RobustPrediction (version 0.1.7)

tuneandtrainRobustTuneCLasso: Tune and Train RobustTuneC Lasso

Description

This function tunes and trains a Lasso classifier using the glmnet package and the "RobustTuneC" method. The function uses K-fold cross-validation to evaluate a sequence of lambda (regularization) values and selects the best model based on the Area Under the Curve (AUC).

Usage

tuneandtrainRobustTuneCLasso(
  data,
  dataext,
  K = 5,
  maxit = 120000,
  nlambda = 100
)

Value

A list containing the best lambda value (`best_lambda`), the final trained model (`best_model`), the number of active coefficients (`active_set_Train`), and the chosen c value(`best_c`).

Arguments

data

A data frame containing the training data. The first column should be the response variable (factor), and the remaining columns should be the predictor variables.

dataext

A data frame containing the external validation data. The first column should be the response variable (factor), and the remaining columns should be the predictor variables.

K

Number of folds to use in cross-validation. Default is 5.

maxit

Maximum number of iterations. Default is 120000.

nlambda

The number of lambda values to use for cross-validation. Default is 100.

Details

This function trains a logistic Lasso model using the training dataset and validates it through cross-validation. After selecting the best lambda value based on the training data, the model is then applied to an external validation dataset to compute the final AUC. The lambda value that results in the highest AUC on the external validation dataset is chosen as the best model.

Examples

# Load sample data
data(sample_data_train)
data(sample_data_extern)

# Example usage
result <- tuneandtrainRobustTuneCLasso(sample_data_train, sample_data_extern, 
  K = 5, maxit = 120000, nlambda = 100)
result$best_lambda
result$best_model
result$best_c