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README.html
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<!DOCTYPE html>
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}
.pl-mh,
.pl-mh .pl-en,
.pl-ms {
color: #1d3e81;
font-weight: bold;
}
.pl-mq {
color: #008080;
}
.pl-mi {
color: #333;
font-style: italic;
}
.pl-mb {
color: #333;
font-weight: bold;
}
.pl-md {
background-color: #ffecec;
color: #bd2c00;
}
.pl-mi1 {
background-color: #eaffea;
color: #55a532;
}
.pl-mdr {
color: #795da3;
font-weight: bold;
}
.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="spoccupancy-">spOccupancy
<a href="https://doserlab.com/files/spoccupancy-web/"><img role="img" src="data:image/png;base64,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" 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<p>spOccupancy fits single-species, multi-species, and integrated
spatial occupancy models using Markov chain Monte Carlo (MCMC). Models
are fit using Pólya-Gamma data augmentation. Spatial models are fit
using either Gaussian processes or Nearest Neighbor Gaussian Processes
(NNGP) for large spatial datasets. The package provides functionality
for data integration of multiple single-species occupancy data sets
using a joint likelihood framework. For multi-species models,
spOccupancy provides functions to account for residual species
correlations in a joint species distribution model framework while
accounting for imperfect detection. <code>spOccupancy</code> also
provides functions for multi-season (i.e., spatio-temporal)
single-species occupancy models. Below we give 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.</p>
<h2 id="installation">Installation</h2>
<p>You can install the released version of <code>spOccupancy</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">"spOccupancy"</span>)</span></code></pre></div>
<h2 id="functionality">Functionality</h2>
<table>
<thead>
<tr class="header">
<th><code>spOccupancy</code> Function</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><code>PGOcc()</code></td>
<td>Single-species occupancy model</td>
</tr>
<tr class="even">
<td><code>spPGOcc()</code></td>
<td>Single-species spatial occupancy model</td>
</tr>
<tr class="odd">
<td><code>intPGOcc()</code></td>
<td>Single-species occupancy model with multiple data sources</td>
</tr>
<tr class="even">
<td><code>spIntPGOcc()</code></td>
<td>Single-species spatial occupancy model with multiple data
sources</td>
</tr>
<tr class="odd">
<td><code>msPGOcc()</code></td>
<td>Multi-species occupancy model</td>
</tr>
<tr class="even">
<td><code>spMsPGOcc()</code></td>
<td>Multi-species spatial occupancy model</td>
</tr>
<tr class="odd">
<td><code>lfJSDM()</code></td>
<td>Joint species distribution model without imperfect detection</td>
</tr>
<tr class="even">
<td><code>sfJSDM()</code></td>
<td>Spatial joint species distribution model without imperfect
detection</td>
</tr>
<tr class="odd">
<td><code>lfMsPGOcc()</code></td>
<td>Multi-species occupancy model with species correlations</td>
</tr>
<tr class="even">
<td><code>sfMsPGOcc()</code></td>
<td>Multi-species spatial occupancy model with species correlations</td>
</tr>
<tr class="odd">
<td><code>intMsPGOcc()</code></td>
<td>Multi-species occupancy model with multiple data sources</td>
</tr>
<tr class="even">
<td><code>tPGOcc()</code></td>
<td>Single-species multi-season occupancy model</td>
</tr>
<tr class="odd">
<td><code>stPGOcc()</code></td>
<td>Single-species multi-season spatio-temporal occupancy model</td>
</tr>
<tr class="even">
<td><code>svcPGBinom()</code></td>
<td>Single-species spatially-varying coefficient GLM</td>
</tr>
<tr class="odd">
<td><code>svcPGOcc()</code></td>
<td>Single-species spatially-varying coefficient occupancy model</td>
</tr>
<tr class="even">
<td><code>svcTPGBinom()</code></td>
<td>Single-species spatially-varying coefficient multi-season GLM</td>
</tr>
<tr class="odd">
<td><code>svcTPGOcc()</code></td>
<td>Single-species spatially-varying coefficient multi-season occupancy
model</td>
</tr>
<tr class="even">
<td><code>svcMsPGOcc()</code></td>
<td>Multi-species spatially-varying coefficient occupancy model</td>
</tr>
<tr class="odd">
<td><code>tMsPGOcc()</code></td>
<td>Multi-species, multi-season occupancy model</td>
</tr>
<tr class="even">
<td><code>stMsPGOcc()</code></td>
<td>Multi-species, multi-season spatial occupancy model</td>
</tr>
<tr class="odd">
<td><code>svcTMsPGOcc()</code></td>
<td>Multi-species, multi-season spatially-varying coefficient occupancy
model</td>
</tr>
<tr class="even">
<td><code>tIntPGOcc()</code></td>
<td>Multi-season occupancy model with multiple data sources</td>
</tr>
<tr class="odd">
<td><code>stIntPGOcc()</code></td>
<td>Spatial multi-season occupancy model with multiple data sources</td>
</tr>
<tr class="even">
<td><code>svcTIntPGOcc()</code></td>
<td>SVC multi-season occupancy model with multiple data sources</td>
</tr>
<tr class="odd">
<td><code>postHocLM()</code></td>
<td>Fit a linear (mixed) model using estimates from a previous model
fit</td>
</tr>
<tr class="even">
<td><code>ppcOcc()</code></td>
<td>Posterior predictive check using Bayesian p-values</td>
</tr>
<tr class="odd">
<td><code>waicOcc()</code></td>
<td>Compute Widely Applicable Information Criterion (WAIC)</td>
</tr>
<tr class="even">
<td><code>updateMCMC()</code></td>
<td>Update an existing model object with more MCMC samples (in
development)</td>
</tr>
<tr class="odd">
<td><code>simOcc()</code></td>
<td>Simulate single-species occupancy data</td>
</tr>
<tr class="even">
<td><code>simTOcc()</code></td>
<td>Simulate single-species multi-season occupancy data</td>
</tr>
<tr class="odd">
<td><code>simBinom()</code></td>
<td>Simulate detection-nondetection data with perfect detection</td>
</tr>
<tr class="even">
<td><code>simTBinom()</code></td>
<td>Simulate multi-season detection-nondetection data with perfect
detection</td>
</tr>
<tr class="odd">
<td><code>simMsOcc()</code></td>
<td>Simulate multi-species occupancy data</td>
</tr>
<tr class="even">
<td><code>simTMsOcc()</code></td>
<td>Simulate multi-species, multi-season occupancy data</td>
</tr>
<tr class="odd">
<td><code>simIntOcc()</code></td>
<td>Simulate single-species occupancy data from multiple data
sources</td>
</tr>
<tr class="even">
<td><code>simIntMsOcc()</code></td>
<td>Simulate multi-species occupancy data from multiple data
sources</td>
</tr>
<tr class="odd">
<td><code>simTIntOcc()</code></td>
<td>Simulate multi-season occupancy data from multiple data sources</td>
</tr>
</tbody>
</table>
<h2 id="example-usage">Example usage</h2>
<h3 id="load-package-and-data">Load package and data</h3>
<p>To get started with <code>spOccupancy</code> we load the package and
an example data set. We use data on twelve foliage-gleaning birds from
the <a href="https://hubbardbrook.org/">Hubbard Brook Experimental
Forest</a>, which is available in the <code>spOccupancy</code> package
as the <code>hbef2015</code> object. Here we will only work with one
bird species, the black-throated blue warbler (BTBW), and so we subset
the <code>hbef2015</code> object to only include this species.</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><span class="fu">library</span>(spOccupancy)</span>
<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a><span class="fu">data</span>(hbef2015)</span>
<span id="cb2-3"><a href="#cb2-3" tabindex="-1"></a>sp.names <span class="ot"><-</span> <span class="fu">dimnames</span>(hbef2015<span class="sc">$</span>y)[[<span class="dv">1</span>]]</span>
<span id="cb2-4"><a href="#cb2-4" tabindex="-1"></a>btbwHBEF <span class="ot"><-</span> hbef2015</span>
<span id="cb2-5"><a href="#cb2-5" tabindex="-1"></a>btbwHBEF<span class="sc">$</span>y <span class="ot"><-</span> btbwHBEF<span class="sc">$</span>y[sp.names <span class="sc">==</span> <span class="st">"BTBW"</span>, , ]</span></code></pre></div>
<h3 id="fit-a-spatial-occupancy-model-using-sppgocc">Fit a spatial
occupancy model using <code>spPGOcc()</code></h3>
<p>Below we fit a single-species spatial occupancy model to the BTBW
data using a Nearest Neighbor Gaussian Process. We use the default
priors and initial values for the occurrence (<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 occurrence
values (<code>z</code>). We assume occurrence is a function of linear
and quadratic elevation along with a spatial random intercept. We model
detection as a function of linear and quadratic day of survey and linear
time of day the survey occurred.</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="co"># Specify model formulas</span></span>
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a>btbw.occ.formula <span class="ot"><-</span> <span class="er">~</span> <span class="fu">scale</span>(Elevation) <span class="sc">+</span> <span class="fu">I</span>(<span class="fu">scale</span>(Elevation)<span class="sc">^</span><span class="dv">2</span>)</span>
<span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a>btbw.det.formula <span class="ot"><-</span> <span class="er">~</span> <span class="fu">scale</span>(day) <span class="sc">+</span> <span class="fu">scale</span>(tod) <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></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 for 20,000
iterations split into 800 batches each of length 25. For each chain, we
discard the first 8000 iterations as burn-in and use a thinning rate of
4 for a resulting 9000 samples from the joint posterior. We fit the
model using 5 nearest neighbors and an exponential correlation function.
We also specify the <code>k.fold</code> argument to perform 2-fold
cross-validation after fitting the full model. Run <code>?spPGOcc</code>
for more detailed information on all function arguments.</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"># Run the model</span></span>
<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a>out <span class="ot"><-</span> <span class="fu">spPGOcc</span>(<span class="at">occ.formula =</span> btbw.occ.formula,</span>
<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a> <span class="at">det.formula =</span> btbw.det.formula,</span>
<span id="cb4-4"><a href="#cb4-4" tabindex="-1"></a> <span class="at">data =</span> btbwHBEF, <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="cb4-5"><a href="#cb4-5" 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="cb4-6"><a href="#cb4-6" tabindex="-1"></a> <span class="at">NNGP =</span> <span class="cn">TRUE</span>, <span class="at">n.neighbors =</span> <span class="dv">5</span>, <span class="at">n.burn =</span> <span class="dv">8000</span>, </span>
<span id="cb4-7"><a href="#cb4-7" tabindex="-1"></a> <span class="at">n.thin =</span> <span class="dv">4</span>, <span class="at">n.chains =</span> <span class="dv">3</span>, <span class="at">verbose =</span> <span class="cn">FALSE</span>, </span>
<span id="cb4-8"><a href="#cb4-8" tabindex="-1"></a> <span class="at">k.fold =</span> <span class="dv">2</span>, <span class="at">k.fold.threads =</span> <span class="dv">2</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="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="fu">summary</span>(out)</span>
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a><span class="co">#> Call:</span></span>
<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a><span class="co">#> spPGOcc(occ.formula = btbw.occ.formula, det.formula = btbw.det.formula, </span></span>
<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a><span class="co">#> data = btbwHBEF, cov.model = "exponential", NNGP = TRUE, </span></span>
<span id="cb5-6"><a href="#cb5-6" tabindex="-1"></a><span class="co">#> n.neighbors = 5, n.batch = 800, batch.length = 25, accept.rate = 0.43, </span></span>
<span id="cb5-7"><a href="#cb5-7" tabindex="-1"></a><span class="co">#> verbose = FALSE, n.burn = 8000, n.thin = 4, n.chains = 3, </span></span>
<span id="cb5-8"><a href="#cb5-8" tabindex="-1"></a><span class="co">#> k.fold = 2, k.fold.threads = 2)</span></span>
<span id="cb5-9"><a href="#cb5-9" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb5-10"><a href="#cb5-10" tabindex="-1"></a><span class="co">#> Samples per Chain: 20000</span></span>
<span id="cb5-11"><a href="#cb5-11" tabindex="-1"></a><span class="co">#> Burn-in: 8000</span></span>
<span id="cb5-12"><a href="#cb5-12" tabindex="-1"></a><span class="co">#> Thinning Rate: 4</span></span>
<span id="cb5-13"><a href="#cb5-13" tabindex="-1"></a><span class="co">#> Number of Chains: 3</span></span>
<span id="cb5-14"><a href="#cb5-14" tabindex="-1"></a><span class="co">#> Total Posterior Samples: 9000</span></span>
<span id="cb5-15"><a href="#cb5-15" tabindex="-1"></a><span class="co">#> Run Time (min): 1.3642</span></span>
<span id="cb5-16"><a href="#cb5-16" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb5-17"><a href="#cb5-17" tabindex="-1"></a><span class="co">#> Occurrence (logit scale): </span></span>
<span id="cb5-18"><a href="#cb5-18" tabindex="-1"></a><span class="co">#> Mean SD 2.5% 50% 97.5% Rhat ESS</span></span>
<span id="cb5-19"><a href="#cb5-19" tabindex="-1"></a><span class="co">#> (Intercept) 3.9946 0.5810 3.0233 3.9337 5.2932 1.0302 354</span></span>
<span id="cb5-20"><a href="#cb5-20" tabindex="-1"></a><span class="co">#> scale(Elevation) -0.5235 0.2193 -0.9785 -0.5145 -0.1082 1.0013 1368</span></span>
<span id="cb5-21"><a href="#cb5-21" tabindex="-1"></a><span class="co">#> I(scale(Elevation)^2) -1.1673 0.2117 -1.6341 -1.1489 -0.8003 1.0026 571</span></span>
<span id="cb5-22"><a href="#cb5-22" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb5-23"><a href="#cb5-23" tabindex="-1"></a><span class="co">#> Detection (logit scale): </span></span>
<span id="cb5-24"><a href="#cb5-24" tabindex="-1"></a><span class="co">#> Mean SD 2.5% 50% 97.5% Rhat ESS</span></span>
<span id="cb5-25"><a href="#cb5-25" tabindex="-1"></a><span class="co">#> (Intercept) 0.6621 0.1136 0.4429 0.6602 0.8872 1.0009 8235</span></span>
<span id="cb5-26"><a href="#cb5-26" tabindex="-1"></a><span class="co">#> scale(day) 0.2912 0.0701 0.1526 0.2910 0.4294 1.0019 9000</span></span>
<span id="cb5-27"><a href="#cb5-27" tabindex="-1"></a><span class="co">#> scale(tod) -0.0306 0.0699 -0.1672 -0.0299 0.1057 1.0025 9000</span></span>
<span id="cb5-28"><a href="#cb5-28" tabindex="-1"></a><span class="co">#> I(scale(day)^2) -0.0753 0.0861 -0.2456 -0.0753 0.0927 0.9999 9000</span></span>
<span id="cb5-29"><a href="#cb5-29" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb5-30"><a href="#cb5-30" tabindex="-1"></a><span class="co">#> Spatial Covariance: </span></span>
<span id="cb5-31"><a href="#cb5-31" tabindex="-1"></a><span class="co">#> Mean SD 2.5% 50% 97.5% Rhat ESS</span></span>
<span id="cb5-32"><a href="#cb5-32" tabindex="-1"></a><span class="co">#> sigma.sq 1.1864 0.9200 0.2306 0.9314 3.5575 1.0336 160</span></span>
<span id="cb5-33"><a href="#cb5-33" tabindex="-1"></a><span class="co">#> phi 0.0075 0.0075 0.0007 0.0044 0.0272 1.0668 111</span></span></code></pre></div>
<h3 id="posterior-predictive-check">Posterior predictive check</h3>
<p>The function <code>ppcOcc</code> performs a posterior predictive
check on the resulting list from the call to <code>spPGOcc</code>. For
binary data, we need to perform Goodness of Fit assessments on some
binned form of the data rather than the raw binary data. 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="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a>ppc.out <span class="ot"><-</span> <span class="fu">ppcOcc</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="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="fu">summary</span>(ppc.out)</span>
<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a><span class="co">#> Call:</span></span>
<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a><span class="co">#> ppcOcc(object = out, fit.stat = "freeman-tukey", group = 1)</span></span>
<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb6-7"><a href="#cb6-7" tabindex="-1"></a><span class="co">#> Samples per Chain: 20000</span></span>
<span id="cb6-8"><a href="#cb6-8" tabindex="-1"></a><span class="co">#> Burn-in: 8000</span></span>
<span id="cb6-9"><a href="#cb6-9" tabindex="-1"></a><span class="co">#> Thinning Rate: 4</span></span>
<span id="cb6-10"><a href="#cb6-10" tabindex="-1"></a><span class="co">#> Number of Chains: 3</span></span>
<span id="cb6-11"><a href="#cb6-11" tabindex="-1"></a><span class="co">#> Total Posterior Samples: 9000</span></span>
<span id="cb6-12"><a href="#cb6-12" tabindex="-1"></a><span class="co">#> </span></span>
<span id="cb6-13"><a href="#cb6-13" tabindex="-1"></a><span class="co">#> Bayesian p-value: 0.4833 </span></span>
<span id="cb6-14"><a href="#cb6-14" tabindex="-1"></a><span class="co">#> Fit statistic: freeman-tukey</span></span></code></pre></div>
<h3 id="model-selection-using-waic-and-k-fold-cross-validation">Model
selection using WAIC and k-fold cross-validation</h3>
<p>The <code>waicOcc</code> function computes the Widely Applicable
Information Criterion (WAIC) for use in model selection and assessment
(note that due to Monte Carlo error your results will differ
slightly).</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><span class="fu">waicOcc</span>(out)</span>
<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a><span class="co">#> elpd pD WAIC </span></span>
<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a><span class="co">#> -680.80100 21.87208 1405.34616</span></span></code></pre></div>
<p>Alternatively, we can perform k-fold cross-validation (CV) directly
in our call to <code>spPGOcc</code> using the <code>k.fold</code>
argument and compare models using a deviance scoring rule. We fit the
model with <code>k.fold = 2</code> and so below we access the deviance
scoring rule from the 2-fold cross-validation. If we have additional
candidate models to compare this model with, then we might select for
inference the one with the lowest value of this CV score.</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>out<span class="sc">$</span>k.fold.deviance</span>
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a><span class="co">#> [1] 1414.027</span></span></code></pre></div>
<h3 id="prediction">Prediction</h3>
<p>Prediction is possible using the <code>predict</code> function, a set
of occurrence covariates at the new locations, and the spatial
coordinates of the new locations. The object <code>hbefElev</code>
contains elevation data across the entire Hubbard Brook Experimental
Forest. Below we predict BTBW occurrence across the forest, which are
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>elev.pred <span class="ot"><-</span> (hbefElev<span class="sc">$</span>val <span class="sc">-</span> <span class="fu">mean</span>(btbwHBEF<span class="sc">$</span>occ.covs[, <span class="dv">1</span>])) <span class="sc">/</span> <span class="fu">sd</span>(btbwHBEF<span class="sc">$</span>occ.covs[, <span class="dv">1</span>])</span>
<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a>coords<span class="fl">.0</span> <span class="ot"><-</span> <span class="fu">as.matrix</span>(hbefElev[, <span class="fu">c</span>(<span class="st">'Easting'</span>, <span class="st">'Northing'</span>)])</span>
<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a>X<span class="fl">.0</span> <span class="ot"><-</span> <span class="fu">cbind</span>(<span class="dv">1</span>, elev.pred, elev.pred<span class="sc">^</span><span class="dv">2</span>)</span>
<span id="cb9-5"><a href="#cb9-5" 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("modelFitting")</code> provides a more detailed
description and tutorial of the core functions in
<code>spOccupancy</code>. For full statistical details on the MCMC
samplers for core functions in <code>spOccupancy</code>, see
<code>vignette("mcmcSamplers")</code>. In addition, see <a href="https://doi.org/10.1111/2041-210X.13897">the introductory
spOccupancy paper</a> that describes the package in more detail (Doser
et al. 2022). For a detailed description and tutorial of joint species
distribution models in <code>spOccupancy</code> that account for
residual species correlations, see
<code>vignette("factorModels")</code>,
<code>vignette("mcmcFactorModels")</code>, and our <a href="https://doi.org/10.1002/ecy.4137">open-access paper</a> (Doser et
al. 2023). For a description and tutorial of multi-season
(spatio-temporal) occupancy models in <code>spOccupancy</code>, see
<code>vignette("spaceTimeModels")</code>. For a tutorial on
spatially-varying coefficient models in <code>spOccupancy</code>, see
<code>vignette("svcModels")</code> and
<code>vignette(mcmcSVCModels)</code> and our associated papers that
describe the <a href="https://doserlab.com/files/pubs/doser2024JABES.pdf">methods</a>
(Doser et al. 2024A) and <a href="https://onlinelibrary.wiley.com/doi/epdf/10.1111/geb.13814">applications
to ecology</a> (Doser et al. 2024B) in much more detail.</p>
<h2 id="references">References</h2>
<p>Doser, J. W., Finley, A. O., Kery, 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>
<p>Doser, J. W., Finley, A. O., and Banerjee, S. (2023). Joint species
distribution models with imperfect detection for high-dimensional
spatial data. Ecology, 104(9), e4137. <a href="https://doi.org/10.1002/ecy.4137">https://doi.org/10.1002/ecy.4137</a>.</p>
<p>Doser, J. W., Finley, A. O., Saunders, S. P., Kéry, M., Weed, A. S.,
& Zipkin, E. F. (2024A). Modeling complex species-environment
relationships through spatially-varying coefficient occupancy models.
Journal of Agricultural, Biological and Environmental Statistics. <a href="https://doi.org/10.1007/s13253-023-00595-6">https://doi.org/10.1007/s13253-023-00595-6</a>.</p>
<p>Doser, J. W., Kéry, M., Saunders, S. P., Finley, A. O., Bateman, B.
L., Grand, J., Reault, S., Weed, A. S., & Zipkin, E. F. (2024B).
Guidelines for the use of spatially varying coefficients in species
distribution models. Global Ecology and Biogeography, 33, e13814. <a href="https://doi.org/10.1111/geb.13814">https://doi.org/10.1111/geb.13814</a></p>
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