adalasso.net(X, k = 10,use.Gram=FALSE,both=TRUE,verbose=FALSE,intercept=TRUE)X contain the
samples, the columns of X contain the observed variables.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 value is k=10. use.Gram=FALSE. verbose=FALSEintercept=TRUE.X, a regression model based on
(adaptive) lasso is computed. 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! Finally, the results of the regression models are
transformed via the function Beta2parcor.
N. Kraemer, J. Schaefer, A.-L. Boulesteix (2009) "Regularized Estimation of Large-Scale Gene Regulatory Networks using Gaussian Graphical Models", BMC Bioinformatics, 10:384
Beta2parcor, adalasson<-20
p<-10
X<-matrix(rnorm(n*p),ncol=p)
pc<-adalasso.net(X,k=5)
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