datasets

Implement Bayesian multilevel modelling for compositional data.
Compute multilevel compositional data and
perform log-ratio transforms at between and within-person levels,
fit Bayesian multilevel models for compositional predictors and outcomes,
and run post-hoc analyses such as isotemporal substitution models.
References:
Le, Stanford, Dumuid, and Wiley (2025) <doi:10.1037/met0000750>,
Le, Dumuid, Stanford, and Wiley (2025) <doi:10.1080/00273171.2025.2565598>.

Flora Le

multilevelcoda

Estimate Bayesian Multilevel Models for Compositional Data

Joshua F. Wiley

sim function

A list with 5 columns and 4 rows.<dl>
<dt>brmcoda_tab</dt>
<dd>Simulation results for brmcoda() for tables</dd><dt>sub_tab</dt>
<dd>Simulation results for substitution() for tables</dd><dt>brmcoda_plot</dt>
<dd>Simulation results for brmcoda() for graphs</dd><dt>sub_plot</dt>
<dd>Simulation results for substitution() for graphs</dd>
</dl>

Format

multilevelcoda Simulation Study results — sim

A list with 5 columns and 4 rows.<dl>
<dt>brmcoda_tab</dt>
<dd>Simulation results for brmcoda() for tables</dd>

<dt>sub_tab</dt>
<dd>Simulation results for substitution() for tables</dd>

<dt>brmcoda_plot</dt>
<dd>Simulation results for brmcoda() for graphs</dd>

<dt>sub_plot</dt>
<dd>Simulation results for substitution() for graphs</dd>


</dl>

sim: multilevelcoda Simulation Study results

Description

Usage

Arguments

Format