plot.pmc: Plot samples from the output of PMC

Description Usage Arguments Details Author(s) See Also Examples

View source: R/plot.pmc.R

Description

This may be used to plot, or save plots of, samples in an object of class pmc. Plots include a trace plot and density plot for parameters, a density plot for deviance and monitored variables, and convergence plots.

Usage

1
2
## S3 method for class 'pmc'
plot(x, BurnIn=0, Data, PDF=FALSE, Parms, ...)

Arguments

x

This required argument is an object of class pmc.

BurnIn

This argument requires zero or a positive integer that indicates the number of iterations to discard as burn-in for the purposes of plotting.

Data

This required argument must receive the list of data that was supplied to PMC to create the object of class pmc.

PDF

This logical argument indicates whether or not the user wants Laplace's Demon to save the plots as a .pdf file.

Parms

This argument accepts a vector of quoted strings to be matched for selecting parameters for plotting. This argument defaults to NULL and selects every parameter for plotting. Each quoted string is matched to one or more parameter names with the grep function. For example, if the user specifies Parms=c("eta", "tau"), and if the parameter names are beta[1], beta[2], eta[1], eta[2], and tau, then all parameters will be selected, because the string eta is within beta. Since grep is used, string matching uses regular expressions, so beware of meta-characters, though these are acceptable: ".", "[", and "]".

...

Additional arguments are unused.

Details

The plots are arranged in a 2 x 2 matrix. Each row represents a parameter, the deviance, or a monitored variable. For parameters, the left column displays trace plots and the right column displays kernel density plots.

Trace plots show the history of the distribution of independent importance samples. When multiple mixture components are used, each mixture component has a different color. These plots are unavailable for the deviance and monitored variables.

Kernel density plots depict the marginal posterior distribution. Although there is no distributional assumption about this density, kernel density estimation uses Gaussian basis functions.

Following these plots are three plots for convergence. First, ESSN (red) and perplexity (black) are plotted by iteration. Convergence occurs when both of these seem to stabilize, and higher is better. The second plot shows the distribution of the normalized importance weights by iteration. The third plot appears only when multiple mixture components are used. The third plot displays the probabilities of each mixture component by iteration. Although the last two plots are not formally convergence plots, they are provided so the user can verify the distribution of importance weights and the mixture probabilities have become stable.

Author(s)

Statisticat, LLC. [email protected]

See Also

ESS and PMC.

Examples

1
### See the PMC function for an example.

LaplacesDemon documentation built on July 1, 2018, 9:02 a.m.