print.hbmc_results

Print a summary for a model goodness of fit and prior sensitivity

Implementing Hierarchical Bayesian Small Area Estimation
models using the 'brms' package as the computational backend. The
modeling framework follows the methodological foundations described in area-level
models. This package is designed to facilitate a principled Bayesian workflow,
enabling users to conduct prior predictive checks, model fitting, posterior
predictive checks, model comparison, and sensitivity analysis in a coherent and
reproducible manner. It supports flexible model specifications via 'brms' and
promotes transparency in model development, aligned with the recommendations of
modern Bayesian data analysis practices, implementing methods described in
Rao and Molina (2015) <doi:10.1002/9781118735855>.

Achmad Syahrul Choir

hbsaems

Hierarchical Bayes Small Area Estimation Model using 'Stan'

Saniyyah Sri Nurhayati

Sofi Zamzanah

Arsyka Laila Oktalia Siregar

print.hbmc_results function

<dl><dt>x</dt>
<dd>A <code>hbmc_results</code> object</dd>
<dt>...</dt>
<dd>Other potential arguments</dd></dl>

Arguments

Print a summary for a model goodness of fit and prior sensitivity — print.hbmc_results

<dl>

<dt>x</dt>
<dd>A <code>hbmc_results</code> object</dd>


<dt>...</dt>
<dd>Other potential arguments</dd>

</dl>

print.hbmc_results: Print a summary for a model goodness of fit and prior sensitivity

Description

Usage

Value

Arguments