tidy_posterior_predict

Draw from the posterior predictive distribution of the model outcome

Implements Bayesian models to analyze data from tracer addition
experiments. The implemented method was originally described in the article
"A New Method to Reconstruct Quantitative Food Webs and Nutrient Flows from
Isotope Tracer Addition Experiments" by López-Sepulcre et al. (2020)
<doi:10.1086/708546>.

Mattieu Bruneaux

isotracer

Isotopic Tracer Analysis Using MCMC

Andrés López-Sepulcre

Matthieu Bruneaux

tidy_posterior_predict function

<dl><dt>object</dt>
<dd>A networkModelStanfit object.</dd>
<dt>newdata</dt>
<dd>The original model used to fit the networkStanfit object.</dd>
<dt>draw</dt>
<dd>Integer, number of draws to sample from the posterior. Default
is 100.</dd>
<dt>cores</dt>
<dd>Number of cores to use for parallel calculations. Default is
<code>NULL</code>, which means to use the value stored in
<code>options()[["mc.cores"]]</code> (or 1 if this value is not set).</dd>
<dt>...</dt>
<dd>Not used for now.</dd></dl>

Arguments

Draw from the posterior predictive distribution of the model outcome — tidy_posterior_predict

<dl>

<dt>object</dt>
<dd>A networkModelStanfit object.</dd>


<dt>newdata</dt>
<dd>The original model used to fit the networkStanfit object.</dd>


<dt>draw</dt>
<dd>Integer, number of draws to sample from the posterior. Default
is 100.</dd>


<dt>cores</dt>
<dd>Number of cores to use for parallel calculations. Default is
<code>NULL</code>, which means to use the value stored in
<code>options()[["mc.cores"]]</code> (or 1 if this value is not set).</dd>


<dt>...</dt>
<dd>Not used for now.</dd>

</dl>

tidy_posterior_predict: Draw from the posterior predictive distribution of the model outcome

Description

Usage

Value

Arguments