fetchMCMC {demest} | R Documentation

## Create a list of objects for analysis with package "coda".

### Description

Create an object of class `"mcmc.list"`, or a list of objects of class `"mcmc.list"`, which can be analysed using the diagnostic functions in package coda.

### Usage

    fetchMCMC(filename, where = NULL, nSample = 25, sample = NULL, thinned = TRUE)

### Arguments

`filename` | The name of the file where the output from the `estimate` function is kept.
`where` | A character vector used to select a single parameter or batch of parameters. See below for details.
`nSample` | Number of parameters to be sampled from a batch of parameters. Defaults to 25. Ignored when batch has fewer than 25 parameters (including having only one parameter.)
`sample` | Indices of parameters to be sampled. An alternative to `nSample` when more control over sampling is required.
`thinned` | Logical. If `TRUE`, the default, the `"mcmc.list"` object or objects describe the thinned process.

### Details

If no `where` argument is supplied, `mcmc.list` objects are returned for every parameter or batch of parameters that were estimated. If a `where` argument is supplied, it must describe the path to parameter or parameters. See `fetch` for more on specifying paths.

If a batch of parameters has many elements, then calculating MCMC diagnositics for all those elements can be very slow. To speed things up, when applied to a batch of parameters, `fetchMCMC` randomly selects only `nSample` of these parameters. `nSample` defaults to 25.

Alternatively, the indices of the parameters to be selected can be specified using the `sample` argument. See below for an example.

If `thinned` is `TRUE`, then the `thin` argument in coda function `mcmc` is set to 1; otherwise `thin` is set to `nThin`, extracted from `object`.

If the model in question contains structural zeros (see `Poisson`), some parameters of the model may be undefined. These parameters are omitted from the results, unless they are specifically requested via the `sample` argument.

### Value

A single object of class `"mcmc.list"`, or a named list of such objects.

### See Also

Other functions for examining output from calls to `estimateModel`, `estimateCounts`, and `estimateAccount` include `fetch`, `fetchFiniteSD`, and `listContents`.

### Examples

    library(demdata)
    ## fit model
    deaths <- Counts(round(VADeaths2))
    popn <- Counts(VAPopn)
    filename <- tempfile()
    estimateModel(Model(y ~ Poisson(mean ~ age)),
                  y = deaths,
                  exposure = popn,
                  filename = filename,
                  nBurnin = 20,
                  nSim = 20,
                  nChain = 2,
                  parallel = FALSE)

    ## create a list containing an "mcmc.list" object for
    ## every element of the results that was estimated
    l <- fetchMCMC(filename)
    names(l)
    sapply(l, class)

    ## analyse using functions from 'coda'
    ## Not run:
    library(coda)
    plot(l$model.likelihood.rate)
    plot(l$model.likelihood.rate, ask = FALSE)
    plot(l$model.likelihood.rate, ask = FALSE, smooth = FALSE)
    gelman.diag(l$model.likelihood.rate)

    ## End(Not run)

    ## create a single "mcmc.list" object
    mean.mcmc <- fetchMCMC(filename,
                      where = c("model", "likelihood", "rate"))

    ## only write part of each name
    mean.mcmc <- fetchMCMC(filename, where = c("mod", "like", "r"))

    ## sample 6 randomly-chosen values
    mean.mcmc.5 <- fetchMCMC(filename,
                        where = c("model", "likelihood", "rate"),
                        nSample = 6)

    ## sample the first 5 values
    mean.mcmc.5 <- fetchMCMC(filename,
                        where = c("model", "likelihood", "rate"),
                        sample = 1:5)

[Package demest version 0.0.0.3.1 Index]