adalasso: Adaptive Lasso

In parcor: Regularized estimation of partial correlation matrices

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.

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. 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.

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)

Example output

Loading required package: MASS
Loading required package: glmnet
Loading required package: Matrix
Loading required package: foreach
Loaded glmnet 2.0-16

Loading required package: ppls
Loading required package: splines
Loading required package: Epi
Loading required package: GeneNet
Loading required package: corpcor
Loading required package: longitudinal
Loading required package: fdrtool

parcor documentation built on May 1, 2019, 9:10 p.m.