mylars {parcor} | R Documentation |
This function computes the cross-validation-optimal regression coefficients for lasso.
mylars(X, y, k = 10,use.Gram=TRUE,normalize=TRUE)
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. |
normalize |
Should the columns of X be scaled? Default is normalize=TRUE. |
We use the glmnet() function from the glmnet package to compute the fit. Note that in Kraemer et. al. (2009), we used the lars() function from the lars package, which is much slower than glmnet().
lambda |
vector of paramter values from which the optimal parameter is selected |
cv |
cross-validated error for all lambda values |
lambda.opt |
cross-validation optimal parameter |
cv.lasso |
cv error for the optimal model. |
intercept |
cross-validation optimal intercept |
coefficients |
cross-validation optimal regression coefficients, without intercept |
Nicole Kraemer
R. Tibshirani (1997) "Regression Shrinkage and Selection via the Lasso", Journal of the Royal Statistical Society B, 58 (1)
N. Kraemer, J. Schaefer, A.-L. Boulesteix (2009) "Regularized Estimation of Large-Scale Gene Regulatory Networks with Gaussian Graphical Models", BMC Bioinformatics, 10:384
http://www.biomedcentral.com/1471-2105/10/384/
n<-20 p<-50 X<-matrix(rnorm(n*p),ncol=p) y<-rnorm(n) dummy<-mylars(X,y)