MRG | R Documentation |
MRG
generates and plots maximal ribbonless graphs (a modification
of MC graph to use m-separation) after marginalisation and conditioning.
MRG(amat,M=c(),C=c(),showmat=TRUE,plot=FALSE, plotfun = plotGraph, ...)
amat |
An adjacency matrix, or a graph that can be a |
M |
A subset of the node set of |
C |
Another disjoint subset of the node set of |
showmat |
A logical value. |
plot |
A logical value, |
plotfun |
Function to plot the graph when |
... |
Further arguments passed to |
This function uses the functions RG
and Max
.
A matrix that consists 4 different integers as an ij
-element: 0 for a missing
edge between i
and j
, 1 for an arrow from i
to j
, 10 for a full line between
i
and j
, and 100 for a bi-directed arrow between i
and j
. These numbers are
added to be associated with multiple edges of different types. The matrix is
symmetric w.r.t full lines and bi-directed arrows.
Kayvan Sadeghi
Koster, J.T.A. (2002). Marginalizing and conditioning in graphical models. Bernoulli, 8(6), 817-840.
Richardson, T.S. and Spirtes, P. (2002). Ancestral graph Markov models. Annals of Statistics, 30(4), 962-1030.
Sadeghi, K. (2013). Stable mixed graphs. Bernoulli 19(5B), 2330–2358.
Sadeghi, K. and Lauritzen, S.L. (2014). Markov properties for loopless mixed graphs. Bernoulli 20(2), 676-696.
MAG
, Max
, 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)
MRG(ex, M, C, plot = TRUE)
###################################################
H <- matrix(c( 0, 100, 1, 0,
100, 0, 100, 0,
0, 100, 0, 100,
0, 1, 100, 0), 4,4)
Max(H)
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