ElasticNetCMA-methods: Classfication and variable selection by the ElasticNet

In CMA: Synthesis of microarray-based classification

Description

Zou and Hastie (2004) proposed a combined L1/L2 penalty for regularization and variable selection. The Elastic Net penalty encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model together. The computation is done with the function glmpath from the package of the same name.

Methods

X = "matrix", y = "numeric", f = "missing"

signature 1

X = "matrix", y = "factor", f = "missing"

signature 2

X = "data.frame", y = "missing", f = "formula"

signature 3

X = "ExpressionSet", y = "character", f = "missing"

signature 4

For references, further argument and output information, consult ElasticNetCMA

CMA documentation built on Nov. 8, 2020, 5:02 p.m.