grpreg-package {grpreg}R Documentation

Regularization paths for regression models with grouped covariates

Description

This package fits regularization paths for linear or logistic regression models penalized by the group lasso, group bridge, or group MCP methods. The algorithm is based on the idea of a locally approximated coordinate descent, and is stable and very fast.

Details

Package: grpreg
Type: Package
Version: 1.0
Date: 2008-11-11
License: GPL-2

Accepts a list Data containing the response, design matrix, family and covariate groupings, and produces the regularization path over a grid of values for the tuning parameter lambda. Also provides methods for selecting the optimal point along the path using a variety of information criteria and for plotting the paths.

Author(s)

Patrick Breheny <patrick-breheny@uiowa.edu>

References

Breheny, P. and Huang, J. (2008) Penalized Methods for Bi-level variable selection. Tech report No. 393, Department of Statistics and Actuarial Science, University of Iowa.http://www.stat.uiowa.edu/techrep/tr393.pdf

Examples

data(birthwt.grpreg)
Data.gaussian <- list(y=birthwt.grpreg$bwt,
                      X=as.matrix(birthwt.grpreg[,c(-1,-2)]),
                      family="gaussian",
                      group=c(1,1,1,2,2,2,3,3,4,5,5,6,7,8,8,8))
Data.binomial <- list(y=birthwt.grpreg$low,
                      X=as.matrix(birthwt.grpreg[,c(-1,-2)]),
                      family="binomial",
                      group=c(1,1,1,2,2,2,3,3,4,5,5,6,7,8,8,8))

fit1.gLasso <- grpreg(Data.gaussian,"gLasso")
fit1.gBridge <- grpreg(Data.gaussian,"gBridge",lambda.max=0.08)
fit1.gMCP <- grpreg(Data.gaussian,"gMCP")

fit2.gLasso <- grpreg(Data.binomial,"gLasso")
fit2.gBridge <- grpreg(Data.binomial,"gBridge",lambda.max=0.06)
fit2.gMCP <- grpreg(Data.binomial,"gMCP")

plot(fit1.gMCP)
select(fit2.gLasso)

[Package grpreg version 1.0 Index]