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abn

plot-abn

Plot an ABN graphic

Description

Plot an ABN DAG using formula statement or a matrix in using Rgraphviz through the graphAM class.

Usage

plotAbn(dag, data.dists=NULL, markov.blanket.node=NULL, fitted.values=NULL,
        digits=2, edge.strength=NULL, edge.direction="pc", edge.color="black",
        edge.arrowsize=0.5, edge.fontsize=5, node.fontsize=10,
        node.fillcolor=c("lightblue","brown3","chartreuse3"), node.fillcolor.list=NULL,
        node.shape=c("circle","box","ellipse","diamond"), plot=TRUE , ...)

Arguments

`dag`	a matrix or a formula statement (see details for format) defining the network structure, a Directed Acyclic Graph (DAG). Note that rownames must be set or given in `data.dists`.
`data.dists`	a named list giving the distribution for each node in the network, see details.
`markov.blanket.node`	name of variables to display its Markov blanket.
`fitted.values`	modes or coefficents outputted from `fitAbn`.
`digits`	number of digits to display the `fitted.values`.
`edge.strength`	a named matrix containing evaluations of edge strength which will change the arcs width (could be Mutual information, p-values, number of bootstrap retrieve samples or the outcome of the `link.strength`).
`edge.direction`	character giving the direction in which arcs should be plotted, `pc` (parent to child) or `cp` (child to parent) or `undirected`.
`node.fillcolor`	the colour of the node. Second and third element is used for the Markov blanket and node of the Markov blanket.
`node.shape`	the shape of the nodes according the Gaussian, binomial, Poisson and multinomial distributions.
`edge.color`	the colour of the edge.
`edge.arrowsize`	the thickness of the arrows. Not relevant if `arc.strength` is provided.
`node.fontsize`	the font size of the nodes names.
`edge.fontsize`	the font size of the arcs fitted values.
`plot`	logical variable, if set to `TRUE` then the graph is plotted.
`node.fillcolor.list`	the list of node that should be coloured.
`...`	arguments passed to the plotting function.

Details

By default binomial nodes are squares, multinoial nodes are empty, Gaussian nodes are circles and poison nodes are ellipses.

The dag can be provided using a formula statement (similar to glm). A typical formula is ~ node1|parent1:parent2 + node2:node3|parent3. The formula statement have to start with ~. In this example, node1 has two parents (parent1 and parent2). node2 and node3 have the same parent3. The parents names have to exactly match those given in name. : is the separator between either children or parents, | separates children (left side) and parents (right side), + separates terms, . replaces all the variables in name.

Value

A matrix giving the DAG definition of the most probable posterior structure.

Author(s)

Gilles Kratzer

References

Further information about abn can be found at:
http://r-bayesian-networks.org

Examples

#Define distribution list
dist <- list(a="gaussian", b="gaussian", c="gaussian", d="gaussian", e="binomial", f="binomial")

#Define a matrix formulation
arc.strength <- matrix(c(0,0.5,0.5,0.7,0.1,0,
                              0,0,0.3,0.1,0,0.8,
                              0,0,0,0.35,0.66,0,
                              0,0,0,0,0.9,0,
                              0,0,0,0,0,0.8,
                              0,0,0,0,0,0),nrow = 6L, ncol = 6L, byrow = TRUE)

#Naming of the matrix
colnames(arc.strength) <- rownames(arc.strength) <- names(dist)

#Plot from a formula
plotAbn(dag = ~a|b:c:e+b|c:d:f+e|f, data.dist = dist)

#Plot form a matrix
plotAbn(dag = arc.strength, data.dist = dist)

#Arc strength
plotAbn(dag = ~a|b:c:e+b|c:d:f+e|f, data.dist = dist, arc.strength = arc.strength)

#Markov blanket
plotAbn(dag = ~a|b:c:e+b|c:d:f+e|f, data.dists = dist, markov.blanket.node = "e")

abn

Modelling Multivariate Data with Additive Bayesian Networks

v2.5-0

GPL (>= 2)

Authors

Gilles Kratzer [aut, cre] (<https://orcid.org/0000-0002-5929-8935>), Fraser Iain Lewis [aut] (<https://orcid.org/0000-0003-4580-2712>), Reinhard Furrer [ctb] (<https://orcid.org/0000-0002-6319-2332>), Marta Pittavino [ctb] (<https://orcid.org/0000-0002-1232-1034>)

Initial release

2021-04-21