print.cat_gibbs

This function prints a summary of the <code>cat_gibbs</code> model, displaying details about the formula, covariate dimensions,
family, coefficients, and Gibbs sampling settings.

To improve estimation accuracy and stability in statistical modeling, catalytic prior distributions are employed, integrating observed data with synthetic data generated from a simpler model's predictive distribution. This approach enhances model robustness, stability, and flexibility in complex data scenarios. The catalytic prior distributions are introduced by 'Huang et al.' (2020, <doi:10.1073/pnas.1920913117>), Li and Huang (2023, <doi:10.48550/arXiv.2312.01411>).

Dongming Huang

catalytic

Tools for Applying Catalytic Priors in Statistical Modeling

Yitong Wu

Weihao Li

Ministry of Education, Singapore 

print.cat_gibbs function

<dl><dt>x</dt>
<dd>A <code>cat_gibbs</code> model object containing the results of a Bayesian GLM fitted using Gibbs sampling.</dd>
<dt>digit</dt>
<dd>An integer indicating the number of decimal places for printing
coefficient estimates. Default is 3.</dd>
<dt>detail</dt>
<dd>A logical value indicating whether to include additional detailed output at the end of the summary.
If <code>TRUE</code>, it will print additional interpretation help.</dd>
<dt>...</dt>
<dd>Additional parameters to pass to other functions.</dd></dl>

Arguments

Print Summary of cat_gibbs Model — print.cat_gibbs

<dl>

<dt>x</dt>
<dd>A <code>cat_gibbs</code> model object containing the results of a Bayesian GLM fitted using Gibbs sampling.</dd>


<dt>digit</dt>
<dd>An integer indicating the number of decimal places for printing
coefficient estimates. Default is 3.</dd>


<dt>detail</dt>
<dd>A logical value indicating whether to include additional detailed output at the end of the summary.
If <code>TRUE</code>, it will print additional interpretation help.</dd>


<dt>...</dt>
<dd>Additional parameters to pass to other functions.</dd>

</dl>

print.cat_gibbs: Print Summary of `cat_gibbs` Model

Description

Usage

Value

Arguments

Details