grpreg-package {grpreg} | R Documentation |
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.
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.
Patrick Breheny <patrick-breheny@uiowa.edu>
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
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)