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README.html
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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
<meta name="viewport" content="width=device-width, initial-scale=1">
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color: #777;
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h6 .anchor {
line-height: 1.1;
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margin-bottom: 16px;
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margin: 16px 0;
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color: #777;
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padding: 6px 13px;
border: 1px solid #ddd;
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border-top: 1px solid #ccc;
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background-color: #f8f8f8;
}
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max-width: 100%;
box-sizing: content-box;
background-color: #fff;
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padding: 0;
padding-top: 0.2em;
padding-bottom: 0.2em;
margin: 0;
font-size: 85%;
background-color: rgba(0,0,0,0.04);
border-radius: 3px;
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margin: 0;
font-size: 100%;
word-break: normal;
white-space: pre;
background: transparent;
border: 0;
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margin-bottom: 16px;
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padding: 16px;
overflow: auto;
font-size: 85%;
line-height: 1.45;
background-color: #f7f7f7;
border-radius: 3px;
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margin-bottom: 0;
word-break: normal;
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word-wrap: normal;
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max-width: initial;
padding: 0;
margin: 0;
overflow: initial;
line-height: inherit;
word-wrap: normal;
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border: solid 1px #ccc;
border-bottom-color: #bbb;
border-radius: 3px;
box-shadow: inset 0 -1px 0 #bbb;
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}
.pl-mo {
color: #1d3e81;
}
kbd {
display: inline-block;
padding: 3px 5px;
font: 11px Consolas, "Liberation Mono", Menlo, Courier, monospace;
line-height: 10px;
color: #555;
vertical-align: middle;
background-color: #fcfcfc;
border: solid 1px #ccc;
border-bottom-color: #bbb;
border-radius: 3px;
box-shadow: inset 0 -1px 0 #bbb;
}
.task-list-item {
list-style-type: none;
}
.task-list-item+.task-list-item {
margin-top: 3px;
}
.task-list-item input {
margin: 0 0.35em 0.25em -1.6em;
vertical-align: middle;
}
:checked+.radio-label {
z-index: 1;
position: relative;
border-color: #4078c0;
}
.sourceLine {
display: inline-block;
}
code .kw { color: #000000; }
code .dt { color: #ed6a43; }
code .dv { color: #009999; }
code .bn { color: #009999; }
code .fl { color: #009999; }
code .ch { color: #009999; }
code .st { color: #183691; }
code .co { color: #969896; }
code .ot { color: #0086b3; }
code .al { color: #a61717; }
code .fu { color: #63a35c; }
code .er { color: #a61717; background-color: #e3d2d2; }
code .wa { color: #000000; }
code .cn { color: #008080; }
code .sc { color: #008080; }
code .vs { color: #183691; }
code .ss { color: #183691; }
code .im { color: #000000; }
code .va {color: #008080; }
code .cf { color: #000000; }
code .op { color: #000000; }
code .bu { color: #000000; }
code .ex { color: #000000; }
code .pp { color: #999999; }
code .at { color: #008080; }
code .do { color: #969896; }
code .an { color: #008080; }
code .cv { color: #008080; }
code .in { color: #008080; }
</style>
<style>
body {
box-sizing: border-box;
min-width: 200px;
max-width: 980px;
margin: 0 auto;
padding: 45px;
padding-top: 0px;
}
</style>
</head>
<body>
<!-- README.md is generated from README.Rmd. Please edit that file -->
<h1 id="spabundance-">spAbundance
<a href="https://www.doserlab.com/files/spabundance-web/"><img role="img" src="data:image/png;base64,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<p><code>spAbundance</code> fits univariate (i.e., single-species) and
multivariate (i.e., multi-species) spatial N-mixture models,
hierarchical distance sampling models, and generalized linear mixed
models using Markov chain Monte Carlo (MCMC). Spatial models are fit
using Nearest Neighbor Gaussian Processes (NNGPs) to facilitate model
fitting to large spatial datasets. <code>spAbundance</code> uses
analogous syntax to its “sister package” <a href="https://www.doserlab.com/files/spoccupancy-web/">spOccupancy</a>
(Doser et al. 2022). Below we provide a very brief introduction to some
of the package’s functionality, and illustrate just one of the model
fitting functions. For more information, see the resources referenced at
the bottom of this page and the “Articles” tab at the top of the page.
Please also consider joining the <a href="https://groups.google.com/g/spocc-spabund-users"><code>spAbundance</code>
and <code>spOccupancy</code> users google group</a>.</p>
<h2 id="installation">Installation</h2>
<p>You can install the released version of <code>spAbundance</code> from
<a href="https://CRAN.R-project.org">CRAN</a> with</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">install.packages</span>(<span class="st">"spAbundance"</span>)</span></code></pre></div>
<p>To download the development version of the package, you can use
<code>devtools</code> as follows:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a>devtools<span class="sc">::</span><span class="fu">install_github</span>(<span class="st">"biodiverse/spAbundance"</span>)</span></code></pre></div>
<p>Note that because we implement the MCMC in C++, you will need a C++
compiler on your computer to install the package from GitHub. To compile
C++ on Windows, you can install <a href="https://cran.r-project.org/bin/windows/Rtools/"><code>RTools</code></a>.
To compile C++ on a Mac, you can install <code>XCode</code> from the Mac
app store.</p>
<h2 id="functionality">Functionality</h2>
<table>
<thead>
<tr class="header">
<th><code>spAbundance</code> Function</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>DS()</code></td>
<td>Single-species hierarchical distance sampling (HDS) model</td>
</tr>
<tr class="even">
<td><code>spDS()</code></td>
<td>Single-species spatial HDS model</td>
</tr>
<tr class="odd">
<td><code>msDS()</code></td>
<td>Multi-species HDS model</td>
</tr>
<tr class="even">
<td><code>lfMsDS()</code></td>
<td>Multi-species HDS model with species correlations</td>
</tr>
<tr class="odd">
<td><code>sfMsDS()</code></td>
<td>Multi-species spatial HDS model with species correlations</td>
</tr>
<tr class="even">
<td><code>NMix()</code></td>
<td>Single-species N-mixture model</td>
</tr>
<tr class="odd">
<td><code>spNMix()</code></td>
<td>Single-species spatial N-mixture model</td>
</tr>
<tr class="even">
<td><code>msNMix()</code></td>
<td>Multi-species N-mixture model</td>
</tr>
<tr class="odd">
<td><code>lfMsNMix()</code></td>
<td>Multi-species N-mixture model with species correlations</td>
</tr>
<tr class="even">
<td><code>sfMsNMix()</code></td>
<td>Multi-species spatial N-mixture model with species correlations</td>
</tr>
<tr class="odd">
<td><code>abund()</code></td>
<td>Univariate GLMM</td>
</tr>
<tr class="even">
<td><code>spAbund()</code></td>
<td>Univariate spatial GLMM</td>
</tr>
<tr class="odd">
<td><code>svcAbund()</code></td>
<td>Univariate spatially-varying coefficient GLMM</td>
</tr>
<tr class="even">
<td><code>msAbund()</code></td>
<td>Multivariate GLMM</td>
</tr>
<tr class="odd">
<td><code>lfMsAbund()</code></td>
<td>Multivariate GLMM with species correlations</td>
</tr>
<tr class="even">
<td><code>sfMsAbund()</code></td>
<td>Multivariate spatial GLMM with species correlations</td>
</tr>
<tr class="odd">
<td><code>svcMsAbund()</code></td>
<td>Multivariate spatially-varying coefficient GLMM with species
correlations</td>
</tr>
<tr class="even">
<td><code>ppcAbund()</code></td>
<td>Posterior predictive check using Bayesian p-values</td>
</tr>
<tr class="odd">
<td><code>waicAbund()</code></td>
<td>Calculate Widely Applicable Information Criterion (WAIC)</td>
</tr>
<tr class="even">
<td><code>simDS()</code></td>
<td>Simulate single-species distance sampling data</td>
</tr>
<tr class="odd">
<td><code>simMsDS()</code></td>
<td>Simulate multi-species distance sampling data</td>
</tr>
<tr class="even">
<td><code>simNMix()</code></td>
<td>Simulate single-species repeated count data</td>
</tr>
<tr class="odd">
<td><code>simMsNMix()</code></td>
<td>Simulate multi-species repeated count data</td>
</tr>
<tr class="even">
<td><code>simAbund()</code></td>
<td>Simulate single-species count data</td>
</tr>
<tr class="odd">
<td><code>simMsAbund()</code></td>
<td>Simulate multi-species count data</td>
</tr>
</tbody>
</table>
<p>All model fitting functions allow for Poisson and negative binomial
distributions for the abundance portion of the model. All GLM(M)s also
allow for Gaussian and zero-inflated Gaussian models. Note the
multi-species spatailly-varying coefficient models are only available
for Gaussian and zero-inflated Gaussian models.</p>
<h2 id="example-usage">Example usage</h2>
<h3 id="load-package-and-data">Load package and data</h3>
<p>To get started with <code>spAbundance</code> we load the package and
an example data set. We use data on 16 birds from the <a href="https://www.neonscience.org/field-sites/dsny">Disney Wilderness
Preserve</a> in Central Florida, USA, which is available in the
<code>spAbundance</code> package as the <code>neonDWP</code> object.
Here we will only work with one bird species, the Mourning Dove (MODO),
and so we subset the <code>neonDWP</code> object to only include this
species.</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="fu">library</span>(spAbundance)</span>
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="co"># Set seed to get exact same results</span></span>
<span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">500</span>)</span>
<span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a><span class="fu">data</span>(neonDWP)</span>
<span id="cb3-5"><a href="#cb3-5" tabindex="-1"></a>sp.names <span class="ot"><-</span> <span class="fu">dimnames</span>(neonDWP<span class="sc">$</span>y)[[<span class="dv">1</span>]]</span>
<span id="cb3-6"><a href="#cb3-6" tabindex="-1"></a>dat.MODO <span class="ot"><-</span> neonDWP</span>
<span id="cb3-7"><a href="#cb3-7" tabindex="-1"></a>dat.MODO<span class="sc">$</span>y <span class="ot"><-</span> dat.MODO<span class="sc">$</span>y[sp.names <span class="sc">==</span> <span class="st">"MODO"</span>, , ]</span></code></pre></div>
<h3 id="fit-a-spatial-hierarchical-distance-sampling-model-using-spds">Fit a
spatial hierarchical distance sampling model using
<code>spDS()</code></h3>
<p>Below we fit a single-species spatially-explicit hierarchical
distance sampling model to the MODO data using a Nearest Neighbor
Gaussian Process. We use the default priors and initial values for the
abundance (<code>beta</code>) and detection (<code>alpha</code>)
coefficients, the spatial variance (<code>sigma.sq</code>), the spatial
decay parameter (<code>phi</code>), the spatial random effects
(<code>w</code>), and the latent abundance values (<code>N</code>). We
also include an offset in <code>dat.MODO</code> to provide estimates of
density on a per hectare basis. We model abundance as a function of
local forest cover and grassland cover, along with a spatial random
intercept. We model detection probability as a function of linear and
quadratic day of survey and a linear effect of wind.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="co"># Specify model formulas</span></span>
<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a>MODO.abund.formula <span class="ot"><-</span> <span class="er">~</span> <span class="fu">scale</span>(forest) <span class="sc">+</span> <span class="fu">scale</span>(grass) </span>
<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a>MODO.det.formula <span class="ot"><-</span> <span class="er">~</span> <span class="fu">scale</span>(day) <span class="sc">+</span> <span class="fu">I</span>(<span class="fu">scale</span>(day)<span class="sc">^</span><span class="dv">2</span>) <span class="sc">+</span> <span class="fu">scale</span>(wind)</span></code></pre></div>
<p>We run the model using an Adaptive MCMC sampler with a target
acceptance rate of 0.43. We run 3 chains of the model each for 20,000
iterations split into 800 batches each of length 25. For each chain, we
discard the first 10,000 iterations as burn-in and use a thinning rate
of 5 for a resulting 6,000 samples from the joint posterior. We fit the
model using 15 nearest neighbors and an exponential correlation
function. Run <code>?spDS</code> for more detailed information on all
function arguments.</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="co"># Run the model (Approx run time: 1 min)</span></span>
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a>out <span class="ot"><-</span> <span class="fu">spDS</span>(<span class="at">abund.formula =</span> MODO.abund.formula,</span>
<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a> <span class="at">det.formula =</span> MODO.det.formula,</span>
<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a> <span class="at">data =</span> dat.MODO, <span class="at">n.batch =</span> <span class="dv">800</span>, <span class="at">batch.length =</span> <span class="dv">25</span>,</span>
<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a> <span class="at">accept.rate =</span> <span class="fl">0.43</span>, <span class="at">cov.model =</span> <span class="st">"exponential"</span>,</span>
<span id="cb5-6"><a href="#cb5-6" tabindex="-1"></a> <span class="at">transect =</span> <span class="st">'point'</span>, <span class="at">det.func =</span> <span class="st">'halfnormal'</span>,</span>
<span id="cb5-7"><a href="#cb5-7" tabindex="-1"></a> <span class="at">NNGP =</span> <span class="cn">TRUE</span>, <span class="at">n.neighbors =</span> <span class="dv">15</span>, <span class="at">n.burn =</span> <span class="dv">10000</span>,</span>
<span id="cb5-8"><a href="#cb5-8" tabindex="-1"></a> <span class="at">n.thin =</span> <span class="dv">5</span>, <span class="at">n.chains =</span> <span class="dv">3</span>, <span class="at">verbose =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
<p>This will produce a large output object, and you can use
<code>str(out)</code> to get an overview of what’s in there. Here we use
the <code>summary()</code> function to print a concise but informative
summary of the model fit.</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="fu">summary</span>(out)</span>
<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a><span class="co">#> Call:</span></span>
<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a><span class="co">#> spDS(abund.formula = MODO.abund.formula, det.formula = MODO.det.formula, </span></span>
<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a><span class="co">#> data = dat.MODO, cov.model = "exponential", NNGP = TRUE, </span></span>
<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="co">#> n.neighbors = 15, n.batch = 800, batch.length = 25, accept.rate = 0.43, </span></span>
<span id="cb6-7"><a href="#cb6-7" tabindex="-1"></a><span class="co">#> transect = "point", det.func = "halfnormal", verbose = FALSE, </span></span>
<span id="cb6-8"><a href="#cb6-8" tabindex="-1"></a><span class="co">#> n.burn = 10000, n.thin = 5, n.chains = 3)</span></span>
<span id="cb6-9"><a href="#cb6-9" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb6-10"><a href="#cb6-10" tabindex="-1"></a><span class="co">#> Samples per Chain: 20000</span></span>
<span id="cb6-11"><a href="#cb6-11" tabindex="-1"></a><span class="co">#> Burn-in: 10000</span></span>
<span id="cb6-12"><a href="#cb6-12" tabindex="-1"></a><span class="co">#> Thinning Rate: 5</span></span>
<span id="cb6-13"><a href="#cb6-13" tabindex="-1"></a><span class="co">#> Number of Chains: 3</span></span>
<span id="cb6-14"><a href="#cb6-14" tabindex="-1"></a><span class="co">#> Total Posterior Samples: 6000</span></span>
<span id="cb6-15"><a href="#cb6-15" tabindex="-1"></a><span class="co">#> Run Time (min): 0.7582</span></span>
<span id="cb6-16"><a href="#cb6-16" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb6-17"><a href="#cb6-17" tabindex="-1"></a><span class="co">#> Abundance (log scale): </span></span>
<span id="cb6-18"><a href="#cb6-18" tabindex="-1"></a><span class="co">#> Mean SD 2.5% 50% 97.5% Rhat ESS</span></span>
<span id="cb6-19"><a href="#cb6-19" tabindex="-1"></a><span class="co">#> (Intercept) -1.8186 0.3428 -2.5560 -1.8020 -1.1956 1.0692 64</span></span>
<span id="cb6-20"><a href="#cb6-20" tabindex="-1"></a><span class="co">#> scale(forest) -0.1999 0.2056 -0.5818 -0.2102 0.2443 1.0292 160</span></span>
<span id="cb6-21"><a href="#cb6-21" tabindex="-1"></a><span class="co">#> scale(grass) 0.1206 0.1939 -0.2720 0.1244 0.4938 1.0210 229</span></span>
<span id="cb6-22"><a href="#cb6-22" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb6-23"><a href="#cb6-23" tabindex="-1"></a><span class="co">#> Detection (log scale): </span></span>
<span id="cb6-24"><a href="#cb6-24" tabindex="-1"></a><span class="co">#> Mean SD 2.5% 50% 97.5% Rhat ESS</span></span>
<span id="cb6-25"><a href="#cb6-25" tabindex="-1"></a><span class="co">#> (Intercept) -2.5392 0.1196 -2.7602 -2.5436 -2.2815 1.0850 204</span></span>
<span id="cb6-26"><a href="#cb6-26" tabindex="-1"></a><span class="co">#> scale(day) -0.1658 0.0807 -0.3380 -0.1629 -0.0187 1.0341 364</span></span>
<span id="cb6-27"><a href="#cb6-27" tabindex="-1"></a><span class="co">#> I(scale(day)^2) 0.0011 0.0828 -0.1530 -0.0011 0.1648 1.0391 352</span></span>
<span id="cb6-28"><a href="#cb6-28" tabindex="-1"></a><span class="co">#> scale(wind) -0.1352 0.0769 -0.2931 -0.1344 0.0126 1.0037 534</span></span>
<span id="cb6-29"><a href="#cb6-29" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb6-30"><a href="#cb6-30" tabindex="-1"></a><span class="co">#> Spatial Covariance: </span></span>
<span id="cb6-31"><a href="#cb6-31" tabindex="-1"></a><span class="co">#> Mean SD 2.5% 50% 97.5% Rhat ESS</span></span>
<span id="cb6-32"><a href="#cb6-32" tabindex="-1"></a><span class="co">#> sigma.sq 0.4941 0.2648 0.1725 0.431 1.1929 1.0156 169</span></span>
<span id="cb6-33"><a href="#cb6-33" tabindex="-1"></a><span class="co">#> phi 0.0016 0.0018 0.0003 0.001 0.0072 1.0644 102</span></span></code></pre></div>
<h3 id="posterior-predictive-check">Posterior predictive check</h3>
<p>The function <code>ppcAbund</code> performs a posterior predictive
check on the resulting list from the call to <code>spDS</code>. We
provide options to group, or bin, the data in different ways prior to
performing the posterior predictive check, which can help reveal
different types of inadequate model fit. Below we perform a posterior
predictive check on the data grouped by site with a Freeman-Tukey fit
statistic, and then use the <code>summary</code> function to summarize
the check with a Bayesian p-value.</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a>ppc.out <span class="ot"><-</span> <span class="fu">ppcAbund</span>(out, <span class="at">fit.stat =</span> <span class="st">'freeman-tukey'</span>, <span class="at">group =</span> <span class="dv">1</span>)</span>
<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a><span class="fu">summary</span>(ppc.out)</span>
<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a><span class="co">#> Call:</span></span>
<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a><span class="co">#> ppcAbund(object = out, fit.stat = "freeman-tukey", group = 1)</span></span>
<span id="cb7-6"><a href="#cb7-6" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb7-7"><a href="#cb7-7" tabindex="-1"></a><span class="co">#> Samples per Chain: 20000</span></span>
<span id="cb7-8"><a href="#cb7-8" tabindex="-1"></a><span class="co">#> Burn-in: 10000</span></span>
<span id="cb7-9"><a href="#cb7-9" tabindex="-1"></a><span class="co">#> Thinning Rate: 5</span></span>
<span id="cb7-10"><a href="#cb7-10" tabindex="-1"></a><span class="co">#> Number of Chains: 3</span></span>
<span id="cb7-11"><a href="#cb7-11" tabindex="-1"></a><span class="co">#> Total Posterior Samples: 6000</span></span>
<span id="cb7-12"><a href="#cb7-12" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb7-13"><a href="#cb7-13" tabindex="-1"></a><span class="co">#> Bayesian p-value: 0.535 </span></span>
<span id="cb7-14"><a href="#cb7-14" tabindex="-1"></a><span class="co">#> Fit statistic: freeman-tukey</span></span></code></pre></div>
<h3 id="model-selection-using-waic">Model selection using WAIC</h3>
<p>The <code>waicAbund</code> function computes the Widely Applicable
Information Criterion (WAIC) for use in model selection and
assessment.</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="fu">waicAbund</span>(out)</span>
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a><span class="co">#> N.max not specified. Setting upper index of integration of N to 10 plus</span></span>
<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a><span class="co">#> the largest estimated abundance value at each site in object$N.samples</span></span>
<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a><span class="co">#> elpd pD WAIC </span></span>
<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a><span class="co">#> -167.74186 14.03248 363.54866</span></span></code></pre></div>
<h3 id="prediction">Prediction</h3>
<p>Prediction is possible using the <code>predict</code> function, a set
of covariates at the desired prediction locations, and the spatial
coordinates of the locations. The object <code>neonPredData</code>
contains percent forest cover and grassland cover across the Disney
Wildnerness Preserve. Below we predict MODO density across the preserve,
which is stored in the <code>out.pred</code> object.</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="co"># First standardize elevation using mean and sd from fitted model</span></span>
<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a>forest.pred <span class="ot"><-</span> (neonPredData<span class="sc">$</span>forest <span class="sc">-</span> <span class="fu">mean</span>(dat.MODO<span class="sc">$</span>covs<span class="sc">$</span>forest)) <span class="sc">/</span></span>
<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a> <span class="fu">sd</span>(dat.MODO<span class="sc">$</span>covs<span class="sc">$</span>forest)</span>
<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a>grass.pred <span class="ot"><-</span> (neonPredData<span class="sc">$</span>grass <span class="sc">-</span> <span class="fu">mean</span>(dat.MODO<span class="sc">$</span>covs<span class="sc">$</span>grass)) <span class="sc">/</span></span>
<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a> <span class="fu">sd</span>(dat.MODO<span class="sc">$</span>covs<span class="sc">$</span>grass)</span>
<span id="cb9-6"><a href="#cb9-6" tabindex="-1"></a>X<span class="fl">.0</span> <span class="ot"><-</span> <span class="fu">cbind</span>(<span class="dv">1</span>, forest.pred, grass.pred)</span>
<span id="cb9-7"><a href="#cb9-7" tabindex="-1"></a><span class="fu">colnames</span>(X<span class="fl">.0</span>) <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">'(Intercept)'</span>, <span class="st">'forest'</span>, <span class="st">'grass'</span>)</span>
<span id="cb9-8"><a href="#cb9-8" tabindex="-1"></a>coords<span class="fl">.0</span> <span class="ot"><-</span> neonPredData[, <span class="fu">c</span>(<span class="st">'easting'</span>, <span class="st">'northing'</span>)]</span>
<span id="cb9-9"><a href="#cb9-9" tabindex="-1"></a>out.pred <span class="ot"><-</span> <span class="fu">predict</span>(out, X<span class="fl">.0</span>, coords<span class="fl">.0</span>, <span class="at">verbose =</span> <span class="cn">FALSE</span>)</span></code></pre></div>
<h2 id="learn-more">Learn more</h2>
<p>The <code>vignette("distanceSampling")</code>,
<code>vignette("nMixtureModels")</code>, and
<code>vignette("glmm")</code> provide detailed descriptions and
tutorials of all hierarchical distance sampling models, N-mixture
models, and generalized linear mixed models in <code>spAbundance</code>,
respectively. Given the similarity in syntax to fitting occupancy models
in the <code>spOccupancy</code> package, much of the documentation on
the <a href="https://www.doserlab.com/files/spoccupancy-web/"><code>spOccupancy</code>
website</a> will also be helpful for fitting models in
<code>spAbundance</code>. Please also consider joining the <a href="https://groups.google.com/g/spocc-spabund-users"><code>spAbundance</code>
and <code>spOccupancy</code> users google group</a> to learn from others
who use the two packages.</p>
<h2 id="citing-spabundance">Citing <code>spAbundance</code></h2>
<p>Please cite <code>spAbundance</code> as:</p>
<p>Doser, J. W., Finley A. O., Kéry, M., & Zipkin E. F. (2024).
spAbundance: An R package for single-species and multi-species spatially
explicit abundance models, Methods in Ecology and Evolution. 15,
1024-1033. <a href="https://doi.org/10.1111/2041-210X.14332">https://doi.org/10.1111/2041-210X.14332</a>“)</p>
<h2 id="references">References</h2>
<p>Doser, J. W., Finley, A. O., Kéry, M., and Zipkin, E. F. (2022).
spOccupancy: An R package for single-species, multi-species, and
integrated spatial occupancy models. Methods in Ecology and Evolution,
13(8), 1670-1678. <a href="https://doi.org/10.1111/2041-210X.13897">https://doi.org/10.1111/2041-210X.13897</a>.</p>
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