bnlearn - Bayesian network structure learning

bn.fit plots {bnlearn}

R Documentation

Plot fitted Bayesian networks

Description

Plot functions for the bn.fit, bn.fit.dnode and bn.fit.gnode classes, based on the lattice package.

Usage

## for Gaussian Bayesian networks.
bn.fit.qqplot(fitted, xlab = "Theoretical Quantiles",
  ylab = "Sample Quantiles", main = "Normal Q-Q Plot", ...)
bn.fit.histogram(fitted, density = TRUE, xlab = "Residuals",
  ylab = ifelse(density, "Density", ""),
  main = "Histogram of the residuals", ...)
bn.fit.xyplot(fitted, xlab = "Fitted values",
  ylab = "Residuals", main = "Residuals vs Fitted", ...)
## for discrete Bayesian networks
bn.fit.barchart(fitted, xlab = "Probabilities",
  ylab = "Levels", main = "Conditional Probabilities", ...)
bn.fit.dotplot(fitted, xlab = "Probabilities",
  ylab = "Levels", main = "Conditional Probabilities", ...)

Arguments

`fitted`	an object of class `bn.fit`, `bn.fit.dnode` or `bn.fit.gnode`.
`xlab, ylab, main`	the label of the x axis, of the y axis, and the plot title.
`density`	a boolean value. If `TRUE` the histogram is plotted using relative frequencies, and the matching normal density is added to the plot.
`...`	additional arguments to be passed to lattice functions.

Details

bn.fit.qqplot draws a quantile-quantile plot of the residuals.

bn.fit.histogram draws a histogram of the residuals, using either absolute or relative frequencies.

bn.fit.xyplot plots the residuals versus the fitted values.

bn.fit.barchart and bn.fit.dotplot plot the probabilities in the conditional probability table associated with each node.

Value

The lattice plot objects. Note that if auto-printing is turned off (for example when the code is loaded with the source function), the return value must be printed explicitly for the plot to be displayed.

Author(s)

Marco Scutari