Description Usage Arguments Details Value Author(s) References See Also Examples
This function computes the matrix of partial correlations based on an estimation of the corresponding regression models via lasso and adaptive lasso respectively.
1 | adalasso.net(X, k = 10,use.Gram=FALSE,both=TRUE,verbose=FALSE,intercept=TRUE)
|
X |
matrix of observations. The rows of |
k |
the number of splits in |
use.Gram |
When the number of variables is very large, you may not want LARS to precompute the Gram matrix. Default is |
both |
Logical. If both=FALSE, only the lasso solution is computed. Default is both=TRUE. |
verbose |
Print information on conflicting signs etc. Default is |
intercept |
Should an intercept be included in the regression models? Default is |
For each of the columns of 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
.
pcor.adalasso |
estimated matrix of partial correlation coefficients for adaptive lasso. |
pcor.lasso |
estimated matrix of partial correlation coefficients for lasso. |
...
Nicole Kraemer
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/
1 2 3 4 | n<-20
p<-10
X<-matrix(rnorm(n*p),ncol=p)
pc<-adalasso.net(X,k=5)
|
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