sinBG: SIN for bidirected graphs

In SIN: A SINful Approach to Selection of Gaussian Graphical Markov Models

Description

This function computes the matrix of simultaneous p-values for SIN model selection for bidirected (or covariance) graphs.

Usage

sinBG(S,n,holm=TRUE)

Arguments

S	a covariance or correlation matrix.
n	the sample size,
holm	Boolean variable indicating whether Holm's p-value adjustment should be used (holm=TRUE) or not (holm=FALSE).

Value

A matrix of simultaneous p-values with NA on the diagonal.

References

Drton, M. \& Perlman, M.D. (2004) Model Selection for Gaussian Concentration Graphs. Biometrika 91(3): 591-602.

Drton, M. \& Perlman, M.D. (2008) A SINful Approach to Gaussian Graphical Model Selection. J. Statist. Plann. Inference 138(4): 1179-1200.

See Also

plotBGpvalues

Examples

data(stressful)
sinBG(stressful$corr,stressful$n)
sinBG(stressful$corr,stressful$n,holm=FALSE)

Example output

                    cognitive avoidance vigilance  blunting monitoring
cognitive avoidance                  NA 0.3207896 0.0001806  0.9956171
vigilance                     0.3207896        NA 1.0000000  0.0001359
blunting                      0.0001806 1.0000000        NA  0.5056668
monitoring                    0.9956171 0.0001359 0.5056668         NA
                    cognitive avoidance vigilance  blunting monitoring
cognitive avoidance                  NA 0.4402340 0.0002167  0.9999999
vigilance                     0.4402340        NA 1.0000000  0.0001359
blunting                      0.0002167 1.0000000        NA  0.7556347
monitoring                    0.9999999 0.0001359 0.7556347         NA

SIN documentation built on May 2, 2019, 8:23 a.m.