| 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)