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 |
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 |
S |
a covariance or correlation matrix. |
n |
the sample size. |
type |
a string equal to either |
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.
1 2 3 4 5 6 7 | 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)
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