IterativeQuadratureThis 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.
# S3 method for iterquad
plot(x, Data, PDF=FALSE, Parms, …)This required argument is an object of class iterquad.
This required argument must receive the list of data that was
supplied to IterativeQuadrature to create the object
of class iterquad.
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. 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.
# NOT RUN {
### See the IterativeQuadrature function for an example.
# }
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