Maximum cardinality search on undirected graph.
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
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 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"))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.