PlotAUC: Plot AUC ROC Curves

In IOBR: Immune Oncology Biological Research

Plot AUC ROC Curves

Description

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)
}

IOBR documentation built on May 30, 2026, 5:07 p.m.