pls.net: Partial Correlations with Partial Least Squares
Description
This function computes the matrix of partial correlations via an
estimation of the corresponding regression models via Partial Least Squares.
Usage
pls.net(X, scale = TRUE, k = 10, ncomp = 15,verbose=FALSE)
Arguments
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
Value
pcor
estimated matrix of partial correlation coefficients.
m
optimal number of components for each of the ncol(X) regression models.
Details
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.
References
N. Kraemer, J. Schaefer, A.-L. Boulesteix (2009) "Regularized Estimation of
Large-Scale Gene Regulatory Networks using Gaussian Graphical Models", BMC Bioinformatics, 10:384