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SG
generates and plots summary graphs after marginalization
and conditioning.SG(amat,M=c(),C=c(),showmat=TRUE,plot=FALSE, plotfun = plotGraph, ...)
graphNEL
or an igraph
object
or a vector of length $3e$, where $e$ is the number of edges of the graph,
that is a sequence of triples (type, node1a
that is going to be marginalised overa
that is going to be
conditioned on.TRUE
(by default) to print the generated matrix.FALSE
(by default). TRUE
to plot
the generated graph.plot == TRUE
. Can be plotGraph
(the default) or drawGraph
.plotfun
.Wermuth, N. (2011). Probability distributions with summary graph structure. Bernoulli, 17(3),845-879.
AG
, MSG
, RG
ex <- matrix(c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, ##The adjacency matrix of a DAG
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,
0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,
0,0,0,0,1,0,1,0,1,1,0,0,0,0,0,0,
1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,
0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0),16,16, byrow = TRUE)
M <- c(3,5,6,15,16)
C <- c(4,7)
SG(ex, M, C, plot = TRUE)
SG(ex, M, C, plot = TRUE, plotfun = drawGraph, adjust = FALSE)
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