LassoCMA-methods: L1 penalized logistic regression

In CMA: Synthesis of microarray-based classification

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.

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