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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