bn.fit.plots: Plot fitted Bayesian networks

In vspinu/bnlearn: Bayesian network structure learning, parameter learning and inference

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 (multinomial and ordinal) 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

See Also

bn.fit, bn.fit class.

vspinu/bnlearn documentation built on May 3, 2019, 7:08 p.m.