R: Maximal ancestral graph

MAG {ggm} | R Documentation

## Maximal ancestral graph

### Description

`MAG` generates and plots maximal ancestral graphs after marginalisation and conditioning.

### Usage

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

### Arguments

`amat` | An adjacency matrix, or a graph that can be a `graphNEL` 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`.

### Details

This function uses the functions `AG` and `Max`.

### Value

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.

### 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. (2011). Stable classes of graphs containing directed acyclic graphs. Submitted.

Sadeghi, K. and Lauritzen, S.L. (2011). Markov properties for loopless mixed graphs. Submitted. URL http://arxiv.org/abs/1109.5909.

### See Also

`AG`, `Max`, `MRG`, `MSG`

### Examples

    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)
    MAG(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)

[Package ggm version 2.3 Index]