simplifyposteriors

A function that ads up posteriors for the same expression written
in different character form in different parallel runs of the algorithm
(mainly for Logic Regression and Deep Regression contexts)

models

methods

Implementation of the Mode Jumping Markov Chain Monte Carlo algorithm from Hubin, A., Storvik, G. (2018) <doi:10.1016/j.csda.2018.05.020>, Genetically Modified Mode Jumping Markov Chain Monte Carlo from Hubin, A., Storvik, G., & Frommlet, F. (2020) <doi:10.1214/18-BA1141>, Hubin, A., Storvik, G., & Frommlet, F. (2021) <doi:10.1613/jair.1.13047>, and Hubin, A., Heinze, G., & De Bin, R. (2023) <doi:10.3390/fractalfract7090641>, and Reversible Genetically Modified Mode Jumping Markov Chain Monte Carlo from Hubin, A., Frommlet, F., & Storvik, G. (2021) <doi:10.48550/arXiv.2110.05316>, which allow for estimating posterior model probabilities and Bayesian model averaging across a wide set of Bayesian models including linear, generalized linear, generalized linear mixed, generalized nonlinear, generalized nonlinear mixed, and logic regression models.

Waldir Leoncio

EMJMCMC

Evolutionary Mode Jumping Markov Chain Monte Carlo Expert
Toolbox

Aliaksandr Hubin

Geir Storvik

Florian Frommlet

simplifyposteriors function

<dl><dt>X</dt>
<dd>a data.frame containing the data on the covariates</dd>
<dt>posteriors</dt>
<dd>a data.frame with expressions in the first column and their posteriors in the second column from all of the runs</dd>
<dt>th</dt>
<dd>initial filtering before summary threshold</dd>
<dt>thf</dt>
<dd>threshold for final filtering after summary</dd>
<dt>resp</dt>
<dd>the response to be addressed</dd></dl>

Arguments

A function that ads up posteriors for the same expression written
in different character form in different parallel runs of the algorithm
(mainly for Logic Regression and Deep Regression contexts) — simplifyposteriors

<dl>

<dt>X</dt>
<dd>a data.frame containing the data on the covariates</dd>


<dt>posteriors</dt>
<dd>a data.frame with expressions in the first column and their posteriors in the second column from all of the runs</dd>


<dt>th</dt>
<dd>initial filtering before summary threshold</dd>


<dt>thf</dt>
<dd>threshold for final filtering after summary</dd>


<dt>resp</dt>
<dd>the response to be addressed</dd>

</dl>

A function that ads up posteriors for the same expression written
in different character form in different parallel runs of the algorithm
(mainly for Logic Regression and Deep Regression contexts)

simplifyposteriors: A function that ads up posteriors for the same expression written in different character form in different parallel runs of the algorithm (mainly for Logic Regression and Deep Regression contexts)

Description

Usage

Value

Arguments

See Also