adalasso {parcor} | R Documentation |
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.
adalasso(X, y, k = 10, use.Gram = TRUE,both=TRUE)
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. |
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!
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. |
Nicole Kraemer, Juliane Schaefer
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/
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)