plot.iterquad {LaplacesDemon}

Plot the output of IterativeQuadrature

Description

This may be used to plot, or save plots of, the iterated history of the parameters and, if posterior samples were taken, density plots of parameters and monitors in an object of class iterquad.

Usage

## S3 method for class 'iterquad'
plot(x, Data, PDF=FALSE, Parms, ...)

Arguments

x

This required argument is an object of class iterquad.

Data

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

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 \times 2 matrix. The purpose of the iterated history plots is to show how the value of each parameter and the deviance changed by iteration as the IterativeQuadrature attempted to fit a normal distribution to the marginal posterior distributions.

The plots on the right show several densities, described below.

• The transparent black density is the normalized quadrature weights for non-standard normal distributions, M. For multivariate quadrature, there are often multiple weights at a given node, and the average M is shown. Vertical black lines indicate the nodes.

• The transparent red density is the normalized LP weights. For multivariate quadrature, there are often multiple weights at a given node, and the average normalized and weighted LP is shown. Vertical red lines indicate the nodes.

• The transparent green density is the normal density implied given the conditional mean and conditional variance.

• The transparent blue density is the kernel density estimate of posterior samples generated with Sampling Importance Resampling. This is plotted only if the algorithm converged, and if sir=TRUE.

Author(s)

Statisticat, LLC. [e-mail]

See Also

IterativeQuadrature

Examples

### See the IterativeQuadrature function for an example.