View source: R/ranger_crossRF_plot_util.R
plot_cross_appl | R Documentation |
The heatmap indicating ML performance (e.g., MAE) in the self-validation and cross-applications.
plot_cross_appl( cross_appl_res, metric = "MAE", outdir = NULL, plot_width = 8, plot_height = 7 )
cross_appl_res |
The output object from |
metric |
The classification performance metric applied. |
outdir |
The outputh directory, default is NULL. |
plot_width |
The width (inches) of heatmap |
plot_height |
The height (inches) of heatmap. |
A heat map showing ML performance in self-validation and cross-applications.
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))))) df0 <- data.frame(t(rmultinom(120, 600,c(.001,.6,.2,.3,.299)))) metadata<-data.frame(f_s=factor(rep(c("A", "B"), 60)), f_s1=factor(c(rep(TRUE, 60), rep(FALSE, 60))), f_c=factor(c(rep("C", 30), rep("H", 30), rep("D", 30), rep("P", 30))), age=c(1:60, 2:61) ) table(metadata[, c('f_s', 'f_c')]) clf_res<-rf_clf.by_datasets(df, metadata, nfolds=5, s_category='f_c', c_category='f_s') clf_cross_appl_res <- rf_clf.cross_appl(clf_res$rf_model_list, x_list=clf_res$x_list, y_list=clf_res$y_list) plot_cross_appl(clf_cross_appl_res, metric="AUROC") reg_res<-rf_reg.by_datasets(df, metadata, nfolds=5, s_category='f_c', c_category='age') reg_cross_appl_res <- rf_reg.cross_appl(reg_res, x_list=reg_res$x_list, y_list=reg_res$y_list) plot_cross_appl(reg_cross_appl_res, metric="MAE")
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