sinCG {SIN} | R Documentation |
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.
sinCG(blocks, S, n, type="AMP", holm=TRUE)
blocks |
a list of integer vectors with entries amongst
1,\ldots,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.
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)