`IterativeQuadrature`

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`

.

```
# S3 method for iterquad
plot(x, Data, PDF=FALSE, Parms, …)
```

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.

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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