glmlep-package: Regularization paths for LEP-penalized regression models

In glmlep: Fit GLM with LEP-Based Penalized Maximum Likelihood

Description

Efficient algorithms for fitting regularization paths for linear or logistic regression models penalized by LEP.

Details

Package:	glmlep
Type:	Package
Version:	0.1
Date:	2013-06-05
License:	GPL-2

Accepts a design matrix X and vector of responses y, produces the regularization path over a grid of values for the tuning parameter lambda. Also provides methods for plotting and for determining locally convex regions of the coefficients paths.

Author(s)

Canhong Wen, Hao Lin, Shaoli Wang and Xueqin Wang.

Maintainer: Canhong Wen <[e-mail]>

References

Wen, C., Wang, X., & Wang, S. (2013). Laplace Error Penalty based variable selection in ultra high-dimension. In press.

Examples

## generate data
require(mvtnorm)
n <- 100;
beta <- c(3,1.5,0,0,2,0,0,0)

set.seed(100)
p <- length(beta);
corr_data <- diag(rep(1,p));

x <- as.matrix(rmvnorm(n,rep(0,p),corr_data))
noise <- rnorm(n)

y <- tcrossprod(x,t(beta)) + noise;
fit <- glmlep(x,y,family="gaussian")

glmlep documentation built on May 1, 2019, 9:14 p.m.