graphmcs  R Documentation 
Returns (if it exists) a perfect ordering of the vertices in an undirected graph.
mcs(object, root = NULL, index = FALSE) ## Default S3 method: mcs(object, root = NULL, index = FALSE) mcsMAT(amat, vn = colnames(amat), root = NULL, index = FALSE) mcs_marked(object, discrete = NULL, index = FALSE) ## Default S3 method: mcs_marked(object, discrete = NULL, index = FALSE) mcs_markedMAT(amat, vn = colnames(amat), discrete = NULL, index = FALSE)
object 
An undirected graph represented either as a

root 
A vector of variables. The first variable in the perfect ordering will be the first variable on 'root'. The ordering of the variables given in 'root' will be followed as far as possible. 
index 
If TRUE, then a permutation is returned 
amat 
Adjacency matrix 
vn 
Nodes in the graph given by adjacency matrix 
discrete 
A vector indicating which of the nodes are discrete. See 'details' for more information. 
An undirected graph is decomposable iff there exists a
perfect ordering of the vertices. The maximum cardinality
search algorithm returns a perfect ordering of the vertices if
it exists and hence this algorithm provides a check for
decomposability. The mcs()
functions finds such an
ordering if it exists.
The notion of strong decomposability is used in connection with e.g. mixed interaction models where some vertices represent discrete variables and some represent continuous variables. Such graphs are said to be marked. The \code{mcsmarked()} function will return a perfect ordering iff the graph is strongly decomposable. As graphs do not know about whether vertices represent discrete or continuous variables, this information is supplied in the \code{discrete} argument.
A vector with a linear ordering (obtained by maximum cardinality search) of the variables or character(0) if such an ordering can not be created.
The workhorse is the mcsMAT
function.
Søren Højsgaard, sorenh@math.aau.dk
moralize
, junction_tree
,
rip
, ug
, dag
uG < ug(~ me:ve + me:al + ve:al + al:an + al:st + an:st) mcs(uG) mcsMAT(as(uG, "matrix")) ## Same as uG < ug(~ me:ve + me:al + ve:al + al:an + al:st + an:st, result="matrix") mcsMAT(uG) ## Marked graphs uG1 < ug(~ a:b + b:c + c:d) uG2 < ug(~ a:b + a:d + c:d) ## Not strongly decomposable: mcs_marked(uG1, discrete=c("a","d")) ## Strongly decomposable: mcs_marked(uG2, discrete=c("a","d"))
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