tidy_dpp

This function prepares both tidy data from a model and tidy posterior
predictions from a model fit. Having those two tibbles prepared at the same
time allows to merge them to ensure that observed data, predicted data and
original variables other than observations are all in sync when using y and
y_rep objects for bayesplot functions.

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_dpp function

<dl><dt>model</dt>
<dd>A networkModel object.</dd>
<dt>fit</dt>
<dd>A networkModelStanfit object.</dd>
<dt>draw</dt>
<dd>Integer, number of draws to sample from the posterior.</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></dl>

Arguments

Prepare tidy data and posterior predictions — tidy_dpp

<dl>

<dt>model</dt>
<dd>A networkModel object.</dd>


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


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

</dl>

tidy_dpp: Prepare tidy data and posterior predictions

Description

Usage

Value

Arguments