PlotTimeROC: Plot Time-Dependent ROC Curves

In IOBR: Immune Oncology Biological Research

Plot Time-Dependent ROC Curves

Description

Generates time-dependent ROC curves for evaluating prognostic accuracy of survival models. Plots training and testing ROC curves at the 90th percentile survival time.

Usage

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

Arguments

train.x	Matrix or data frame of training predictors.
train.y	Training survival outcomes (time and status).
test.x	Matrix or data frame of testing predictors.
test.y	Testing survival outcomes (time and status).
model	Fitted survival model object.
modelname	Character string for model identification.
cols	Optional vector of colors for plotting.
palette	Character string specifying color palette. Default is '"jama"'.

Value

A 'ggplot' object representing the ROC curve plot.

Author(s)

Dongqiang Zeng

Examples

if (requireNamespace("glmnet", quietly = TRUE) &&
  requireNamespace("survival", quietly = TRUE) &&
  requireNamespace("timeROC", quietly = TRUE)) {
  library(survival)
  set.seed(123)
  train_x <- matrix(rnorm(100 * 5), ncol = 5)
  train_y <- data.frame(time = rexp(100), status = rbinom(100, 1, 0.5))
  test_x <- matrix(rnorm(50 * 5), ncol = 5)
  test_y <- data.frame(time = rexp(50), status = rbinom(50, 1, 0.5))
  fit <- glmnet::cv.glmnet(train_x, Surv(train_y$time, train_y$status), family = "cox")
  p <- PlotTimeROC(train_x, train_y, test_x, test_y, fit, "Cox Model")
  print(p)
}

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