bnlearn (version 5.2)

strength.plot: Arc strength plot

Description

Plot a Bayesian network and format its arcs according to the strength of the dependencies they represent. Requires the Rgraphviz package.

Usage

strength.plot(x, strength, threshold, cutpoints, highlight = NULL, groups,
  layout = "dot", shape = "rectangle", fontsize = 12, main = NULL, sub = NULL,
  render = TRUE, debug = FALSE)

Value

graphviz.plot() returns invisibly the graph object produced by

Rgraphviz. It can be further modified using the commands present in the

graph and Rgraphviz packages, and it contains the arc strengths in the edge weight attribute.

Arguments

x

an object of class bn.

strength

an object of class bn.strength computed from the object of class bn corresponding to the x argument.

threshold

a numeric value. See below.

cutpoints

an array of numeric values. See below.

highlight

a list, see graphviz.plot for details.

groups

a list of character vectors, representing groups of node labels of nodes that should be plotted close to each other.

layout

a character string, the layout argument that will be passed to Rgraphviz. Possible values are "dot", "neato", "twopi", "circo" and "fdp". See Rgraphviz documentation for details.

shape

a character string, the shape of the nodes. Can be "circle", "ellipse" or "rectangle".

fontsize

a positive number, the font size for the node labels.

main

a character string, the main title of the graph. It's plotted at the top of the graph.

sub

a character string, a subtitle which is plotted at the bottom of the graph.

debug

a boolean value. If TRUE, a lot of debugging output is printed. Otherwise, the function is completely silent.

render

a logical value. If TRUE, strength.plot() actually draws the figure in addition to returning the corresponding graph object. If FALSE, no figure is produced.

Author

Marco Scutari

Details

The threshold argument is used to determine which arcs are supported strongly enough by the data to be deemed significant:

• If arc strengths have been computed using conditional independence tests, any strength coefficient (which is the p-value of the test) less than or equal to the threshold is considered significant.

• If arc strengths have been computed using network scores, any strength coefficient (which is the increase/decrease of the network score caused by the removal of the arc) less than the threshold is considered significant.

• If arc strengths have been computed using bootstrap or using Bayes factors, any strength coefficient (which can be interpreted as a probability for inclusion) greater than or equal to the threshold is considered significant.

The default value is the value of the strength attribute of the bn.strength object passed via the strength argument.

Non-significant arcs are plotted as dashed lines.

The cutpoints argument is an array of numeric values used to divide the range of strength coefficients into intervals. The interval each strength coefficient falls into determines the line width of the corresponding arc in the plot. The default intervals are delimited by

unique(c(0, threshold/c(10, 5, 2, 1.5, 1), 1))

if the coefficients are computed from conditional independence tests, by

unique(c(0, (1 - threshold)/c(10, 5, 2, 1.5, 1), 1))

for bootstrap estimates or by the quantiles

quantile(s[s < threshold], 1 - c(0.50, 0.75, 0.90, 0.95, 1))

of the significant differences if network scores are used.

Examples

if (FALSE) {
# plot the network learned by hc().
dag = hc(learning.test)
strength = arc.strength(dag, learning.test, criterion = "x2")
strength.plot(dag, strength)
# add another (non-significant) arc and plot the network again.
dag = set.arc(dag, "A", "C")
strength = arc.strength(dag, learning.test, criterion = "x2")
strength.plot(dag, strength)
}