LassoCMA-methods: 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.