calc_roc.rfsrc: Receiver Operator Characteristic calculator

In ggRandomForests: Visually Exploring Random Forests

Receiver Operator Characteristic calculator

Description

Receiver Operator Characteristic calculator

Usage

## S3 method for class 'rfsrc'
calc_roc(object, dta, which_outcome = "all", oob = TRUE, ...)

Arguments

object	rfsrc or predict.rfsrc object containing predicted response
dta	True response variable
which_outcome	If defined, only show ROC for this response.
oob	Use OOB estimates, the normal validation method (TRUE)
...	extra arguments passed to helper functions

Details

For a randomForestSRC prediction and the actual response value, calculate the specificity (1-False Positive Rate) and sensitivity (True Positive Rate) of a predictor.

This is a helper function for the gg_roc functions, and not intended for use by the end user.

Value

A gg_roc object

See Also

calc_auc gg_roc

plot.gg_roc

Examples

## Taken from the gg_roc example
rfsrc_iris <- rfsrc(Species ~ ., data = iris)

gg_dta <- calc_roc(rfsrc_iris, rfsrc_iris$yvar,
     which_outcome=1, oob=TRUE)
gg_dta <- calc_roc(rfsrc_iris, rfsrc_iris$yvar,
     which_outcome=1, oob=FALSE)

rf_iris <- randomForest(Species ~ ., data = iris)
gg_dta <- calc_roc(rf_iris, rf_iris$yvar,
     which_outcome=1)
gg_dta <- calc_roc(rf_iris, rf_iris$yvar,
     which_outcome=2)

ggRandomForests documentation built on Sept. 1, 2022, 5:07 p.m.