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.
# S3 method for pmc
plot(x, BurnIn=0, Data, PDF=FALSE, Parms, …)This required argument is an object of class pmc.
This argument requires zero or a positive integer that indicates the number of iterations to discard as burn-in for the purposes of plotting.
This required argument must receive the list of data that
was supplied to PMC to create the object of class
pmc.
This logical argument indicates whether or not the user wants Laplace's Demon to save the plots as a .pdf file.
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.
The plots are arranged in a \(2 \times 2\) 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.
# NOT RUN {
### See the PMC function for an example.
# }
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