estimate.elnet

A test function to work with elastic networks in future, be omitted so far

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

estimate.elnet function

<dl><dt>formula</dt>
<dd>a formula object for the model to be addressed</dd>
<dt>response</dt>
<dd>response in a formula</dd>
<dt>data</dt>
<dd>a data frame object containing variables and observations corresponding to the formula used</dd>
<dt>family</dt>
<dd>distribution of the response family object</dd>
<dt>alpha</dt>
<dd>regularization parameter in [0,1]</dd></dl>

Arguments

A test function to work with elastic networks in future, be omitted so far — estimate.elnet

<dl>

<dt>formula</dt>
<dd>a formula object for the model to be addressed</dd>


<dt>response</dt>
<dd>response in a formula</dd>


<dt>data</dt>
<dd>a data frame object containing variables and observations corresponding to the formula used</dd>


<dt>family</dt>
<dd>distribution of the response family object</dd>


<dt>alpha</dt>
<dd>regularization parameter in [0,1]</dd>

</dl>

estimate.elnet: A test function to work with elastic networks in future, be omitted so far

Description

Usage

Value

Arguments

See Also