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<title>2. Confirmation of Bayesian skills</title>

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<h1 class="title toc-ignore">2. Confirmation of Bayesian skills</h1>


<div id="TOC">
<ul>
<li><a href="#correlations">Correlations</a>
<ul>
<li><a href="#frequentist-version">Frequentist version</a></li>
<li><a href="#bayesian-correlation">Bayesian correlation</a></li>
<li><a href="#bayes-factor-bf">Bayes Factor (BF)</a></li>
<li><a href="#visualise-the-bayes-factor">Visualise the Bayes factor</a></li>
</ul></li>
<li><a href="#t-tests"><em>t</em>-tests</a>
<ul>
<li><a href="#versicolor-vs.-virginica">Versicolor <em>vs.</em> virginica</a></li>
<li><a href="#compute-the-bayesian-t-test">Compute the Bayesian <em>t</em>-test</a></li>
</ul></li>
<li><a href="#logistic-model">Logistic Model</a>
<ul>
<li><a href="#fit-the-model">Fit the model</a></li>
<li><a href="#visualise-the-model">Visualise the model</a></li>
<li><a href="#performance-and-parameters">Performance and Parameters</a></li>
<li><a href="#visualise-the-indices">Visualise the indices</a></li>
<li><a href="#diagnostic-indices">Diagnostic Indices</a></li>
</ul></li>
</ul>
</div>

<p>This vignette can be referred to by citing the package:</p>
<ul>
<li>Makowski, D., Ben-Shachar, M. S., &amp; Lüdecke, D. (2019). <em>bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework</em>. Journal of Open Source Software, 4(40), 1541. <a href="https://doi.org/10.21105/joss.01541" class="uri">https://doi.org/10.21105/joss.01541</a></li>
</ul>
<hr />
<p>Now that <a href="https://easystats.github.io/bayestestR/articles/example1.html"><strong>describing and understanding posterior distributions</strong></a> of linear regressions is not that mysterious to you, we will take one step back and study some simpler models: <strong>correlations</strong> and <strong><em>t</em>-tests</strong>.</p>
<p>But before we do that, let us take a moment to remind ourselves and appreciate the fact that <strong>all basic statistical procedures</strong> such as correlations, <em>t</em>-tests, ANOVAs, or chi-square tests <strong>are</strong> linear regressions (we strongly recommend <a href="https://lindeloev.github.io/tests-as-linear/">this excellent demonstration</a>). Nevertheless, these simple models will provide a good pretext to introduce a few more complex indices, such as the <strong>Bayes factor</strong>.</p>
<div id="correlations" class="section level2">
<h2>Correlations</h2>
<div id="frequentist-version" class="section level3">
<h3>Frequentist version</h3>
<p>Once again, let us begin with a <strong>frequentist correlation</strong> between two continuous variables, the <strong>width</strong> and the <strong>length</strong> of the sepals of some flowers. The data is available in <code>R</code> as the <code>iris</code> dataset (the same that was used in the <a href="https://easystats.github.io/bayestestR/articles/example1.html">previous tutorial</a>).</p>
<p>We will compute a Pearson’s correlation test, store the results in an object called <code>result</code>, and then display it:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a>result <span class="ot">&lt;-</span> <span class="fu">cor.test</span>(iris<span class="sc">$</span>Sepal.Width, iris<span class="sc">$</span>Sepal.Length)</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>result</span></code></pre></div>
<pre><code>&gt; 
&gt;   Pearson&#39;s product-moment correlation
&gt; 
&gt; data:  iris$Sepal.Width and iris$Sepal.Length
&gt; t = -1, df = 148, p-value = 0.2
&gt; alternative hypothesis: true correlation is not equal to 0
&gt; 95 percent confidence interval:
&gt;  -0.273  0.044
&gt; sample estimates:
&gt;   cor 
&gt; -0.12</code></pre>
<p>As you can see in the output, the test actually compared <strong>two</strong> hypotheses: - the <strong>null hypothesis</strong> (<em>h0</em>; no correlation), - the <strong>alternative hypothesis</strong> (<em>h1</em>; a non-null correlation).</p>
<p>Based on the <em>p</em>-value, the null hypothesis cannot be rejected: the correlation between the two variables is <strong>negative but non-significant</strong> (<span class="math inline">\(r = -.12, p &gt; .05\)</span>).</p>
</div>
<div id="bayesian-correlation" class="section level3">
<h3>Bayesian correlation</h3>
<p>To compute a Bayesian correlation test, we will need the <a href="https://richarddmorey.github.io/BayesFactor/"><code>BayesFactor</code></a> package (you can install it by running <code>install.packages(&quot;BayesFactor&quot;)</code>). We can then load this package, compute the correlation using the <code>correlationBF()</code> function, and store the result.</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(BayesFactor)</span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>result <span class="ot">&lt;-</span> <span class="fu">correlationBF</span>(iris<span class="sc">$</span>Sepal.Width, iris<span class="sc">$</span>Sepal.Length)</span></code></pre></div>
<p>Now, let us run our <code>describe_posterior()</code> function on that:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="fu">describe_posterior</span>(result)</span></code></pre></div>
<pre><code>&gt; Summary of Posterior Distribution
&gt; 
&gt; Parameter | Median |        95% CI |     pd |          ROPE | % in ROPE |    BF |         Prior
&gt; -----------------------------------------------------------------------------------------------
&gt; rho       |  -0.11 | [-0.26, 0.05] | 92.25% | [-0.05, 0.05] |    20.84% | 0.509 | Beta (3 +- 3)</code></pre>
<p>We see again many things here, but the important indices for now are the <strong>median</strong> of the posterior distribution, <code>-.11</code>. This is (again) quite close to the frequentist correlation. We could, as previously, describe the <a href="https://easystats.github.io/bayestestR/articles/credible_interval.html"><strong>credible interval</strong></a>, the <a href="https://easystats.github.io/bayestestR/articles/probability_of_direction.html"><strong>pd</strong></a> or the <a href="https://easystats.github.io/bayestestR/articles/region_of_practical_equivalence.html"><strong>ROPE percentage</strong></a>, but we will focus here on another index provided by the Bayesian framework, the <strong>Bayes Factor (BF)</strong>.</p>
</div>
<div id="bayes-factor-bf" class="section level3">
<h3>Bayes Factor (BF)</h3>
<p>We said previously that a correlation test actually compares two hypotheses, a null (absence of effect) with an alternative one (presence of an effect). The <a href="https://easystats.github.io/bayestestR/articles/bayes_factors.html"><strong>Bayes factor (BF)</strong></a> allows the same comparison and determines <strong>under which of these two models the observed data are more probable</strong>: a model with the effect of interest, and a null model without the effect of interest. So, in the context of our correlation example, the null hypothesis would be no correlation between the two variables (<span class="math inline">\(h0: \rho = 0\)</span>; where <span class="math inline">\(\rho\)</span> stands for Bayesian correlation coefficient), while the alternative hypothesis would be that there is a correlation <strong>different</strong> than 0 - positive or negative (<span class="math inline">\(h1: \rho \neq 0\)</span>).</p>
<p>We can use <code>bayesfactor()</code> to specifically compute the Bayes factor comparing those models:</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="fu">bayesfactor</span>(result)</span></code></pre></div>
<pre><code>&gt; Bayes Factors for Model Comparison
&gt; 
&gt;     Model         BF
&gt; [2] (rho != 0) 0.509
&gt; 
&gt; * Against Denominator: [1] (rho = 0)
&gt; *   Bayes Factor Type: JZS (BayesFactor)</code></pre>
<p>We got a <em>BF</em> of <code>0.51</code>. What does it mean?</p>
<p>Bayes factors are <strong>continuous measures of <em>relative</em> evidence</strong>, with a Bayes factor greater than 1 giving evidence in favour of one of the models (often referred to as <em>the numerator</em>), and a Bayes factor smaller than 1 giving evidence in favour of the other model (<em>the denominator</em>).</p>
<blockquote>
<p><strong>Yes, you heard that right, evidence in favour of the <em>null</em>!</strong></p>
</blockquote>
<p>That’s one of the reason why the Bayesian framework is sometimes considered as superior to the frequentist framework. Remember from your stats lessons, that the <strong><em>p</em>-value can only be used to reject <em>h0</em></strong>, but not <em>accept</em> it. With the <strong>Bayes factor</strong>, you can measure <strong>evidence against - and in favour of - the null</strong>. In other words, in the frequentist framework, if the <em>p</em>-value is not significant, we can conclude that <strong>evidence for the effect is absent</strong>, but not that there is <strong>evidence for the absence of the effect</strong>. In Bayesian framework, we can do the latter. This is important since sometimes our hypotheses are about no effect.</p>
<p>BFs representing evidence for the alternative against the null can be reversed using <span class="math inline">\(BF_{01}=1/BF_{10}\)</span> (the <em>01</em> and <em>10</em> correspond to <em>h0</em> against <em>h1</em> and <em>h1</em> against <em>h0</em>, respectively) to provide evidence of the null against the alternative. This improves human readability<a href="#fn1" class="footnote-ref" id="fnref1"><sup>1</sup></a> in cases where the BF of the alternative against the null is smaller than 1 (i.e., in support of the null).</p>
<p>In our case, <code>BF = 1/0.51 = 2</code>, indicates that the data are <strong>2 times more probable under the null compared to the alternative hypothesis</strong>, which, though favouring the null, is considered only <a href="https://easystats.github.io/effectsize/reference/interpret_bf.html">anecdotal evidence against the null</a>.</p>
<p>We can thus conclude that there is <strong>anecdotal evidence in favour of an absence of correlation between the two variables (r<sub>median</sub> = 0.11, BF = 0.51)</strong>, which is a much more informative statement that what we can do with frequentist statistics.</p>
<p><strong>And that’s not all!</strong></p>
</div>
<div id="visualise-the-bayes-factor" class="section level3">
<h3>Visualise the Bayes factor</h3>
<p>In general, <strong>pie charts are an absolute no-go in data visualisation</strong>, as our brain’s perceptive system heavily distorts the information presented in such way<a href="#fn2" class="footnote-ref" id="fnref2"><sup>2</sup></a>. Nevertheless, there is one exception: pizza charts.</p>
<p>It is an intuitive way of interpreting the strength of evidence provided by BFs as an amount of surprise.</p>
<div class="figure" style="text-align: center">
<img src="data:image/jpeg;base64,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" alt="Wagenmakers&#39; pizza poking analogy. From the great &lt;www.bayesianspectacles.org&gt; blog." width="80%" />
<p class="caption">
Wagenmakers’ pizza poking analogy. From the great &lt;www.bayesianspectacles.org&gt; blog.
</p>
</div>
<p>Such “pizza plots” can be directly created through the <a href="https://github.com/easystats/see"><code>see</code></a> visualisation companion package for <code>easystats</code> (you can install it by running <code>install.packages(&quot;see&quot;)</code>):</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(see)</span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(<span class="fu">bayesfactor</span>(result)) <span class="sc">+</span></span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_pizza</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" width="100%" /></p>
<p>So, after seeing this pizza, how much would you be surprised by the outcome of a blinded poke?</p>
</div>
</div>
<div id="t-tests" class="section level2">
<h2><em>t</em>-tests</h2>
<blockquote>
<p><strong>“I know that I know nothing, and especially not if <em>versicolor</em> and <em>virginica</em> differ in terms of their Sepal.Width” - Socrates</strong>.</p>
</blockquote>
<p>Time to finally answer this crucial question!</p>
<div id="versicolor-vs.-virginica" class="section level3">
<h3>Versicolor <em>vs.</em> virginica</h3>
<p>Bayesian <em>t</em>-tests can be performed in a very similar way to correlations. As we are particularly interested in two levels of the <code>Species</code> factor, <em>versicolor</em> and <em>virginica</em>. We will start by filtering out from <code>iris</code> the non-relevant observations corresponding to the <em>setosa</em> specie, and we will then visualise the observations and the distribution of the <code>Sepal.Width</code> variable.</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(dplyr)</span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(ggplot2)</span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a><span class="co"># Select only two relevant species</span></span>
<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a>data <span class="ot">&lt;-</span> iris <span class="sc">%&gt;%</span></span>
<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(Species <span class="sc">!=</span> <span class="st">&quot;setosa&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">droplevels</span>()</span>
<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-9"><a href="#cb9-9" aria-hidden="true" tabindex="-1"></a><span class="co"># Visualise distributions and observations</span></span>
<span id="cb9-10"><a href="#cb9-10" aria-hidden="true" tabindex="-1"></a>data <span class="sc">%&gt;%</span></span>
<span id="cb9-11"><a href="#cb9-11" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(<span class="at">x =</span> Species, <span class="at">y =</span> Sepal.Width, <span class="at">fill =</span> Species)) <span class="sc">+</span></span>
<span id="cb9-12"><a href="#cb9-12" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_violindot</span>(<span class="at">fill_dots =</span> <span class="st">&quot;black&quot;</span>, <span class="at">size_dots =</span> <span class="dv">1</span>) <span class="sc">+</span></span>
<span id="cb9-13"><a href="#cb9-13" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_material</span>() <span class="sc">+</span></span>
<span id="cb9-14"><a href="#cb9-14" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_modern</span>()</span></code></pre></div>
<p><img 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" width="100%" /></p>
<p>It <em>seems</em> (visually) that <em>virgnica</em> flowers have, on average, a slightly higer width of sepals. Let’s assess this difference statistically by using the <code>ttestBF()</code> function in the <code>BayesFactor</code> package.</p>
</div>
<div id="compute-the-bayesian-t-test" class="section level3">
<h3>Compute the Bayesian <em>t</em>-test</h3>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>result <span class="ot">&lt;-</span> BayesFactor<span class="sc">::</span><span class="fu">ttestBF</span>(<span class="at">formula =</span> Sepal.Width <span class="sc">~</span> Species, <span class="at">data =</span> data)</span>
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a><span class="fu">describe_posterior</span>(result)</span></code></pre></div>
<pre><code>&gt; Summary of Posterior Distribution
&gt; 
&gt; Parameter  | Median |       95% CI |     pd |          ROPE | % in ROPE |    BF |              Prior
&gt; ----------------------------------------------------------------------------------------------------
&gt; Difference |   0.19 | [0.06, 0.31] | 99.75% | [-0.03, 0.03] |        0% | 17.72 | Cauchy (0 +- 0.71)</code></pre>
<p>From the indices, we can say that the difference of <code>Sepal.Width</code> between <em>virginica</em> and <em>versicolor</em> has a probability of <strong>100% of being negative</strong> [<em>from the pd and the sign of the median</em>] (median = -0.19, 89% CI [-0.29, -0.092]). The data provides a <strong>strong evidence against the null hypothesis</strong> (BF = 18).</p>
<p>Keep that in mind as we will see another way of investigating this question.</p>
</div>
</div>
<div id="logistic-model" class="section level2">
<h2>Logistic Model</h2>
<p>A hypothesis for which one uses a <em>t</em>-test can also be tested using a binomial model (<em>e.g.</em>, a <strong>logistic model</strong>). Indeed, it is possible to reformulate the following hypothesis, “<em>there is an important difference in this variable between the two groups</em>” with the hypothesis “<em>this variable is able to discriminate between (or classify) the two groups</em>.” However, these models are much more powerful than a <em>t</em>-test.</p>
<p>In the case of the difference of <code>Sepal.Width</code> between <em>virginica</em> and <em>versicolor</em>, the question becomes, <em>how well can we classify the two species using only</em> <code>Sepal.Width</code>.</p>
<div id="fit-the-model" class="section level3">
<h3>Fit the model</h3>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(rstanarm)</span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>model <span class="ot">&lt;-</span> <span class="fu">stan_glm</span>(Species <span class="sc">~</span> Sepal.Width, <span class="at">data =</span> data, <span class="at">family =</span> <span class="st">&quot;binomial&quot;</span>, <span class="at">refresh =</span> <span class="dv">0</span>)</span></code></pre></div>
</div>
<div id="visualise-the-model" class="section level3">
<h3>Visualise the model</h3>
<p>Using the <a href="https://github.com/easystats/modelbased"><code>modelbased</code></a> package.</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(modelbased)</span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>means <span class="ot">&lt;-</span> <span class="fu">estimate_means</span>(model, <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">&quot;Sepal.Width&quot;</span>), <span class="at">length =</span> <span class="dv">2</span>)</span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a>means</span></code></pre></div>
<pre><code>&gt; Sepal.Width | Probability |       95% CI
&gt; ----------------------------------------
&gt; 2.00        |        0.13 | [0.03, 0.31]
&gt; 3.80        |        0.88 | [0.70, 0.98]</code></pre>
</div>
<div id="performance-and-parameters" class="section level3">
<h3>Performance and Parameters</h3>
<p>Once again, we can extract all indices of interest for the posterior distribution using our old pal <code>describe_posterior()</code>.</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="fu">describe_posterior</span>(model, <span class="at">test =</span> <span class="fu">c</span>(<span class="st">&quot;pd&quot;</span>, <span class="st">&quot;ROPE&quot;</span>, <span class="st">&quot;BF&quot;</span>))</span></code></pre></div>
<pre><code>&gt; Summary of Posterior Distribution
&gt; 
&gt; Parameter   | Median |          95% CI |     pd |          ROPE | % in ROPE |    BF |  Rhat |     ESS
&gt; -----------------------------------------------------------------------------------------------------
&gt; (Intercept) |  -6.15 | [-10.26, -2.13] | 99.92% | [-0.18, 0.18] |        0% |  7.23 | 1.001 | 2651.00
&gt; Sepal.Width |   2.13 | [  0.77,  3.60] | 99.95% | [-0.18, 0.18] |        0% | 20.31 | 1.001 | 2639.00</code></pre>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(performance)</span>
<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a><span class="fu">model_performance</span>(model)</span></code></pre></div>
<pre><code>&gt; # Indices of model performance
&gt; 
&gt; ELPD    | ELPD_SE |   LOOIC | LOOIC_SE |    WAIC |    R2 |  RMSE | Sigma | Log_loss | Score_log | Score_spherical
&gt; -----------------------------------------------------------------------------------------------------------------
&gt; -66.265 |   3.071 | 132.530 |    6.142 | 132.519 | 0.099 | 0.477 | 1.000 |    0.643 |   -34.666 |           0.014</code></pre>
</div>
<div id="visualise-the-indices" class="section level3">
<h3>Visualise the indices</h3>
<p>TO DO.</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(see)</span>
<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a><span class="fu">plot</span>(<span class="fu">rope</span>(result))</span></code></pre></div>
<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA8cAAAJACAMAAACXE+S8AAABHVBMVEUAAAAAADoAAGYAOjoAOmYAOpAAZpAAZrYAv8QwT1AwrrI6AAA6ADo6AGY6OgA6Ojo6OmY6ZmY6ZpA6ZrY6kJA6kLY6kNtNTU1NTW5NTY5NbqtNjshmAABmADpmOgBmOjpmZgBmZjpmZmZmZpBmkLZmkNtmtrZmtttmtv9uTU1uTW5uTY5uq+SOTU2OyP+QOgCQOjqQZgCQZjqQttuQ2/+rbk2rjk2r5P+vzs+2ZgC2Zjq2kDq2kJC2tma2tpC2tra227a229u22/+2/7a2///Ijk3I///bkDrbkGbbtmbbtpDbtrbb25Db2//b/7bb/9vb///kq27k///4dm3/tmb/yI7/25D/27b/29v/5Kv//7b//8j//9v//+T///8yTmMtAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAcp0lEQVR4nO3dC3sj112A8fE2rr0VtybghWICFNq4kOJSNoDCtcmmamhpd6nWm7iKv//HYM6Z+2juc27/M+/7PIktSxqN5Pn5zEWaTZ6ISHqJ7xkgotXhmEh+OCaSH46J5IdjIvnhmEh+OCaSH46J5LfE8SHJ+tYHr/tucrp/9mrq5B4/TJKr5pT/6LOxO51+NvAgtSuKKZYPMXVO5zyDesf0bmcv0Luf7pLk4oP/bcySfpKN+Ttc9r6gREOtcdxrY56CfTqhm9aUL14O3+fdh1dhOn68vTl/0F/s8mf1UWOW1JNs3PR0f7PgIYkWOr7TX/9nN8ZtSimYcir5lE+f9v+FyHrYDd2g4fhu8WwtcbxXQ2rrBTomiRqKv/o00T9vPMnm/B1NvKC0wdY4Tr8xMH6c7qu1yWLKo4hCdfywq0nNX6DH20Lnw05Ns/Ekm/N3uh/5+0XUmQHH6cCSXPyttvjuw+TiB8pYpuD06S7dKnytrf5KXVVNo7zqqNYpC8nllPdqEX/2+YdJOla/+8v0Fu/9k55++u37xZ3usgcpfvZU3bDXcWv+Hm8vX6vv9hqautSeRvnc6s+gesTaU6/mu/0C1f7a5ZfrT7L5d+bAgExLWuP4S73a+HibFBQf9HbgdwsnageWUvEqvZhtA5YLdHVVp+NsqHr2V+qaY779eFdMP71xzXH5s6fqhn2OW/NXOs5udExnrzWN6rnVnkH1iLWnnj+t26vzF2hf2dSrEQPjcXoDtpBpQev3c+2TdFhNB6Ybtayn336pfqyX0X1y+dnTu3t9Mf329G/VEl9d1bVe/dU/ZpuOCsDp/kKNjnv1E/VQ6Z3uchD5g+Q/q92waz9XOq32/JWOs7X01Ft7Go3nVjyDai6qq/OO2betnVfVCroe8ltPsrFnr/5iEE1u1XEntfs1H4LUAviw0wvhIVfweKsXYPUl9fEyX4p1tatajhu7cmtj0zGdaHb/YzYm5kJqP6tu2OO4NX+VYz0L1dyV06g9t+oZVI9Yu7p8tLuOF6h6fvkQ3LW/Oh+098uOdtHGW7xe/e4+O4rykB9TSZ69yjUV25/Fvig9zhVssinUrupyfPH+Z0+1VeKvfvnvf71LadV2bpWOGzu8ihv2rFe35q9yrDdL82vr06g9t+oO1SPWrs5/kq9CN16g+vPT33c/yeYUiGa1fPs43TjMtiqNOr5rP4zaN5Wvoo44rm4427HaLNWCmtOoPbcux7Wr85/UHRcvUDaSF7NdrVd3POMnHNOyVuznOurltwbJiuN0w/S9v/mvnx1HHdduONvx6f5Kz1xrGrUH7B6P20eJGo7zF6h/f/XZM37CMS1rzf7qfXZ0tDQztH3cdNy/fXy2iOdYDuX2sf5/a/u45uvQ7zh//OoWaoaz7/bP/qPc7D57BqrqGVSPWLu6+WiNF6j3+PH5M35i+5iWtcbxQ3ZYRe16fvpSk0j+5HX6Xdf+6qbjkf3V9Qvpsv+ZPk6b768+7TNwevTMdynrn9Vu2HfcaZ9c5fOniaWPXzg+XvyBei7tadSfW/kMqrmors4foNxfXX+Bet7P1TF/zZ1iRJNb+T6Qq/IgasagsYWaX6PH3Jbj6qoJ69VZehQu7qTu/1Ht+PGzV/Ubdr6/upyhb6lBV2/bfvu+AqqPibWnUT232jNozkVSfzN46/hx9gIp6fmurR90PMnGRjbHj2lRqxxne3JO6sM8euer3k303n/ubs7fz9VyXF01up/r9FO1b/e/b5vv3frFLrlpvZ+rumGfY32TD36lV57TCbz/m9Jxvgp8No3yudWfQe39XNVTz2q9nyvf1ZUOxTv16afi8079jnk/Fy3KwuePj0nYY8rwe7PXTnvVc+f91bQsk4711mU6Jgc+pth0nH3eaXF83omWZdJxsXX5gcFpWsiqY/3546UxHNPCjK5Xn/Rm4EcmJ2khq471+UCWxvlAaGGcn4tIfjgmkh+OieSHYyL54ZhIfjgmkh+OieSHYyL54ZhIfjgmkh+OieSHYyL54ZhIfjgmkh+OieSHYyL54ZhIfjgmkh+OieSHYyL54ZhIfjgmkh+OieSHYyL54ZhIfgsce6T/r/by96SIVodjHJP8cIxjkh+OcUzywzGOSX44xjHJD8c4JvnhGMckPxzjmOSHYxyT/HCMY5IfjnFM8sMxjkl+OMYxyQ/HOCb54RjHJD8c45jkh2Mck/xwjGOSH45xTPLDMY5JfjiOzHGS5Xs2yG04jslxCvi3WUjeVjiOyHGBOJPse27IYTiOx3GDMUPypsJxNI6TlmNG5A2F41gctxUDeUvhOBLHHYxxvJ1wHLFjIG8mHMfhuJMx+7o2E46jcNzDmBF5K+E4Bse9jIG8kXAcuWMgbyIcR+B4iDGONxGOY3cM5C2EY/mOhxnjeAvhWLzjEcZA3kI4jt8xkOMPx9IdjzMGcvzhGMckPxwLdzyFMZCjD8c4JvnhWLbjaYyBHHs43oZjIMcdjnFM8sOxaMeTGQM57nCMY5IfjiU7nsEYx1GH4604BnLM4Viw41mMcRxzON6MYyBHHI7lOp7JGMcRh+PtOAZyvOFYrOPZjHEcbzjekGMgRxuOpTpewBjH0YbjLTkGcqzheFOOgRxpOBbqeBljHEcajrflGMhxhmOZjpcyxnGc4XhjjoEcZTgW6Xg5YxxHGY635hjIMYZjiY7XMMZxjOF4c46BHGE4Fuh4HWMcRxiOt+cYyPGFY3mO1zLGcXzheIOOgRxdOBbneD1jHEcXjrfoGMixhWMck/xwLM2xCcY4ji0cb9IxkCMLx8Icm2GM48jCMY5JfjiW5dgQYyBHFo5xTPLDsSjHxhgDOa5wjGOSH44lOTbIGMhRhWMck/xwLMixUcZAjikc45jkh2M5jg0zBnJE4RjHJD8ci3FsnDGQ4wnHUhxbYIzjaMLxlh0DOZZwLMSxFcY4jiUcb9oxkCMJxzIcW2KM40jCsQjHthgDOZJwjGOSH44lOLbHGMhxhGMck/xwvHXHQI4hHAtwbJUxjmMIx5t3DOQIwnH4ji0zxnEE4RjHOJYfjoN3bJsxkCMIx6E7ts8YyPLDMY5xLD8cB+7YBWMgiw/HOMax/HActmM3jIEsPRzjGMfyw3HQjl0xBrLwcIxjIMsPxyE7dsgYx6LDMY6BLD8cB+zYKWMcSw7HOAay/HCMYyDLD8fhOnbNGMdywzGOgSw/HAfr2D1jIIsNxzgGsvxwHKpjL4xxLDQc4xjI8sMxjoEsPxwH6tgXYyCLDMc4xrH8cBymY3+MgSwxHOMYx/LDcZCOfTIGssBwjGMgyw/HOAay/HAcomPfjHEsLRzjGMjywzGOgSw/HOO4OyBLCscBOg6BMY5FhWMc9wRkQeEYx30BWU44xnFvQBYTjsNzHApjHMsJxzjuD8hSwnFwjsNhDGQx4RjHA+FYSDjG8VBAlhGOQ3McFGMcCwnHOB4MyCLCMY4Hw7GIcIzj4YAsIRwH5jg0xjgWEY5xPBKQBYTjsByHxxjHEsIxjscCcvjhGMdj4Tj8cByU4xAZA1lAOMbxaDgOPhzjeDwghx6OcTwejkMPxyE5DpQxkIMPxzieEI4DD8cBOQ6WMY5DD8c4nhKQww7HOJ4SjsMOx+E4DpgxjgMPxzieFJCDDsc4nhSOgw7HOJ4WkEMOx8E4DpsxjoMOxzieFo5DDsehOA6cMZCDDsc4nhiOAw7HOJ4YjgMOxzieGpDDDceBOA6fMY4DDsc4nhyQgw3HOJ4cjoMNxzieHI6DDcdhOJbAGMjhhmMcTw/HoYZjHE8Px6GGYxxPD8ehhuMgHMtgDORgwzGOZ4TjQMNxCI6lMMZxqOEYx3MCcpjhGMdzwnGY4TgAx3IYAznQcIzjWeE4yHCM41nhOMhwjON5ATnEcOzfsSjGOA4yHON4XjgOMRx7dyyLMZCDrP5LOd3fzb2L43AcQDgOsPov5fH2Zu5dHIfjEAJyeDXHYxy7TxxjHJvo8TZJksvX6XfHZNJq8HCN38nD772cexe34TiEcLy+Q3KjBs6LlxYc6z8Rqmevpt7FcTgOIiCv7WGnV30fb9MR2fx4bO0upsJxEOF4bft8qPzlaxvr1dbuYqoIHQtkjOO16XG4yILjh126Vn0xspGMY5PheIM93l5VF8w7PqqN73QTfHi6ODaYRMZAXptdx6f7bOr72qA/chfH4TiMcLyu073N9erifSAH9lc7S6ZjIK+s2M+l1n0Zj+U7FsoYxyvLjzvpcZntYxz7Cscrs/k+EPZXO0+qYyCvTVOz9L5Ma3cxFY5DCcdhhWOfjsUyxnFg4RjHiwJyUJW/jsfby1/zOQm3CWaM47BiPMbxsoAcUpzXB8fLwnFIcV4ff45FM8ZxUHFeHxwvDMgBxXl9cLwwHAcU5/XB8dKAHE7sr8bx0nC8uKSzNRN0chdTReVYOmMgL6/zd2/Ocbpmffmbsb1dODYTjrebXcfHi5eHy9f5ZyOn3cVtOA4qHC/NqmN13Cl1rP+beBfHxeRYPmMgL86qY/U+EO2Y/dUOwvGGszweX2nHnNfHQTEwxvHSBhwf8jN5ZEeBr/QZBxTI4XdNt87rc5c65rw+LorCMZAX1u94nyJ+2N1Vb8pKt3b1ifOOV4MTbFzSfwM4r4+LcLzleh1nn3FQa8XHfOtWeU4vn34yyJLjx34cx8EYxwvrdXzUo6j6/yEff3PHw8MxjnG8KiAvasxxumm7/8N03fgmX6++GRmOm/ur/yK7Lfur7YfjTdfrWG8aq1NPZ/+U2/4m3881Mhzj2I/jWBgDeVlj+7mKXc3FRnI6HD/eDn1+qfw1nO6r92vzfi7b4XjbDRx32ifJs8/zETUfnvXO6v3V0AHhrvF4bB4WzruBYnEcD2MgL2rkfSAPv5+PvPnBJz0c35Sjc9cEF8zD/LuYCsfhheMFjTjWn3JQI3EuNx2O5zjWb+jK/7WKgXlY9QxWheMAA/L8Bo4fX+U7rfdqz9Y+++fcfpJdnrherf41x4fdVfHPLk66i9twHGA4nl//eKzeipW9EyvdUM53d+lDyRP3cz1ln3dSR67YX225qBgDeUH2P++k3kmCY7vheOtZd6z+oXQ+f2y5yBwDeXZ2zweyv/z17dXo6ehxvLLYGON4dnbPs3e6Tzelx3Zz4XhtOCbTcfzYuePoGAN5dpz3FscBhuOZJdcdmXP8sOM8AtaL0DGQZ2bX8VF/QoLz+lgtRsY4nplVx8UeLs6zZ7MoHQN5XlYdFweceB+IzXBMjMfiHcfJGMjzGnScf2rxWOyqKr4ZOgEu28c4NhKO5zTk+PFWrw+rDz3pDz4V3wyeAJf91W4dx8oYyLMacHzM/v3xk/7nEvdX1TeDJ8Dl+DGOzYTjGfU7TteJ9ekC8hPuqU8SF98MnQAXxzg2FJCnN7h9nDnWZ/Q5Znz1N4MnwK29+Ifyc8sj87D2SSxPvOOIGeN4RuOOyxPSV2emHzoBbu2cQPqWI/+GefMuzsNxwOF4eoscq/pOgFs7723xD8uMzoOJJ7IsHIcckCe3ZL06u67nBLjla5+9CWTgjHznd3EfjkMOx5Ob4PhsP5e6qvcEuG3HYwednnC8oqgZA3l6447Pjzvpq/pOgItjHBsLx1Mbd3z+PpCnoRPg4tih48gZA3lyExyr40eZxvKbgRPg4hjH5sLxxOx9TkKd/7qIzztZKXrGQJ6a5fOB2LqLqXAceDieFo5xHHI4nhbn2ZPreAOMgewrHOPYZDj2E45xbDQgewnHrhxvgzGO/YRjHJsNyD7CsSPHW2GMYy/hGMdmw7GPcOzG8WYYA9lLOMax4XDsIRzj2HRAdh+OnTjeEmMcewjHODYekJ2HYxeOt8UYx+7DMY6Nh2Pn4diB440xBrL7cIxj8+HYdTjGsYWA7Dgc23e8PcY4dh2OcWwhHDsOx9Ydb5AxkF2HYxzbCMduw7Ftx5tkjGPH4RjHVgKy03Bs2fFGGePYbTjGsZVw7DQc23W8VcZAdhuOcWwnHLsMx1Ydb5cxkJ2GYxxbCscOwzGOLYVjh+HYpuMtMwayy3CMY1vh2F04tuh424xx7DAc49haQHYWju053jpjHLsLxzi2F5BdhWNrjmGMY2fhGMcWA7KjcGzLMYx/i2Nn4RjHFsOxo3BsyTGMdUB2E45xbDMcuwnHdhzDOA/ITsIxjq2GYyfh2IpjGBfh2Ek4xrHdgOwiHNtwDOMqHLsIxzi2HJAdhGMLjmFcD8cOwjGObQdk++HYvGMYN8Ox/XCMY+sB2Xo4Nu4Yxu1wbD0c49h6OLYejk07hvFZOLYejnFsPyDbDsc4th+ObYdjw45h3BWQLYdjHDsIx5bDMY5dBGS74disYxh3h2O74RjHTgKy1XBs1DGM+8Kx1XCMYzcB2WY4NukYxv3h2GY4xrGjgGwxHBt0DOOhcGwxHOPYVUC2F47NOYbxcDi2F45x7CwgWwvHxhzDeCwcWwvHOHYXkG2FY1OOYTwejm2FYxw7DMiWwrEhxzCeEo4thWMcuwzIdsIxjp0GZCvh2IxjGE8Mx1bCMY7dBmQb4diIYxhPDsc2wjGOHQdkC+HYhGMYzwnI5sMxjl2HY/Ph2IBjGM8LyMbDMY6dh2Pj4RjHzsOx8XC83jGM5wZk0+EYxx4CsuFwvNoxjOeHY8PhGMc+ArLZcLzWMYyXhGOz4RjHXgKy0XC80jGMFwZkk+EYx54CssFwvM4xjBeHY4PhGMe+ArK5cLzKMYxXhGNz4RjH3gKysXC8xjGM1wVkU+EYx/7CsalwvMIxjNcGZEPhGMc+A7KZcLzcMYwNBGQj4XixYxibCMdGwjGOvXYNZBPheKljGBsJx0bCMY69dg1kE+F4oWMYm+kayCbC8TLHMDYUjo2EYxx77RrIJsLxIscwNhWOjYTjJY5hbKxrIJsIxwscw9hc10A2EY5x7LVrIJsIx/Mdw9hgODYSjnHstWsgmwjHsx3D2GTXQDYRjuc6hrHRroFsIhzPdAxjs+HYSDie5zjBsdmugWwiHM9yjGLTXQPZRDie4xjGxrsGsolwPMMxK9XmuwayiXA83TGKLYRjI+F4smMY2+gayCbC8VTHMLbSNZBNhOOJjmFsp7ZjIC8Kx9Mcw9hSODYSjqc4Zke1tc4cA3lJOJ7gGMX2OncM5AXheNQxg7HNOhwDeX44HnOcXF/7XtZjrssxkGeH42HHScoYxxbrdAzkueF40HGilyrfy3rMdTsG8sxwPOQ4Y4xji/U4BvK8cDzgOMGx9focA3lWOO53nBSLlO9lPeZ6HQN5TjjudVwyxrHF+h0DeUY47nGcVIxxbDEcGwnH3Y6T+gLle1mPuQHHQJ4ejrscJw3GOLbYkOPrBMkTw3GH46S1OPle1mNu0DFD8tRwfOY4aTPGscVGHDMkTwvHbcdninFsszHHjMiTwnHT8flgjGOrjToG8pRw3HDcpRjHNht3zKr1hHBcd9zNGMcWm+CYIXk8HFeOO9epcWy3SY4ZksfCcem4TzGObTbNMUPySDjO6x2McWy1qY4ZkgfDcdbzoUXI97Iec5MdMyQPhWOF+Pnz7+DYTzMcp0MylHvCcar4O2k49tMcx9eMyX3hWCvGsa9mOmYzubuNO37+PGeMY0/NdczadWfbdlwgxrG35ju+Zu36vA07rsZiHPtrkWPG5HabddxAjGNvLXMM5VbbdPy8rRjHvlrsOKOM5awNOu5AjGNvrXFcWAbz9hx3Isaxt9Y6LjH7WypDaFuOu4diHHvMiOPC8nY1b8jxAGIce8uY40qzv+XTX5tw/Fw3gBjH3jLseKuWI3c8RTCOfWbBcWF5S5ijdPy8bIpgHPvMkuMSs7+F1WkROV6iF8e+s+m4sLwBzeIdr9OLY9/ZdlzXPJa/BXt14hw/b7VOL45958bxtASztuV42h/ABfn4/fpe1mPOx+9zYmOL4nw69pI1HhNRVzgmkh+OieSHYyL54ZhIfjgmkh+OieSHYyL54ZhIfjgmkh+OieSHYyL54ZhIfjgmkh+OieSHYyL54ZhIfjgmkh+OieSHYyL5LXFMRNMyL7YHpasHIiJr4ZhIfjgmkh+OieSHYyL54ZhIfjgmkp9Jx29fvPjTf6kufv33Pzc48YWz0Zonb/Ph7+Vozsg3n7x48eLjAGbk6U0wv5mnL773f35mxFwGHb9NX5u31evzux//mZcFtzEbrXnyNh/+Xo7mjHzzSfrNmxd/7n1Gnt6kL0cYv5mUNY6rvvlE/Zn/olhE0r95Xhbcxmy05snbfPh7OVoz8vWP/uEpM+R5Rn7344/VTwL4zaSzguNazUXk7YuP33pZWhqz4XGxDeTl6HwN/IyD5zPix3F7Pt58759xXPX136mFo7a0enJcn42zefI0H0++5qJrRtKxyP9vRvfGz9+T5nykF9k+rpX9ka/9qfez4DZm42yePM2H/oEnx+evwVs/O7raM/LW0w635nyotWwc1wpkwcXx6Ix43M3V/GV884kPQM35eJPOAo5rBbIiyXr1yIx4Go07fxle/sKeLyE4znqjjkme7cZgP1cAL8fZjLzxdvS445eR/cjnfKhFV+VhPoxm7biTrwU30ONO/hw3Z+SNvyW24wCYl9fkfKFgPK53dnid94EE8HI0Z+TrH/kajVszou1kovzORzEvwjP5vsz8nXbFUUFfC25jNjy++y+Ql6MxI/lqpKeXpPGKfOFvbbb1m8ExEYUQjonkh2Mi+eGYSH44JpIfjonkh2Mi+eGYSH44JpIfjonkh2Mi+eGYSH44JpIfjonkh2Mi+eGYSH44JpIfjonkh2Mi+eGYSH44JpIfjonkh2Mi+eGYSH44JpIfjonkh2Mi+eGYSH44JpIfjonkh2Mi+eHYWftEd/l66DaXrx9vb7LvH3Z3Zh74cDPpZsYej9yHY2ftn71K//94OwhZ3cCw43KCI+FYcDh2Vub46XDxcvBmOKb54dhZueNjknI5pCvYN/pCkqgfF1/1evUf3+ortavihqrH2x/e5pf2+Qr6/tv3Sfp3Ib/4sFM3uEsnpv9WZPd92OlJFxPK75E+pP6/epDy3nen+6snPRPnc0hBh2Nn1cbjffrfw+5GKzrdX74uvmaOU+jFlYf8hrryGi1Nm9tnNvOLD7v0IfapST2t4r56PC4ntE/KqSmzh2evqnvXHB9ac+jlFaPJ4dhZmeOH3VUuM6VyzEe64mvmOPeVEspvmF9bvyb/sZ5meVHfPBvv8wvqG+W4mtC+HFzVpRRu7d6V4/L2R8ZiEeHYWfn+6qvaKm26zqu3SYuvmeO74sq7at1XlV2T/ex0r1d39/lImV3UN9TXK4HFfZXj2kp0ObaqS9mky3uXjs/mkMIOx86qRsJKlRKkoBRfW44PGf2k4TjRW7TPXh1Kx8XFhuPyvspxNaHKsUKrLtXuXTqubl/MGQUdjp3VdnzMeKRQqq+l4/Qm5XhcVF5zTMq1ZKWyvNgxHj9l+6urCVWO0xt9rq6q3bs1HucVc0jhhmNnVY7L7eP80l35tXwfSH37uEBVXqN/cLovHRcXG47L+1bbx+qqmuOH3ffTH9TvrR1nO97qD8wRqeDDsbMqx/n+6qvcXDrA5l8zx7VdxeqG2e6tJ+VYsdOHkq7UgaHCcXmx4bi8r+ZfTqjmWO/abt5bzeNR/bS4fTlnFHQ4dlbNcXl0Vn3VUvKv2vEPW8ePc8apq+/usm1VdVD38stydC0uNh2X9z0Ux4/Lg8N5x/IIcXFvtTV8dSiOH1/V5pCCDsdymvrGLNpeOJYTjqkvHMsJx9QXjonkh2Mi+eGYSH44JpIfjonkh2Mi+eGYSH7/D1ZLTg2HW/j0AAAAAElFTkSuQmCC" width="100%" /></p>
</div>
<div id="diagnostic-indices" class="section level3">
<h3>Diagnostic Indices</h3>
<p>About diagnostic indices such as Rhat and ESS.</p>
</div>
</div>
<div class="footnotes">
<hr />
<ol>
<li id="fn1"><p>If the effect is really strong, the BF values can be extremely high. So don’t be surprised if you see BF values that have been log-transformed to make them more human readable.<a href="#fnref1" class="footnote-back">↩︎</a></p></li>
<li id="fn2"><p>An exception would be when the pie slices are well-labeled so that our brain’s perception system does not have to do the decoding work.<a href="#fnref2" class="footnote-back">↩︎</a></p></li>
</ol>
</div>



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