cvVAR_ENET: Cross Validation for Elastic Net VAR Estimation

In VARcpDetectOnline: Sequential Change Point Detection for High-Dimensional VAR Models

Cross Validation for Elastic Net VAR Estimation

Description

This internal function performs cross validation using elastic net (ENET) estimation via the glmnet package. It supports parallel processing if requested.

Usage

cvVAR_ENET(X, y, nvar, opt)

Arguments

X	A numeric matrix of predictors.
y	Numeric vector of responses.
nvar	Integer. The number of variables in the original VAR (number of columns in data).
opt	List. A list of options including: alpha: The elastic net mixing parameter (default = 1). nlambda: Number of lambda values (default = 100). type.measure: Error measure for CV (default = "mse"). nfolds: Number of folds for CV (default = 10). parallel: Logical. Whether to use parallel processing (default = FALSE). ncores: Number of cores for parallel processing (default = 1). lambdas_list: Optionally, a user-specified list of lambdas. folds_ids: Optionally, user-specified fold IDs for CV.

Value

An object of class cv.glmnet as returned by glmnet::cv.glmnet.

VARcpDetectOnline documentation built on April 12, 2025, 1:44 a.m.