Plot ROC curve with AUC annotation — plot_roc

Generates a ROC curve from predicted vs observed response with AUC value annotation.

Usage

plot_roc_curve(
  response,
  predictor,
  smooth_curve = TRUE,
  base_size = 15,
  auc_digits = 3,
  title = NULL,
  tooltip = TRUE
)

Arguments

response: Factor or numeric. True response labels (e.g., "R"/"NR" or 0/1).
predictor: Numeric. Predicted values (e.g., viability scores).
smooth_curve: Logical. Whether to smooth the ROC curve. Default = TRUE.
base_size: Numeric. Base font size. Default = 15.
auc_digits: Integer. Number of digits for AUC display. Default = 3.
title: Character. Plot title. Default = NULL.
tooltip: Logical. If TRUE (default) and ggiraph is installed, curve points get hover tooltips (FPR / TPR).

Value

A ggplot object.

Examples

if (FALSE) { # \dontrun{
  response <- factor(c("R", "NR", "R", "NR", "R"))
  predictor <- c(0.8, 0.2, 0.7, 0.3, 0.9)
  plot_roc_curve(response, predictor)
} # }