Learn R Programming

parcor (version 0.2-6)

adalasso: 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,intercept=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.
intercept
Should an intercept be included? Default is intercept=TRUE.

Value

intercept.lasso
intercept for lasso. If intercept=FALSE was specified, the intercept is set to 0.
intercept.adalasso
intercept for adaptive lasso. If intercept=FALSE was specified, the intercept is set to 0.
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.

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!

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

Run this code
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)

Run the code above in your browser using DataLab