plot_clf_ROC: plot_ROC

In shihuang047/crossRanger: Cross-Application of Ranger Models using microbiome data

plot_ROC

Description

plot_ROC

Usage

plot_clf_ROC(
  y,
  rf_clf_model,
  positive_class = NA,
  prefix = "train",
  outdir = NULL
)

Arguments

y	A factor of classes to be used as the true results
rf_clf_model	A list object of a random forest model.
positive_class	an optional character string for the factor level that corresponds to a "positive" result (if that makes sense for your data). If there are only two factor levels, the first level will be used as the "positive" result.
prefix	The prefix of data set.
outdir	The output directory.

Author(s)

Shi Huang

Examples

set.seed(123)
x <- data.frame(rbind(t(rmultinom(7, 75, c(.201,.5,.02,.18,.099))),
            t(rmultinom(8, 75, c(.201,.4,.12,.18,.099))),
            t(rmultinom(15, 75, c(.011,.3,.22,.18,.289))),
            t(rmultinom(15, 75, c(.091,.2,.32,.18,.209))),
            t(rmultinom(15, 75, c(.001,.1,.42,.18,.299)))))
x0 <- data.frame(rbind(t(rmultinom(7, 75, c(.011,.3,.22,.18,.289))),
            t(rmultinom(8, 75, c(.011,.3,.22,.18,.289))),
            t(rmultinom(15, 75, c(.011,.3,.22,.18,.289))),
            t(rmultinom(15, 75, c(.011,.3,.22,.18,.289))),
            t(rmultinom(15, 75, c(.011,.3,.22,.18,.289)))))
y<-factor(c(rep("A", 20), rep("B", 20), rep("C", 20)))
y0<-factor(c(rep("A", 5), rep("B", 55)))
rf_clf_model<-rf.out.of.bag(x, y)
plot_clf_ROC(y, rf_clf_model, positive_class="A", outdir='./A')
plot_clf_ROC(y, rf_clf_model, positive_class="B", outdir='./B')
plot_clf_PRC(y, rf_clf_model, positive_class="A", outdir='./A')
plot_clf_PRC(y, rf_clf_model, positive_class="B", outdir='./B')
rf_clf_model0<-rf.out.of.bag(x0, y0)
plot_clf_ROC(y0, rf_clf_model0, positive_class="A", outdir='./A')
plot_clf_ROC(y0, rf_clf_model0, positive_class="B", outdir='./B')
plot_clf_PRC(y0, rf_clf_model0, positive_class="A", outdir='./A')
plot_clf_PRC(y0, rf_clf_model0, positive_class="B", outdir='./B')
pred_df<-data.frame(y=y0, prediction=rf_clf_model0$probabilities[, positive_class])
ggplot(pred_df, aes(prediction, fill = y)) +
geom_histogram(alpha = 0.5, position = 'identity')

shihuang047/crossRanger documentation built on Feb. 7, 2023, 10:03 p.m.