tuneandtrainExtRidge {RobustPrediction}R Documentation

Tune and Train External Ridge

Description

This function tunes and trains a Ridge classifier using the glmnet package. It provides two strategies for tuning the regularization parameter lambda based on the estperf argument:

Usage

tuneandtrainExtRidge(
  data,
  dataext,
  estperf = FALSE,
  maxit = 120000,
  nlambda = 100
)

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.

estperf

A logical value indicating whether to use internal tuning with external validation (TRUE) or external tuning (FALSE). Default is FALSE.

maxit

An integer specifying the maximum number of iterations. Default is 120000.

nlambda

An integer specifying the number of lambda values to use in the Ridge model. Default is 100.

Value

A list containing the following components:

Examples

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

# Example usage with external tuning (default)
result <- tuneandtrainExtRidge(sample_data_train, sample_data_extern, maxit = 120000, nlambda = 100)
print(result$best_lambda)       # Optimal lambda
print(result$best_model)        # Final trained model
# Note: est_auc is not returned when estperf = FALSE

# Example usage with internal tuning and external validation
result_internal <- tuneandtrainExtRidge(sample_data_train, sample_data_extern, 
  estperf = TRUE, maxit = 120000, nlambda = 100)
print(result_internal$best_lambda)  # Optimal lambda
print(result_internal$best_model)   # Final trained model
print(result_internal$est_auc)      # AUC on external validation dataset

[Package RobustPrediction version 0.1.7 Index]