gRbase (version 1.6-11)

triangulate: Triangulation of an undirected graph

Description

This function will triangulate an undirected graph by adding fill-ins.

Usage

triangulate(object, ...)
## S3 method for class 'graphNEL':
triangulate(object, method="mcwh", nLevels = rep(2,length(nodes(object))), 
                               result="graphNEL",...)
## S3 method for class 'matrix':
triangulate(object, method="mcwh", nLevels = rep(2,ncol(object)), result="matrix",...)
## S3 method for class 'Matrix':
triangulate(object, method="mcwh", nLevels = rep(2,ncol(object)), result="Matrix",...)
triangulateMAT(amat, method="mcwh", nLevels=rep(2,ncol(amat)), result=NULL, ...)

Arguments

object

An undirected graph represented either as a graphNEL object, a (dense) matrix, a (sparse) dgCMatrix

method

Triangulation method. Only valid option is "mcwh" (minimum clique weight heuristic). See section 'details'.

nLevels

Typically, the number of levels of the variables (nodes) when these are discrete. Used in determining the triangulation using a "minimum clique weight heuristic". See section 'details'.

result

The type (representation) of the result. Same possible representations as the input graph, and default is the same type as the input graph.

...

Additional arguments, currently not used.

amat

Adjacency matrix; a (dense) matrix, or a (sparse) dgCMatrix.

Value

• A triangulated graph represented either as a graphNEL, a (dense) matrix or a (sparse) dgCMatrix.

Details

The triangulation is made so as the total state space is kept low by applying a minimum clique weight heuristic: When a fill-in is necessary, the algorithm will search for an edge to add such that the complete set to be formed will have as small a state-space as possible. If nLevels is the same for all nodes then the heuristic aims at keeping the clique sizes small.

See Also

ug dag mcs, mcsMAT rip, ripMAT, moralize, moralizeMAT

Examples

## graphNEL
uG1 <- ug(~a:b+b:c+c:d+d:e+e:f+f:a)
tuG1 <- triangulate(uG1)

## adjacency matrix
uG2 <- ug(~a:b+b:c+c:d+d:e+e:f+f:a, result="matrix")
tuG2 <- triangulate(uG2)

## adjacency matrix (sparse)
uG2 <- ug(~a:b+b:c+c:d+d:e+e:f+f:a, result="Matrix")
tuG2 <- triangulate(uG2)