pls.net {parcor} | R Documentation |
This function computes the matrix of partial correlations via an estimation of the corresponding regression models via Partial Least Squares.
pls.net(X, scale = TRUE, k = 10, ncomp = 15,verbose=FALSE)
X |
matrix of observations. The rows of X contain the
samples, the columns of X contain the observed variables. |
scale |
Scale the columns of X? Default is scale=TRUE. |
k |
Number of splits in k -fold cross-validation. Default value is k =10. |
ncomp |
Maximal number of components. Default is 15. |
verbose |
Print information on conflicting signs etc. Default is verbose=FALSE |
For each of the columns of X
, a regression model based on
Partial Least Squares is computed. The optimal model is determined via
cross-validation. The results of the regression models are
transformed via the function Beta2parcor
.
pcor |
estimated matrix of partial correlation coefficients. |
m |
optimal number of components for each of the ncol(X) regression models. |
Nicole Kraemer
N. Kraemer, J. Schaefer, A.-L. Boulesteix (2009) "Regularized Estimation of Large-Scale Gene Regulatory Networks using Gaussian Graphical Models", preprint
http://ml.cs.tu-berlin.de/~nkraemer/publications.html
n<-50 p<-10 X<-matrix(rnorm(n*p),ncol=p) pc<-pls.net(X,ncomp=5,k=5)