Regression Result Computation

Computes regression results with coefficients at lambda.min and lambda.1se, and evaluates AUC for binomial outcomes. Returns a comprehensive summary of model performance on both training and testing datasets.

Usage

RegressionResult(train.x, train.y, test.x, test.y, model)

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 object.

Value

List containing:

model

The fitted cv.glmnet model

coefs

Data frame with feature names and coefficients at lambda.min and lambda.1se

AUC

Matrix of AUC values for train/test sets at both lambda values

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  set.seed(123)
  train_data <- matrix(rnorm(100 * 10), ncol = 10)
  train_outcome <- rbinom(100, 1, 0.5)
  test_data <- matrix(rnorm(50 * 10), ncol = 10)
  test_outcome <- rbinom(50, 1, 0.5)
  fitted_model <- glmnet::cv.glmnet(train_data, train_outcome, family = "binomial", nfolds = 5)
  results <- RegressionResult(
    train.x = train_data, train.y = train_outcome,
    test.x = test_data, test.y = test_outcome, model = fitted_model
  )
}