.cv.glmnet

see <code><a rd-options="glmnet" href="/link/cv.glmnet?package=starnet&version=0.0.3&to=glmnet" data-mini-rdoc="glmnet::cv.glmnet">cv.glmnet</a></code>

maximum number of non-zero coefficients:
positive integer

nzero

Wrapper for <code><a rd-options="glmnet" href="/link/cv.glmnet?package=starnet&version=0.0.3&to=glmnet" data-mini-rdoc="glmnet::cv.glmnet">cv.glmnet</a></code>,
with different handling of sparsity constraints.

Implements stacked elastic net regression (Rauschenberger 2020, <doi:10.1093/bioinformatics/btaa535>). The elastic net generalises ridge and lasso regularisation (Zou 2005, <doi:10.1111/j.1467-9868.2005.00503.x>). Instead of fixing or tuning the mixing parameter alpha, we combine multiple alpha by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>).

Armin Rauschenberger

starnet

Stacked Elastic Net

.cv.glmnet function

see <code><a rd-options='glmnet' href='cv.glmnet'>cv.glmnet</a></code>

Wrapper for <code><a rd-options='glmnet' href='cv.glmnet'>cv.glmnet</a></code>,
with different handling of sparsity constraints.

.cv.glmnet: glmnet::cv.glmnet

Description

Usage

Arguments

Value

Examples