mylars {parcor}R Documentation

Cross-validation for Lasso

Description

This function computes the cross-validation-optimal regression coefficients for lasso.

Usage

mylars(X, y, k = 10,fraction = seq(from = 0, to = 1, length = 1000),use.Gram=TRUE,normalize=TRUE)

Arguments

X matrix of observations. The rows of X contain the samples, the columns of X contain the observed variables
y vector of responses. The length of y must equal the number of rows of X
k the number of splits in k-fold cross-validation. Default is k=10.
use.Gram When the number of variables is very large, you may not want LARS to precompute the Gram matrix. Default is use.Gram=TRUE.
fraction vector of possible regularization parameters, in the range from 0 to 1.
normalize Should the columns of X be scaled? Default is normalize=TRUE.

Details

This is a variation of the cv.lars function of the lars package. Here, we adjust the regularization parameter fraction in order to avoid its peaking behavior in the n=p case. See Kraemer (2009) for more details.

Value

coefficients cross-validation optimal regression coefficients, without intercept.
cv.lasso cv error for the optimal model.

Author(s)

Nicole Kraemer

References

R. Tibshirani (1997) "Regression Shrinkage and Selection via the Lasso", Journal of the Royal Statistical Society B, 58 (1)

N. Kraemer (2009) "On the Peaking Phenomenon of the Lasso in Model Selection", preprint, http://ml.cs.tu-berlin.de/~nkraemer/publications.html

N. Kraemer, J. Schaefer, A.-L. Boulesteix (2009) "Regularized Estimation of Large-Scale Gene Regulatory Networks with Gaussian Graphical Models", preprint

http://ml.cs.tu-berlin.de/~nkraemer/publications.html

See Also

Beta2parcor, adalasso

Examples


n<-50
p<-10
X<-matrix(rnorm(n*p),ncol=p)
y<-rnorm(n)
dummy<-mylars(X,y,k=5)


[Package parcor version 0.1 Index]