BASiCS_DiagHist {BASiCS} | R Documentation

## Create diagnostic plots of MCMC parameters

### Description

Plot a histogram of effective sample size or Geweke's diagnostic z-statistic. See effectiveSize and geweke.diag for more details.

### Usage

    BASiCS_DiagHist(
      object,
      Parameter = NULL,
      Measure = c("ess", "geweke.diag"),
      na.rm = TRUE
    )

    BASiCS_diagHist(...)

### Arguments

`object` | an object of class `BASiCS_Summary`
`Parameter` | Optional name of a chain parameter to restrict the histogram; if not supplied, all parameters will be assessed. Possible values: `'mu'`, `'delta'`, `'phi'`, `'s'`, `'nu'`, `'theta'`, `'beta'`, `'sigma2'` and `'epsilon'`. Default `Parameter = NULL`.
`Measure` | Character scalar specifying the diagnostic measure to plot. Current options are effective sample size and the Geweke diagnostic criterion.
`na.rm` | Logical value indicating whether NA values should be removed before calculating effective sample size.
`...` | Unused.

### Value

A ggplot object.

### Author(s)

Alan O'Callaghan

### References

Geweke, J. Evaluating the accuracy of sampling-based approaches to calculating posterior moments. In _Bayesian Statistics 4_ (ed JM Bernado, JO Berger, AP Dawid and AFM Smith). Clarendon Press, Oxford, UK.

### See Also

`BASiCS_Chain`

### Examples

    # Built-in example chain
    data(ChainSC)

    # See effective sample size distribution across all parameters
    BASiCS_DiagHist(ChainSC)
    # For mu only
    BASiCS_DiagHist(ChainSC, Parameter = "mu")

[Package BASiCS version 2.2.4 Index]