# bn.fit.plots: Plot fitted Bayesian networks In 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

 ```1 2 3 4 5 6 7 8 9``` ```## for Gaussian Bayesian networks. bn.fit.qqplot(fitted, xlab = "Theoretical Quantiles", ylab = "Sample Quantiles", main, ...) bn.fit.histogram(fitted, density = TRUE, xlab = "Residuals", ylab = ifelse(density, "Density", ""), main, ...) bn.fit.xyplot(fitted, xlab = "Fitted values", ylab = "Residuals", main, ...) ## for discrete (multinomial and ordinal) Bayesian networks. bn.fit.barchart(fitted, xlab = "Probabilities", ylab = "Levels", main, ...) bn.fit.dotplot(fitted, xlab = "Probabilities", ylab = "Levels", main, ...) ```

## 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

`bn.fit`, `bn.fit class`.