Calculate AUC for Binomial Model

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)
}
#> [1] 0.4879227