bnlearn: graphviz.chart – R documentation

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graphviz.chart

Plotting networks with probability bars

Description

Plot a Bayesian network as a graph whose nodes are barplots representing the marginal probability distributions of the corresponding variables. Requires the Rgraphviz and gRain packages.

Usage

graphviz.chart(x, type = "barchart", layout = "dot", draw.levels = TRUE,
  grid = FALSE, scale = c(0.75, 1.1), col = "black", bg = "transparent",
  text.col = "black", bar.col = "black", strip.bg = bg, main = NULL,
  sub = NULL)

Arguments

`x`	an object of class `bn.fit` representing a discrete Bayesian network.
`type`	a character string, the type of graph used to plot the probability distributions in the nodes. Possible values are `barchart`, `dotplot` and `barprob` (a barchart with probabilities printed over the bars).
`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.
`draw.levels`	a boolean value, whether to print the labels of the levels of each variable.
`grid`	a boolean value, whether to draw to a reference grid for the probability distributions. If `grid` is `TRUE`, a vertical grid is drawn at probabilities `c(0, 0.25,` `0.50, 0.75)`. If `grid` is a numeric vector, a verical grid is drawn at the specified probabilities.
`scale`	a vector of two positive numbers, used by Rgraphviz to determine the size and the aspect ratio of the nodes.
`col, bg, text.col, bar.col, strip.bg`	the colours of the node border, of the barchart background, of the text, of the bars and of the strip background.
`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.

Value

graphviz.chart() invisibly returns NULL.

Author(s)

Marco Scutari

Examples

## Not run: 
modelstring = paste("[HIST|LVF][CVP|LVV][PCWP|LVV][HYP][LVV|HYP:LVF][LVF]",
  "[STKV|HYP:LVF][ERLO][HRBP|ERLO:HR][HREK|ERCA:HR][ERCA][HRSA|ERCA:HR][ANES]",
  "[APL][TPR|APL][ECO2|ACO2:VLNG][KINK][MINV|INT:VLNG][FIO2][PVS|FIO2:VALV]",
  "[SAO2|PVS:SHNT][PAP|PMB][PMB][SHNT|INT:PMB][INT][PRSS|INT:KINK:VTUB][DISC]",
  "[MVS][VMCH|MVS][VTUB|DISC:VMCH][VLNG|INT:KINK:VTUB][VALV|INT:VLNG][ACO2|VALV]",
  "[CCHL|ACO2:ANES:SAO2:TPR][HR|CCHL][CO|HR:STKV][BP|CO:TPR]", sep = "")
dag = model2network(modelstring)
fitted = bn.fit(dag, alarm)

# Netica style.
graphviz.chart(fitted, grid = TRUE, bg = "beige", bar.col = "black")
# Hugin style.
graphviz.chart(fitted, type = "barprob", grid = TRUE, bar.col = "green",
  strip.bg = "lightyellow")
# GeNIe style.
graphviz.chart(fitted, col = "darkblue", bg = "azure", bar.col = "darkblue")
# personal favourites.
graphviz.chart(fitted, type = "barprob", grid = TRUE, bar.col = "darkgreen",
  strip.bg = "lightskyblue")
graphviz.chart(fitted, type = "barprob", grid = TRUE, bar.col = "gold",
  strip.bg = "lightskyblue")
# dot-plot version.
graphviz.chart(fitted, type = "dotplot")

## End(Not run)

bnlearn

Bayesian Network Structure Learning, Parameter Learning and Inference

v4.6.1

GPL (>= 2)

Authors

Marco Scutari [aut, cre], Robert Ness [ctb]

Initial release

2020-09-16