BASiCS_DiagPlot {BASiCS} | R Documentation

## Create diagnostic plots of MCMC parameters

### Description

Plot parameter values and effective sample size. See effectiveSize for more details on this diagnostic measure.

### Usage

    BASiCS_DiagPlot(
      object,
      Parameter = "mu",
      Measure = c("ess", "geweke.diag"),
      x = NULL,
      y = NULL,
      LogX = isTRUE(x %in% c("mu", "delta")),
      LogY = isTRUE(y %in% c("mu", "delta")),
      Smooth = TRUE,
      na.rm = TRUE
    )

    BASiCS_diagPlot(...)

### 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 = 'mu'`
`Measure` | Character scalar specifying the diagnostic measure to plot. Current options are effective sample size and the Geweke diagnostic criterion.
`x, y` | Optional MCMC parameter values to be plotted on the x or y axis, respectively. If neither is supplied, Parameter will be plotted on the x axis and effective sample size will be plotted on the y axis as a density plot.
`LogX, LogY` | A logical value indicating whether to use a log10 transformation for the x or y axis, respectively.
`Smooth` | A logical value indicating whether to use smoothing (specifically hexagonal binning using `geom_hex`).
`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

### See Also

`BASiCS_Chain`

### Examples

    # Built-in example chain
    data(ChainSC)

    # Point estimates versus effective sample size
    BASiCS_DiagPlot(ChainSC, Parameter = "mu")
    # Effective sample size as colour, mu as x, delta as y.
    BASiCS_DiagPlot(ChainSC, x = "mu", y = "delta")

[Package BASiCS version 2.2.4 Index]