Plot AUC ROC Curves

Generates ROC curves for model evaluation comparing training and testing performance at both lambda.min and lambda.1se. Creates a ggplot visualization with AUC values in the legend.

Usage

PlotAUC(
  train.x,
  train.y,
  test.x,
  test.y,
  model,
  modelname,
  cols = NULL,
  palette = "jama"
)

Arguments

train.x

Training predictors matrix.

train.y

Training outcomes (binary factor).

test.x

Testing predictors matrix.

test.y

Testing outcomes (binary factor).

model

Fitted cv.glmnet model.

modelname

Character string for plot title.

cols

Optional color vector for ROC curves.

palette

Color palette name from IOBR palettes. Default is `"jama"`.

Value

ggplot object of ROC curves.

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  set.seed(123)
  train_data <- matrix(rnorm(100 * 5), ncol = 5)
  train_outcome <- rbinom(100, 1, 0.5)
  test_data <- matrix(rnorm(50 * 5), ncol = 5)
  test_outcome <- rbinom(50, 1, 0.5)
  fitted_model <- glmnet::cv.glmnet(train_data, train_outcome, family = "binomial", nfolds = 5)
  p <- PlotAUC(train_data, train_outcome, test_data, test_outcome, fitted_model, "MyModel")
  print(p)
}