data_lnln

This dataset is a simulated data created for demonstrating the implementation of
Hierarchical Bayesian Small Area Estimation (HB SAE) using a lognormal-lognormal model.
It includes area-level covariates, random effects, direct estimates, and spatial components,
for testing SAE models with lognormal assumptions and spatial correlation.

datasets

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

data_lnln function

A data frame with 100 rows and 13 variables:<dl>
<dt>group</dt>
<dd>Area ID (1–100) for random effects formula specifying the grouping structure in the data.</dd><dt>x1, x2, x3</dt>
<dd>Auxiliary area-level covariates</dd><dt>u_true</dt>
<dd>True unstructured area-level random effect on the log scale.</dd><dt>teta_true</dt>
<dd>True linear predictor on the log scale (meanlog for lognormal distribution).</dd><dt>mu_orig_true</dt>
<dd>True mean on the original scale, calculated from <code>eta_true</code> and <code>sigma_e</code>.</dd><dt>n</dt>
<dd>Sample size per area.</dd><dt>y_obs</dt>
<dd>Simulated observed mean per area, generated from a lognormal distribution.</dd><dt>lambda_dir</dt>
<dd>Direct estimator of the mean per area (same as <code>y_obs</code>).</dd><dt>y_log_obs</dt>
<dd>Log-transformed direct estimator.</dd><dt>psi_i</dt>
<dd>Approximate sampling variance of <code>y_obs</code>.</dd><dt>sre</dt>
<dd>An optional grouping factor mapping observations to spatial locations.</dd>
</dl>

Format

Simulation Data for Lognormal-Lognormal Model — data_lnln

A data frame with 100 rows and 13 variables:<dl>
<dt>group</dt>
<dd>Area ID (1–100) for random effects formula specifying the grouping structure in the data.</dd>

<dt>x1, x2, x3</dt>
<dd>Auxiliary area-level covariates</dd>

<dt>u_true</dt>
<dd>True unstructured area-level random effect on the log scale.</dd>

<dt>teta_true</dt>
<dd>True linear predictor on the log scale (meanlog for lognormal distribution).</dd>

<dt>mu_orig_true</dt>
<dd>True mean on the original scale, calculated from <code>eta_true</code> and <code>sigma_e</code>.</dd>

<dt>n</dt>
<dd>Sample size per area.</dd>

<dt>y_obs</dt>
<dd>Simulated observed mean per area, generated from a lognormal distribution.</dd>

<dt>lambda_dir</dt>
<dd>Direct estimator of the mean per area (same as <code>y_obs</code>).</dd>

<dt>y_log_obs</dt>
<dd>Log-transformed direct estimator.</dd>

<dt>psi_i</dt>
<dd>Approximate sampling variance of <code>y_obs</code>.</dd>

<dt>sre</dt>
<dd>An optional grouping factor mapping observations to spatial locations.</dd>


</dl>

data_lnln: Simulation Data for Lognormal-Lognormal Model

Description

Usage

Arguments

Format