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

In covTest: Computes covariance test for adaptive linear modelling

Description

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

Usage

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

Arguments

x	N by p matrix of predictors
y	N-vector of outcome values
lambda2	Value of L2 penalty parameter
normalize	Should columns of x be standardized?

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).

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

Author(s)

Rob Tibshirani

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),

See Also

predict.lars, covTest

Examples

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)
  

covTest documentation built on May 29, 2017, 8:30 p.m.