From 16b822f6150bf62b6e7dcdd4c989ae53a61deff2 Mon Sep 17 00:00:00 2001
From: Quarto GHA Workflow Runner <quarto-github-actions-publish@example.com>
Date: Thu, 23 Nov 2023 18:12:41 +0000
Subject: [PATCH] Built site for gh-pages

---
 .nojekyll                               |   2 +-
 MarineSDMs.docx                         | Bin 3269321 -> 3268897 bytes
 MarineSDMs.pdf                          | Bin 2828100 -> 2821467 bytes
 calibrate.html                          |  93 ++---------------
 ensemble.html                           |   2 +-
 evaluate.html                           |  34 +++---
 explorations.html                       |   8 +-
 explorations/am-envelope.html           |   2 +-
 explorations/am-fine.html               |   2 +-
 explorations/obis-top-spp-by-class.html |   2 +-
 explorations/sdm-1_predicts.html        |   2 +-
 fit.html                                | 131 +++---------------------
 mosaic.html                             |   4 +-
 occ.html                                |   4 +-
 predict.html                            |  80 ++-------------
 search.json                             |  45 +++-----
 software.html                           |   6 +-
 split.html                              |  14 ++-
 18 files changed, 89 insertions(+), 342 deletions(-)

diff --git a/.nojekyll b/.nojekyll
index 541b1f7..732e178 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-452d69b8
\ No newline at end of file
+6fee117f
\ No newline at end of file
diff --git a/MarineSDMs.docx b/MarineSDMs.docx
index 943de53e8f43d548f754f00ef152239fddb2c8cf..69f1af5e28d53a0c78a8042bc99fa56eeda2dec4 100644
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diff --git a/MarineSDMs.pdf b/MarineSDMs.pdf
index 1d5fe6c7ce1eca0a9c4730ea6cc928dd1a5e84e2..4b777253d1f932bb8ec46c38365d94a9f90957f6 100644
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diff --git a/calibrate.html b/calibrate.html
index 98c71c1..3184120 100644
--- a/calibrate.html
+++ b/calibrate.html
@@ -96,9 +96,6 @@
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@@ -415,14 +412,7 @@
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 <!-- margin-sidebar -->
     <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
-        <nav id="TOC" role="doc-toc" class="toc-active">
-    <h2 id="toc-title">Table of contents</h2>
-   
-  <ul>
-  <li><a href="#predict" id="toc-predict" class="nav-link active" data-scroll-target="#predict"><span class="header-section-number">9.1</span> Predict</a></li>
-  <li><a href="#evaluate" id="toc-evaluate" class="nav-link" data-scroll-target="#evaluate"><span class="header-section-number">9.2</span> Evaluate</a></li>
-  </ul>
-<div class="toc-actions"><div><i class="bi bi-github"></i></div><div class="action-links"><p><a href="https://github.com/marinebon/sdm-book/edit/main/calibrate.qmd" class="toc-action">Edit this page</a></p></div></div></nav>
+        
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 <main class="content" id="quarto-document-content">
@@ -430,6 +420,7 @@ <h2 id="toc-title">Table of contents</h2>
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
 <div class="quarto-title-block"><div><h1 class="title"><span class="chapter-number">9</span>&nbsp; <span class="chapter-title">Calibrate</span></h1><button type="button" class="btn code-tools-button" id="quarto-code-tools-source"><i class="bi"></i> Code</button></div></div>
+<p class="subtitle lead">Calibrate model fit, i.e., model selection</p>
 </div>
 
 
@@ -446,50 +437,6 @@ <h2 id="toc-title">Table of contents</h2>
 
 <p>The process of refining the model to only the most relevant environmental predictor terms is commonly called “Model Selection.” One of the most cited scientific paper of all time <span class="citation" data-cites="akaike1974">(<a href="references.html#ref-akaike1974" role="doc-biblioref">Akaike 1974</a>)</span> is based on taking a most parsimonious approach to this process – the so called Akaike Information Criteria (AIC).</p>
 <p>It is important to avoid using environmental predictors that are correlated with each other, since the effect of a predictor on the response could be the ecologically inverse, the result of explaining variance on the residuals of the other correlated predictor.</p>
-<section id="predict" class="level2" data-number="9.1">
-<h2 data-number="9.1" class="anchored" data-anchor-id="predict"><span class="header-section-number">9.1</span> Predict</h2>
-<p>The prediction step applies the environmental relationships from the fitted model to a new set of data, typically the seascape of interest, and perhaps with some sort of temporal snapshot (e.g., climatic annual or monthly average).</p>
-</section>
-<section id="evaluate" class="level2" data-number="9.2">
-<h2 data-number="9.2" class="anchored" data-anchor-id="evaluate"><span class="header-section-number">9.2</span> Evaluate</h2>
-<p>Model evaluation uses the set aside test data from the earlier splitting to evaluate how well the model predicts the response of presence or absence. Since the test response data is binary [0,1] and the prediction from the model is continuous [0-1], a threshold needs to be applied to assign to convert the continuous response to binary. This is often performed through a Receiver Operator Characteristic (<strong>ROC</strong>) curve (<a href="evaluate.html#fig-rocr">Figure&nbsp;<span>11.1</span></a>), which evaluates at each threshold the <strong>confusion matrix</strong> (<a href="evaluate.html#tbl-confusion-matrix">Table&nbsp;<span>11.1</span></a>).</p>
-<div id="tbl-confusion-matrix" class="anchored">
-<table class="table">
-<caption>Table&nbsp;9.1: Confusion matrix to understand predicted versus observed.</caption>
-<tbody>
-<tr class="odd">
-<td></td>
-<td></td>
-<td>Predicted</td>
-<td></td>
-</tr>
-<tr class="even">
-<td></td>
-<td></td>
-<td>0 (absence)</td>
-<td>1 (presence)</td>
-</tr>
-<tr class="odd">
-<td>Observed</td>
-<td>0 (absence)</td>
-<td>True absence</td>
-<td>False presence</td>
-</tr>
-<tr class="even">
-<td></td>
-<td>1 (presence)</td>
-<td>False absence</td>
-<td>True presence</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div id="fig-rocr" class="quarto-figure quarto-figure-center anchored">
-<figure class="figure">
-<p><a href="figures/rocr.png" class="lightbox" data-gallery="quarto-lightbox-gallery-1" title="ROC curve generated by showing rates of false positive vs false negative as function of changing the threshold value (rainbow colors). Source: ROCR: visualizing classifier performance in R"><img src="figures/rocr.png" class="img-fluid figure-img"></a></p>
-<figcaption class="figure-caption">Figure&nbsp;9.1: ROC curve generated by showing rates of false positive vs false negative as function of changing the threshold value (rainbow colors). Source: <a href="https://cran.rstudio.com/web/packages/ROCR/vignettes/ROCR.html">ROCR: visualizing classifier performance in R</a></figcaption>
-</figure>
-</div>
 
 
 <!-- -->
@@ -499,7 +446,6 @@ <h2 data-number="9.2" class="anchored" data-anchor-id="evaluate"><span class="he
 Akaike, H. 1974. <span>“A New Look at the Statistical Model Identification.”</span> <em>IEEE Transactions on Automatic Control</em> 19 (6): 716723.
 </div>
 </div>
-</section>
 
 </main> <!-- /main -->
 <script id="quarto-html-after-body" type="application/javascript">
@@ -913,30 +859,14 @@ <h2 data-number="9.2" class="anchored" data-anchor-id="evaluate"><span class="he
       </a>
   </div>
 </nav><div class="modal fade" id="quarto-embedded-source-code-modal" tabindex="-1" aria-labelledby="quarto-embedded-source-code-modal-label" aria-hidden="true"><div class="modal-dialog modal-dialog-scrollable"><div class="modal-content"><div class="modal-header"><h5 class="modal-title" id="quarto-embedded-source-code-modal-label">Source Code</h5><button class="btn-close" data-bs-dismiss="modal"></button></div><div class="modal-body"><div class="">
-<div class="sourceCode" id="cb1" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu"># Calibrate</span></span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>The process of refining the model to only the most relevant environmental predictor terms is commonly called "Model Selection." One of the most cited scientific paper of all time <span class="co">[</span><span class="ot">@akaike1974</span><span class="co">]</span> is based on taking a most parsimonious approach to this process -- the so called Akaike Information Criteria (AIC).</span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>It is important to avoid using environmental predictors that are correlated with each other, since the effect of a predictor on the response could be the ecologically inverse, the result of explaining variance on the residuals of the other correlated predictor.</span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="fu">## Predict</span></span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>The prediction step applies the environmental relationships from the fitted model to a new set of data, typically the seascape of interest, and perhaps with some sort of temporal snapshot (e.g., climatic annual or monthly average).</span>
-<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a><span class="fu">## Evaluate</span></span>
-<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a>Model evaluation uses the set aside test data from the earlier splitting to evaluate how well the model predicts the response of presence or absence. Since the test response data is binary <span class="sc">\[</span>0,1<span class="sc">\]</span> and the prediction from the model is continuous <span class="sc">\[</span>0-1<span class="sc">\]</span>, a threshold needs to be applied to assign to convert the continuous response to binary. This is often performed through a Receiver Operator Characteristic (**ROC**) curve (@fig-rocr), which evaluates at each threshold the **confusion matrix** (@tbl-confusion-matrix).</span>
-<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a>|          |              |               |                |</span>
-<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a>|----------|--------------|---------------|----------------|</span>
-<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a>|          |              | Predicted     |                |</span>
-<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a>|          |              | 0 (absence)   | 1 (presence)   |</span>
-<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a>| Observed | 0 (absence)  | True absence  | False presence |</span>
-<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a>|          | 1 (presence) | False absence | True presence  |</span>
-<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-22"><a href="#cb1-22" aria-hidden="true" tabindex="-1"></a>: Confusion matrix to understand predicted versus observed. {#tbl-confusion-matrix}</span>
-<span id="cb1-23"><a href="#cb1-23" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-24"><a href="#cb1-24" aria-hidden="true" tabindex="-1"></a>!<span class="co">[</span><span class="ot">ROC curve generated by showing rates of false positive vs false negative as function of changing the threshold value (rainbow colors). Source: [ROCR: visualizing classifier performance in R](https://cran.rstudio.com/web/packages/ROCR/vignettes/ROCR.html)</span><span class="co">](figures/rocr.png)</span>{#fig-rocr}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb1" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> "Calibrate"</span></span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="an">subtitle:</span><span class="co"> "Calibrate model fit, i.e., model selection"</span></span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>The process of refining the model to only the most relevant environmental predictor terms is commonly called "Model Selection." One of the most cited scientific paper of all time <span class="co">[</span><span class="ot">@akaike1974</span><span class="co">]</span> is based on taking a most parsimonious approach to this process -- the so called Akaike Information Criteria (AIC).</span>
+<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>It is important to avoid using environmental predictors that are correlated with each other, since the effect of a predictor on the response could be the ecologically inverse, the result of explaining variance on the residuals of the other correlated predictor.</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div></div></div></div></div>
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@@ -944,11 +874,10 @@ <h2 data-number="9.2" class="anchored" data-anchor-id="evaluate"><span class="he
     <div class="nav-footer-left">© <a href="https://bbest.github.io/cv/">Ben Best</a></div>   
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+    <div class="toc-actions"><div><i class="bi bi-github"></i></div><div class="action-links"><p><a href="https://github.com/marinebon/sdm-book/edit/main/calibrate.qmd" class="toc-action">Edit this page</a></p></div></div></div>
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diff --git a/ensemble.html b/ensemble.html
index 822de93..5fd51be 100644
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diff --git a/evaluate.html b/evaluate.html
index 3272c1e..ea1badb 100644
--- a/evaluate.html
+++ b/evaluate.html
@@ -404,6 +404,7 @@
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
 <div class="quarto-title-block"><div><h1 class="title"><span class="chapter-number">11</span>&nbsp; <span class="chapter-title">Evaluate</span></h1><button type="button" class="btn code-tools-button" id="quarto-code-tools-source"><i class="bi"></i> Code</button></div></div>
+<p class="subtitle lead">Evaluate performance of the predicted model with the test data</p>
 </div>
 
 
@@ -873,20 +874,23 @@
       </a>
   </div>
 </nav><div class="modal fade" id="quarto-embedded-source-code-modal" tabindex="-1" aria-labelledby="quarto-embedded-source-code-modal-label" aria-hidden="true"><div class="modal-dialog modal-dialog-scrollable"><div class="modal-content"><div class="modal-header"><h5 class="modal-title" id="quarto-embedded-source-code-modal-label">Source Code</h5><button class="btn-close" data-bs-dismiss="modal"></button></div><div class="modal-body"><div class="">
-<div class="sourceCode" id="cb1" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu"># Evaluate</span></span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>Model evaluation uses the set aside test data from the earlier splitting to evaluate how well the model predicts the response of presence or absence. Since the test response data is binary <span class="sc">\[</span>0,1<span class="sc">\]</span> and the prediction from the model is continuous <span class="sc">\[</span>0-1<span class="sc">\]</span>, a threshold needs to be applied to assign to convert the continuous response to binary. This is often performed through a Receiver Operator Characteristic (**ROC**) curve (@fig-rocr), which evaluates at each threshold the **confusion matrix** (@tbl-confusion-matrix).</span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>|          |              |               |                |</span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>|----------|--------------|---------------|----------------|</span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>|          |              | Predicted     |                |</span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>|          |              | 0 (absence)   | 1 (presence)   |</span>
-<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>| Observed | 0 (absence)  | True absence  | False presence |</span>
-<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>|          | 1 (presence) | False absence | True presence  |</span>
-<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>: Confusion matrix to understand predicted versus observed. {#tbl-confusion-matrix}</span>
-<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>!<span class="co">[</span><span class="ot">ROC curve generated by showing rates of false positive vs false negative as function of changing the threshold value (rainbow colors). Source: [ROCR: visualizing classifier performance in R](https://cran.rstudio.com/web/packages/ROCR/vignettes/ROCR.html)</span><span class="co">](figures/rocr.png)</span>{#fig-rocr}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb1" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> "Evaluate"</span></span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="an">subtitle:</span><span class="co"> "Evaluate performance of the predicted model with the test data"</span></span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>Model evaluation uses the set aside test data from the earlier splitting to evaluate how well the model predicts the response of presence or absence. Since the test response data is binary <span class="sc">\[</span>0,1<span class="sc">\]</span> and the prediction from the model is continuous <span class="sc">\[</span>0-1<span class="sc">\]</span>, a threshold needs to be applied to assign to convert the continuous response to binary. This is often performed through a Receiver Operator Characteristic (**ROC**) curve (@fig-rocr), which evaluates at each threshold the **confusion matrix** (@tbl-confusion-matrix).</span>
+<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>|          |              |               |                |</span>
+<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>|----------|--------------|---------------|----------------|</span>
+<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>|          |              | Predicted     |                |</span>
+<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>|          |              | 0 (absence)   | 1 (presence)   |</span>
+<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>| Observed | 0 (absence)  | True absence  | False presence |</span>
+<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a>|          | 1 (presence) | False absence | True presence  |</span>
+<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a>: Confusion matrix to understand predicted versus observed. {#tbl-confusion-matrix}</span>
+<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a>!<span class="co">[</span><span class="ot">ROC curve generated by showing rates of false positive vs false negative as function of changing the threshold value (rainbow colors). Source: [ROCR: visualizing classifier performance in R](https://cran.rstudio.com/web/packages/ROCR/vignettes/ROCR.html)</span><span class="co">](figures/rocr.png)</span>{#fig-rocr}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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-<div class="g-col-1" data-index="1" data-categories="data: AquaMaps,tech: R" data-listing-file-modified-sort="1700762093360" data-listing-reading-time-sort="1">
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+<div class="g-col-1" data-index="3" data-categories="data: OBIS,tech: R,model: Maxent" data-listing-file-modified-sort="1700762885743" data-listing-reading-time-sort="1">
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diff --git a/explorations/am-fine.html b/explorations/am-fine.html
index bbf7f1e..f002944 100644
--- a/explorations/am-fine.html
+++ b/explorations/am-fine.html
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diff --git a/explorations/obis-top-spp-by-class.html b/explorations/obis-top-spp-by-class.html
index 13f7d62..8c87641 100644
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+++ b/explorations/obis-top-spp-by-class.html
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diff --git a/explorations/sdm-1_predicts.html b/explorations/sdm-1_predicts.html
index 2144a90..b9c2a3d 100644
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diff --git a/fit.html b/fit.html
index aaed7f1..2e94776 100644
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-}
-div.csl-indent {
-  margin-left: 2em;
-}</style>
+</style>
 
 
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 <!-- margin-sidebar -->
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-        <nav id="TOC" role="doc-toc" class="toc-active">
-    <h2 id="toc-title">Table of contents</h2>
-   
-  <ul>
-  <li><a href="#calibrate" id="toc-calibrate" class="nav-link active" data-scroll-target="#calibrate"><span class="header-section-number">8.1</span> Calibrate</a></li>
-  <li><a href="#predict" id="toc-predict" class="nav-link" data-scroll-target="#predict"><span class="header-section-number">8.2</span> Predict</a></li>
-  <li><a href="#evaluate" id="toc-evaluate" class="nav-link" data-scroll-target="#evaluate"><span class="header-section-number">8.3</span> Evaluate</a></li>
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+        
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 <main class="content" id="quarto-document-content">
@@ -431,6 +401,7 @@ <h2 id="toc-title">Table of contents</h2>
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
 <div class="quarto-title-block"><div><h1 class="title"><span class="chapter-number">8</span>&nbsp; <span class="chapter-title">Fit</span></h1><button type="button" class="btn code-tools-button" id="quarto-code-tools-source"><i class="bi"></i> Code</button></div></div>
+<p class="subtitle lead">Fit environmental relationship distinguishing presence from absence of species</p>
 </div>
 
 
@@ -447,65 +418,10 @@ <h2 id="toc-title">Table of contents</h2>
 
 <p>Model fitting in theory is quite complex, but quite simple in practice, with feeding the prepared data into the modeling function.</p>
 <p>However there are MANY modeling techniques from which to choose. For instance check out 238 entries in <a href="https://topepo.github.io/caret/available-models.html">6 Available Models | The caret Package</a>.</p>
-<section id="calibrate" class="level2" data-number="8.1">
-<h2 data-number="8.1" class="anchored" data-anchor-id="calibrate"><span class="header-section-number">8.1</span> Calibrate</h2>
-<p>The process of refining the model to only the most relevant environmental predictor terms is commonly called “Model Selection.” One of the most cited scientific paper of all time <span class="citation" data-cites="akaike1974">(<a href="references.html#ref-akaike1974" role="doc-biblioref">Akaike 1974</a>)</span> is based on taking a most parsimonious approach to this process – the so called Akaike Information Criteria (AIC).</p>
-<p>It is important to avoid using environmental predictors that are correlated with each other, since the effect of a predictor on the response could be the ecologically inverse, the result of explaining variance on the residuals of the other correlated predictor.</p>
-</section>
-<section id="predict" class="level2" data-number="8.2">
-<h2 data-number="8.2" class="anchored" data-anchor-id="predict"><span class="header-section-number">8.2</span> Predict</h2>
-<p>The prediction step applies the environmental relationships from the fitted model to a new set of data, typically the seascape of interest, and perhaps with some sort of temporal snapshot (e.g., climatic annual or monthly average).</p>
-</section>
-<section id="evaluate" class="level2" data-number="8.3">
-<h2 data-number="8.3" class="anchored" data-anchor-id="evaluate"><span class="header-section-number">8.3</span> Evaluate</h2>
-<p>Model evaluation uses the set aside test data from the earlier splitting to evaluate how well the model predicts the response of presence or absence. Since the test response data is binary [0,1] and the prediction from the model is continuous [0-1], a threshold needs to be applied to assign to convert the continuous response to binary. This is often performed through a Receiver Operator Characteristic (<strong>ROC</strong>) curve (<a href="evaluate.html#fig-rocr">Figure&nbsp;<span>11.1</span></a>), which evaluates at each threshold the <strong>confusion matrix</strong> (<a href="evaluate.html#tbl-confusion-matrix">Table&nbsp;<span>11.1</span></a>).</p>
-<div id="tbl-confusion-matrix" class="anchored">
-<table class="table">
-<caption>Table&nbsp;8.1: Confusion matrix to understand predicted versus observed.</caption>
-<tbody>
-<tr class="odd">
-<td></td>
-<td></td>
-<td>Predicted</td>
-<td></td>
-</tr>
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-<td></td>
-<td></td>
-<td>0 (absence)</td>
-<td>1 (presence)</td>
-</tr>
-<tr class="odd">
-<td>Observed</td>
-<td>0 (absence)</td>
-<td>True absence</td>
-<td>False presence</td>
-</tr>
-<tr class="even">
-<td></td>
-<td>1 (presence)</td>
-<td>False absence</td>
-<td>True presence</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div id="fig-rocr" class="quarto-figure quarto-figure-center anchored">
-<figure class="figure">
-<p><a href="figures/rocr.png" class="lightbox" title="ROC curve generated by showing rates of false positive vs false negative as function of changing the threshold value (rainbow colors). Source: ROCR: visualizing classifier performance in R" data-gallery="quarto-lightbox-gallery-1"><img src="figures/rocr.png" class="img-fluid figure-img"></a></p>
-<figcaption class="figure-caption">Figure&nbsp;8.1: ROC curve generated by showing rates of false positive vs false negative as function of changing the threshold value (rainbow colors). Source: <a href="https://cran.rstudio.com/web/packages/ROCR/vignettes/ROCR.html">ROCR: visualizing classifier performance in R</a></figcaption>
-</figure>
-</div>
 
 
 <!-- -->
 
-<div id="refs" class="references csl-bib-body hanging-indent" role="list" style="display: none">
-<div id="ref-akaike1974" class="csl-entry" role="listitem">
-Akaike, H. 1974. <span>“A New Look at the Statistical Model Identification.”</span> <em>IEEE Transactions on Automatic Control</em> 19 (6): 716723.
-</div>
-</div>
-</section>
 
 </main> <!-- /main -->
 <script id="quarto-html-after-body" type="application/javascript">
@@ -919,36 +835,14 @@ <h2 data-number="8.3" class="anchored" data-anchor-id="evaluate"><span class="he
       </a>
   </div>
 </nav><div class="modal fade" id="quarto-embedded-source-code-modal" tabindex="-1" aria-labelledby="quarto-embedded-source-code-modal-label" aria-hidden="true"><div class="modal-dialog modal-dialog-scrollable"><div class="modal-content"><div class="modal-header"><h5 class="modal-title" id="quarto-embedded-source-code-modal-label">Source Code</h5><button class="btn-close" data-bs-dismiss="modal"></button></div><div class="modal-body"><div class="">
-<div class="sourceCode" id="cb1" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu"># Fit</span></span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>Model fitting in theory is quite complex, but quite simple in practice, with feeding the prepared data into the modeling function.</span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>However there are MANY modeling techniques from which to choose. For instance check out 238 entries in <span class="co">[</span><span class="ot">6 Available Models | The caret Package</span><span class="co">](https://topepo.github.io/caret/available-models.html)</span>. </span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="fu">## Calibrate</span></span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>The process of refining the model to only the most relevant environmental predictor terms is commonly called "Model Selection." One of the most cited scientific paper of all time <span class="co">[</span><span class="ot">@akaike1974</span><span class="co">]</span> is based on taking a most parsimonious approach to this process -- the so called Akaike Information Criteria (AIC).</span>
-<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>It is important to avoid using environmental predictors that are correlated with each other, since the effect of a predictor on the response could be the ecologically inverse, the result of explaining variance on the residuals of the other correlated predictor.</span>
-<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a><span class="fu">## Predict</span></span>
-<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a>The prediction step applies the environmental relationships from the fitted model to a new set of data, typically the seascape of interest, and perhaps with some sort of temporal snapshot (e.g., climatic annual or monthly average).</span>
-<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a><span class="fu">## Evaluate</span></span>
-<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a>Model evaluation uses the set aside test data from the earlier splitting to evaluate how well the model predicts the response of presence or absence. Since the test response data is binary <span class="sc">\[</span>0,1<span class="sc">\]</span> and the prediction from the model is continuous <span class="sc">\[</span>0-1<span class="sc">\]</span>, a threshold needs to be applied to assign to convert the continuous response to binary. This is often performed through a Receiver Operator Characteristic (**ROC**) curve (@fig-rocr), which evaluates at each threshold the **confusion matrix** (@tbl-confusion-matrix).</span>
-<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a>|          |              |               |                |</span>
-<span id="cb1-22"><a href="#cb1-22" aria-hidden="true" tabindex="-1"></a>|----------|--------------|---------------|----------------|</span>
-<span id="cb1-23"><a href="#cb1-23" aria-hidden="true" tabindex="-1"></a>|          |              | Predicted     |                |</span>
-<span id="cb1-24"><a href="#cb1-24" aria-hidden="true" tabindex="-1"></a>|          |              | 0 (absence)   | 1 (presence)   |</span>
-<span id="cb1-25"><a href="#cb1-25" aria-hidden="true" tabindex="-1"></a>| Observed | 0 (absence)  | True absence  | False presence |</span>
-<span id="cb1-26"><a href="#cb1-26" aria-hidden="true" tabindex="-1"></a>|          | 1 (presence) | False absence | True presence  |</span>
-<span id="cb1-27"><a href="#cb1-27" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-28"><a href="#cb1-28" aria-hidden="true" tabindex="-1"></a>: Confusion matrix to understand predicted versus observed. {#tbl-confusion-matrix}</span>
-<span id="cb1-29"><a href="#cb1-29" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-30"><a href="#cb1-30" aria-hidden="true" tabindex="-1"></a>!<span class="co">[</span><span class="ot">ROC curve generated by showing rates of false positive vs false negative as function of changing the threshold value (rainbow colors). Source: [ROCR: visualizing classifier performance in R](https://cran.rstudio.com/web/packages/ROCR/vignettes/ROCR.html)</span><span class="co">](figures/rocr.png)</span>{#fig-rocr}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb1" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> "Fit"</span></span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="an">subtitle:</span><span class="co"> "Fit environmental relationship distinguishing presence from absence of species"</span></span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>Model fitting in theory is quite complex, but quite simple in practice, with feeding the prepared data into the modeling function.</span>
+<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>However there are MANY modeling techniques from which to choose. For instance check out 238 entries in <span class="co">[</span><span class="ot">6 Available Models \| The caret Package</span><span class="co">](https://topepo.github.io/caret/available-models.html)</span>.</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 <div id="fig-sdm-response-hierarchy" class="quarto-figure quarto-figure-center anchored">
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-<p><a href="figures/sdm-response-hierarchy.png" class="lightbox" data-gallery="quarto-lightbox-gallery-1" title="Hierarchy of preferred model outputs based on response type and age."><img src="figures/sdm-response-hierarchy.png" class="img-fluid figure-img"></a></p>
+<p><a href="figures/sdm-response-hierarchy.png" class="lightbox" title="Hierarchy of preferred model outputs based on response type and age." data-gallery="quarto-lightbox-gallery-1"><img src="figures/sdm-response-hierarchy.png" class="img-fluid figure-img"></a></p>
 <figcaption class="figure-caption">Figure&nbsp;14.1: Hierarchy of preferred model outputs based on response type and age.</figcaption>
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diff --git a/occ.html b/occ.html
index 8fe6366..b594131 100644
--- a/occ.html
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@@ -436,7 +436,7 @@ <h2 id="toc-title">Table of contents</h2>
 <em>Tidy and Geospatial Kernel Smoothing for spatially filtering outlier observations</em></p>
 <div id="fig-eks" class="quarto-figure quarto-figure-center anchored">
 <figure class="figure">
-<p><a href="figures/software/eks.png" class="lightbox" title="Source: Kernel density estimates for tidy and geospatial data in the eks package" data-gallery="quarto-lightbox-gallery-1"><img src="figures/software/eks.png" class="img-fluid figure-img"></a></p>
+<p><a href="figures/software/eks.png" class="lightbox" data-gallery="quarto-lightbox-gallery-1" title="Source: Kernel density estimates for tidy and geospatial data in the eks package"><img src="figures/software/eks.png" class="img-fluid figure-img"></a></p>
 <figcaption class="figure-caption">Figure&nbsp;3.1: Source: Kernel density estimates for tidy and geospatial data in the eks package</figcaption>
 </figure>
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@@ -897,7 +897,7 @@ <h2 data-number="3.2" class="anchored" data-anchor-id="filter-occurrences"><span
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diff --git a/predict.html b/predict.html
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 <!-- margin-sidebar -->
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-        <nav id="TOC" role="doc-toc" class="toc-active">
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-  <li><a href="#evaluate" id="toc-evaluate" class="nav-link active" data-scroll-target="#evaluate"><span class="header-section-number">10.1</span> Evaluate</a></li>
-  </ul>
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@@ -410,6 +401,7 @@ <h2 id="toc-title">Table of contents</h2>
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 <div class="quarto-title">
 <div class="quarto-title-block"><div><h1 class="title"><span class="chapter-number">10</span>&nbsp; <span class="chapter-title">Predict</span></h1><button type="button" class="btn code-tools-button" id="quarto-code-tools-source"><i class="bi"></i> Code</button></div></div>
+<p class="subtitle lead">Predict distribution of the species with environmental relationship from fitted model</p>
 </div>
 
 
@@ -425,51 +417,10 @@ <h2 id="toc-title">Table of contents</h2>
 </header>
 
 <p>The prediction step applies the environmental relationships from the fitted model to a new set of data, typically the seascape of interest, and perhaps with some sort of temporal snapshot (e.g., climatic annual or monthly average).</p>
-<section id="evaluate" class="level2" data-number="10.1">
-<h2 data-number="10.1" class="anchored" data-anchor-id="evaluate"><span class="header-section-number">10.1</span> Evaluate</h2>
-<p>Model evaluation uses the set aside test data from the earlier splitting to evaluate how well the model predicts the response of presence or absence. Since the test response data is binary [0,1] and the prediction from the model is continuous [0-1], a threshold needs to be applied to assign to convert the continuous response to binary. This is often performed through a Receiver Operator Characteristic (<strong>ROC</strong>) curve (<a href="evaluate.html#fig-rocr">Figure&nbsp;<span>11.1</span></a>), which evaluates at each threshold the <strong>confusion matrix</strong> (<a href="evaluate.html#tbl-confusion-matrix">Table&nbsp;<span>11.1</span></a>).</p>
-<div id="tbl-confusion-matrix" class="anchored">
-<table class="table">
-<caption>Table&nbsp;10.1: Confusion matrix to understand predicted versus observed.</caption>
-<tbody>
-<tr class="odd">
-<td></td>
-<td></td>
-<td>Predicted</td>
-<td></td>
-</tr>
-<tr class="even">
-<td></td>
-<td></td>
-<td>0 (absence)</td>
-<td>1 (presence)</td>
-</tr>
-<tr class="odd">
-<td>Observed</td>
-<td>0 (absence)</td>
-<td>True absence</td>
-<td>False presence</td>
-</tr>
-<tr class="even">
-<td></td>
-<td>1 (presence)</td>
-<td>False absence</td>
-<td>True presence</td>
-</tr>
-</tbody>
-</table>
-</div>
-<div id="fig-rocr" class="quarto-figure quarto-figure-center anchored">
-<figure class="figure">
-<p><a href="figures/rocr.png" class="lightbox" data-gallery="quarto-lightbox-gallery-1" title="ROC curve generated by showing rates of false positive vs false negative as function of changing the threshold value (rainbow colors). Source: ROCR: visualizing classifier performance in R"><img src="figures/rocr.png" class="img-fluid figure-img"></a></p>
-<figcaption class="figure-caption">Figure&nbsp;10.1: ROC curve generated by showing rates of false positive vs false negative as function of changing the threshold value (rainbow colors). Source: <a href="https://cran.rstudio.com/web/packages/ROCR/vignettes/ROCR.html">ROCR: visualizing classifier performance in R</a></figcaption>
-</figure>
-</div>
 
 
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-</section>
 
 </main> <!-- /main -->
 <script id="quarto-html-after-body" type="application/javascript">
@@ -883,24 +834,12 @@ <h2 data-number="10.1" class="anchored" data-anchor-id="evaluate"><span class="h
       </a>
   </div>
 </nav><div class="modal fade" id="quarto-embedded-source-code-modal" tabindex="-1" aria-labelledby="quarto-embedded-source-code-modal-label" aria-hidden="true"><div class="modal-dialog modal-dialog-scrollable"><div class="modal-content"><div class="modal-header"><h5 class="modal-title" id="quarto-embedded-source-code-modal-label">Source Code</h5><button class="btn-close" data-bs-dismiss="modal"></button></div><div class="modal-body"><div class="">
-<div class="sourceCode" id="cb1" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu"># Predict</span></span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>The prediction step applies the environmental relationships from the fitted model to a new set of data, typically the seascape of interest, and perhaps with some sort of temporal snapshot (e.g., climatic annual or monthly average).</span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="fu">## Evaluate</span></span>
-<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a>Model evaluation uses the set aside test data from the earlier splitting to evaluate how well the model predicts the response of presence or absence. Since the test response data is binary <span class="sc">\[</span>0,1<span class="sc">\]</span> and the prediction from the model is continuous <span class="sc">\[</span>0-1<span class="sc">\]</span>, a threshold needs to be applied to assign to convert the continuous response to binary. This is often performed through a Receiver Operator Characteristic (**ROC**) curve (@fig-rocr), which evaluates at each threshold the **confusion matrix** (@tbl-confusion-matrix).</span>
-<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>|          |              |               |                |</span>
-<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>|----------|--------------|---------------|----------------|</span>
-<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>|          |              | Predicted     |                |</span>
-<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>|          |              | 0 (absence)   | 1 (presence)   |</span>
-<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a>| Observed | 0 (absence)  | True absence  | False presence |</span>
-<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>|          | 1 (presence) | False absence | True presence  |</span>
-<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a>: Confusion matrix to understand predicted versus observed. {#tbl-confusion-matrix}</span>
-<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a>!<span class="co">[</span><span class="ot">ROC curve generated by showing rates of false positive vs false negative as function of changing the threshold value (rainbow colors). Source: [ROCR: visualizing classifier performance in R](https://cran.rstudio.com/web/packages/ROCR/vignettes/ROCR.html)</span><span class="co">](figures/rocr.png)</span>{#fig-rocr}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb1" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> "Predict"</span></span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="an">subtitle:</span><span class="co"> "Predict distribution of the species with environmental relationship from fitted model"</span></span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>The prediction step applies the environmental relationships from the fitted model to a new set of data, typically the seascape of interest, and perhaps with some sort of temporal snapshot (e.g., climatic annual or monthly average).</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div></div></div></div></div>
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 <footer class="footer">
@@ -908,11 +847,10 @@ <h2 data-number="10.1" class="anchored" data-anchor-id="evaluate"><span class="h
     <div class="nav-footer-left">© <a href="https://bbest.github.io/cv/">Ben Best</a></div>   
     <div class="nav-footer-center">
       &nbsp;
-    </div>
+    <div class="toc-actions"><div><i class="bi bi-github"></i></div><div class="action-links"><p><a href="https://github.com/marinebon/sdm-book/edit/main/predict.qmd" class="toc-action">Edit this page</a></p></div></div></div>
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diff --git a/search.json b/search.json
index dc8f2d2..d945996 100644
--- a/search.json
+++ b/search.json
@@ -126,46 +126,25 @@
     "text": "Data is often split so that ~20% of the observations (presence and absence) are set aside from the model fitting to be used for model evaluation.\nThe k-fold function is often used to split the data into k groups, and then the model is fit k times, each time using a different group as the test data and the remaining groups as the training data."
   },
   {
-    "objectID": "fit.html#calibrate",
-    "href": "fit.html#calibrate",
+    "objectID": "fit.html",
+    "href": "fit.html",
     "title": "8  Fit",
-    "section": "8.1 Calibrate",
-    "text": "8.1 Calibrate\nThe process of refining the model to only the most relevant environmental predictor terms is commonly called “Model Selection.” One of the most cited scientific paper of all time (Akaike 1974) is based on taking a most parsimonious approach to this process – the so called Akaike Information Criteria (AIC).\nIt is important to avoid using environmental predictors that are correlated with each other, since the effect of a predictor on the response could be the ecologically inverse, the result of explaining variance on the residuals of the other correlated predictor."
-  },
-  {
-    "objectID": "fit.html#predict",
-    "href": "fit.html#predict",
-    "title": "8  Fit",
-    "section": "8.2 Predict",
-    "text": "8.2 Predict\nThe prediction step applies the environmental relationships from the fitted model to a new set of data, typically the seascape of interest, and perhaps with some sort of temporal snapshot (e.g., climatic annual or monthly average)."
-  },
-  {
-    "objectID": "fit.html#evaluate",
-    "href": "fit.html#evaluate",
-    "title": "8  Fit",
-    "section": "8.3 Evaluate",
-    "text": "8.3 Evaluate\nModel evaluation uses the set aside test data from the earlier splitting to evaluate how well the model predicts the response of presence or absence. Since the test response data is binary [0,1] and the prediction from the model is continuous [0-1], a threshold needs to be applied to assign to convert the continuous response to binary. This is often performed through a Receiver Operator Characteristic (ROC) curve (Figure 11.1), which evaluates at each threshold the confusion matrix (Table 11.1).\n\n\nTable 8.1: Confusion matrix to understand predicted versus observed.\n\n\n\n\nPredicted\n\n\n\n\n\n0 (absence)\n1 (presence)\n\n\nObserved\n0 (absence)\nTrue absence\nFalse presence\n\n\n\n1 (presence)\nFalse absence\nTrue presence\n\n\n\n\n\n\n\nFigure 8.1: ROC curve generated by showing rates of false positive vs false negative as function of changing the threshold value (rainbow colors). Source: ROCR: visualizing classifier performance in R\n\n\n\n\n\n\n\n\nAkaike, H. 1974. “A New Look at the Statistical Model Identification.” IEEE Transactions on Automatic Control 19 (6): 716723."
-  },
-  {
-    "objectID": "calibrate.html#predict",
-    "href": "calibrate.html#predict",
-    "title": "9  Calibrate",
-    "section": "9.1 Predict",
-    "text": "9.1 Predict\nThe prediction step applies the environmental relationships from the fitted model to a new set of data, typically the seascape of interest, and perhaps with some sort of temporal snapshot (e.g., climatic annual or monthly average)."
+    "section": "",
+    "text": "Model fitting in theory is quite complex, but quite simple in practice, with feeding the prepared data into the modeling function.\nHowever there are MANY modeling techniques from which to choose. For instance check out 238 entries in 6 Available Models | The caret Package."
   },
   {
-    "objectID": "calibrate.html#evaluate",
-    "href": "calibrate.html#evaluate",
+    "objectID": "calibrate.html",
+    "href": "calibrate.html",
     "title": "9  Calibrate",
-    "section": "9.2 Evaluate",
-    "text": "9.2 Evaluate\nModel evaluation uses the set aside test data from the earlier splitting to evaluate how well the model predicts the response of presence or absence. Since the test response data is binary [0,1] and the prediction from the model is continuous [0-1], a threshold needs to be applied to assign to convert the continuous response to binary. This is often performed through a Receiver Operator Characteristic (ROC) curve (Figure 11.1), which evaluates at each threshold the confusion matrix (Table 11.1).\n\n\nTable 9.1: Confusion matrix to understand predicted versus observed.\n\n\n\n\nPredicted\n\n\n\n\n\n0 (absence)\n1 (presence)\n\n\nObserved\n0 (absence)\nTrue absence\nFalse presence\n\n\n\n1 (presence)\nFalse absence\nTrue presence\n\n\n\n\n\n\n\nFigure 9.1: ROC curve generated by showing rates of false positive vs false negative as function of changing the threshold value (rainbow colors). Source: ROCR: visualizing classifier performance in R\n\n\n\n\n\n\n\n\nAkaike, H. 1974. “A New Look at the Statistical Model Identification.” IEEE Transactions on Automatic Control 19 (6): 716723."
+    "section": "",
+    "text": "The process of refining the model to only the most relevant environmental predictor terms is commonly called “Model Selection.” One of the most cited scientific paper of all time (Akaike 1974) is based on taking a most parsimonious approach to this process – the so called Akaike Information Criteria (AIC).\nIt is important to avoid using environmental predictors that are correlated with each other, since the effect of a predictor on the response could be the ecologically inverse, the result of explaining variance on the residuals of the other correlated predictor.\n\n\n\n\n\n\nAkaike, H. 1974. “A New Look at the Statistical Model Identification.” IEEE Transactions on Automatic Control 19 (6): 716723."
   },
   {
-    "objectID": "predict.html#evaluate",
-    "href": "predict.html#evaluate",
+    "objectID": "predict.html",
+    "href": "predict.html",
     "title": "10  Predict",
-    "section": "10.1 Evaluate",
-    "text": "10.1 Evaluate\nModel evaluation uses the set aside test data from the earlier splitting to evaluate how well the model predicts the response of presence or absence. Since the test response data is binary [0,1] and the prediction from the model is continuous [0-1], a threshold needs to be applied to assign to convert the continuous response to binary. This is often performed through a Receiver Operator Characteristic (ROC) curve (Figure 11.1), which evaluates at each threshold the confusion matrix (Table 11.1).\n\n\nTable 10.1: Confusion matrix to understand predicted versus observed.\n\n\n\n\nPredicted\n\n\n\n\n\n0 (absence)\n1 (presence)\n\n\nObserved\n0 (absence)\nTrue absence\nFalse presence\n\n\n\n1 (presence)\nFalse absence\nTrue presence\n\n\n\n\n\n\n\nFigure 10.1: ROC curve generated by showing rates of false positive vs false negative as function of changing the threshold value (rainbow colors). Source: ROCR: visualizing classifier performance in R"
+    "section": "",
+    "text": "The prediction step applies the environmental relationships from the fitted model to a new set of data, typically the seascape of interest, and perhaps with some sort of temporal snapshot (e.g., climatic annual or monthly average)."
   },
   {
     "objectID": "evaluate.html",
diff --git a/software.html b/software.html
index a7cb69c..1cc1d4f 100644
--- a/software.html
+++ b/software.html
@@ -432,12 +432,12 @@ <h2 data-number="17.1" class="anchored" data-anchor-id="r"><span class="header-s
 <ul>
 <li><p><a href="https://biomodhub.github.io/biomod2/"><code>biomod2</code></a><br>
 <em>Species distribution modeling, calibration and evaluation, ensemble modeling<br>
-</em><a href="https://biomodhub.github.io/biomod2/articles/pictures/SCHEMA_BIOMOD2_WORKFLOW_functions.png" class="lightbox" data-gallery="quarto-lightbox-gallery-1" title="Graphical overview of `biomod2` functions for data formatting, single SDMs, ensemble methods and visualization. Source: [`biomod2`](https://biomodhub.github.io/biomod2/)"><img src="https://biomodhub.github.io/biomod2/articles/pictures/SCHEMA_BIOMOD2_WORKFLOW_functions.png" class="img-fluid" alt="Graphical overview of `biomod2` functions for data formatting, single SDMs, ensemble methods and visualization. Source: [`biomod2`](https://biomodhub.github.io/biomod2/)"></a></p></li>
+</em><a href="https://biomodhub.github.io/biomod2/articles/pictures/SCHEMA_BIOMOD2_WORKFLOW_functions.png" class="lightbox" title="Graphical overview of `biomod2` functions for data formatting, single SDMs, ensemble methods and visualization. Source: [`biomod2`](https://biomodhub.github.io/biomod2/)" data-gallery="quarto-lightbox-gallery-1"><img src="https://biomodhub.github.io/biomod2/articles/pictures/SCHEMA_BIOMOD2_WORKFLOW_functions.png" class="img-fluid" alt="Graphical overview of `biomod2` functions for data formatting, single SDMs, ensemble methods and visualization. Source: [`biomod2`](https://biomodhub.github.io/biomod2/)"></a></p></li>
 <li><p><a href="https://cran.r-project.org/web/packages/eks/vignettes/tidysf_kde.html"><code>eks</code></a><br>
 <em>Tidy and Geospatial Kernel Smoothing for spatially filtering outlier observations</em></p>
 <div id="fig-eks" class="quarto-figure quarto-figure-center anchored">
 <figure class="figure">
-<p><a href="figures/software/eks.png" class="lightbox" data-gallery="quarto-lightbox-gallery-2" title="Source: Kernel density estimates for tidy and geospatial data in the eks package"><img src="figures/software/eks.png" class="img-fluid figure-img"></a></p>
+<p><a href="figures/software/eks.png" class="lightbox" title="Source: Kernel density estimates for tidy and geospatial data in the eks package" data-gallery="quarto-lightbox-gallery-2"><img src="figures/software/eks.png" class="img-fluid figure-img"></a></p>
 <figcaption class="figure-caption">Figure&nbsp;17.1: Source: <a href="https://cran.r-project.org/web/packages/eks/vignettes/tidysf_kde.html">Kernel density estimates for tidy and geospatial data in the eks package</a></figcaption>
 </figure>
 </div></li>
@@ -914,7 +914,7 @@ <h2 data-number="17.3" class="anchored" data-anchor-id="google-earth-engine"><sp
     <div class="nav-footer-right">Built with <a href="https://quarto.org/" target="_blank">Quarto</a></div>
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diff --git a/split.html b/split.html
index 377a576..fba4979 100644
--- a/split.html
+++ b/split.html
@@ -401,6 +401,7 @@
 <header id="title-block-header" class="quarto-title-block default">
 <div class="quarto-title">
 <div class="quarto-title-block"><div><h1 class="title"><span class="chapter-number">7</span>&nbsp; <span class="chapter-title">Split</span></h1><button type="button" class="btn code-tools-button" id="quarto-code-tools-source"><i class="bi"></i> Code</button></div></div>
+<p class="subtitle lead">Split data into training (to fit) and test (to evaluate prediction)</p>
 </div>
 
 
@@ -834,11 +835,14 @@
       </a>
   </div>
 </nav><div class="modal fade" id="quarto-embedded-source-code-modal" tabindex="-1" aria-labelledby="quarto-embedded-source-code-modal-label" aria-hidden="true"><div class="modal-dialog modal-dialog-scrollable"><div class="modal-content"><div class="modal-header"><h5 class="modal-title" id="quarto-embedded-source-code-modal-label">Source Code</h5><button class="btn-close" data-bs-dismiss="modal"></button></div><div class="modal-body"><div class="">
-<div class="sourceCode" id="cb1" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu"># Split</span></span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>Data is often split so that \~20% of the observations (presence and absence) are set aside from the model fitting to be used for model evaluation.</span>
-<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a>The <span class="in">`k-fold`</span> function is often used to split the data into k groups, and then the model is fit k times, each time using a different group as the test data and the remaining groups as the training data.</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb1" data-shortcodes="false"><pre class="sourceCode markdown code-with-copy"><code class="sourceCode markdown"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="an">title:</span><span class="co"> "Split"</span></span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="an">subtitle:</span><span class="co"> "Split data into training (to fit) and test (to evaluate prediction)"</span></span>
+<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="co">---</span></span>
+<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a>Data is often split so that \~20% of the observations (presence and absence) are set aside from the model fitting to be used for model evaluation.</span>
+<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a>The <span class="in">`k-fold`</span> function is often used to split the data into k groups, and then the model is fit k times, each time using a different group as the test data and the remaining groups as the training data.</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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