BinomialAUC: Calculate AUC for Binomial Model

In IOBR: Immune Oncology Biological Research

Calculate AUC for Binomial Model

Description

Computes Area Under the ROC Curve (AUC) for model predictions using the ROCR package. Handles binary classification models from glmnet.

Usage

BinomialAUC(model, newx, s, acture.y)

Arguments

model	Fitted glmnet model object.
newx	New data matrix for prediction.
s	Lambda value for prediction (e.g., "lambda.min" or numeric).
acture.y	Actual binary outcomes (numeric 0/1 or factor).

Value

Numeric AUC value between 0 and 1.

Examples

if (requireNamespace("glmnet", quietly = TRUE) &&
  requireNamespace("ROCR", 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)
  auc_value <- BinomialAUC(fitted_model, test_data, fitted_model$lambda.min, test_outcome)
  print(auc_value)
}

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