R: Arc strength plot

strength.plot {bnlearn} | R Documentation

## 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,
      layout = "dot", shape = "circle", main = NULL, sub = NULL, debug = FALSE)

### 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.
`layout` | a character string, the layout argument that will be passed to Rgraphviz. Possible values are `dots`, `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`.
`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.

### 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) lesser or equal than the threshold is considered significant. In this case the default value of `threshold` is equal to the value of the `alpha` argument used in the call to `arc.strength()`, which in turn defaults to the one used by the learning algorithm (if any) or to `0.05`.

- 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) lesser than the threshold is considered significant. In this case the default value of `threshold` is `0`.

- if arc strengths have been computed using bootstrap, any strength coefficient (which is the relative frequency of the arc in the networks learned from the bootstrap replicates) greater or equal than the threshold is considered significant. In this case the default value of `threshold` is `0.5`.

Non-significant arcs are plotted as dashed lines.

The `cutpoints` argument is an array of numeric values used to divide the range of the 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

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

for bootstrap estimates or by the quantiles

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

of the significant coefficients if network scores are used.

### Value

The object of class `graphAM` used to format and render the plot. It can be further modified using the commands present in the graph and Rgraphviz packages.

### Author(s)

Marco Scutari

### Examples

    ## Not run:
    # plot the network learned by gs().
    res = set.arc(gs(learning.test), "A", "B")
    strength = arc.strength(res, learning.test, criterion = "x2")
    strength.plot(res, strength)
    # add another (non-significant) arc and plot the network again.
    res = set.arc(res, "A", "C")
    strength = arc.strength(res, learning.test, criterion = "x2")
    strength.plot(res, strength)

    ## End(Not run)

[Package bnlearn version 4.3 Index]