lars.en: Function to fit least angle regression path of solution for the elastic net.

Description

Function to fit least angle regression path of solution for the elastic net.

Usage

lars.en(x, y, lambda2,normalize=TRUE)

Arguments

N by p matrix of predictors

N-vector of outcome values

lambda2

Value of L2 penalty parameter

normalize

Should columns of x be standardized?

Value

beta: Matrix whose rows of contain the estimated coefficients for each lambda value
larsobj: Result of call to lars on augmented data
mx: Column means of x
sdx: Column standard deviations of x
normalize: Value of normalize argument in call to lars.en
lambda: Values of lambda used
lambda2: Value of lambda2 used
act: Actions (predictor added) at each step
maxp: Maximum number of predictors entered
call: Call to lars.en

Details

This function estimates the least angle regression path of solution for Ll-penalized (lasso) logistic regression and the Cox proportional hazards model, using the R functions enpath and coxpath. These latter functions use the predictor-corrector strategy devised by Park and Hastie (2007).

References

Zou, H. and Hastie, Trevor (2005) Regularization and Variable Selection via the Elastic Net. JRSSB 301-320,

Park, M. Y. & Hastie, T. (2007). l1-regularization path algorithm for generalized linear models, Journal of the Royal Statistical Society Series B 69(4),

Examples

Run this code

set.seed(432)
x=matrix(rnorm(100*10),ncol=10)
x=scale(x,TRUE,TRUE)/sqrt(99)
beta=c(3,rep(0,9))
y=x%*%beta+.4*rnorm(100)
a=lars.en(x,y,lambda2=.5)

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