# 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

 `1` ```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

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),

 ```1 2 3 4 5 6 7``` ```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) ```