loo.brmcoda

Perform approximate leave-one-out cross-validation based
on the posterior likelihood using the loo package.
For more details see <code>loo</code>.

Implement Bayesian Multilevel Modelling for compositional data
in a multilevel framework. Compute multilevel compositional data and
Isometric log ratio (ILR) 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 (2024) <doi:10.48550/arXiv.2405.03985>,
Le, Dumuid, Stanford, and Wiley (2024) <doi:10.48550/arXiv.2411.12407>.

Flora Le

multilevelcoda

Estimate Bayesian Multilevel Models for Compositional Data

Joshua F. Wiley

loo.brmcoda function

<dl><dt>x</dt>
<dd>A <code>brmcoda</code> object.</dd>
<dt>...</dt>
<dd>More <code>brmsfit</code> objects or further arguments
passed to the underlying post-processing functions.
In particular, see <code>prepare_predictions</code> for further
supported arguments.</dd></dl>

Arguments

Efficient approximate leave-one-out cross-validation (LOO) — loo.brmcoda

<dl>

<dt>x</dt>
<dd>A <code>brmcoda</code> object.</dd>


<dt>...</dt>
<dd>More <code>brmsfit</code> objects or further arguments
passed to the underlying post-processing functions.
In particular, see <code>prepare_predictions</code> for further
supported arguments.</dd>

</dl>

loo.brmcoda: Efficient approximate leave-one-out cross-validation (LOO)

Description

Usage

Value

Arguments

See Also