sinCG: SIN for chain graphs
In SIN: A SINful Approach to Selection of Gaussian Graphical Markov Models

Description Usage Arguments Value References See Also Examples

This function computes the matrix of simultaneous p-values for SIN model selection for chain graphs. SIN assumes that a dependence chain or blocking of the variables is available.

1	sinCG(blocks, S, n, type="AMP", holm=TRUE)

`blocks`	a list of integer vectors with entries amongst 1,…,p where p is the number of variables. Each one of the integer vectors specifies a set of variables that form a block in the chain graph. Furthermore, a partial ordering of the variables is specified by the convention that variables in one block are ordered smaller than variables in a block succeeding in the list `blocks`.
`S`	a covariance or correlation matrix.
`n`	the sample size.
`type`	a string equal to either `"AMP"` or `"LWF"` that determines whether the chain graph is interpreted with respect to the Andersson, Madigan, Perlman Markov property or the Lauritzen, Wermuth, Frydenberg Markov property.
`holm`	Boolean variable indicating whether Holm's p-value adjustment should be used (holm=TRUE) or not (holm=FALSE).

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

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.

Andersson, S.A., Madigan, D. \& Perlman, M.D. (2001) Alternative Markov Properties for Chain Graphs. Scandinavian Journal of Statistics 28(1): 33-85.

Lauritzen, S. (1996) Graphical Models. Oxford University Press: Oxford.

plotCGpvalues

data(fowlbones)
p <- dim(fowlbones$corr)[1]
blocks <- list(1:2,3:4,5:6)
sinCG(blocks,fowlbones$corr,fowlbones$n, type="AMP")
sinCG(blocks,fowlbones$corr,fowlbones$n, type="LWF")
sinCG(blocks,fowlbones$corr,fowlbones$n, type="AMP", holm=FALSE)
sinCG(blocks,fowlbones$corr,fowlbones$n, type="LWF", holm=FALSE)