This function computes the matrix of partial correlation coefficients based on the results of the corresponding regression models.
matrix of regression coefficients
print information on conflicting signs etc. Default is
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.
matrix of partial correlation coefficients
This is an internal function.
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
# this is an internal function and should not be called by the user