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, Friedman, Hastie and Tibshirani (2008) have developped and algorithm to compute the entire solution path of the Lasso for an arbitrary generalized linear model, implemented in the package `glmnet`. 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 1.48.0 Index]