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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta name="generator" content="pandoc" />
<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="date" content="2024-09-01" />
<title>TidyModel Practice</title>
<script>// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
// be compatible with the behavior of Pandoc < 2.8).
document.addEventListener('DOMContentLoaded', function(e) {
var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
var i, h, a;
for (i = 0; i < hs.length; i++) {
h = hs[i];
if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6
a = h.attributes;
while (a.length > 0) h.removeAttribute(a[0].name);
}
});
</script>
<style type="text/css">code{white-space: pre;}</style>
<style type="text/css" data-origin="pandoc">
pre > code.sourceCode { white-space: pre; position: relative; }
pre > code.sourceCode > span { line-height: 1.25; }
pre > code.sourceCode > span:empty { height: 1.2em; }
.sourceCode { overflow: visible; }
code.sourceCode > span { color: inherit; text-decoration: inherit; }
div.sourceCode { margin: 1em 0; }
pre.sourceCode { margin: 0; }
@media screen {
div.sourceCode { overflow: auto; }
}
@media print {
pre > code.sourceCode { white-space: pre-wrap; }
pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
}
pre.numberSource code
{ counter-reset: source-line 0; }
pre.numberSource code > span
{ position: relative; left: -4em; counter-increment: source-line; }
pre.numberSource code > span > a:first-child::before
{ content: counter(source-line);
position: relative; left: -1em; text-align: right; vertical-align: baseline;
border: none; display: inline-block;
-webkit-touch-callout: none; -webkit-user-select: none;
-khtml-user-select: none; -moz-user-select: none;
-ms-user-select: none; user-select: none;
padding: 0 4px; width: 4em;
color: #aaaaaa;
}
pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; }
div.sourceCode
{ }
@media screen {
pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; }
}
code span.al { color: #ff0000; font-weight: bold; }
code span.an { color: #60a0b0; font-weight: bold; font-style: italic; }
code span.at { color: #7d9029; }
code span.bn { color: #40a070; }
code span.bu { color: #008000; }
code span.cf { color: #007020; font-weight: bold; }
code span.ch { color: #4070a0; }
code span.cn { color: #880000; }
code span.co { color: #60a0b0; font-style: italic; }
code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; }
code span.do { color: #ba2121; font-style: italic; }
code span.dt { color: #902000; }
code span.dv { color: #40a070; }
code span.er { color: #ff0000; font-weight: bold; }
code span.ex { }
code span.fl { color: #40a070; }
code span.fu { color: #06287e; }
code span.im { color: #008000; font-weight: bold; }
code span.in { color: #60a0b0; font-weight: bold; font-style: italic; }
code span.kw { color: #007020; font-weight: bold; }
code span.op { color: #666666; }
code span.ot { color: #007020; }
code span.pp { color: #bc7a00; }
code span.sc { color: #4070a0; }
code span.ss { color: #bb6688; }
code span.st { color: #4070a0; }
code span.va { color: #19177c; }
code span.vs { color: #4070a0; }
code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; }
a.sourceLine {
pointer-events: auto;
}
</style>
<script>
// apply pandoc div.sourceCode style to pre.sourceCode instead
(function() {
var sheets = document.styleSheets;
for (var i = 0; i < sheets.length; i++) {
if (sheets[i].ownerNode.dataset["origin"] !== "pandoc") continue;
try { var rules = sheets[i].cssRules; } catch (e) { continue; }
for (var j = 0; j < rules.length; j++) {
var rule = rules[j];
// check if there is a div.sourceCode rule
if (rule.type !== rule.STYLE_RULE || rule.selectorText !== "div.sourceCode") continue;
var style = rule.style.cssText;
// check if color or background-color is set
if (rule.style.color === '' && rule.style.backgroundColor === '') continue;
// replace div.sourceCode by a pre.sourceCode rule
sheets[i].deleteRule(j);
sheets[i].insertRule('pre.sourceCode{' + style + '}', j);
}
}
})();
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<h1 class="title toc-ignore">TidyModel Practice</h1>
<h3 class="author">Thomas Reinke</h3>
<h3 class="date">2024-09-01</h3>
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</header>
<div id="TOC" class="toc">
<div class="inner">
<ul>
<li><a href="#intro-to-tidymodels" id="toc-intro-to-tidymodels">Intro to
Tidymodels</a>
<ul>
<li><a href="#frequentist" id="toc-frequentist">Frequentist</a>
<ul>
<li><a href="#sea-urchin-data" id="toc-sea-urchin-data">Sea Urchin
Data</a></li>
<li><a href="#scatterplot-by-regime" id="toc-scatterplot-by-regime">Scatterplot by Regime</a></li>
<li><a href="#build-and-fit-a-model" id="toc-build-and-fit-a-model">Build and Fit a Model</a></li>
<li><a href="#view-fit" id="toc-view-fit">View Fit</a></li>
<li><a href="#dot-whisker-plot" id="toc-dot-whisker-plot">Dot &
Whisker Plot</a></li>
<li><a href="#prediction" id="toc-prediction">Prediction</a>
<ul>
<li><a href="#prediction-points" id="toc-prediction-points">Prediction
Points</a></li>
<li><a href="#mean-prediction" id="toc-mean-prediction">Mean
Prediction</a></li>
<li><a href="#confidence-interval" id="toc-confidence-interval">Confidence Interval</a></li>
<li><a href="#plot-intervals" id="toc-plot-intervals">Plot
Intervals</a></li>
</ul></li>
</ul></li>
<li><a href="#bayes" id="toc-bayes">Bayes</a>
<ul>
<li><a href="#build-model" id="toc-build-model">Build Model</a></li>
<li><a href="#confidence-intervals" id="toc-confidence-intervals">Confidence Intervals</a></li>
<li><a href="#plot-error-bars" id="toc-plot-error-bars">Plot Error
Bars</a></li>
</ul></li>
</ul></li>
<li><a href="#preprocessing" id="toc-preprocessing">Preprocessing</a>
<ul>
<li><a href="#introduction" id="toc-introduction">Introduction</a></li>
<li><a href="#the-new-york-city-flight-data" id="toc-the-new-york-city-flight-data">The New York City Flight
Data</a></li>
<li><a href="#data-splitting" id="toc-data-splitting">Data
Splitting</a></li>
<li><a href="#create-recipe-and-roles" id="toc-create-recipe-and-roles">Create Recipe and Roles</a></li>
<li><a href="#create-features" id="toc-create-features">Create
Features</a></li>
<li><a href="#fit-a-model-with-a-recipe" id="toc-fit-a-model-with-a-recipe">Fit a Model with a Recipe</a></li>
<li><a href="#use-a-trained-workflow-to-predict" id="toc-use-a-trained-workflow-to-predict">Use a Trained Workflow to
Predict</a></li>
</ul></li>
</ul>
</div>
</div>
<div id="content-wrapper">
<div class="inner clearfix">
<section id="main-content">
<div id="intro-to-tidymodels" class="section level1">
<h1>Intro to Tidymodels</h1>
<div id="frequentist" class="section level2">
<h2>Frequentist</h2>
<div id="sea-urchin-data" class="section level3">
<h3>Sea Urchin Data</h3>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a>url <span class="ot"><-</span> <span class="st">"https://tidymodels.org/start/models/urchins.csv"</span></span>
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a>urchins <span class="ot"><-</span></span>
<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a> <span class="fu">read_csv</span>(url) <span class="sc">%>%</span> </span>
<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a> <span class="co"># Change the names to be a little more verbose</span></span>
<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a> <span class="fu">setNames</span>(<span class="fu">c</span>(<span class="st">"food_regime"</span>, <span class="st">"initial_volume"</span>, <span class="st">"width"</span>)) <span class="sc">%>%</span> </span>
<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a> <span class="co"># Factors are very helpful for modeling</span></span>
<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a> <span class="co"># so we convert one column</span></span>
<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a> <span class="fu">mutate</span>(</span>
<span id="cb1-9"><a href="#cb1-9" tabindex="-1"></a> <span class="at">food_regime =</span> <span class="fu">factor</span>(</span>
<span id="cb1-10"><a href="#cb1-10" tabindex="-1"></a> food_regime,</span>
<span id="cb1-11"><a href="#cb1-11" tabindex="-1"></a> <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">"Initial"</span>, <span class="st">"Low"</span>, <span class="st">"High"</span>)</span>
<span id="cb1-12"><a href="#cb1-12" tabindex="-1"></a> )</span>
<span id="cb1-13"><a href="#cb1-13" tabindex="-1"></a> )</span></code></pre></div>
</div>
<div id="scatterplot-by-regime" class="section level3">
<h3>Scatterplot by Regime</h3>
<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">ggplot</span>(urchins,</span>
<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a> <span class="fu">aes</span>(<span class="at">x =</span> initial_volume, </span>
<span id="cb2-3"><a href="#cb2-3" tabindex="-1"></a> <span class="at">y =</span> width, </span>
<span id="cb2-4"><a href="#cb2-4" tabindex="-1"></a> <span class="at">group =</span> food_regime, </span>
<span id="cb2-5"><a href="#cb2-5" tabindex="-1"></a> <span class="at">col =</span> food_regime)) <span class="sc">+</span> </span>
<span id="cb2-6"><a href="#cb2-6" tabindex="-1"></a> <span class="fu">geom_point</span>() <span class="sc">+</span> </span>
<span id="cb2-7"><a href="#cb2-7" tabindex="-1"></a> <span class="fu">geom_smooth</span>(<span class="at">method =</span> lm, <span class="at">se =</span> <span class="cn">FALSE</span>) <span class="sc">+</span></span>
<span id="cb2-8"><a href="#cb2-8" tabindex="-1"></a> <span class="fu">scale_color_viridis_d</span>(<span class="at">option =</span> <span class="st">"plasma"</span>, <span class="at">end =</span> .<span class="dv">7</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="build-and-fit-a-model" class="section level3">
<h3>Build and Fit a Model</h3>
<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"># linear_reg() |> </span></span>
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="co"># set_engine("keras")</span></span>
<span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a>lm_mod <span class="ot"><-</span> <span class="fu">linear_reg</span>()</span>
<span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a></span>
<span id="cb3-5"><a href="#cb3-5" tabindex="-1"></a>lm_fit <span class="ot"><-</span> </span>
<span id="cb3-6"><a href="#cb3-6" tabindex="-1"></a> lm_mod <span class="sc">%>%</span> </span>
<span id="cb3-7"><a href="#cb3-7" tabindex="-1"></a> <span class="fu">fit</span>(width <span class="sc">~</span> initial_volume <span class="sc">*</span> food_regime, <span class="at">data =</span> urchins)</span>
<span id="cb3-8"><a href="#cb3-8" tabindex="-1"></a><span class="co">#lm_fit</span></span></code></pre></div>
</div>
<div id="view-fit" class="section level3">
<h3>View Fit</h3>
<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="fu">tidy</span>(lm_fit)</span></code></pre></div>
<div class="kable-table">
<table style="width:100%;">
<colgroup>
<col width="42%" />
<col width="15%" />
<col width="13%" />
<col width="15%" />
<col width="13%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">term</th>
<th align="right">estimate</th>
<th align="right">std.error</th>
<th align="right">statistic</th>
<th align="right">p.value</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">(Intercept)</td>
<td align="right">0.0331216</td>
<td align="right">0.0096186</td>
<td align="right">3.4434873</td>
<td align="right">0.0010020</td>
</tr>
<tr class="even">
<td align="left">initial_volume</td>
<td align="right">0.0015546</td>
<td align="right">0.0003978</td>
<td align="right">3.9077643</td>
<td align="right">0.0002220</td>
</tr>
<tr class="odd">
<td align="left">food_regimeLow</td>
<td align="right">0.0197824</td>
<td align="right">0.0129883</td>
<td align="right">1.5230864</td>
<td align="right">0.1325145</td>
</tr>
<tr class="even">
<td align="left">food_regimeHigh</td>
<td align="right">0.0214111</td>
<td align="right">0.0145318</td>
<td align="right">1.4733993</td>
<td align="right">0.1453970</td>
</tr>
<tr class="odd">
<td align="left">initial_volume:food_regimeLow</td>
<td align="right">-0.0012594</td>
<td align="right">0.0005102</td>
<td align="right">-2.4685525</td>
<td align="right">0.0161638</td>
</tr>
<tr class="even">
<td align="left">initial_volume:food_regimeHigh</td>
<td align="right">0.0005254</td>
<td align="right">0.0007020</td>
<td align="right">0.7484702</td>
<td align="right">0.4568356</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="dot-whisker-plot" class="section level3">
<h3>Dot & Whisker Plot</h3>
<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">tidy</span>(lm_fit) <span class="sc">%>%</span> </span>
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a> <span class="fu">dwplot</span>(<span class="at">dot_args =</span> <span class="fu">list</span>(<span class="at">size =</span> <span class="dv">2</span>, <span class="at">color =</span> <span class="st">"black"</span>),</span>
<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a> <span class="at">whisker_args =</span> <span class="fu">list</span>(<span class="at">color =</span> <span class="st">"black"</span>),</span>
<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a> <span class="at">vline =</span> <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="dv">0</span>, <span class="at">colour =</span> <span class="st">"grey50"</span>, <span class="at">linetype =</span> <span class="dv">2</span>))</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="prediction" class="section level3">
<h3>Prediction</h3>
<div id="prediction-points" class="section level4">
<h4>Prediction Points</h4>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a>new_points <span class="ot"><-</span> <span class="fu">expand.grid</span>(<span class="at">initial_volume =</span> <span class="dv">20</span>, </span>
<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a> <span class="at">food_regime =</span> <span class="fu">c</span>(<span class="st">"Initial"</span>, <span class="st">"Low"</span>, <span class="st">"High"</span>))</span>
<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a>new_points</span></code></pre></div>
<div class="kable-table">
<table>
<thead>
<tr class="header">
<th align="right">initial_volume</th>
<th align="left">food_regime</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="right">20</td>
<td align="left">Initial</td>
</tr>
<tr class="even">
<td align="right">20</td>
<td align="left">Low</td>
</tr>
<tr class="odd">
<td align="right">20</td>
<td align="left">High</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="mean-prediction" class="section level4">
<h4>Mean Prediction</h4>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a>mean_pred <span class="ot"><-</span> <span class="fu">predict</span>(lm_fit, <span class="at">new_data =</span> new_points)</span>
<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a>mean_pred</span></code></pre></div>
<div class="kable-table">
<table>
<thead>
<tr class="header">
<th align="right">.pred</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="right">0.0642144</td>
</tr>
<tr class="even">
<td align="right">0.0588094</td>
</tr>
<tr class="odd">
<td align="right">0.0961334</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="confidence-interval" class="section level4">
<h4>Confidence Interval</h4>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a>conf_int_pred <span class="ot"><-</span> <span class="fu">predict</span>(lm_fit, </span>
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a> <span class="at">new_data =</span> new_points, </span>
<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a> <span class="at">type =</span> <span class="st">"conf_int"</span>)</span>
<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a>conf_int_pred</span></code></pre></div>
<div class="kable-table">
<table>
<thead>
<tr class="header">
<th align="right">.pred_lower</th>
<th align="right">.pred_upper</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="right">0.0554993</td>
<td align="right">0.0729295</td>
</tr>
<tr class="even">
<td align="right">0.0498625</td>
<td align="right">0.0677563</td>
</tr>
<tr class="odd">
<td align="right">0.0869623</td>
<td align="right">0.1053045</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="plot-intervals" class="section level4">
<h4>Plot Intervals</h4>
<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"># Now combine: </span></span>
<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a>plot_data <span class="ot"><-</span> </span>
<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a> new_points <span class="sc">%>%</span> </span>
<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a> <span class="fu">bind_cols</span>(mean_pred) <span class="sc">%>%</span> </span>
<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a> <span class="fu">bind_cols</span>(conf_int_pred)</span>
<span id="cb9-6"><a href="#cb9-6" tabindex="-1"></a></span>
<span id="cb9-7"><a href="#cb9-7" tabindex="-1"></a><span class="co"># and plot:</span></span>
<span id="cb9-8"><a href="#cb9-8" tabindex="-1"></a><span class="fu">ggplot</span>(plot_data, <span class="fu">aes</span>(<span class="at">x =</span> food_regime)) <span class="sc">+</span> </span>
<span id="cb9-9"><a href="#cb9-9" tabindex="-1"></a> <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">y =</span> .pred)) <span class="sc">+</span> </span>
<span id="cb9-10"><a href="#cb9-10" tabindex="-1"></a> <span class="fu">geom_errorbar</span>(<span class="fu">aes</span>(<span class="at">ymin =</span> .pred_lower, </span>
<span id="cb9-11"><a href="#cb9-11" tabindex="-1"></a> <span class="at">ymax =</span> .pred_upper),</span>
<span id="cb9-12"><a href="#cb9-12" tabindex="-1"></a> <span class="at">width =</span> .<span class="dv">2</span>) <span class="sc">+</span> </span>
<span id="cb9-13"><a href="#cb9-13" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">y =</span> <span class="st">"urchin size"</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
</div>
</div>
<div id="bayes" class="section level2">
<h2>Bayes</h2>
<div id="build-model" class="section level3">
<h3>Build Model</h3>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a><span class="co"># set the prior distribution</span></span>
<span id="cb10-2"><a href="#cb10-2" tabindex="-1"></a><span class="fu">options</span>(<span class="at">mc.cores =</span> parallel<span class="sc">::</span><span class="fu">detectCores</span>())</span>
<span id="cb10-3"><a href="#cb10-3" tabindex="-1"></a>prior_dist <span class="ot"><-</span> rstanarm<span class="sc">::</span><span class="fu">student_t</span>(<span class="at">df =</span> <span class="dv">1</span>)</span>
<span id="cb10-4"><a href="#cb10-4" tabindex="-1"></a></span>
<span id="cb10-5"><a href="#cb10-5" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">123</span>)</span>
<span id="cb10-6"><a href="#cb10-6" tabindex="-1"></a></span>
<span id="cb10-7"><a href="#cb10-7" tabindex="-1"></a><span class="co"># make the parsnip model</span></span>
<span id="cb10-8"><a href="#cb10-8" tabindex="-1"></a>bayes_mod <span class="ot"><-</span> </span>
<span id="cb10-9"><a href="#cb10-9" tabindex="-1"></a> <span class="fu">linear_reg</span>() <span class="sc">%>%</span> </span>
<span id="cb10-10"><a href="#cb10-10" tabindex="-1"></a> <span class="fu">set_engine</span>(<span class="st">"stan"</span>, </span>
<span id="cb10-11"><a href="#cb10-11" tabindex="-1"></a> <span class="at">prior_intercept =</span> prior_dist, </span>
<span id="cb10-12"><a href="#cb10-12" tabindex="-1"></a> <span class="at">prior =</span> prior_dist) </span>
<span id="cb10-13"><a href="#cb10-13" tabindex="-1"></a></span>
<span id="cb10-14"><a href="#cb10-14" tabindex="-1"></a><span class="co"># train the model</span></span>
<span id="cb10-15"><a href="#cb10-15" tabindex="-1"></a>bayes_fit <span class="ot"><-</span> </span>
<span id="cb10-16"><a href="#cb10-16" tabindex="-1"></a> bayes_mod <span class="sc">%>%</span> </span>
<span id="cb10-17"><a href="#cb10-17" tabindex="-1"></a> <span class="fu">fit</span>(width <span class="sc">~</span> initial_volume <span class="sc">*</span> food_regime, <span class="at">data =</span> urchins)</span>
<span id="cb10-18"><a href="#cb10-18" tabindex="-1"></a></span>
<span id="cb10-19"><a href="#cb10-19" tabindex="-1"></a><span class="fu">print</span>(bayes_fit, <span class="at">digits =</span> <span class="dv">5</span>)</span></code></pre></div>
<pre class="bg-warning"><code>## parsnip model object
##
## stan_glm
## family: gaussian [identity]
## formula: width ~ initial_volume * food_regime
## observations: 72
## predictors: 6
## ------
## Median MAD_SD
## (Intercept) 0.03305 0.00983
## initial_volume 0.00155 0.00040
## food_regimeLow 0.02026 0.01327
## food_regimeHigh 0.02111 0.01467
## initial_volume:food_regimeLow -0.00128 0.00051
## initial_volume:food_regimeHigh 0.00053 0.00071
##
## Auxiliary parameter(s):
## Median MAD_SD
## sigma 0.02134 0.00190
##
## ------
## * For help interpreting the printed output see ?print.stanreg
## * For info on the priors used see ?prior_summary.stanreg</code></pre>
</div>
<div id="confidence-intervals" class="section level3">
<h3>Confidence Intervals</h3>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a><span class="fu">tidy</span>(bayes_fit, <span class="at">conf.int =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
<div class="kable-table">
<table>
<colgroup>
<col width="41%" />
<col width="14%" />
<col width="13%" />
<col width="14%" />
<col width="14%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">term</th>
<th align="right">estimate</th>
<th align="right">std.error</th>
<th align="right">conf.low</th>
<th align="right">conf.high</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">(Intercept)</td>
<td align="right">0.0330492</td>
<td align="right">0.0098253</td>
<td align="right">0.0165267</td>
<td align="right">0.0487526</td>
</tr>
<tr class="even">
<td align="left">initial_volume</td>
<td align="right">0.0015548</td>
<td align="right">0.0004013</td>
<td align="right">0.0008991</td>
<td align="right">0.0022505</td>
</tr>
<tr class="odd">
<td align="left">food_regimeLow</td>
<td align="right">0.0202582</td>
<td align="right">0.0132657</td>
<td align="right">-0.0016500</td>
<td align="right">0.0419710</td>
</tr>
<tr class="even">
<td align="left">food_regimeHigh</td>
<td align="right">0.0211120</td>
<td align="right">0.0146666</td>
<td align="right">-0.0024255</td>
<td align="right">0.0459320</td>
</tr>
<tr class="odd">
<td align="left">initial_volume:food_regimeLow</td>
<td align="right">-0.0012756</td>
<td align="right">0.0005076</td>
<td align="right">-0.0021153</td>
<td align="right">-0.0004111</td>
</tr>
<tr class="even">
<td align="left">initial_volume:food_regimeHigh</td>
<td align="right">0.0005349</td>
<td align="right">0.0007120</td>
<td align="right">-0.0006557</td>
<td align="right">0.0017073</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="plot-error-bars" class="section level3">
<h3>Plot Error Bars</h3>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a>bayes_plot_data <span class="ot"><-</span> </span>
<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a> new_points <span class="sc">%>%</span> </span>
<span id="cb13-3"><a href="#cb13-3" tabindex="-1"></a> <span class="fu">bind_cols</span>(<span class="fu">predict</span>(bayes_fit, <span class="at">new_data =</span> new_points)) <span class="sc">%>%</span> </span>
<span id="cb13-4"><a href="#cb13-4" tabindex="-1"></a> <span class="fu">bind_cols</span>(<span class="fu">predict</span>(bayes_fit, <span class="at">new_data =</span> new_points, <span class="at">type =</span> <span class="st">"conf_int"</span>))</span>
<span id="cb13-5"><a href="#cb13-5" tabindex="-1"></a></span>
<span id="cb13-6"><a href="#cb13-6" tabindex="-1"></a><span class="fu">ggplot</span>(bayes_plot_data, <span class="fu">aes</span>(<span class="at">x =</span> food_regime)) <span class="sc">+</span> </span>
<span id="cb13-7"><a href="#cb13-7" tabindex="-1"></a> <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">y =</span> .pred)) <span class="sc">+</span> </span>
<span id="cb13-8"><a href="#cb13-8" tabindex="-1"></a> <span class="fu">geom_errorbar</span>(<span class="fu">aes</span>(<span class="at">ymin =</span> .pred_lower, <span class="at">ymax =</span> .pred_upper), <span class="at">width =</span> .<span class="dv">2</span>) <span class="sc">+</span> </span>
<span id="cb13-9"><a href="#cb13-9" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">y =</span> <span class="st">"urchin size"</span>) <span class="sc">+</span> </span>
<span id="cb13-10"><a href="#cb13-10" tabindex="-1"></a> <span class="fu">ggtitle</span>(<span class="st">"Bayesian model with t(1) prior distribution"</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
</div>
</div>
<div id="preprocessing" class="section level1">
<h1>Preprocessing</h1>
<div id="introduction" class="section level2">
<h2>Introduction</h2>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a><span class="fu">library</span>(nycflights13)</span>
<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a><span class="fu">library</span>(skimr)</span>
<span id="cb14-3"><a href="#cb14-3" tabindex="-1"></a><span class="fu">library</span>(timeDate)</span></code></pre></div>
</div>
<div id="the-new-york-city-flight-data" class="section level2">
<h2>The New York City Flight Data</h2>
<p>Goal: predict whether or not a plane arrives 30+ minutes late.</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">123</span>)</span>
<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a></span>
<span id="cb15-3"><a href="#cb15-3" tabindex="-1"></a>flight_data <span class="ot"><-</span></span>
<span id="cb15-4"><a href="#cb15-4" tabindex="-1"></a> flights <span class="sc">|></span></span>
<span id="cb15-5"><a href="#cb15-5" tabindex="-1"></a> <span class="fu">mutate</span>(</span>
<span id="cb15-6"><a href="#cb15-6" tabindex="-1"></a> <span class="at">arr_delay =</span> <span class="fu">ifelse</span>(arr_delay <span class="sc">>=</span> <span class="dv">30</span>, <span class="st">"late"</span>, <span class="st">"on_time"</span>),</span>
<span id="cb15-7"><a href="#cb15-7" tabindex="-1"></a> <span class="at">arr_delay =</span> <span class="fu">factor</span>(arr_delay),</span>
<span id="cb15-8"><a href="#cb15-8" tabindex="-1"></a> <span class="at">date =</span> <span class="fu">as_date</span>(time_hour)</span>
<span id="cb15-9"><a href="#cb15-9" tabindex="-1"></a> ) <span class="sc">|></span></span>
<span id="cb15-10"><a href="#cb15-10" tabindex="-1"></a> <span class="fu">inner_join</span>(weather, <span class="at">by =</span> <span class="fu">c</span>(<span class="st">"origin"</span>, <span class="st">"time_hour"</span>)) <span class="sc">|></span></span>
<span id="cb15-11"><a href="#cb15-11" tabindex="-1"></a> <span class="fu">select</span>(dep_time, flight, origin, dest, air_time,</span>
<span id="cb15-12"><a href="#cb15-12" tabindex="-1"></a> distance, carrier, date, arr_delay, time_hour) <span class="sc">|></span></span>
<span id="cb15-13"><a href="#cb15-13" tabindex="-1"></a> <span class="fu">na.omit</span>() <span class="sc">|></span></span>
<span id="cb15-14"><a href="#cb15-14" tabindex="-1"></a> <span class="fu">mutate_if</span>(is.character, as.factor)</span></code></pre></div>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a>flight_data <span class="sc">|></span> </span>
<span id="cb16-2"><a href="#cb16-2" tabindex="-1"></a> <span class="fu">count</span>(arr_delay) <span class="sc">|></span></span>
<span id="cb16-3"><a href="#cb16-3" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">prop =</span> n<span class="sc">/</span><span class="fu">sum</span>(n))</span></code></pre></div>
<div class="kable-table">
<table>
<thead>
<tr class="header">
<th align="left">arr_delay</th>
<th align="right">n</th>
<th align="right">prop</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">late</td>
<td align="right">52540</td>
<td align="right">0.1612552</td>
</tr>
<tr class="even">
<td align="left">on_time</td>
<td align="right">273279</td>
<td align="right">0.8387448</td>
</tr>
</tbody>
</table>
</div>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" tabindex="-1"></a><span class="fu">glimpse</span>(flight_data)</span></code></pre></div>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a>flight_data <span class="sc">|></span> <span class="fu">skim</span>(dest, carrier)</span></code></pre></div>
<table>
<caption>Data summary</caption>
<tbody>
<tr class="odd">
<td align="left">Name</td>
<td align="left">flight_data</td>
</tr>
<tr class="even">
<td align="left">Number of rows</td>
<td align="left">325819</td>
</tr>
<tr class="odd">
<td align="left">Number of columns</td>
<td align="left">10</td>
</tr>
<tr class="even">
<td align="left">_______________________</td>
<td align="left"></td>
</tr>
<tr class="odd">
<td align="left">Column type frequency:</td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">factor</td>
<td align="left">2</td>
</tr>
<tr class="odd">
<td align="left">________________________</td>
<td align="left"></td>
</tr>
<tr class="even">
<td align="left">Group variables</td>
<td align="left">None</td>
</tr>
</tbody>
</table>
<p><strong>Variable type: factor</strong></p>
<table>
<colgroup>
<col width="13%" />
<col width="9%" />
<col width="13%" />
<col width="7%" />
<col width="8%" />
<col width="46%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">skim_variable</th>
<th align="right">n_missing</th>
<th align="right">complete_rate</th>
<th align="left">ordered</th>
<th align="right">n_unique</th>
<th align="left">top_counts</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">dest</td>
<td align="right">0</td>
<td align="right">1</td>
<td align="left">FALSE</td>
<td align="right">104</td>
<td align="left">ATL: 16771, ORD: 16507, LAX: 15942, BOS: 14948</td>
</tr>
<tr class="even">
<td align="left">carrier</td>
<td align="right">0</td>
<td align="right">1</td>
<td align="left">FALSE</td>
<td align="right">16</td>
<td align="left">UA: 57489, B6: 53715, EV: 50868, DL: 47465</td>
</tr>
</tbody>
</table>
</div>
<div id="data-splitting" class="section level2">
<h2>Data Splitting</h2>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">222</span>)</span>
<span id="cb19-2"><a href="#cb19-2" tabindex="-1"></a></span>
<span id="cb19-3"><a href="#cb19-3" tabindex="-1"></a>data_split <span class="ot"><-</span> <span class="fu">initial_split</span>(flight_data, <span class="at">prop =</span> <span class="dv">3</span><span class="sc">/</span><span class="dv">4</span>)</span>
<span id="cb19-4"><a href="#cb19-4" tabindex="-1"></a></span>
<span id="cb19-5"><a href="#cb19-5" tabindex="-1"></a>train_data <span class="ot"><-</span> <span class="fu">training</span>(data_split)</span>
<span id="cb19-6"><a href="#cb19-6" tabindex="-1"></a>test_data <span class="ot"><-</span> <span class="fu">testing</span>(data_split)</span></code></pre></div>
</div>
<div id="create-recipe-and-roles" class="section level2">
<h2>Create Recipe and Roles</h2>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" tabindex="-1"></a>flights_rec <span class="ot"><-</span></span>
<span id="cb20-2"><a href="#cb20-2" tabindex="-1"></a> <span class="fu">recipe</span>(arr_delay <span class="sc">~</span> ., <span class="at">data =</span> train_data) <span class="sc">|></span></span>
<span id="cb20-3"><a href="#cb20-3" tabindex="-1"></a> <span class="fu">update_role</span>(flight, time_hour, <span class="at">new_role =</span> <span class="st">"ID"</span>)</span></code></pre></div>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a><span class="fu">summary</span>(flights_rec)</span></code></pre></div>
</div>
<div id="create-features" class="section level2">
<h2>Create Features</h2>
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" tabindex="-1"></a>flight_data <span class="sc">|></span></span>
<span id="cb22-2"><a href="#cb22-2" tabindex="-1"></a> <span class="fu">distinct</span>(date) <span class="sc">|></span> </span>
<span id="cb22-3"><a href="#cb22-3" tabindex="-1"></a> <span class="fu">mutate</span>(<span class="at">numeric_date =</span> <span class="fu">as.numeric</span>(date))</span></code></pre></div>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a>flights_rec <span class="ot"><-</span></span>
<span id="cb23-2"><a href="#cb23-2" tabindex="-1"></a> flights_rec <span class="sc">|></span> </span>
<span id="cb23-3"><a href="#cb23-3" tabindex="-1"></a> <span class="fu">step_date</span>(date, <span class="at">features =</span> <span class="fu">c</span>(<span class="st">"dow"</span>, <span class="st">"month"</span>)) <span class="sc">|></span></span>
<span id="cb23-4"><a href="#cb23-4" tabindex="-1"></a> <span class="fu">step_holiday</span>(date,</span>
<span id="cb23-5"><a href="#cb23-5" tabindex="-1"></a> <span class="at">holidays =</span> <span class="fu">listHolidays</span>(<span class="st">"US"</span>),</span>
<span id="cb23-6"><a href="#cb23-6" tabindex="-1"></a> <span class="at">keep_original_cols =</span> <span class="cn">FALSE</span>) <span class="sc">|></span></span>
<span id="cb23-7"><a href="#cb23-7" tabindex="-1"></a> <span class="fu">step_dummy</span>(<span class="fu">all_nominal_predictors</span>())</span></code></pre></div>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" tabindex="-1"></a>test_data <span class="sc">|></span></span>
<span id="cb24-2"><a href="#cb24-2" tabindex="-1"></a> <span class="fu">distinct</span>(dest) <span class="sc">|></span></span>
<span id="cb24-3"><a href="#cb24-3" tabindex="-1"></a> <span class="fu">anti_join</span>(train_data)</span></code></pre></div>
<pre><code>## Joining with `by = join_by(dest)`</code></pre>
<div class="kable-table">
<table>
<thead>
<tr class="header">
<th align="left">dest</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">LEX</td>
</tr>
</tbody>
</table>
</div>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" tabindex="-1"></a>flights_rec <span class="ot"><-</span></span>
<span id="cb26-2"><a href="#cb26-2" tabindex="-1"></a> flights_rec <span class="sc">|></span></span>
<span id="cb26-3"><a href="#cb26-3" tabindex="-1"></a> <span class="fu">step_zv</span>(<span class="fu">all_predictors</span>())</span></code></pre></div>
</div>
<div id="fit-a-model-with-a-recipe" class="section level2">
<h2>Fit a Model with a Recipe</h2>
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" tabindex="-1"></a>lr_mod <span class="ot"><-</span></span>
<span id="cb27-2"><a href="#cb27-2" tabindex="-1"></a> <span class="fu">logistic_reg</span>() <span class="sc">|></span></span>
<span id="cb27-3"><a href="#cb27-3" tabindex="-1"></a> <span class="fu">set_engine</span>(<span class="st">"glm"</span>)</span></code></pre></div>
<ol style="list-style-type: decimal">
<li>Process recipe using training set</li>
<li>Apply recipe to training set</li>
<li>Apply recipe to test set</li>
</ol>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" tabindex="-1"></a>flights_wflow <span class="ot"><-</span></span>
<span id="cb28-2"><a href="#cb28-2" tabindex="-1"></a> <span class="fu">workflow</span>() <span class="sc">|></span></span>
<span id="cb28-3"><a href="#cb28-3" tabindex="-1"></a> <span class="fu">add_model</span>(lr_mod) <span class="sc">|></span></span>
<span id="cb28-4"><a href="#cb28-4" tabindex="-1"></a> <span class="fu">add_recipe</span>(flights_rec)</span>
<span id="cb28-5"><a href="#cb28-5" tabindex="-1"></a></span>
<span id="cb28-6"><a href="#cb28-6" tabindex="-1"></a>flights_wflow</span></code></pre></div>
<pre class="bg-warning"><code>## ══ Workflow ════════════════════════════════════════════════════════════════════
## Preprocessor: Recipe
## Model: logistic_reg()
##
## ── Preprocessor ────────────────────────────────────────────────────────────────
## 4 Recipe Steps
##
## • step_date()
## • step_holiday()
## • step_dummy()
## • step_zv()
##
## ── Model ───────────────────────────────────────────────────────────────────────
## Logistic Regression Model Specification (classification)
##
## Computational engine: glm</code></pre>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" tabindex="-1"></a>flights_fit <span class="ot"><-</span></span>
<span id="cb30-2"><a href="#cb30-2" tabindex="-1"></a> flights_wflow <span class="sc">|></span></span>
<span id="cb30-3"><a href="#cb30-3" tabindex="-1"></a> <span class="fu">fit</span>(<span class="at">data =</span> train_data)</span></code></pre></div>
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" tabindex="-1"></a>flights_fit <span class="sc">|></span></span>
<span id="cb31-2"><a href="#cb31-2" tabindex="-1"></a> <span class="fu">extract_fit_parsnip</span>() <span class="sc">|></span></span>
<span id="cb31-3"><a href="#cb31-3" tabindex="-1"></a> <span class="fu">tidy</span>()</span></code></pre></div>
<div class="kable-table">
<table>
<colgroup>
<col width="39%" />
<col width="14%" />
<col width="14%" />
<col width="17%" />
<col width="13%" />
</colgroup>
<thead>
<tr class="header">
<th align="left">term</th>
<th align="right">estimate</th>
<th align="right">std.error</th>
<th align="right">statistic</th>
<th align="right">p.value</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">(Intercept)</td>
<td align="right">7.2764465</td>
<td align="right">2.7276474</td>
<td align="right">2.6676639</td>
<td align="right">0.0076381</td>
</tr>
<tr class="even">
<td align="left">dep_time</td>
<td align="right">-0.0016641</td>
<td align="right">0.0000141</td>
<td align="right">-118.0582446</td>
<td align="right">0.0000000</td>
</tr>
<tr class="odd">
<td align="left">air_time</td>
<td align="right">-0.0440138</td>
<td align="right">0.0005629</td>
<td align="right">-78.1899512</td>
<td align="right">0.0000000</td>
</tr>
<tr class="even">
<td align="left">distance</td>
<td align="right">0.0050708</td>
<td align="right">0.0015015</td>
<td align="right">3.3772393</td>
<td align="right">0.0007322</td>
</tr>
<tr class="odd">
<td align="left">date_USChristmasDay</td>
<td align="right">1.3293362</td>
<td align="right">0.1774995</td>
<td align="right">7.4892394</td>
<td align="right">0.0000000</td>
</tr>
<tr class="even">
<td align="left">date_USColumbusDay</td>
<td align="right">0.7239272</td>
<td align="right">0.1702983</td>
<td align="right">4.2509349</td>
<td align="right">0.0000213</td>
</tr>
<tr class="odd">
<td align="left">date_USCPulaskisBirthday</td>
<td align="right">0.8071650</td>
<td align="right">0.1391266</td>
<td align="right">5.8016587</td>
<td align="right">0.0000000</td>
</tr>
<tr class="even">
<td align="left">date_USDecorationMemorialDay</td>
<td align="right">0.5846944</td>
<td align="right">0.1173840</td>
<td align="right">4.9810404</td>
<td align="right">0.0000006</td>
</tr>
<tr class="odd">
<td align="left">date_USElectionDay</td>
<td align="right">0.9476518</td>
<td align="right">0.1901650</td>
<td align="right">4.9833137</td>
<td align="right">0.0000006</td>
</tr>
<tr class="even">
<td align="left">date_USGoodFriday</td>
<td align="right">1.2468109</td>
<td align="right">0.1673769</td>
<td align="right">7.4491205</td>
<td align="right">0.0000000</td>
</tr>
<tr class="odd">
<td align="left">date_USInaugurationDay</td>
<td align="right">0.2289467</td>
<td align="right">0.1358693</td>
<td align="right">1.6850504</td>
<td align="right">0.0919789</td>
</tr>
<tr class="even">
<td align="left">date_USIndependenceDay</td>
<td align="right">2.1197469</td>
<td align="right">0.2029918</td>
<td align="right">10.4425237</td>
<td align="right">0.0000000</td>
</tr>
<tr class="odd">
<td align="left">date_USLaborDay</td>
<td align="right">-1.9337374</td>
<td align="right">0.0967105</td>
<td align="right">-19.9951196</td>
<td align="right">0.0000000</td>
</tr>
<tr class="even">
<td align="left">date_USLincolnsBirthday</td>
<td align="right">0.5837500</td>
<td align="right">0.1416181</td>
<td align="right">4.1220003</td>
<td align="right">0.0000376</td>
</tr>
<tr class="odd">
<td align="left">date_USMemorialDay</td>
<td align="right">1.5192168</td>
<td align="right">0.1812174</td>
<td align="right">8.3833929</td>
<td align="right">0.0000000</td>
</tr>
<tr class="even">
<td align="left">date_USMLKingsBirthday</td>
<td align="right">0.4285855</td>
<td align="right">0.1206922</td>
<td align="right">3.5510604</td>
<td align="right">0.0003837</td>
</tr>
<tr class="odd">
<td align="left">date_USNewYearsDay</td>
<td align="right">0.2042030</td>
<td align="right">0.1207265</td>
<td align="right">1.6914516</td>
<td align="right">0.0907506</td>
</tr>
<tr class="even">
<td align="left">date_USPresidentsDay</td>
<td align="right">0.4837968</td>
<td align="right">0.1356693</td>
<td align="right">3.5660001</td>
<td align="right">0.0003625</td>
</tr>
<tr class="odd">
<td align="left">date_USThanksgivingDay</td>
<td align="right">0.1529775</td>
<td align="right">0.1626502</td>
<td align="right">0.9405309</td>
<td align="right">0.3469453</td>
</tr>
<tr class="even">
<td align="left">date_USVeteransDay</td>
<td align="right">0.7178950</td>
<td align="right">0.1594695</td>
<td align="right">4.5017705</td>
<td align="right">0.0000067</td>
</tr>
<tr class="odd">
<td align="left">date_USWashingtonsBirthday</td>
<td align="right">0.0433049</td>
<td align="right">0.1087822</td>
<td align="right">0.3980884</td>
<td align="right">0.6905650</td>
</tr>
<tr class="even">
<td align="left">origin_JFK</td>
<td align="right">0.1072886</td>
<td align="right">0.0289085</td>
<td align="right">3.7113160</td>
<td align="right">0.0002062</td>
</tr>
<tr class="odd">
<td align="left">origin_LGA</td>
<td align="right">0.0109610</td>
<td align="right">0.0279198</td>
<td align="right">0.3925872</td>
<td align="right">0.6946244</td>
</tr>
<tr class="even">
<td align="left">dest_ACK</td>
<td align="right">-1.7379545</td>
<td align="right">2.4563444</td>
<td align="right">-0.7075370</td>
<td align="right">0.4792329</td>
</tr>
<tr class="odd">
<td align="left">dest_ALB</td>
<td align="right">-1.6795509</td>
<td align="right">2.5136619</td>
<td align="right">-0.6681690</td>
<td align="right">0.5040257</td>
</tr>
<tr class="even">
<td align="left">dest_ANC</td>
<td align="right">-1.2039951</td>
<td align="right">2.5846481</td>
<td align="right">-0.4658256</td>
<td align="right">0.6413404</td>
</tr>
<tr class="odd">
<td align="left">dest_ATL</td>
<td align="right">-1.6077154</td>
<td align="right">1.6105404</td>
<td align="right">-0.9982459</td>
<td align="right">0.3181601</td>
</tr>
<tr class="even">
<td align="left">dest_AUS</td>
<td align="right">-0.7973039</td>
<td align="right">0.5070358</td>
<td align="right">-1.5724804</td>