adalasso {parcor}R Documentation

Adaptive Lasso

Description

This function computes the lasso and adaptive lasso solution based on k-fold cross-validation. The initial weights for adaptive lasso are computed from a lasso fit.

Usage

adalasso(X, y, k = 10, use.Gram = TRUE,both=TRUE)

Arguments

X matrix of input 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. The same k is used for the estimation of the weights and the estimation of the penalty term for adaptive lasso. 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.
both Logical. If both=FALSE, only the lasso coefficients are computed. Default is both=TRUE.

Details

In each of the k-fold cross-validation steps, the weights for adaptive lasso are computed in terms of a lasso fit. (The optimal value of the penalty term is selected via k-fold cross-validation). Note that this implies that a lasso solution is computed k*k times!

Value

intercept.lasso intercept for lasso.
intercept.adalasso intercept for adaptive lasso.
coefficients.adalasso regression coefficients for adaptive lasso.
coefficients.lasso regression coefficients for lasso.
cv.lasso cv error for the optimal lasso model.
cv.adalasso cv error for the optimal adaptive lasso model.
lambda.lasso optimal lambda value for lasso-
lambda.adalasso optimal lambda value for adaptive lasso.

Author(s)

Nicole Kraemer, Juliane Schaefer

References

H. Zou (2006) "The Adaptive Lasso and its Oracle Property", Journal of the American Statistical Association 101 (476): 1418-1429.

N. Kraemer, J. Schaefer, A.-L. Boulesteix (2009) "Regularized Estimation of Large-Scale Gene Regulatory Networks using Gaussian Graphical Models", BMC Bioinformatics, 10:384

http://www.biomedcentral.com/1471-2105/10/384/

See Also

Beta2parcor, adalasso.net

Examples

n<-100 # number of observations
p<-60 # number of variables
X<-matrix(rnorm(n*p),ncol=p) 
y<-rnorm(n)
ada.object<-adalasso(X,y,k=10)

[Package parcor version 0.2-2 Index]