ggRandomForests (version 3.1.2)

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, ..., panel = c("overlay", "facet"))

Value

A ggplot object. The x-axis is 1 - Specificity (FPR), the y-axis is Sensitivity (TPR), and a dashed red diagonal marks the random-classifier baseline. Single-class curves carry the AUC as an annotation; multi-class plots colour and style each class curve distinctly.

Arguments

x

A gg_roc object, or a raw rfsrc or randomForest classification forest. Hand it a forest and gg_roc is called for you.

which_outcome

Integer; for multi-class problems, the index of the class to plot. When NULL (default) and the forest has more than two classes, the curves for all classes are overlaid in one plot. For binary forests, NULL defaults to class index 2.

...

Additional arguments passed to gg_roc when x is a raw forest (e.g. oob = FALSE).

panel

Character; layout for per-class ROC objects, the ones from gg_roc(..., per_class = TRUE). "overlay" (default) draws every class curve in one panel, coloured by class; "facet" gives each class its own panel. Ignored for single-class gg_roc objects.

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. randomForestSRC: Random Forests for Survival, Regression and Classification. R package version >= 3.4.0. https://cran.r-project.org/package=randomForestSRC

See Also

gg_roc calc_roc calc_auc rfsrc randomForest

Examples

## ------------------------------------------------------------
## classification example
## ------------------------------------------------------------
## -------- iris data
# Build a small classification forest (ntree=50 keeps example fast)
set.seed(42)
rfsrc_iris <- randomForestSRC::rfsrc(Species ~ ., data = iris, ntree = 50)

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

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

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

# Plot all three ROC curves in one call by iterating over outcome indices
n_cls <- ncol(rfsrc_iris$predicted)
for (i in seq_len(n_cls)) print(plot(gg_roc(rfsrc_iris, which_outcome = i)))