plot.gmjmcmc

Function to plot the results, works both for results from gmjmcmc and
merged results from merge.results

Implements the Mode Jumping Markov Chain Monte Carlo algorithm described in <doi:10.1016/j.csda.2018.05.020> and its Genetically Modified counterpart described in <doi:10.1613/jair.1.13047> as well as the sub-sampling versions described in <doi:10.1016/j.ijar.2022.08.018> for flexible Bayesian model selection and model averaging.

Jon Lachmann

FBMS

Flexible Bayesian Model Selection and Model Averaging

Aliaksandr Hubin

plot.gmjmcmc function

<dl><dt>x</dt>
<dd>The results to use</dd>
<dt>count</dt>
<dd>The number of features to plot, defaults to all</dd>
<dt>pop</dt>
<dd>The population to plot, defaults to last</dd>
<dt>tol</dt>
<dd>The tolerance to use for the correlation when finding equivalent features, default is 0.0000001</dd>
<dt>data</dt>
<dd>Data to merge on, important if pre-filtering was used</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Function to plot the results, works both for results from gmjmcmc and
merged results from merge.results — plot.gmjmcmc

<dl>

<dt>x</dt>
<dd>The results to use</dd>


<dt>count</dt>
<dd>The number of features to plot, defaults to all</dd>


<dt>pop</dt>
<dd>The population to plot, defaults to last</dd>


<dt>tol</dt>
<dd>The tolerance to use for the correlation when finding equivalent features, default is 0.0000001</dd>


<dt>data</dt>
<dd>Data to merge on, important if pre-filtering was used</dd>


<dt>...</dt>
<dd>Not used.</dd>

</dl>

plot.gmjmcmc: Function to plot the results, works both for results from gmjmcmc and merged results from merge.results

Description

Usage

Value

Arguments

Examples