ROC plot generic function for a gg_roc object.

ROC plot generic function for a gg_roc object.

Usage

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

Arguments

x

A gg_roc object, or a raw rfsrc classification forest or randomForest classification object. When a forest is supplied, gg_roc is called automatically.

which_outcome

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

...

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

Value

A ggplot object. The x-axis shows 1 - Specificity (FPR) and the y-axis shows Sensitivity (TPR). A dashed red diagonal reference line marks the random-classifier baseline. The AUC value is annotated on the plot for single-class curves. Multi-class plots colour and style each class curve distinctly.

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 <- 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)))