gg_roc.rfsrc: ROC (Receiver operator curve) data from a classification...

In ehrlinger/ggRandomForests: Visually Exploring Random Forests

ROC (Receiver operator curve) data from a classification random forest.

Description

The sensitivity and specificity of a randomForest classification object.

Usage

## S3 method for class 'rfsrc'
gg_roc(object, which_outcome, oob, ...)

Arguments

object	an rfsrc classification object
which_outcome	select the classification outcome of interest.
oob	use oob estimates (default TRUE)
...	extra arguments (not used)

Value

gg_roc data.frame for plotting ROC curves.

See Also

plot.gg_roc rfsrc randomForest

Examples

## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------
## -------- iris data
rfsrc_iris <- rfsrc(Species ~ ., data = iris)

# ROC for setosa
gg_dta <- gg_roc(rfsrc_iris, which_outcome=1)
plot(gg_dta)

# ROC for versicolor
gg_dta <- gg_roc(rfsrc_iris, which_outcome=2)
plot(gg_dta)

# ROC for virginica
gg_dta <- gg_roc(rfsrc_iris, which_outcome=3)
plot(gg_dta)

## -------- iris data
rf_iris <- randomForest::randomForest(Species ~ ., data = iris)

# ROC for setosa
gg_dta <- gg_roc(rf_iris, which_outcome=1)
plot(gg_dta)

# ROC for versicolor
gg_dta <- gg_roc(rf_iris, which_outcome=2)
plot(gg_dta)

# ROC for virginica
gg_dta <- gg_roc(rf_iris, which_outcome=3)
plot(gg_dta)

ehrlinger/ggRandomForests documentation built on June 12, 2025, 10:59 a.m.