pls.net: Partial Correlations with Partial Least Squares

Description Usage Arguments Details Value Author(s) References Examples

Description

This function computes the matrix of partial correlations via an estimation of the corresponding regression models via Partial Least Squares.

Usage

1
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

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.

Value

pcor

estimated matrix of partial correlation coefficients.

m

optimal number of components for each of the ncol(X) regression models.

Author(s)

Nicole Kraemer

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

http://www.biomedcentral.com/1471-2105/10/384/

Examples

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n<-20
p<-40
X<-matrix(rnorm(n*p),ncol=p)
pc<-pls.net(X,ncomp=10,k=5)

parcor documentation built on May 19, 2017, 9:23 a.m.

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