LassoCMA-methods {CMA}R Documentation

L1 penalized logistic regression

Description

The Lasso (Tibshirani, 1996) is one of the most popular tools for simultaneous shrinkage and variable selection. Recently, Young-Park and Hastie (2007) have developped and algorithm to compute the entire solution path of the Lasso for an arbitrary generalized linear model, implemented in the package glmpath. The method can be used for variable selection alone, s. GeneSelection

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 LassoCMA.


[Package CMA version 1.0.0 Index]