AG {ggm}

Ancestral graph

Description

AG generates and plots ancestral graphs after marginalization and conditioning.

Usage

AG(amat,M=c(),C=c(),showmat=TRUE,plot=FALSE, plotfun = plotGraph, ...)

Arguments

amat

An adjacency matrix, or a graph that can be of class graphNEL-class 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, node1label, node2label). The type of edge can be "a" (arrows from node1 to node2), "b" (arcs), and "l" (lines).

M

A subset of the node set of a that is going to be marginalized over

C

Another disjoint subset of the node set of a that is going to be conditioned on.

showmat

A logical value. TRUE (by default) to print the generated matrix.

plot

A logical value, FALSE (by default). TRUE to plot the generated graph.

plotfun

Function to plot the graph when plot == TRUE. Can be plotGraph (the default) or drawGraph.

...

Further arguments passed to plotfun.

Value

A matrix that is the adjacency matrix of the generated graph. It consists of 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.

Author(s)

Kayvan Sadeghi

References

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.

See Also

MAG, RG, SG

Examples

##The adjacency matrix of a DAG
ex<-matrix(c(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,
             0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
             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)
AG(ex, M, C, plot = TRUE)