get_classes-DownSamplingConsensusPartition-method.RdGet subgroup labels
# S4 method for DownSamplingConsensusPartition
get_classes(object, k = object@k, p_cutoff = 0.05, reduce = FALSE)A DownSamplingConsensusPartition-class object.
Number of subgroups.
Cutoff of p-values of class label prediction. It is only used when k is a vector.
Used internally.
If k is a scalar, it returns a data frame with two columns:
the class labels
the p-value for the prediction of class labels.
If k is a vector, it returns a data frame of class labels for each k. The class
label with prediction p-value > p_cutoff is set to NA.
data(golub_cola_ds)
get_classes(golub_cola_ds, k = 3)
#> class p
#> sample_39 2 0.000
#> sample_40 3 0.000
#> sample_42 1 0.000
#> sample_47 1 0.000
#> sample_48 1 0.000
#> sample_49 3 0.000
#> sample_41 1 0.000
#> sample_43 1 0.000
#> sample_44 1 0.000
#> sample_45 1 0.000
#> sample_46 1 0.000
#> sample_70 1 1.000
#> sample_71 2 0.502
#> sample_72 1 0.000
#> sample_68 1 0.000
#> sample_69 1 0.000
#> sample_67 1 0.000
#> sample_55 3 0.000
#> sample_56 3 0.000
#> sample_59 1 0.000
#> sample_52 2 0.000
#> sample_53 2 0.000
#> sample_51 2 0.000
#> sample_50 2 0.000
#> sample_54 1 0.000
#> sample_57 2 0.000
#> sample_58 2 0.000
#> sample_60 1 0.000
#> sample_61 2 0.000
#> sample_65 2 0.000
#> sample_66 1 0.000
#> sample_63 2 0.000
#> sample_64 2 0.000
#> sample_62 2 0.000
#> sample_1 3 0.000
#> sample_2 1 0.000
#> sample_3 3 0.000
#> sample_4 3 0.000
#> sample_5 1 0.000
#> sample_6 3 0.000
#> sample_7 3 0.000
#> sample_8 3 0.000
#> sample_9 1 0.000
#> sample_10 3 0.000
#> sample_11 1 0.000
#> sample_12 2 0.000
#> sample_13 1 0.000
#> sample_14 1 0.000
#> sample_15 1 0.000
#> sample_16 1 0.000
#> sample_17 1 0.000
#> sample_18 3 0.000
#> sample_19 1 0.000
#> sample_20 1 0.000
#> sample_21 1 0.000
#> sample_22 2 0.000
#> sample_23 3 0.000
#> sample_24 1 0.000
#> sample_25 2 0.000
#> sample_26 1 0.000
#> sample_27 3 0.000
#> sample_34 2 0.000
#> sample_35 2 0.000
#> sample_36 2 0.000
#> sample_37 2 0.000
#> sample_38 2 0.000
#> sample_28 2 0.000
#> sample_29 1 0.000
#> sample_30 2 0.000
#> sample_31 2 0.000
#> sample_32 2 0.000
#> sample_33 2 0.000
get_classes(golub_cola_ds)
#> k=2 k=3 k=4 k=5 k=6
#> sample_39 2 2 2 2 NA
#> sample_40 2 3 3 3 NA
#> sample_42 1 1 4 4 3
#> sample_47 1 1 1 1 1
#> sample_48 1 1 1 1 1
#> sample_49 2 3 3 NA 2
#> sample_41 1 1 1 1 1
#> sample_43 1 1 1 1 1
#> sample_44 1 1 1 1 1
#> sample_45 1 1 1 1 1
#> sample_46 1 1 1 NA NA
#> sample_70 1 NA NA NA NA
#> sample_71 1 NA 4 4 3
#> sample_72 1 1 4 4 3
#> sample_68 1 1 1 1 1
#> sample_69 1 1 1 1 1
#> sample_67 1 1 4 4 3
#> sample_55 2 3 3 NA NA
#> sample_56 2 3 3 NA NA
#> sample_59 1 1 NA NA NA
#> sample_52 2 2 2 NA NA
#> sample_53 2 2 2 NA NA
#> sample_51 2 2 2 2 2
#> sample_50 2 2 2 2 2
#> sample_54 1 1 1 NA NA
#> sample_57 2 2 2 2 2
#> sample_58 2 2 2 2 2
#> sample_60 NA 1 NA NA NA
#> sample_61 2 2 2 2 2
#> sample_65 2 2 2 2 2
#> sample_66 1 1 1 1 1
#> sample_63 2 2 2 2 2
#> sample_64 2 2 2 2 2
#> sample_62 2 2 2 2 2
#> sample_1 2 3 3 3 NA
#> sample_2 1 1 4 4 3
#> sample_3 2 3 3 3 3
#> sample_4 2 3 3 3 NA
#> sample_5 1 1 1 1 1
#> sample_6 2 3 3 3 NA
#> sample_7 2 3 3 3 2
#> sample_8 2 3 3 NA 2
#> sample_9 1 1 1 NA NA
#> sample_10 2 3 3 NA 3
#> sample_11 1 1 4 4 3
#> sample_12 2 2 2 2 2
#> sample_13 1 1 1 1 1
#> sample_14 1 1 1 1 NA
#> sample_15 1 1 1 1 1
#> sample_16 1 1 1 1 1
#> sample_17 1 1 1 1 NA
#> sample_18 2 3 3 NA NA
#> sample_19 1 1 1 1 1
#> sample_20 1 1 1 1 1
#> sample_21 1 1 1 1 1
#> sample_22 2 2 NA NA NA
#> sample_23 2 3 3 3 3
#> sample_24 1 1 1 1 1
#> sample_25 2 2 2 NA NA
#> sample_26 1 1 NA NA NA
#> sample_27 2 3 3 3 2
#> sample_34 2 2 2 2 2
#> sample_35 2 2 2 2 2
#> sample_36 2 2 2 2 2
#> sample_37 2 2 2 2 2
#> sample_38 2 2 2 2 2
#> sample_28 2 2 2 2 NA
#> sample_29 1 1 4 4 3
#> sample_30 2 2 2 NA NA
#> sample_31 2 2 2 2 2
#> sample_32 2 2 2 2 2
#> sample_33 2 2 2 2 2