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2 changes: 1 addition & 1 deletion bayesian-process-models.html
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Expand Up @@ -560,7 +560,7 @@ <h2 data-number="19.1" class="anchored" data-anchor-id="running-mcmc-on-the-fore
<div class="cell">
<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>accept <span class="sc">/</span> (num_iter <span class="sc">*</span> num_pars)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<pre><code>[1] 0.3528889</code></pre>
<pre><code>[1] 0.3535556</code></pre>
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<p>Process MCMC chain by removing the first 500 iterations and pivoting to a long format</p>
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3,001 changes: 3,001 additions & 0 deletions data/terrestrial_daily-2025-02-19-bookcast_forest.csv

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2 changes: 1 addition & 1 deletion evaluating-forecasts.html
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Expand Up @@ -483,7 +483,7 @@ <h3 data-number="11.3.2" class="anchored" data-anchor-id="example-of-a-crps-calc
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(scoringRules)</span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="fu">crps_sample</span>(<span class="at">y =</span> y, <span class="at">dat =</span> x)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<pre><code>[1] 0.2316588</code></pre>
<pre><code>[1] 0.2384334</code></pre>
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</section>
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4 changes: 2 additions & 2 deletions index.html
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<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">

<meta name="author" content="R. Quinn Thomas">
<meta name="dcterms.date" content="2025-02-18">
<meta name="dcterms.date" content="2025-02-19">

<title>A Practical Guide to Ecosystem Forecasting</title>
<style>
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<div>
<div class="quarto-title-meta-heading">Published</div>
<div class="quarto-title-meta-contents">
<p class="date">February 18, 2025</p>
<p class="date">February 19, 2025</p>
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</div>

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2 changes: 1 addition & 1 deletion process-model-forecast-evaluation.html
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Expand Up @@ -436,7 +436,7 @@ <h2 data-number="22.2" class="anchored" data-anchor-id="aggregated-scores"><span
<pre><code># A tibble: 1 × 1
mean_crps
&lt;dbl&gt;
1 0.873</code></pre>
1 0.872</code></pre>
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</section>
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2 changes: 1 addition & 1 deletion process-model-forecast.html
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Expand Up @@ -576,7 +576,7 @@ <h2 data-number="21.4" class="anchored" data-anchor-id="step-4-save-forecast-and
<pre><code>validating that file matches required standard</code></pre>
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<pre><code>data/terrestrial_daily-2025-02-18-bookcast_forest.csv</code></pre>
<pre><code>data/terrestrial_daily-2025-02-19-bookcast_forest.csv</code></pre>
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<pre><code>✔ file has model_id column
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22 changes: 11 additions & 11 deletions project1.html
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Expand Up @@ -884,16 +884,16 @@ <h4 data-number="12.2.2.2" class="anchored" data-anchor-id="step-2-pre-process-d
<pre><code># A tibble: 68 × 5
datetime site_id air_temperature temperature doy
&lt;date&gt; &lt;chr&gt; &lt;dbl&gt; &lt;lgl&gt; &lt;dbl&gt;
1 2025-02-18 BARC 14.0 NA 49
2 2025-02-19 BARC 17.1 NA 50
3 2025-02-20 BARC 12.8 NA 51
4 2025-02-21 BARC 13.5 NA 52
5 2025-02-22 BARC 17.5 NA 53
6 2025-02-23 BARC 18.0 NA 54
7 2025-02-24 BARC 18.2 NA 55
8 2025-02-25 BARC 15.5 NA 56
9 2025-02-26 BARC 14.0 NA 57
10 2025-02-27 BARC 15.8 NA 58
1 2025-02-19 BARC 17.1 NA 50
2 2025-02-20 BARC 16.5 NA 51
3 2025-02-21 BARC 13.4 NA 52
4 2025-02-22 BARC 12.9 NA 53
5 2025-02-23 BARC 15.0 NA 54
6 2025-02-24 BARC 19.0 NA 55
7 2025-02-25 BARC 14.1 NA 56
8 2025-02-26 BARC 11.9 NA 57
9 2025-02-27 BARC 12.3 NA 58
10 2025-02-28 BARC 14.7 NA 59
# ℹ 58 more rows</code></pre>
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<pre><code># A tibble: 1 × 2
start_date h
&lt;date&gt; &lt;dbl&gt;
1 2025-01-30 54</code></pre>
1 2025-01-30 55</code></pre>
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<p>The targets have to be converted into a time-series tibble (called a <code>tsibble</code>). To make a <code>tsibble</code> you need to define the grouping variables (<code>key</code>) and the common that defines the time-series (<code>index</code>). Many modeling approaches require the datetime to be continuous (no gaps) but allow na values for values in the gaps. The <code>fill_gaps()</code> function fills the gaps with <code>NA</code>s.</p>
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12 changes: 6 additions & 6 deletions search.json

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