Beta2parcor: Computation of partial correlation coefficients

In parcor: Regularized estimation of partial correlation matrices

Description

This function computes the matrix of partial correlation coefficients based on the results of the corresponding regression models.

Usage

Beta2parcor(Beta,verbose=FALSE)

Arguments

Beta	matrix of regression coefficients
verbose	print information on conflicting signs etc. Default is verbose=FALSE.

Details

A well-known result (Whittaker, 1990) shows that the matrix of partial correlation coefficients can be estimated by computing a least squares regression model for each variable. If there are more variables than observations, the least squares problem is ill-posed and needs regularization. The matrix Beta stores the regression coefficients of any user-defined regression method. The function Beta2parcor computes the corresponding matrix of partial correlations.

Value

matrix of partial correlation coefficients

Note

This is an internal function.

Author(s)

Nicole Kraemer

References

J. Whittaker (1990) "Graphical models in applied multivariate statistics", Wiley, New York.

N. Kraemer, J. Schaefer, A.-L. Boulesteix (2009) "Regularized Estimation of Large-Scale Gene Regulatory Networks with Gaussian Graphical Models", BMC Bioinformatics, 10:384

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

See Also

ridge.net, adalasso.net,pls.net

Examples

# this is an internal function and should not be called by the user

parcor documentation built on May 1, 2019, 9:10 p.m.