ROC (Receiver Operating Characteristic) curve data from a classification forest.

Computes sensitivity (true positive rate) and specificity (1 - false positive rate) across all prediction thresholds for one class of a classification rfsrc or randomForest object.

Usage

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

Arguments

object

A classification rfsrc or randomForest object. Only forests with family == "class" (rfsrc) or type == "classification" (randomForest) are supported.

which_outcome

Integer index or character name of the class for which the ROC curve is computed. For binary forests this is typically 1 or 2; for multi-class forests any valid class index. Use which_outcome = 0 to obtain the overall (averaged) ROC.

oob

Logical; if TRUE (default) use out-of-bag predicted probabilities for the curve. Set to FALSE to use full in-bag predictions.

...

Extra arguments (currently unused).

Value

A gg_roc data.frame with one row per unique prediction threshold and columns:

sens

Sensitivity (true positive rate) at each threshold.

spec

Specificity (true negative rate) at each threshold.

yvar

The observed class label for each observation.

Pass to calc_auc for the area under the curve.

See also

plot.gg_roc, calc_roc, calc_auc, 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)