View source: R/ranger_crossRF_plot_util.R
corr_datasets_by_imps | R Documentation |
The correlation of importance scores between datasets
corr_datasets_by_imps(feature_imps_list, ranked = TRUE, plot = FALSE)
feature_imps_list |
a list of feature importance scores from the output of |
ranked |
if transform importance scores into rank for correlation analysis |
plot |
if plot the correlation matrix |
Shi Huang
ranger rf_clf.by_datasets rf_reg.by_datasets
df <- data.frame(rbind(t(rmultinom(14, 14*5, c(.21,.6,.12,.38,.099))), t(rmultinom(16, 16*5, c(.001,.6,.42,.58,.299))), t(rmultinom(30, 30*5, c(.011,.6,.22,.28,.289))), t(rmultinom(30, 30*5, c(.091,.6,.32,.18,.209))), t(rmultinom(30, 30*5, c(.001,.6,.42,.58,.299))))) metadata<-data.frame(f_s=factor(c(rep("A", 15), rep("B", 15), rep("C", 15), rep("D", 15), rep("A", 15), rep("B", 15), rep("C", 15), rep("D", 15))), f_c=factor(c(rep("C", 60), rep("D", 60))), f_d=factor(c(rep("A", 30), rep("B", 30), rep("C", 30), rep("D", 30))), age=c(1:60, 2:61) ) reg_res<-rf_reg.by_datasets(df, metadata, s_category='f_d', c_category='age') corr_datasets_by_imps(reg_res$feature_imps_list, plot=TRUE) clf_res<-rf_clf.by_datasets(df, metadata, s_category='f_s', c_category='f_c') feature_imps_list <- lapply(clf_res$feature_imps_list, function(x) x[,"rf_imps"]) names(feature_imps_list) <- levels(metadata[, 'f_s']) corr_datasets_by_imps(feature_imps_list, plot=TRUE)
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