Enet: Elastic Net Model Fitting

In IOBR: Immune Oncology Biological Research

Elastic Net Model Fitting

Description

Fits elastic net model with cross-validation to find optimal alpha and lambda. Searches across a grid of alpha values (0 to 1) and lambda values to minimize cross-validation error.

Usage

Enet(train.x, train.y, lambdamax, nfold = 10)

Arguments

train.x	Training predictors matrix.
train.y	Training binary outcomes (0/1 or factor).
lambdamax	Maximum lambda value for the grid search.
nfold	Number of CV folds. Default is '10'.

Value

List containing:

chose_alpha

Optimal alpha value (0-1)

chose_lambda

Optimal lambda value

Examples

if (requireNamespace("glmnet", quietly = TRUE)) {
  set.seed(123)
  train_data <- matrix(rnorm(50 * 5), ncol = 5)
  train_outcome <- rbinom(50, 1, 0.5)
  result <- Enet(train.x = train_data, train.y = train_outcome, lambdamax = 1, nfold = 5)
}

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