plot.pmc {LaplacesDemon} | R Documentation |

## Plot samples from the output of PMC

### Description

This may be used to plot, or save plots of, samples in an object of
class `pmc`

. Plots include a trace plot and density plot for
parameters, a density plot for deviance and monitored variables, and
convergence plots.

### Usage

```
## S3 method for class 'pmc'
plot(x, BurnIn=0, Data, PDF=FALSE, Parms, ...)
```

### Arguments

`x` |
This required argument is an object of class |

`BurnIn` |
This argument requires zero or a positive integer that indicates the number of iterations to discard as burn-in for the purposes of plotting. |

`Data` |
This required argument must receive the list of data that
was supplied to |

`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 |

`...` |
Additional arguments are unused. |

### Details

The plots are arranged in a `2 \times 2`

matrix. Each row
represents a parameter, the deviance, or a monitored variable. For
parameters, the left column displays trace plots and the right column
displays kernel density plots.

Trace plots show the history of the distribution of independent importance samples. When multiple mixture components are used, each mixture component has a different color. These plots are unavailable for the deviance and monitored variables.

Kernel density plots depict the marginal posterior distribution. Although there is no distributional assumption about this density, kernel density estimation uses Gaussian basis functions.

Following these plots are three plots for convergence. First, ESSN (red) and perplexity (black) are plotted by iteration. Convergence occurs when both of these seem to stabilize, and higher is better. The second plot shows the distribution of the normalized importance weights by iteration. The third plot appears only when multiple mixture components are used. The third plot displays the probabilities of each mixture component by iteration. Although the last two plots are not formally convergence plots, they are provided so the user can verify the distribution of importance weights and the mixture probabilities have become stable.

### Author(s)

Statisticat, LLC. software@bayesian-inference.com

### See Also

### Examples

`### See the PMC function for an example.`

*LaplacesDemon*version 16.1.6 Index]