summary_gibbs

For abms objects, it returns the posterior mean, standard deviation, and 95% centered credible interval for each parameter. Additionally, it provides all explored models alongside the conditional Bayes factors and marginal Bayes factors estimator between the most probable model and the others that have arisen.

Tools to perform model selection alongside estimation under Linear, Logistic, Negative binomial, Quantile, and Skew-Normal regression. Under the spike-and-slab method, a probability for each possible model is estimated with the posterior mean, credibility interval, and standard deviation of coefficients and parameters under the most probable model.

Francisco Segovia

abms

Augmented Bayesian Model Selection for Regression Models

Luis Gutierres

Ramses Mena

summary_gibbs function

<dl><dt>fit</dt>
<dd>An abms object. Such object is obtained by fitting a regression model with the <code>gibbs_abms()</code> function.</dd>
<dt>BF</dt>
<dd>A logical object. if <code>TRUE</code>, then the Bayes factor comparison is shown.<code>BF=FALSE</code> by default.</dd></dl>

Arguments

Summary function for abms objects — summary_gibbs

<dl>

<dt>fit</dt>
<dd>An abms object. Such object is obtained by fitting a regression model with the <code>gibbs_abms()</code> function.</dd>


<dt>BF</dt>
<dd>A logical object. if <code>TRUE</code>, then the Bayes factor comparison is shown.<code>BF=FALSE</code> by default.</dd>

</dl>

summary_gibbs: Summary function for abms objects

Description

Usage

Value

Arguments

Examples