plot.cv4postpr: Barplot of model misclassification

In abc: Tools for Approximate Bayesian Computation (ABC)

Barplot of model misclassification

Description

Displays a barplot of either the proportion of simulations classified to any of the models or the mean misclassification probabilities of models for all tolerance levels in the "cv4postpr" object.

Usage

## S3 method for class 'cv4postpr'
plot(x, probs = FALSE, file = NULL, postscript
= FALSE, onefile = TRUE, ask = !is.null(deviceIsInteractive()), caption
= NULL, ...)

Arguments

x	an object of class "cv4postpr".
probs	logical, if TRUE the mean posterior model probabilities are plotted. If FALSE the frequencies of the simulations classified to the different models (default).
file	a character string giving the name of the file. See postscript for details on accepted file names. If NULL (the default) plots are printed to the null device (e.g. X11). If not NULL plots are printed on a pdf device. See also postscript.
postscript	logical; if FALSE (default) plots are printed on a pdf device, if TRUE on a postscript device.
onefile	logical, if TRUE (the default) allow multiple figures in one file. If FALSE, generate a file name containing the page number for each page. See postscript for further details.
ask	logical; if TRUE (the default), the user is asked before each plot, see par(ask=.).
caption	captions to appear above the plot(s); character vector of valid graphics annotations, see as.graphicsAnnot. Can be set to "" or NA to suppress all captions.
...	other parameters passed to barplot.

Details

Model are distinguised with different intensities of the gray colour. The first model in alphabetic order has the darkest colour. If the classification of models is perfect (so that the frequency (or probability) of each model is zero for all but the correct model) each bar has a single colour of its corresponding model.

See Also

cv4postpr, summary.cv4postpr

Examples

## see ?cv4postpr for examples

abc documentation built on May 20, 2022, 1:11 a.m.