 0.96850 0.992 0.000 0.008 0.000
#> round_ERR2585245 1 0.0707 0.96379 0.980 0.000 0.000 0.020
#> aberrant_ERR2585353 2 0.0469 0.78879 0.000 0.988 0.000 0.012
#> round_ERR2585258 1 0.0592 0.96535 0.984 0.000 0.000 0.016
#> aberrant_ERR2585354 2 0.0817 0.78393 0.000 0.976 0.000 0.024
#> round_ERR2585249 1 0.0707 0.96379 0.980 0.000 0.000 0.020
#> round_ERR2585268 1 0.0859 0.96663 0.980 0.004 0.008 0.008
#> aberrant_ERR2585356 2 0.1637 0.78421 0.000 0.940 0.000 0.060
#> round_ERR2585266 3 0.4328 0.55274 0.244 0.000 0.748 0.008
#> round_ERR2585231 1 0.0707 0.96379 0.980 0.000 0.000 0.020
#> round_ERR2585230 1 0.0524 0.96893 0.988 0.000 0.008 0.004
#> round_ERR2585267 1 0.0469 0.96652 0.988 0.000 0.000 0.012
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> aberrant_ERR2585320 2 0.6485 0.3404 0.000 0.568 0.036 0.112 0.284
#> aberrant_ERR2585338 3 0.4435 0.4847 0.000 0.320 0.664 0.008 0.008
#> aberrant_ERR2585325 5 0.7853 0.4650 0.000 0.208 0.208 0.120 0.464
#> aberrant_ERR2585283 5 0.4558 0.2583 0.000 0.060 0.000 0.216 0.724
#> aberrant_ERR2585343 2 0.1952 0.6931 0.000 0.912 0.000 0.004 0.084
#> aberrant_ERR2585329 2 0.5105 0.5856 0.000 0.712 0.104 0.008 0.176
#> aberrant_ERR2585317 2 0.3880 0.6283 0.000 0.772 0.004 0.020 0.204
#> aberrant_ERR2585339 3 0.6011 0.1503 0.000 0.344 0.528 0.000 0.128
#> aberrant_ERR2585335 2 0.1557 0.6928 0.000 0.940 0.000 0.008 0.052
#> aberrant_ERR2585287 3 0.5808 0.4602 0.000 0.020 0.624 0.084 0.272
#> aberrant_ERR2585321 2 0.2389 0.6840 0.000 0.880 0.000 0.004 0.116
#> aberrant_ERR2585297 1 0.2249 0.9015 0.896 0.000 0.000 0.096 0.008
#> aberrant_ERR2585337 2 0.6913 0.1049 0.000 0.436 0.356 0.016 0.192
#> aberrant_ERR2585319 2 0.4668 0.3930 0.000 0.624 0.000 0.024 0.352
#> aberrant_ERR2585315 5 0.5498 0.6819 0.000 0.216 0.024 0.080 0.680
#> aberrant_ERR2585336 2 0.5929 0.3841 0.000 0.608 0.276 0.016 0.100
#> aberrant_ERR2585307 3 0.6150 0.1649 0.000 0.364 0.524 0.012 0.100
#> aberrant_ERR2585301 2 0.4722 0.3517 0.000 0.608 0.000 0.024 0.368
#> aberrant_ERR2585326 3 0.7063 -0.2544 0.000 0.288 0.408 0.012 0.292
#> aberrant_ERR2585331 3 0.0613 0.8336 0.000 0.008 0.984 0.004 0.004
#> aberrant_ERR2585346 5 0.3648 0.5066 0.000 0.084 0.000 0.092 0.824
#> aberrant_ERR2585314 2 0.2734 0.6288 0.000 0.892 0.008 0.052 0.048
#> aberrant_ERR2585298 3 0.0451 0.8325 0.000 0.000 0.988 0.008 0.004
#> aberrant_ERR2585345 2 0.6415 0.2813 0.000 0.540 0.296 0.012 0.152
#> aberrant_ERR2585299 1 0.1026 0.9107 0.968 0.000 0.004 0.024 0.004
#> aberrant_ERR2585309 1 0.1892 0.9068 0.916 0.000 0.000 0.080 0.004
#> aberrant_ERR2585303 3 0.6110 0.4085 0.000 0.212 0.616 0.016 0.156
#> aberrant_ERR2585313 2 0.7763 -0.1338 0.000 0.376 0.264 0.060 0.300
#> aberrant_ERR2585318 2 0.1195 0.6726 0.000 0.960 0.000 0.012 0.028
#> aberrant_ERR2585328 2 0.5490 0.0789 0.000 0.484 0.464 0.008 0.044
#> aberrant_ERR2585330 2 0.4734 0.4498 0.000 0.652 0.000 0.036 0.312
#> aberrant_ERR2585293 4 0.3913 1.0000 0.000 0.000 0.000 0.676 0.324
#> aberrant_ERR2585342 2 0.2690 0.6681 0.000 0.844 0.000 0.000 0.156
#> aberrant_ERR2585348 2 0.2795 0.6687 0.000 0.884 0.080 0.008 0.028
#> aberrant_ERR2585352 2 0.0693 0.6800 0.000 0.980 0.008 0.000 0.012
#> aberrant_ERR2585308 1 0.2249 0.8998 0.896 0.000 0.000 0.096 0.008
#> aberrant_ERR2585349 3 0.5579 0.3100 0.008 0.400 0.548 0.012 0.032
#> aberrant_ERR2585316 2 0.3809 0.5801 0.000 0.736 0.000 0.008 0.256
#> aberrant_ERR2585306 5 0.6021 0.5447 0.000 0.188 0.000 0.232 0.580
#> aberrant_ERR2585324 5 0.4129 0.6878 0.000 0.204 0.000 0.040 0.756
#> aberrant_ERR2585310 1 0.7218 0.1522 0.508 0.196 0.004 0.040 0.252
#> aberrant_ERR2585296 1 0.4714 0.7285 0.768 0.152 0.004 0.044 0.032
#> aberrant_ERR2585275 5 0.3234 0.4282 0.000 0.064 0.000 0.084 0.852
#> aberrant_ERR2585311 2 0.4066 0.4614 0.000 0.672 0.000 0.004 0.324
#> aberrant_ERR2585292 4 0.3913 1.0000 0.000 0.000 0.000 0.676 0.324
#> aberrant_ERR2585282 2 0.3586 0.6451 0.000 0.828 0.000 0.076 0.096
#> aberrant_ERR2585305 5 0.5009 0.3050 0.000 0.428 0.000 0.032 0.540
#> aberrant_ERR2585278 5 0.5250 0.3389 0.000 0.416 0.000 0.048 0.536
#> aberrant_ERR2585347 5 0.7216 0.5756 0.000 0.252 0.092 0.124 0.532
#> aberrant_ERR2585332 2 0.4425 0.5843 0.000 0.716 0.000 0.040 0.244
#> aberrant_ERR2585280 5 0.4844 0.5740 0.000 0.116 0.088 0.032 0.764
#> aberrant_ERR2585304 3 0.3890 0.6942 0.004 0.000 0.792 0.036 0.168
#> aberrant_ERR2585322 3 0.6985 0.2995 0.000 0.168 0.552 0.056 0.224
#> aberrant_ERR2585279 3 0.0162 0.8335 0.000 0.000 0.996 0.000 0.004
#> aberrant_ERR2585277 3 0.0579 0.8335 0.000 0.008 0.984 0.000 0.008
#> aberrant_ERR2585295 3 0.2708 0.7965 0.000 0.016 0.892 0.020 0.072
#> aberrant_ERR2585333 5 0.5803 0.4761 0.000 0.368 0.000 0.100 0.532
#> aberrant_ERR2585285 2 0.4849 0.3501 0.000 0.608 0.000 0.032 0.360
#> aberrant_ERR2585286 3 0.1095 0.8322 0.000 0.012 0.968 0.012 0.008
#> aberrant_ERR2585294 5 0.4384 0.6933 0.000 0.228 0.000 0.044 0.728
#> aberrant_ERR2585300 5 0.5439 0.4914 0.000 0.372 0.000 0.068 0.560
#> aberrant_ERR2585334 3 0.0162 0.8333 0.000 0.000 0.996 0.004 0.000
#> aberrant_ERR2585361 2 0.2574 0.6869 0.000 0.876 0.000 0.012 0.112
#> aberrant_ERR2585372 2 0.2629 0.6810 0.000 0.860 0.000 0.004 0.136
#> round_ERR2585217 3 0.5919 0.5919 0.152 0.064 0.708 0.048 0.028
#> round_ERR2585205 1 0.0963 0.9067 0.964 0.000 0.000 0.036 0.000
#> round_ERR2585214 3 0.0000 0.8332 0.000 0.000 1.000 0.000 0.000
#> round_ERR2585202 3 0.1314 0.8282 0.004 0.008 0.960 0.024 0.004
#> aberrant_ERR2585367 2 0.4898 0.5958 0.000 0.748 0.144 0.020 0.088
#> round_ERR2585220 1 0.0162 0.9119 0.996 0.000 0.000 0.004 0.000
#> round_ERR2585238 1 0.0703 0.9142 0.976 0.000 0.000 0.024 0.000
#> aberrant_ERR2585276 5 0.3991 0.6656 0.000 0.172 0.000 0.048 0.780
#> round_ERR2585218 1 0.0609 0.9123 0.980 0.000 0.000 0.020 0.000
#> aberrant_ERR2585363 2 0.1243 0.6708 0.000 0.960 0.008 0.004 0.028
#> round_ERR2585201 3 0.0162 0.8330 0.004 0.000 0.996 0.000 0.000
#> round_ERR2585210 1 0.1701 0.8985 0.936 0.000 0.000 0.048 0.016
#> aberrant_ERR2585362 2 0.2339 0.6309 0.000 0.912 0.008 0.028 0.052
#> aberrant_ERR2585360 2 0.1282 0.6896 0.000 0.952 0.000 0.004 0.044
#> round_ERR2585209 1 0.3365 0.8036 0.836 0.000 0.120 0.044 0.000
#> round_ERR2585242 3 0.0671 0.8316 0.004 0.000 0.980 0.016 0.000
#> round_ERR2585216 1 0.3605 0.8357 0.852 0.044 0.000 0.060 0.044
#> round_ERR2585219 1 0.0880 0.9082 0.968 0.000 0.000 0.032 0.000
#> round_ERR2585237 3 0.5046 0.6376 0.152 0.060 0.752 0.012 0.024
#> round_ERR2585198 3 0.0451 0.8327 0.004 0.000 0.988 0.000 0.008
#> round_ERR2585211 1 0.0771 0.9104 0.976 0.000 0.000 0.020 0.004
#> round_ERR2585206 1 0.0324 0.9128 0.992 0.000 0.000 0.004 0.004
#> aberrant_ERR2585281 3 0.2284 0.7914 0.000 0.004 0.896 0.004 0.096
#> round_ERR2585212 1 0.0865 0.9102 0.972 0.000 0.000 0.024 0.004
#> round_ERR2585221 1 0.2864 0.8791 0.852 0.000 0.000 0.136 0.012
#> round_ERR2585243 1 0.2653 0.8931 0.880 0.000 0.000 0.096 0.024
#> round_ERR2585204 3 0.0162 0.8335 0.000 0.000 0.996 0.000 0.004
#> round_ERR2585213 3 0.0162 0.8335 0.000 0.000 0.996 0.000 0.004
#> aberrant_ERR2585373 2 0.4229 0.5475 0.000 0.704 0.000 0.020 0.276
#> aberrant_ERR2585358 2 0.1357 0.6919 0.000 0.948 0.000 0.004 0.048
#> aberrant_ERR2585365 2 0.1195 0.6674 0.000 0.960 0.012 0.000 0.028
#> aberrant_ERR2585359 2 0.1704 0.6923 0.000 0.928 0.000 0.004 0.068
#> aberrant_ERR2585370 3 0.1365 0.8227 0.000 0.040 0.952 0.004 0.004
#> round_ERR2585215 1 0.0162 0.9119 0.996 0.000 0.000 0.004 0.000
#> round_ERR2585262 3 0.2395 0.8114 0.000 0.040 0.912 0.036 0.012
#> round_ERR2585199 3 0.0324 0.8341 0.000 0.004 0.992 0.000 0.004
#> aberrant_ERR2585369 2 0.3551 0.6183 0.000 0.772 0.000 0.008 0.220
#> round_ERR2585208 1 0.1124 0.9148 0.960 0.000 0.000 0.036 0.004
#> round_ERR2585252 1 0.1270 0.9129 0.948 0.000 0.000 0.052 0.000
#> round_ERR2585236 1 0.0771 0.9153 0.976 0.000 0.004 0.020 0.000
#> aberrant_ERR2585284 2 0.7287 0.2534 0.000 0.536 0.180 0.084 0.200
#> round_ERR2585224 1 0.4268 0.8143 0.772 0.000 0.000 0.144 0.084
#> round_ERR2585260 1 0.0963 0.9145 0.964 0.000 0.000 0.036 0.000
#> round_ERR2585229 1 0.0451 0.9123 0.988 0.000 0.000 0.008 0.004
#> aberrant_ERR2585364 2 0.3013 0.6680 0.000 0.832 0.000 0.008 0.160
#> round_ERR2585253 1 0.0510 0.9140 0.984 0.000 0.000 0.016 0.000
#> aberrant_ERR2585368 3 0.0451 0.8339 0.000 0.008 0.988 0.004 0.000
#> aberrant_ERR2585371 3 0.0162 0.8340 0.000 0.004 0.996 0.000 0.000
#> round_ERR2585239 1 0.2300 0.9052 0.908 0.000 0.000 0.052 0.040
#> round_ERR2585273 1 0.1608 0.9091 0.928 0.000 0.000 0.072 0.000
#> round_ERR2585256 1 0.0162 0.9119 0.996 0.000 0.000 0.004 0.000
#> round_ERR2585272 1 0.0510 0.9141 0.984 0.000 0.000 0.016 0.000
#> round_ERR2585246 1 0.3151 0.8682 0.836 0.000 0.000 0.144 0.020
#> round_ERR2585261 1 0.3044 0.7881 0.840 0.000 0.148 0.008 0.004
#> round_ERR2585254 1 0.0807 0.9111 0.976 0.000 0.012 0.012 0.000
#> round_ERR2585225 3 0.0451 0.8322 0.008 0.000 0.988 0.004 0.000
#> round_ERR2585235 1 0.1830 0.9103 0.924 0.000 0.008 0.068 0.000
#> round_ERR2585271 1 0.0609 0.9106 0.980 0.000 0.000 0.020 0.000
#> round_ERR2585251 1 0.0162 0.9126 0.996 0.000 0.000 0.004 0.000
#> round_ERR2585255 3 0.0510 0.8329 0.000 0.000 0.984 0.016 0.000
#> round_ERR2585257 3 0.0162 0.8334 0.000 0.000 0.996 0.000 0.004
#> round_ERR2585226 1 0.2723 0.8854 0.864 0.000 0.000 0.124 0.012
#> round_ERR2585265 1 0.0000 0.9120 1.000 0.000 0.000 0.000 0.000
#> round_ERR2585259 1 0.3183 0.7666 0.828 0.000 0.156 0.016 0.000
#> round_ERR2585247 1 0.2389 0.8939 0.880 0.000 0.000 0.116 0.004
#> round_ERR2585241 1 0.1484 0.9026 0.944 0.000 0.000 0.048 0.008
#> round_ERR2585263 1 0.5054 0.7506 0.776 0.088 0.016 0.068 0.052
#> round_ERR2585264 1 0.2358 0.8972 0.888 0.000 0.000 0.104 0.008
#> round_ERR2585233 3 0.1202 0.8175 0.032 0.000 0.960 0.004 0.004
#> round_ERR2585223 1 0.2390 0.9034 0.896 0.000 0.000 0.084 0.020
#> round_ERR2585234 3 0.0451 0.8319 0.008 0.000 0.988 0.000 0.004
#> round_ERR2585222 1 0.3033 0.8643 0.864 0.000 0.000 0.052 0.084
#> round_ERR2585228 1 0.0671 0.9111 0.980 0.000 0.000 0.016 0.004
#> round_ERR2585248 1 0.1357 0.9137 0.948 0.000 0.000 0.048 0.004
#> round_ERR2585240 1 0.4973 0.7171 0.724 0.000 0.184 0.080 0.012
#> round_ERR2585270 1 0.1522 0.9024 0.944 0.000 0.000 0.044 0.012
#> round_ERR2585232 1 0.1442 0.9157 0.952 0.000 0.012 0.032 0.004
#> aberrant_ERR2585341 3 0.1588 0.8262 0.000 0.028 0.948 0.016 0.008
#> aberrant_ERR2585355 3 0.1522 0.8213 0.000 0.044 0.944 0.012 0.000
#> round_ERR2585227 1 0.2074 0.9009 0.896 0.000 0.000 0.104 0.000
#> aberrant_ERR2585351 2 0.1026 0.6723 0.000 0.968 0.004 0.004 0.024
#> round_ERR2585269 1 0.2179 0.8996 0.896 0.000 0.000 0.100 0.004
#> aberrant_ERR2585357 3 0.5124 0.5001 0.000 0.260 0.668 0.004 0.068
#> aberrant_ERR2585350 2 0.5257 0.4451 0.000 0.656 0.264 0.004 0.076
#> round_ERR2585250 1 0.1549 0.9102 0.944 0.000 0.000 0.040 0.016
#> round_ERR2585245 1 0.3241 0.8656 0.832 0.000 0.000 0.144 0.024
#> aberrant_ERR2585353 2 0.0833 0.6758 0.000 0.976 0.004 0.004 0.016
#> round_ERR2585258 1 0.3106 0.8742 0.844 0.000 0.000 0.132 0.024
#> aberrant_ERR2585354 2 0.0898 0.6779 0.000 0.972 0.000 0.008 0.020
#> round_ERR2585249 1 0.3106 0.8707 0.840 0.000 0.000 0.140 0.020
#> round_ERR2585268 1 0.1547 0.9028 0.948 0.016 0.000 0.032 0.004
#> aberrant_ERR2585356 2 0.3086 0.6534 0.000 0.816 0.000 0.004 0.180
#> round_ERR2585266 3 0.2900 0.7384 0.092 0.000 0.876 0.020 0.012
#> round_ERR2585231 1 0.3489 0.8583 0.820 0.000 0.000 0.144 0.036
#> round_ERR2585230 1 0.2446 0.8906 0.900 0.000 0.000 0.044 0.056
#> round_ERR2585267 1 0.2674 0.8886 0.868 0.000 0.000 0.120 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> aberrant_ERR2585320 6 0.6825 0.00000 0.000 0.292 0.032 0.004 0.312 0.360
#> aberrant_ERR2585338 3 0.3672 0.52666 0.000 0.004 0.712 0.000 0.276 0.008
#> aberrant_ERR2585325 2 0.6084 -0.23698 0.000 0.508 0.068 0.000 0.076 0.348
#> aberrant_ERR2585283 2 0.4041 0.32482 0.000 0.788 0.000 0.108 0.028 0.076
#> aberrant_ERR2585343 5 0.3544 0.52767 0.000 0.120 0.000 0.000 0.800 0.080
#> aberrant_ERR2585329 5 0.5490 0.23835 0.000 0.312 0.044 0.000 0.584 0.060
#> aberrant_ERR2585317 5 0.4674 0.28109 0.000 0.332 0.000 0.000 0.608 0.060
#> aberrant_ERR2585339 3 0.5688 0.12255 0.000 0.156 0.544 0.000 0.292 0.008
#> aberrant_ERR2585335 5 0.3790 0.47223 0.000 0.104 0.000 0.000 0.780 0.116
#> aberrant_ERR2585287 2 0.6981 -0.03200 0.000 0.472 0.288 0.080 0.012 0.148
#> aberrant_ERR2585321 5 0.2491 0.55782 0.000 0.112 0.000 0.000 0.868 0.020
#> aberrant_ERR2585297 1 0.3288 0.77202 0.724 0.000 0.000 0.276 0.000 0.000
#> aberrant_ERR2585337 5 0.6678 -0.10228 0.000 0.264 0.292 0.000 0.408 0.036
#> aberrant_ERR2585319 5 0.5129 -0.03345 0.000 0.464 0.000 0.008 0.468 0.060
#> aberrant_ERR2585315 2 0.3369 0.32586 0.000 0.800 0.004 0.004 0.172 0.020
#> aberrant_ERR2585336 5 0.6322 0.07026 0.000 0.180 0.240 0.000 0.532 0.048
#> aberrant_ERR2585307 3 0.6414 -0.08639 0.000 0.180 0.472 0.004 0.316 0.028
#> aberrant_ERR2585301 2 0.4920 -0.07464 0.000 0.544 0.000 0.004 0.396 0.056
#> aberrant_ERR2585326 2 0.5941 0.10424 0.000 0.556 0.248 0.000 0.172 0.024
#> aberrant_ERR2585331 3 0.0405 0.82621 0.000 0.000 0.988 0.000 0.004 0.008
#> aberrant_ERR2585346 2 0.2711 0.36047 0.000 0.880 0.000 0.056 0.016 0.048
#> aberrant_ERR2585314 5 0.3892 0.29648 0.004 0.028 0.004 0.000 0.744 0.220
#> aberrant_ERR2585298 3 0.0520 0.82463 0.008 0.000 0.984 0.000 0.000 0.008
#> aberrant_ERR2585345 5 0.6230 -0.03782 0.000 0.288 0.248 0.000 0.452 0.012
#> aberrant_ERR2585299 1 0.2149 0.80038 0.900 0.000 0.000 0.016 0.004 0.080
#> aberrant_ERR2585309 1 0.3499 0.77328 0.728 0.000 0.000 0.264 0.004 0.004
#> aberrant_ERR2585303 3 0.5040 0.52318 0.000 0.104 0.692 0.000 0.172 0.032
#> aberrant_ERR2585313 2 0.7008 -0.11744 0.000 0.456 0.184 0.016 0.288 0.056
#> aberrant_ERR2585318 5 0.2618 0.53723 0.000 0.052 0.000 0.000 0.872 0.076
#> aberrant_ERR2585328 3 0.4999 0.00657 0.000 0.020 0.488 0.000 0.460 0.032
#> aberrant_ERR2585330 5 0.4172 0.23058 0.000 0.424 0.000 0.004 0.564 0.008
#> aberrant_ERR2585293 4 0.4859 1.00000 0.000 0.084 0.000 0.612 0.000 0.304
#> aberrant_ERR2585342 5 0.2896 0.54284 0.000 0.160 0.000 0.000 0.824 0.016
#> aberrant_ERR2585348 5 0.3827 0.48219 0.000 0.036 0.064 0.004 0.816 0.080
#> aberrant_ERR2585352 5 0.1124 0.53830 0.000 0.008 0.000 0.000 0.956 0.036
#> aberrant_ERR2585308 1 0.3398 0.78063 0.740 0.000 0.000 0.252 0.000 0.008
#> aberrant_ERR2585349 3 0.5305 0.11461 0.000 0.000 0.492 0.000 0.404 0.104
#> aberrant_ERR2585316 5 0.4168 0.24960 0.000 0.400 0.000 0.000 0.584 0.016
#> aberrant_ERR2585306 2 0.5877 0.13969 0.000 0.548 0.000 0.276 0.156 0.020
#> aberrant_ERR2585324 2 0.3355 0.34079 0.000 0.836 0.000 0.016 0.072 0.076
#> aberrant_ERR2585310 2 0.7526 -0.02871 0.364 0.388 0.000 0.060 0.096 0.092
#> aberrant_ERR2585296 1 0.5196 0.57139 0.660 0.004 0.004 0.008 0.112 0.212
#> aberrant_ERR2585275 2 0.2796 0.34062 0.000 0.872 0.000 0.048 0.012 0.068
#> aberrant_ERR2585311 5 0.4394 0.11109 0.000 0.484 0.000 0.004 0.496 0.016
#> aberrant_ERR2585292 4 0.4859 1.00000 0.000 0.084 0.000 0.612 0.000 0.304
#> aberrant_ERR2585282 5 0.5631 -0.39037 0.000 0.168 0.000 0.000 0.508 0.324
#> aberrant_ERR2585305 2 0.4886 0.29946 0.004 0.692 0.000 0.084 0.204 0.016
#> aberrant_ERR2585278 2 0.4216 0.26349 0.000 0.720 0.000 0.012 0.228 0.040
#> aberrant_ERR2585347 2 0.5761 -0.11583 0.000 0.540 0.024 0.012 0.072 0.352
#> aberrant_ERR2585332 5 0.5946 -0.29417 0.000 0.352 0.000 0.004 0.452 0.192
#> aberrant_ERR2585280 2 0.3276 0.30613 0.000 0.840 0.032 0.000 0.028 0.100
#> aberrant_ERR2585304 3 0.5775 0.22771 0.000 0.380 0.504 0.036 0.000 0.080
#> aberrant_ERR2585322 2 0.6857 -0.00735 0.000 0.436 0.356 0.008 0.096 0.104
#> aberrant_ERR2585279 3 0.0146 0.82562 0.000 0.000 0.996 0.000 0.000 0.004
#> aberrant_ERR2585277 3 0.0964 0.82202 0.000 0.012 0.968 0.000 0.004 0.016
#> aberrant_ERR2585295 3 0.4545 0.55479 0.000 0.224 0.684 0.000 0.000 0.092
#> aberrant_ERR2585333 2 0.4962 0.13023 0.000 0.632 0.000 0.020 0.292 0.056
#> aberrant_ERR2585285 2 0.4262 -0.14080 0.000 0.508 0.000 0.000 0.476 0.016
#> aberrant_ERR2585286 3 0.0837 0.82549 0.000 0.004 0.972 0.000 0.004 0.020
#> aberrant_ERR2585294 2 0.3883 0.36299 0.000 0.788 0.000 0.040 0.144 0.028
#> aberrant_ERR2585300 2 0.4537 0.18323 0.000 0.640 0.000 0.012 0.316 0.032
#> aberrant_ERR2585334 3 0.0405 0.82573 0.000 0.000 0.988 0.000 0.004 0.008
#> aberrant_ERR2585361 5 0.2709 0.55273 0.000 0.132 0.000 0.000 0.848 0.020
#> aberrant_ERR2585372 5 0.4086 0.43232 0.000 0.244 0.000 0.008 0.716 0.032
#> round_ERR2585217 3 0.6210 0.31431 0.260 0.000 0.548 0.000 0.056 0.136
#> round_ERR2585205 1 0.1663 0.79436 0.912 0.000 0.000 0.000 0.000 0.088
#> round_ERR2585214 3 0.0000 0.82548 0.000 0.000 1.000 0.000 0.000 0.000
#> round_ERR2585202 3 0.2901 0.76925 0.008 0.016 0.868 0.000 0.020 0.088
#> aberrant_ERR2585367 5 0.4600 0.43727 0.000 0.076 0.128 0.004 0.752 0.040
#> round_ERR2585220 1 0.0777 0.81429 0.972 0.000 0.000 0.004 0.000 0.024
#> round_ERR2585238 1 0.1700 0.82466 0.916 0.000 0.000 0.080 0.000 0.004
#> aberrant_ERR2585276 2 0.3371 0.36505 0.000 0.844 0.000 0.056 0.048 0.052
#> round_ERR2585218 1 0.1226 0.81109 0.952 0.004 0.000 0.004 0.000 0.040
#> aberrant_ERR2585363 5 0.1700 0.51094 0.000 0.004 0.000 0.000 0.916 0.080
#> round_ERR2585201 3 0.0363 0.82495 0.012 0.000 0.988 0.000 0.000 0.000
#> round_ERR2585210 1 0.2558 0.75776 0.840 0.000 0.000 0.004 0.000 0.156
#> aberrant_ERR2585362 5 0.2100 0.45702 0.000 0.004 0.000 0.000 0.884 0.112
#> aberrant_ERR2585360 5 0.1572 0.55544 0.000 0.036 0.000 0.000 0.936 0.028
#> round_ERR2585209 1 0.2956 0.75549 0.840 0.000 0.040 0.000 0.000 0.120
#> round_ERR2585242 3 0.1078 0.81894 0.016 0.000 0.964 0.008 0.000 0.012
#> round_ERR2585216 1 0.3590 0.69819 0.776 0.000 0.000 0.004 0.032 0.188
#> round_ERR2585219 1 0.2001 0.79353 0.900 0.004 0.000 0.004 0.000 0.092
#> round_ERR2585237 3 0.5308 0.43545 0.256 0.000 0.632 0.000 0.032 0.080
#> round_ERR2585198 3 0.0146 0.82562 0.000 0.000 0.996 0.000 0.000 0.004
#> round_ERR2585211 1 0.1524 0.80545 0.932 0.000 0.000 0.008 0.000 0.060
#> round_ERR2585206 1 0.0622 0.81624 0.980 0.000 0.000 0.008 0.000 0.012
#> aberrant_ERR2585281 3 0.2308 0.77121 0.000 0.108 0.880 0.000 0.004 0.008
#> round_ERR2585212 1 0.1932 0.79631 0.912 0.000 0.004 0.004 0.004 0.076
#> round_ERR2585221 1 0.4019 0.72964 0.652 0.012 0.000 0.332 0.000 0.004
#> round_ERR2585243 1 0.4391 0.72557 0.644 0.028 0.000 0.320 0.000 0.008
#> round_ERR2585204 3 0.0146 0.82605 0.000 0.000 0.996 0.000 0.000 0.004
#> round_ERR2585213 3 0.0146 0.82562 0.000 0.000 0.996 0.000 0.000 0.004
#> aberrant_ERR2585373 5 0.4555 0.15553 0.000 0.440 0.000 0.012 0.532 0.016
#> aberrant_ERR2585358 5 0.1668 0.55872 0.000 0.060 0.000 0.004 0.928 0.008
#> aberrant_ERR2585365 5 0.1138 0.53725 0.000 0.012 0.004 0.000 0.960 0.024
#> aberrant_ERR2585359 5 0.2088 0.56023 0.000 0.068 0.000 0.000 0.904 0.028
#> aberrant_ERR2585370 3 0.1116 0.81772 0.000 0.028 0.960 0.000 0.008 0.004
#> round_ERR2585215 1 0.1194 0.81643 0.956 0.000 0.000 0.008 0.004 0.032
#> round_ERR2585262 3 0.2228 0.80312 0.000 0.004 0.912 0.024 0.016 0.044
#> round_ERR2585199 3 0.0405 0.82596 0.000 0.000 0.988 0.000 0.004 0.008
#> aberrant_ERR2585369 5 0.3905 0.37138 0.000 0.316 0.000 0.000 0.668 0.016
#> round_ERR2585208 1 0.2006 0.82295 0.892 0.004 0.000 0.104 0.000 0.000
#> round_ERR2585252 1 0.2838 0.80233 0.808 0.000 0.000 0.188 0.000 0.004
#> round_ERR2585236 1 0.2668 0.82623 0.872 0.000 0.004 0.096 0.004 0.024
#> aberrant_ERR2585284 5 0.7398 -0.48656 0.000 0.232 0.108 0.004 0.376 0.280
#> round_ERR2585224 1 0.4890 0.65524 0.580 0.052 0.000 0.360 0.000 0.008
#> round_ERR2585260 1 0.1765 0.82323 0.904 0.000 0.000 0.096 0.000 0.000
#> round_ERR2585229 1 0.1049 0.81628 0.960 0.000 0.000 0.008 0.000 0.032
#> aberrant_ERR2585364 5 0.3224 0.54085 0.000 0.128 0.000 0.008 0.828 0.036
#> round_ERR2585253 1 0.1333 0.82309 0.944 0.000 0.000 0.048 0.000 0.008
#> aberrant_ERR2585368 3 0.0508 0.82608 0.000 0.000 0.984 0.000 0.004 0.012
#> aberrant_ERR2585371 3 0.0508 0.82608 0.000 0.000 0.984 0.000 0.004 0.012
#> round_ERR2585239 1 0.3579 0.79591 0.828 0.036 0.000 0.064 0.000 0.072
#> round_ERR2585273 1 0.3050 0.78760 0.764 0.000 0.000 0.236 0.000 0.000
#> round_ERR2585256 1 0.1003 0.82152 0.964 0.000 0.004 0.028 0.000 0.004
#> round_ERR2585272 1 0.0935 0.82153 0.964 0.000 0.000 0.032 0.000 0.004
#> round_ERR2585246 1 0.3592 0.72657 0.656 0.000 0.000 0.344 0.000 0.000
#> round_ERR2585261 1 0.3503 0.69458 0.788 0.000 0.180 0.012 0.000 0.020
#> round_ERR2585254 1 0.1340 0.81440 0.948 0.000 0.004 0.008 0.000 0.040
#> round_ERR2585225 3 0.0717 0.82282 0.016 0.000 0.976 0.000 0.000 0.008
#> round_ERR2585235 1 0.3404 0.78917 0.760 0.000 0.016 0.224 0.000 0.000
#> round_ERR2585271 1 0.1462 0.80989 0.936 0.000 0.000 0.008 0.000 0.056
#> round_ERR2585251 1 0.1387 0.82375 0.932 0.000 0.000 0.068 0.000 0.000
#> round_ERR2585255 3 0.0458 0.82518 0.000 0.000 0.984 0.000 0.000 0.016
#> round_ERR2585257 3 0.0291 0.82555 0.004 0.000 0.992 0.000 0.000 0.004
#> round_ERR2585226 1 0.3499 0.74482 0.680 0.000 0.000 0.320 0.000 0.000
#> round_ERR2585265 1 0.0790 0.82190 0.968 0.000 0.000 0.032 0.000 0.000
#> round_ERR2585259 1 0.3190 0.75492 0.844 0.000 0.100 0.008 0.004 0.044
#> round_ERR2585247 1 0.3371 0.76234 0.708 0.000 0.000 0.292 0.000 0.000
#> round_ERR2585241 1 0.2975 0.76473 0.840 0.012 0.000 0.016 0.000 0.132
#> round_ERR2585263 1 0.4830 0.51970 0.652 0.008 0.008 0.004 0.040 0.288
#> round_ERR2585264 1 0.3360 0.77406 0.732 0.000 0.000 0.264 0.000 0.004
#> round_ERR2585233 3 0.0862 0.81844 0.016 0.000 0.972 0.008 0.000 0.004
#> round_ERR2585223 1 0.3201 0.79762 0.780 0.012 0.000 0.208 0.000 0.000
#> round_ERR2585234 3 0.0291 0.82550 0.004 0.000 0.992 0.000 0.000 0.004
#> round_ERR2585222 1 0.5138 0.69549 0.712 0.076 0.004 0.072 0.000 0.136
#> round_ERR2585228 1 0.1007 0.80873 0.956 0.000 0.000 0.000 0.000 0.044
#> round_ERR2585248 1 0.2357 0.82071 0.872 0.000 0.000 0.116 0.000 0.012
#> round_ERR2585240 1 0.5982 0.38936 0.468 0.004 0.312 0.216 0.000 0.000
#> round_ERR2585270 1 0.2165 0.78327 0.884 0.000 0.008 0.000 0.000 0.108
#> round_ERR2585232 1 0.2894 0.81988 0.860 0.004 0.036 0.096 0.000 0.004
#> aberrant_ERR2585341 3 0.2039 0.80580 0.000 0.020 0.916 0.000 0.012 0.052
#> aberrant_ERR2585355 3 0.0951 0.82333 0.000 0.004 0.968 0.000 0.020 0.008
#> round_ERR2585227 1 0.3309 0.76831 0.720 0.000 0.000 0.280 0.000 0.000
#> aberrant_ERR2585351 5 0.1444 0.51408 0.000 0.000 0.000 0.000 0.928 0.072
#> round_ERR2585269 1 0.3547 0.75566 0.696 0.000 0.000 0.300 0.000 0.004
#> aberrant_ERR2585357 3 0.5901 0.21513 0.000 0.184 0.568 0.000 0.224 0.024
#> aberrant_ERR2585350 5 0.4684 0.26509 0.000 0.088 0.256 0.000 0.656 0.000
#> round_ERR2585250 1 0.3192 0.80685 0.856 0.012 0.004 0.048 0.004 0.076
#> round_ERR2585245 1 0.3756 0.71734 0.644 0.004 0.000 0.352 0.000 0.000
#> aberrant_ERR2585353 5 0.1268 0.53742 0.000 0.008 0.000 0.004 0.952 0.036
#> round_ERR2585258 1 0.3619 0.74750 0.680 0.004 0.000 0.316 0.000 0.000
#> aberrant_ERR2585354 5 0.1225 0.53698 0.000 0.012 0.000 0.000 0.952 0.036
#> round_ERR2585249 1 0.3515 0.74256 0.676 0.000 0.000 0.324 0.000 0.000
#> round_ERR2585268 1 0.3875 0.73208 0.776 0.012 0.008 0.028 0.000 0.176
#> aberrant_ERR2585356 5 0.3210 0.52932 0.000 0.168 0.000 0.000 0.804 0.028
#> round_ERR2585266 3 0.2635 0.75419 0.036 0.004 0.884 0.068 0.000 0.008
#> round_ERR2585231 1 0.4399 0.69824 0.616 0.028 0.000 0.352 0.000 0.004
#> round_ERR2585230 1 0.3804 0.78049 0.800 0.008 0.004 0.080 0.000 0.108
#> round_ERR2585267 1 0.3578 0.73178 0.660 0.000 0.000 0.340 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n cell_type(p) k
#> ATC:NMF 159 4.69e-29 2
#> ATC:NMF 145 1.67e-21 3
#> ATC:NMF 146 4.69e-19 4
#> ATC:NMF 131 1.51e-16 5
#> ATC:NMF 107 9.83e-12 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
sessionInfo()
#> R version 3.6.0 (2019-04-26)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#>
#> Matrix products: default
#> BLAS: /usr/lib64/libblas.so.3.4.2
#> LAPACK: /usr/lib64/liblapack.so.3.4.2
#>
#> locale:
#> [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8
#> [4] LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
#> [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] grid stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] genefilter_1.66.0 ComplexHeatmap_2.3.1 markdown_1.1 knitr_1.26
#> [5] GetoptLong_0.1.7 cola_1.3.2
#>
#> loaded via a namespace (and not attached):
#> [1] circlize_0.4.8 shape_1.4.4 xfun_0.11 slam_0.1-46
#> [5] lattice_0.20-38 splines_3.6.0 colorspace_1.4-1 vctrs_0.2.0
#> [9] stats4_3.6.0 blob_1.2.0 XML_3.98-1.20 survival_2.44-1.1
#> [13] rlang_0.4.2 pillar_1.4.2 DBI_1.0.0 BiocGenerics_0.30.0
#> [17] bit64_0.9-7 RColorBrewer_1.1-2 matrixStats_0.55.0 stringr_1.4.0
#> [21] GlobalOptions_0.1.1 evaluate_0.14 memoise_1.1.0 Biobase_2.44.0
#> [25] IRanges_2.18.3 parallel_3.6.0 AnnotationDbi_1.46.1 highr_0.8
#> [29] Rcpp_1.0.3 xtable_1.8-4 backports_1.1.5 S4Vectors_0.22.1
#> [33] annotate_1.62.0 skmeans_0.2-11 bit_1.1-14 microbenchmark_1.4-7
#> [37] brew_1.0-6 impute_1.58.0 rjson_0.2.20 png_0.1-7
#> [41] digest_0.6.23 stringi_1.4.3 polyclip_1.10-0 clue_0.3-57
#> [45] tools_3.6.0 bitops_1.0-6 magrittr_1.5 eulerr_6.0.0
#> [49] RCurl_1.95-4.12 RSQLite_2.1.4 tibble_2.1.3 cluster_2.1.0
#> [53] crayon_1.3.4 pkgconfig_2.0.3 zeallot_0.1.0 Matrix_1.2-17
#> [57] xml2_1.2.2 httr_1.4.1 R6_2.4.1 mclust_5.4.5
#> [61] compiler_3.6.0