ggRandomForests (version 2.2.1)

plot.gg_roc: ROC plot generic function for a gg_roc object.

Description

ROC plot generic function for a gg_roc object.

Usage

# S3 method for gg_roc
plot(x, which_outcome = NULL, ...)

Value

ggplot object of the ROC curve

Arguments

x

gg_roc object created from a classification forest

which_outcome

for multiclass problems, choose the class for plotting

...

arguments passed to the gg_roc function

References

Breiman L. (2001). Random forests, Machine Learning, 45:5-32.

Ishwaran H. and Kogalur U.B. (2007). Random survival forests for R, Rnews, 7(2):25-31.

Ishwaran H. and Kogalur U.B. (2013). Random Forests for Survival, Regression and Classification (RF-SRC), R package version 1.4.

See Also

gg_roc rfsrc

Examples

if (FALSE) {
## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------
## -------- iris data
#rfsrc_iris <- rfsrc(Species ~ ., data = iris)
data(rfsrc_iris, package="ggRandomForests")

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

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

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

# Alternatively, you can plot all three outcomes in one go
# by calling the plot function on the forest object.
plot.gg_roc(rfsrc_iris)

}