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<body>

<h1 id="bayes-factors">Bayes Factors</h1>
<ul>
<li><a href="#the-bayes-factor">The Bayes Factor</a>
<ul>
<li><a href="#bayesfactor_parameters">Testing Models’ Parameters with Bayes Factors</a>
<ul>
<li><a href="#testing-against-a-null-region">Testing against a null-<em>region</em></a></li>
<li><a href="#testing-against-the-point-null-0">Testing against the <em>point</em>-null (0)</a></li>
<li><a href="#one-sided-tests">One-sided tests</a></li>
<li><a href="#si">Support intervals</a></li>
</ul></li>
<li><a href="#bayesfactor_models">Comparing Models using Bayes Factors</a>
<ul>
<li><a href="#for-bayesian-models-brms-and-rstanarm">For Bayesian models (<code>brms</code> and <code>rstanarm</code>)</a></li>
<li><a href="#for-frequentist-models-via-the-bic-approximation">For Frequentist models via the BIC approximation</a></li>
<li><a href="#bayesfactor_restricted">Order restricted models</a></li>
</ul></li>
<li><a href="#bayesian-model-averaging">Bayesian Model Averaging</a>
<ul>
<li><a href="#bayesfactor_inclusion">Inclusion Bayes factors</a></li>
<li><a href="#weighted_posteriors">Averaging posteriors</a></li>
</ul></li>
<li><a href="#appendices">Appendices</a>
<ul>
<li><a href="#testing-contrasts-with-emmeans-modelbased">Testing contrasts (with <code>emmeans</code> / <code>modelbased</code>)</a></li>
<li><a href="#contr_bayes">Specifying correct priors for factors</a></li>
</ul></li>
</ul></li>
<li><a href="#references">References</a></li>
</ul>
<p>This vignette can be referred to by citing the following:</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">https://doi.org/10.21105/joss.01541</a></li>
<li>Makowski, D., Ben-Shachar, M. S., Chen, S. H. A., &amp; Lüdecke, D. (2019). <em>Indices of Effect Existence and Significance in the Bayesian Framework</em>. Retrieved from <a href="https://doi.org/10.3389/fpsyg.2019.02767">10.3389/fpsyg.2019.02767</a></li>
</ul>
<hr />
<p>The adoption of the Bayesian framework for applied statistics, especially in the social and psychological sciences, seems to be developing in two distinct directions. One of the key topics marking their separation is their opinion about <strong>the Bayes factor</strong>. In short, some authors (e.g., the “Amsterdam school”, led by <a href="https://www.bayesianspectacles.org/">Wagenmakers</a>) advocate its use and emphasize its qualities as a statistical index, while others point to its limits and prefer, instead, the precise description of posterior distributions (using <a href="https://easystats.github.io/bayestestR/reference/hdi.html">CIs</a>, <a href="https://easystats.github.io/bayestestR/reference/rope.html">ROPEs</a>, etc.).</p>
<p><strong>bayestestR</strong> does not take a side in this debate, rather offering tools to help you in whatever analysis you want to achieve. Instead, it strongly supports the notion of an <em>informed choice:</em> <strong>discover the methods, try them, understand them, learn about them, and decide for yourself</strong>.</p>
<p>Having said that, here’s an introduction to Bayes factors :)</p>
<h1 id="the-bayes-factor">The Bayes Factor</h1>
<p><strong>Bayes factors (BFs) are indices of <em>relative</em> evidence of one “model” over another</strong>, which can be used in the Bayesian framework as alternatives to classical (frequentist) hypothesis testing indices (such as (p-values)).</p>
<p>According to Bayes’ theorem, we can update prior probabilities of some model (M) ((P(M))) to posterior probabilities ((P(M|D))) after observing some datum (D) by accounting for the probability of observing that datum given the model ((P(D|M)), also known as the <em>likelihood</em>):</p>
<p>[ P(M|D) = \frac{P(D|M)\times P(M)}{P(D)} ]</p>
<p>Using this equation, We can compare the probability-odds of two models:</p>
<p>[ \frac{P(M_1|D)}{P(M_2|D)} = \frac{P(D|M_1)}{P(D|M_2)} \times \frac{P(M_1)}{P(M_2)} ] Where the left-most term are the <em>posterior odds</em>, the right-most term are the <em>prior odds</em>, and the middle term is the <em>Bayes factor</em>:</p>
<p>[ BF_{12}=\frac{P(D|M_1)}{P(D|M_2)} ]</p>
<p>Thus, Bayes factors can be seen either as a ratio quantifying <em><strong>the relative probability of some observed data by two models</strong></em> as they can be computed by comparing the marginal likelihoods of the two models, or as <em><strong>the degree by which some prior beliefs about the relative credibility of two models are to be updated</strong></em> as they can be computed by dividing posterior odds by prior odds, as we will soon demonstrate.</p>
<p>Here we provide functions for computing Bayes factors in two different applications: <strong>testing single parameters (coefficients) within a model</strong> and <strong>comparing statistical models themselves</strong>.</p>
<h2 id="testing-models-parameters-with-bayes-factors">Testing Models’ Parameters with Bayes Factors</h2>
<p>A <em><strong>Bayes factor for a single parameter</strong></em> can be used to answer the question:</p>
<blockquote>
<p><strong>Given the observed data, has the null hypothesis of an absence of an effect become more, or less credible?</strong></p>
</blockquote>
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" alt="Bayesian analysis of the Students&#39; (1908) Sleep data set." width="80%" />

<p class="caption">

<p>Bayesian analysis of the Students’ (1908) Sleep data set.</p>
</p>

</div>

<p>Let’s use the Students’ (1908) Sleep data set (<code>data(&quot;sleep&quot;)</code>), in which <strong>people took some drug</strong> and where the researchers measured the <strong>extra hours of sleep</strong> that they slept afterwards. We will try answering the following question:</p>
<p><em>given the observed data, has the hypothesis that the drug (the effect of <code>group</code>) <strong>has no effect</strong> on the numbers of hours of <strong>extra sleep</strong> (variable <code>extra</code>) become more of less credible?</em></p>
<p><img 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" /><!-- --></p>
<p>The <strong>bloxplot</strong> suggests that the second group has a higher number of hours of extra sleep. <em>By how much?</em> Let’s fit a simple <a href="https://easystats.github.io/bayestestR/articles/example1_GLM.html">Bayesian linear model</a>, with a prior of (b_{group} \sim N(0, 3)):</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="kw">library</span>(rstanarm)</a>
<a class="sourceLine" id="cb1-2" title="2"></a>
<a class="sourceLine" id="cb1-3" title="3">model &lt;-<span class="st"> </span><span class="kw">stan_glm</span>(extra <span class="op">~</span><span class="st"> </span>group, <span class="dt">data =</span> sleep,</a>
<a class="sourceLine" id="cb1-4" title="4">                  <span class="dt">prior =</span> <span class="kw">normal</span>(<span class="dv">0</span>, <span class="dv">3</span>, <span class="dt">autoscale =</span> <span class="ot">FALSE</span>))</a></code></pre></div>
<h3 id="testing-against-a-null-region">Testing against a null-<em>region</em></h3>
<p>One way of operationlizing the null-hypothesis is by setting a null region, such that an effect that falls within this interval would be practically equivalent to the the null (Kruschke, 2010). In our case, that means defining a range of effects we would consider equal to the drug having no effect at all. We can then compute the prior probability of the drug’s effect falling <em>within this null-region</em>, and the prior probability of the drug’s effect falling <em>outside the null-region</em> to get our <em>prior odds</em>. Say any effect smaller than an hour of extra sleep is practically equivalent to being no effect at all, we would define our prior odds as:</p>
<p>[ \frac {P(b_{drug} \in [-1, 1])} {P(b_{drug} \notin [-1, 1])} ]</p>
<p>Given our prior has a normal distribution centered at 0 hours with a scale (an SD) of 2.5 hours, our priors would look like this:</p>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>and the prior odds would be 2.2.</p>
<p>By looking at the posterior distribution, can now compute the posterior probability of the drug’s effect falling <em>within the null-region</em>, and the posterior probability of the drug’s effect falling <em>outside the null-region</em> to get our <em>posterior odds</em>:</p>
<p>[ \frac {P(b_{drug} \in [-1,1] | Data)} {P(b_{drug} \notin [-1,1] | Data)} ]</p>
<p><img 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" /><!-- --></p>
<p>We can see that the center of the posterior distribution has shifted away from 0 (to ~1.5). Likewise, the posterior odds are 2 - which seems to favor <strong>the effect being non-null</strong>, but… <em>does this mean the data support the alternative over the null?</em> Hard to say, since even before the data were observed, the priors already favored the alternative - so we need to take our priors into account here!</p>
<p>Let’s compute the Bayes factor as the change from the prior odds to the posterior odds: (BF_{10} = Odds_{posterior} / Odds_{prior} = 0.9)! This BF indicates that the data provide 1/0.9 = 1.1 times more evidence for the effect of the drug being practically nothing than it does for the drug having some clinically significant effect. Thus, although the center of distribution has shifted away from 0, and the posterior distribution seems to favor a non-null effect of the drug, it seems that given the observed data, the probability mass has <em>overall</em> shifted closer to the null interval, making the values in the null interval more probable! (see <em>Non-overlapping Hypotheses</em> in Morey &amp; Rouder, 2011)</p>
<p>Note that <strong>interpretation guides</strong> for Bayes factors can be found <a href="https://easystats.github.io/report/articles/interpret_metrics.html#bayes-factor-bf"><strong>here</strong></a>.</p>
<p>All of this can be achieved with the function <code>bayesfactor_parameters()</code>, which computes a Bayes factor for each of the model’s parameters:</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1">My_first_BF &lt;-<span class="st"> </span><span class="kw">bayesfactor_parameters</span>(model, <span class="dt">null =</span> <span class="kw">c</span>(<span class="op">-</span><span class="dv">1</span>, <span class="dv">1</span>))</a>
<a class="sourceLine" id="cb2-2" title="2">My_first_BF</a></code></pre></div>
<pre><code>&gt; # Bayes Factor (Null-Interval)
&gt; 
&gt; Parameter   |   BF
&gt; ------------------
&gt; (Intercept) | 0.02
&gt; group2      |  0.9
&gt; 
&gt; * Evidence Against The Null: [-1, 1]
</code></pre>
<p>We can also plot using the <code>see</code> package:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1"><span class="kw">library</span>(see)</a>
<a class="sourceLine" id="cb4-2" title="2"><span class="kw">plot</span>(My_first_BF)</a></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<h3 id="testing-against-the-point-null-0">Testing against the <em>point</em>-null (0)</h3>
<p><strong>What if we don’t know what region would be practically equivalent to 0?</strong> Or if we just want the null to be exactly zero? Not a problem - as the width of null region shrinks to a point, the change from the prior probability to the posterior probability of the null can be estimated by comparing the the density of the null value between the two distributions.[1] This ratio is called the <strong>Savage-Dickey ratio</strong>, and has the added benefit of also being an approximation of a Bayes factor comparing the estimated model against the a model in which the parameter of interest has been restricted to a point-null:</p>
<blockquote>
<p>“[…] the Bayes factor for (H_0) versus (H_1) could be obtained by analytically integrating out the model parameter (\theta). However, the Bayes factor may likewise be obtained by only considering (H_1), and dividing the height of the posterior for (\theta) by the height of the prior for (\theta), at the point of interest.” (Wagenmakers, Lodewyckx, Kuriyal, &amp; Grasman, 2010)</p>
</blockquote>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1">My_second_BF &lt;-<span class="st"> </span><span class="kw">bayesfactor_parameters</span>(model, <span class="dt">null =</span> <span class="dv">0</span>)</a>
<a class="sourceLine" id="cb5-2" title="2">My_second_BF</a></code></pre></div>
<pre><code>&gt; # Bayes Factor (Savage-Dickey density ratio)
&gt; 
&gt; Parameter |   BF
&gt; ----------------
&gt; group2    | 1.48
&gt; 
&gt; * Evidence Against The Null: [0]
</code></pre>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1"><span class="kw">plot</span>(My_second_BF)</a></code></pre></div>
<p><img 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Rum0nZe6IkuEPpvn5xce6aL1gs9Qyb0FGO7tWcfPn1y0knrhZ4hE3qKEWzt2V9+p4vWCz1DJvQUY1drzz6tWv+ThMcWegZM6CnGntaeffrdeev/+Fepji30DJjQU4x9rf2Hd2+czFP//UTHFnoGrNfQKz0x7Wztp+8uhvO//HBa+1fSHFvoGbDeQm9IT2zh1laTNtXJ2E9OnkoyeyP05EnoyUygtYvKn1ydhv3dt7/2syTHFnqyJPRkJrxh6uSb763dIvRwRejJTCj0T763PlPz5Udp1lgKPXkSejLjomZwLKEnM4dae/bLhMcWerLUReiVnohCUzcrM/Jf3EgzPb84ttCTpdShN6QnsgOhT3UednFsoSdLQk9m1lv74csvv/y9G9e+9fLS0ydCz0j1t2FK6IlsvbVfLK55sOrZhMcWegZM6CnGRms/+MY3nj45eeIbS99MdkWzC6Fn2HaGPnnnhZ7IDszRpz220DNgQk8xAu8w9e/+ZcJR/NqxhZ4B6zH0Sk9cNkxBmNBTjLXWnv39j381/c9Hq36cbngv9AyZ0FOMtdbOp+fnFzW7YnklIyX0FEPo4UhCT27WW/ubf5z955er/jHhsYWeHAk9uXEyFo4k9ORG6OFIQk9u9rT27O8/+nHaYws9Geqg80JPXMHWfvrvf1a9peC1H6Y8ttCTIaEnO6HWvj9banP2TupFN0JPloSe7ARa+3Ce9y9unHz94st3XL0S1gk92Qm09v153D+ZT9t8ceMpO2MZp10bproKvdITTejqlbPCn71zMku8d5hitPoMvSE9Ue26TPEXN+bjeqFntISeYuwK/WLmxtQN4yX0FCNwPfrZCdhq5mb6y9cTHlvoGTChpxjhVTdP3Ji9WezZhzeSLqQXeoZsR+g76bzQE1WotR/MFtBPB/SzLVMJV1cKPYMm9BQjvDP25Zffmy25+cNnfpr02ELPgPUeeqUnFhc1g6N0E3pDemISejiK0JOfXa31xiMQJPTkJ9TaL9/1VoKwg9CTn0Brv7jhPWNhF6EnP8GLmp08858+WvqxnbGwQujJT+gSCEkXz68eW+jJT0ehV3oi2nWtm06OLfTkR+jJj9BDWK8bpoSemIJz9K90dGyhZ8CEnmIEV90kvDTx2rGFngETeooRaO1vfnFy7Rmrbhg7oacYwVU31tGD0FMOoYcwoacYoambX65yrRtGSugphqtXQpjQUwyhh2N01XmhJ6Jwa7989+nZ5Pzv/rl3mII1Qk+GQq09+6A6C/u7byfdOyX05KfT0Cs9cYRa+/7JyZP/6sY09GfvnFz7YcJjCz3Z6Sz0hvTEE2jtw1ndF1e8mZY+4ZUshZ78CD0ZCl7r5tnLS5s9PEl4OQShJz9CT4ZCG6Zm0zVV6JNeylLoyY/Qk6FdlykWeggRejJ0YET/xQ2hZ6SCG6YeCD0ZOjBH//7J1xMeW+gZsJ2h76bz1lcST3DVzcn3F6GfLahPuJBe6BmyvkNvSE80O9bRP/G9G9f+4/dOTlIO6IWeQRN6ihFs7QeXFyn+/ZTvNSX0DJnQU4xwa3/z4dPTyj/xTNJL3Qg9gyb0FMPVKyFM6CmG0EOY0FMMoYcjdBl6pSeW7daeffrdG7PzsNe+9V7qYws9melwv5TQE89ma88+vHH1xuDX/iTtsYWezHTaeaEnlo3Wnr0z7fs3/+ijjz76H/9utvAm4bUrhZ78CD1Z2mjt+6tL52cbY22YgitCT5bWWzu/+sHa773DFFwRerK03tqta5i97x2m4IrQk6W11p69szmA9w5TsELoydJaa7ffZsT16Bmt0IYpoSdLB0LvHaYYLaGnGEIPYUJPMYQewoSeYgg9hPUfeqUnEqGHMKGnGBuhv/atl9d8z6obxkroKcZG6E+2CD0jJfQUYz303/3Glm8KPSwJPVlK0NrPXp1Mnn/z87XbfvsXt7ZvFHpyI/RkKXprz29P5l5cjfrp4rbJCx+vHVvoyUvHnRd6Ione2tPJzbemo/pbk9eubnt8a/KD/zu/8aW1Yws9eRF68hS7tV+9MXl79uujyXNXo/c7VeAfTW7+aPXYQk9ehJ48xW7tMvDntxfBX/t49cYLoSc7Qk+eYrd2OXi/+mCF0JM3oSdP8UNfzc2frp+OnXt8y9QNOesj9EpPe5FbOx2zV6F/FAj9nfXbhJ4hC2yY6jr0hvTEET/01eRMIPSnk7WZG6Fn0ISeYnQY+mnnX1u7QegZsu3QPxB68tTd1M208z/YOLbQM2Dh0HfaeaEnjq5Oxp7/1eZ4XugZNqGnGLFbexpeXjkd6c82zG4cW+gZsKGEXulpLf6GqcVAfn3J/KzzP9r6s0LPkA0h9Ib0RNHJJRCmnX/u54FjCz0DJvQUo5OLmp2uVn/l2EJPVoSeTCW+TPF8i9R0lH9pdeJe6MmL0JOp+K09X33jkXnoH98SegrQ/TJ6oSeOPlsr9GSlh84LPVEIPdTUR+iVnhiEHmoSenIl9FCT0JMroYeahJ5cCT2EbW2YEnpyJfQQJvQUQ+ghTOgphtBDmNBTDKGHMKGnGEIPYUJPMYQewoYTeqWnJaGHsGGE3pCeCIQe6umn80JPBEIP9Qg92RJ6qEfoyZbQQz1CT7aEHurpMfRKTztCD/X0FHpDetoTeqhH6MmW0EMtfbw1uNATh9BD2MaGqb46L/S0J/QQJvQUQ+ghbCihV3paE3oIE3qKIfQQJvQUQ+ghTOgphtBD2Hroe1tdKfS0J/QQth36fjov9LQm9FCH0JMxoYc6hJ6MCT3UIfRkTOihDqEnY0IPdfQceqWnDaGHOnoMvSE9bQk91CH0ZEzooQ6hJ2NCD2GD2TAl9LQl9BAm9BRD6CFsWKFXeloQeggbTugN6WlJ6CFsLfS9dl7oaUnoIUzoKYbQQ5jQUwyhhzChpxhCDzX0G3qlpx2hhxqEnpwJPdQg9ORM6KEGoSdnQg81CD05E3qoQejJmdBDDUJPzoQeahB6cib0ELa6Yarnzgs97Qg9hA0t9EpPY0IPYUMKvSE9rQg9hAk9xRB6CBN6iiH0ECb0FEPoIWwl9A8GEXqlpymhh7CN0PfaeUN6WhF6OEjoyZvQw0FCT96EHg4SevIm9HDQQEKv9DQk9HBI/4tufm1ITxtCD4cMofNCTwtCD4cIPZkTejhE6Mmc0EPQ9ZnFh0JP5oQeQq4LPeUQegi4fn2l9EJP5oQeKtevX+V9/SOhJ29Cz8hdD9gO/T9bEHqyJPSMV6jxwdBf6rP2Sk9jQs8Y7Uz8Rt+Df7Kn2gs9jQk947Mr7/cvXVwur5z+5u7du7+3sF57oScXQs/I7Mn7Wugri9DfvTe3VvuuUy/0NCb0jEidxq+mfvHhVeg3a9/pJI7Q05jQMxp1G79pI/RV7DufxBF6GhN6xqH2UD7Y+c3Qr7e+m9QLPY0JPWPQuPLhAX0vkzhCT2NCT/FaRH5/6LudxBF6GhN6Ctcy84dC390kjjcTpDGhp2StK18n9FuTOAlL3/f/UPIk9JSrXeZff71+6NcG9qlSL/Q0JfQUqvVgvgr9jkU3B1ov9AyJ0FOkCHM2K6Gv2fnEqRd6mhJ6yhNjZr5h6K9aH3/+xtlYmhJ6ShMn881DP299ktQLPQ0JPUWJVfl2oZ+m/nqC1As9DQk9RYmW+Zahv0p9xNYLPQ0JPQWJV/kIof+96Kk3SU9DQk8xomb+UtPQp1iBI/Q0I/SUIkXlW4U+fuqFnmaEnjIkyny70N+LvNhS6GlG6ClBssy37fyi9bEG9UJPM0JP9tLMzccL/eUKHKGnJ0JP3iKup0wX+ir1redvhJ5mhJ68pc18tNBfbpZt1XqhpxmhJ2eJh/PHXbvyUOrbL6u3kJ5mhJ58pc38fMNUvM5fpr7NXL3Q04jQk6vUg/kEoW+9rF7oaUToyVTizKcI/b3Wy+qFnkaEnjylznyi0N9rt6zeJD2NCD05Sj6cr0If8Vzsauqbz98IPU0IPRnqovOXoY/e+XstltULPU0IPfnppPNJQ994/kboaULoyU4nmU8c+qbzNybpaULoyU1HnZ9LGPqG8zdCTwNCT166mbbpJPSN5m+EngaEnpyk3iTVbeibzN8IPQ0IPRnpOPPpQ3/8/I3Q04DQk43OM59oGf1G6Y9LvdDTQILWfvbqZPL8m59v3f5o8vbGsYWe+rrPfBcD+nnqjym90NNA9Nae357MvbhZ+se3hJ7Gup6c7zL0i9TXLL3Q00D01p5Obr41HdXfmry2fvu080JPU71kvrvQ36u//MZCehqI3dqv3ljk/NHkuY9Xbj7/62nnhZ6Gesn86693MUW/Vvo6qRd6jhe7tcvAn99ezfrjVyeTP70t9DTTz3B+EfpuOl+dk61TeqHneLFbe2fy0sYHM48mL/z8XOhppp/Odxz62runhJ7jxQ99NTd/uno69rd/e3Eh9DTS0/R856GvO1Fvkp7jRW7ttOZV6B9trrsRehroZ7VNP6GvOagXeo4WP/RVzYWe9nrMfBX6Ljtfc/OU0HM0oWe4+sx8L6GvNagXeo5m6obB6jXzPYW+xnXOTNJztG5Oxs4IPcfpufMdbpcKpX7P/I3Qc6zYrT0NLq+cEXqO0nPn54+hn9AfmqoXeo4Vf8PUYiC/nXWh5wj9Ts/fv3ogvZR+/6Be6DlWR5dAuBB6jtDrapsq9NVUeD+h33udM6HnWJ1d1Ezoqav3zM9C/6Dn0O/ZPiX0HCvxZYrvXJ2SFXrq6T/z95edH2bphZ5jxW/t+eobjwg9x+o/8wMJ/c419dZXcixvJciwDKHz94cR+p2rb4SeIwk9gzKIzt8fSOh3pV7oOZLQMyADmJ5fL33Pnb8XXmgp9BxJ6BmMAay2WbrofXnleurXS2+SniMJPQMxoMz3v2FqzfY5WaHnOELPMAwp8zPzB9V34itbpRd6jiP0DMLAMj919/XX+7nSTcDmQktzNxxH6BmCAXZ+SKHfWn0j9BxF6BmA4XV+Hvq+875qfVAv9BxF6OnfADvf28Xod1t9RxKh5yhCT++G2PkBhn51Tb3QcxShp2+D7fzgQn81VS/0HEXo6dkgOz/IAf1MVXqh5yhCT6+Gtnx+aaihX26UVXqOIfT0aFC7YdcMN/TVoF7oOYLQ05vhZn7QoV+UXug5gtDTlwFnftihr87JXu/7CSQfQk9Phpz5eeiHtWFqTVV6qacmoacfg+/8kEM/Tf1dqac+oacPg562uZ9B6KePT+mpTejpwdA7P/zQKz3HEHq6l0Xnhx96pacuoadzQ898teZm+KGvSi/1HCL0dG34nc8h9PPS31d6ahF6ujX4aZv7yyua5RB6pacWoadLQ94Me2XQu6WW7i5Kf1/pOUzo6U4emc8k9NWQ3qCeGoSezmSS+dxCvyy91LOT0NOVXDKfXegN6jlE6OlIXp3PK/RSz35CTzfy6XwmA/r10Es9+wg9Xchmen4mo9AHSi/1bBN60stltc1CJjM3W6E3qGcnoSe1vDJ/NaAf+IapUOktqidM6Ekss8znHXqlJ0joSSu3zF/N3Aw+9JunY+eUngChJ6nsOn91KjaP0O8ovdSzSuhJKb/O5xT64JDe6hu2CT3pZDc9P5N96A3q2SL0pJLbaptKAaGXejYIPWlkmvmVVfQ5h17qWSP0JJFp5rPZFrsQPBtbUXquCD0p5Jr5vDq/N/RSzxWhJ4FsO59Z6A+UXuqpCD3RZTttk13nD4Ve6VkQemLLuPPZhX7f6dgFqedC6Iku587nGfr9pZd6hJ7Ycs58hqE/PKRXeoSeuLIezt8vNPRSj9ATT66bpC6tvedIBhum7tUMvdSPndATS/aZ3xjQFxV6pR83oSeS/DNfdOilftSEnjgKyHyWoT+i9FI/XkJPFMV0vuTQK/1oCT0xFNH5jTU3mYT+qNJL/UgJPe2VMD1/f2ttZU6hr196qR8loaetAlbbLGQa+uOG9N5qcJSEnpZKyXyGm6UqR4beoH6EhJ5Wisn8mEK/TH3fLx46I/S0UFDmMw99s9Jr/VgIPc0V1fl8Q99kSH9ZeqkfBaGnqbIyv7GIPitNQq/0oyL0NFNY5nMe0DcL/VXppb58Qk8jOj8gzUJ/36h+PISeBorLfM4TN/dalN5Sy5EQeo5WXuaDA/psNky1Cr3Uj4LQc6wSO5956NuVXurLJ/Qcp8jMB2ducgt9+9JrfbGEnmOUmfnwqdicQt9ySC/1pRN6aruMQay8DkbwTOyoQm+vbNmEnnqul5t5oZ+xrL5kQk89BWd+xxr67ELfsvTmbwom9NRR8nC+iNBHGNLPaX2ZhJ7Dys585pulliKVXuqLJPQcUHjlq86XEfoopTdbXyChZ5+iT8FWyuh8vCH9SuilvhRCzx4jyHwxnY8X+vtaXxyhZ7cxdD7ry1auiTd3MyP1RRF6dhpD5ssZ0Mcd0s8ofTmEnrBRVL6YM7ELsUsv9cUQegLGcA52oaTOR568WZD6Igg9ATp/L7cNU3MJQn9f60sg9GwZTeb3n4kV+ktSnzuhZ8OIMl9k6JOUXuozJ/SsGlXlD8zQZxj6dKW32DJvQs+l8ZyCrew/E5tt6NOM6T5a3nUAAA7LSURBVNdW1qt9ZoSeyugyf2jFTY6hTzh5c38j9VqfE6FnZvUbOFUmBqfE0Cc7H7sg9ZkSekaa+YNL6PMNfcLSa32ehH70xln5wrZKrUhe+vsuhJMfoR+5sWa+2M53Evr7Wp8ZoR+z0Va+sGvcrOuo9FKfE6EfrxFnvuTOpz4fu8p8fS6EfqTGXPnCO99l6TdSr/VDJfTjs/G92VERhqTwznc2ebMg9RkQ+rEZfeXL73zXpd8a16v94Aj9qBjM3x9D5zsP/YzWD5nQj4jKz9TufJ4bphZSXvNmN6kfLKEfC42v1B7P5xz6nkpvEmeohH4MDOWv1J+3yTr0vczeVMR+eIS+fCp/5Zj5+QJC31fqtX5ohL5wBvOrjjoPm3fo+5u9qUj9kAh9wa6r/JpqkDuW0Pc5e7Ok9gMh9GXa/Kdzv9/uw3Bk5wsJfd+p35rFUfs+CH1xtr6xZH7m2MwXEPreZ2+WAi/Jvr9LxkboSxL4hlL5heM7X4SBlD7YerXvkNAXQuP3GWnmL2dvhpD6+2rfI6HPn8YfcHe0nb83lIn6FcGXq+QnJvRZ0/jDRp35e0Mb1FfUvmNCnyuNr+Pu3bF3fqCln1H77gh9jjS+HpVfGGzq7++rveJHJPT52Pnd0Pf36jDdNZq/dPV/ou9nZQ/JT0jos6Dxx1L5dTmUfkbt0xD6Adv/ou/7W3Kw7q5p3sYCNkytWPlf0vcTdND+F77uN5CgtZ+9Opk8/+bnh28U+k11X+Aiv8vdWJW/V1ro7621PoPaz9T+huj7O3fworf2/PZk7sXPD90o9BdHpF3e97u7rW0Xiwv9Ruozif3CUd8o2r8lemtPJzffmg7gb01eO3Tj6EJ//GtV4w8KBD7WzHyBod9KfV65b/MdNPL4x27tV29M3p79+mjy3Mf7bywx9K1fiBv6/sYaqp11j9X4uSJDfy/Y+syCX4n97TbTd0JSid3aZcvPby/avvvG4YY+xeunpr6/dYbpQNdTRH6m1NDP7ft/2Pfzfbwev2U39B2vXWK39s7kpY0Pdt3YOvR9P6Vx9P0tkoOuC18pOvQzBZV+W9/f2FG0CuSa+KGvpuFPV868Bm8Ueobt9df7fgSMXatCrooc+vPby6Y/ump68MYLoQfYp1UhV8UPfTULvx767RsvBhz6Vg8LGKvBlijj0ANQR8ZTNwDUkfHJWADqiN3a09BKyuCNQg/QifgbphZj9o0NU4EbhR6gEy6BAFA4FzWDsNdf7/sRQCSJL1N8Z/GryxSTH6GnGPFbe776HiN3lrPz3niE3Ag9xfBWghAm9BRD6CFM6CmG0EOY0FMMoYcwoacYQg9hQk8xhB6gcEIPUDihByic0AMUTugBCif0AIUTeoDCCT1A4YQewmyYohhCD2FCTzGEHsKEnmIIPYQJPcUQeggTeooh9BAm9BRD6CFM6ClGv6EHIIm12PZV+fnBAUhirbV9RR6Abgg9QOGEHqBwQg9QOKEHKJzQAxRO6AEKJ/QAhRN6gMIJPUDhhB6gcEK/5dHk7eWHn706mTz/5ud9Ppq+nN+eLDz3cd8PpR8jfu4ro38JlJQCod/0+Nby2V2+0F/M9+lt7qs3Rv1dPurnvjLyl0BZKRD6DdMnd/nsnk5uvjX9UX5r8lq/D6kXj2/l+6KOYNTPfWXkL4GyUiD0a87/evrkVs/udEAz/+DRKEc0p5OX+n4IPRr3c18Z90ugsBQI/arHr04mf3q7enaXz+r57auZuvG4k+/gJYJxP/eVUb8ESkuB0K96NHnh55dP5p3lgObOCEc257dv/qjvx9CjUT/3lXG/BEpLgdCv+u3frvzUvhzQnGZ8Dqapr9547m/+fDK5+YPR/c3nRv3cV8b9EigtBUK/afnsTn+tnt1H2T67zc1ORM2NclQ37ue+Mu6XwExJKRD6TSvPbjUfl++z29zp5OZs0fA//Vm+p59aGPdzXxn3S2CmpBQI/aaSnt32vnpjjGfkPPcrxvkSmCkpBUK/qaR/r0WQ76xkC577VaN8CcyUlAKhX05GLmciSzoDc7T1/xUz+b602xjjc7/TOF8CF2WlQOh3hv40/zVVRxP6hTE+9zuN8yVwUVYKhH7T+dUuicXrO+NdEo1d/WM135d2G2N+7itjfwlclJUCod90+WQWsO+5udPqL311XadRGfVzXxn5S+CirBQI/aarn9r5X8mouelL+4Wfz//y4/xn+5if+8rYXwJlpUDoN109u/lfm7SFR7dG/Jcf+XNfGflLoKwUCP2mlXm489zfbaCN3/7F9Pv8+bfG+Zcf+XNfGflLoKgUCD1A4YQeoHBCD1A4oQconNADFE7oAQon9ACFE3qAwgk9QOGEHqBwQg9QOKEHKJzQAxRO6AEKJ/QAhRN6gMIJPUDhhB6gcEIPUDihByic0AMUTugBCif0AIUTeoDCCT1A4YQeoHBCD1A4oQconNADFE7oAQon9ACFE3qAwgk9QOGEHqBwQg9QOKEHKJzQAxRO6AEKJ/QAhRN6gMIJPUDhhB6gcEIPUDihByic0AMUTugBCif0AIUTeoDCCT1A4YQeoHBCD1A4oQconNADFE7oAQon9ACFE3qAwgk9QOGEHqBwQg9QOKEHKJzQAxRO6AEKJ/QAhRN6gMIJPUDhhB6gcEIPUDihByic0AMUTugBCif0AIUTeoDCCT1A4YQeoHBCD1A4oQconNADFE7oAQon9ACFE3qAwgk9QOGEHqBwQg9QOKEHKJzQAxRO6AEKJ/QAhRN6gMIJPUDhhB6gcEIPUDihByic0AMUTugBCif0AIUTeoDCCT1A4YQeoHBCD1A4oQconNADFE7oAQon9ACFE3qAwgk9QOGEHqBwQs+YPTisw0dz/bAOHw0FEXrGTOgZBaFnzB48+PV+HYf+/n5CTzNCz5gJPaMg9IyZ0DMKQs+YCT2jIPSMmdAzCkLPmAk9oyD0jJnQMwpCT9feP6lc++Z7v1rc9MWNk1e2/+DZf/3Z6m8Xf+h33z55dt+9L74ofIfbhJ5REHq6dhn6qSd/OL8p2OVf3Pja8aGvvqjH0J/9485P/e7b136470tbhv7Q3TNeQk/X3j/5+uKD3/zixsmeNH1ysh76hQOhD3/RTvFD/4sbu3/CCD09EXq6dhn6ebV3d7n70M9vahf6s3fq/VMiaCP089tM3RCB0NO1ldDPplh2Zrvr0F/dOojQX90s9LQl9HRtNfTT3zz1q6sp9X/47snJyRN/PDtF+3Axif/K9HNf+58fzGbzr+bov3z35OTa789P5F5Oxj+czQKtfFF186eze/zmexfLP/vl7PfP/HR5+I3QV1cxG0roq8uYCT1tCT1dWwv9vM/LLn9SnaKdDcpXQ/8v5rddhv5bNxaLdmYT0vtDf/ZBdY/znwrTG19e+dKZ9dA/2Bn6h9OfR3934+pHxNmHTy9/IlW/Wfw0WfwNnl3eWP00enjy7MMb0x9Vy0n01a9efm728Ubor+8M/frDqXv3jJfQ07W10C+KfPnfWRg/vbH4A9UszPTWkz/41dmPr1bdnFz7/uyk59q/BaqfGCtf9Mr8ULN7nOX+69U9PfmTi7P/dnL5CKahv7oc8fZHD1ZC/28XPyJeqR711Q+M6SD+ZNn3y9B/+Z2VP/Fw/rPpqV9VJV776uXnZnc7Df3K9Yi3P1oN/crDqXv3jJfQ07W10C+m3Bdd/qQKUhXrq2Y/tTJLMwv9MrbPHgj95RmAT5b/blhM4L9/OZFfP/TzHxnTfM++cvoYnpyO3//Xd6p/e1yb/ubs7+a/qaZupr9Mf6RcfPnu8l8nT/304pfVspjNr158buaI0K8+nLp3z3gJPV3bE/q1E6lXzV7U+jL01fh0Pr2/N/SXd3h5lGerP9sg9M9e3dHyfhe/W/v7VKH/5OpBPjv/6lcWf372GNe/evm5mWNC/+z2XRy6e8ZL6OnaztA/nM0sX203Wp+F2d4wNf/8vtBPo7s8UvCHwsUxoa9+NMwKPr3fy4cwvdtPTk7+4PJRL0J/dUp2Npt++dXzEm989cOVn25HhH7l4dS+e8ZL6OnaztAv9sxee+Yni0/tDP1lQ6ddOxD65UrMT3aHvvbJ2OqDr/1s9SFMjzabTDo5+dZiZc8i8YubLs8sL796XuKNr364MoF+1MnY7bs4dPeMl9DTtZ0nYy8ufvH04jTi92e/aR36lSX3wdH/Rf3llZe9/OLGaui/mF9wYbm2Z3Ym+ZjQz796T+h3L69cezi1757xEnq6tnt55dTZX353sUCyjxH90nEj+pl/+A9PL1bbXIZ+dWa84Yh+yYietoSeru3ZMLVQrZzsaI6+1iUQ9s3RV87mx7ico1/dvrtW4q1J9B2h33cJhM05+np3z3gJPV3buATCKxdbXV7Uemfov75yP5effb/Wqpv9oQ84vOrmsqwPr0I/+xM/rf4qmyXeWhazM/Tbdq26qXf3jJfQ07X1i5o9Ve1ZXe3y+/tH9ItKLxYOLru/vPnAOvoWoT/5g8119J9+p/qBdO1PFvu8nr0cy88eznuL2ftXNku8/tVNQ7/ycOrePeMl9HTtMvS//HB5meLLhs92GVV1XOZ4O/SrO2NnK3WmyZtmbbkTdPWLtnbGNg/9k99ZuXhCeGfs8vFU/9K43C67UeLNrauNQr/6cOrePeMl9HRt7Y1HFhMcVYIfLuv4+9WNs05uh/6PFn/syfnw/8vFApev/feTq8pdftHWtW6ah/6p//3BjeW1azaudXPxi29MD/HE9+e/O3t3UfzFtW6WF6NZK/HGxWgahX714dS9e8ZL6OnaZeifeOa96qZlgr9898bKQvrpoD0Y+lfmV69cVnb2NdPiLeO98UWLq1f+ZO0ozULfQS+PCb18cwShZ8yEnlEQesZM6BkFoWfMhJ5REHrGTOgZBaFnzB4c1uGjuX5Yh4+Gggg9Yyb0jILQAxRO6AEKJ/QAhRN6gMIJPUDhhB6gcEIPUDihByic0AMUTugBCif0AIUTeoDCCT1A4YQeoHBCD1A4oQconNADFE7oAQon9ACFE3qAwgk9QOGEHqBwQg9QOKEHKNz/B4//ofHRKOjqAAAAAElFTkSuQmCC" /><!-- --></p>
<h3 id="one-sided-tests">One-sided tests</h3>
<p>We can also conduct a directional test (a “one sided” or “one tailed” test) if we have a prior hypotheses about the direction of the effect. This can be done by setting an order restriction on the prior distribution (and thus also on the posterior distribution) of the alternative (Morey &amp; Wagenmakers, 2014). For example, if we have a prior hypothesis that the effect of the drug is an <em>increase</em> in the number of sleep hours, the alternative will be restricted to the region to the right of the null (point or interval):</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" title="1">test_group2_right &lt;-<span class="st"> </span><span class="kw">bayesfactor_parameters</span>(model, <span class="dt">direction =</span> <span class="st">&quot;&gt;&quot;</span>)</a>
<a class="sourceLine" id="cb8-2" title="2">test_group2_right</a></code></pre></div>
<pre><code>&gt; # Bayes Factor (Savage-Dickey density ratio)
&gt; 
&gt; Parameter |   BF
&gt; ----------------
&gt; group2    | 2.82
&gt; 
&gt; * Evidence Against The Null: [0]
&gt; *                 Direction: Right-Sided test
</code></pre>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" title="1"><span class="kw">plot</span>(test_group2_right)</a></code></pre></div>
<p><img 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" /><!-- --></p>
<p>As we can see, given that we have an <em>a priori</em> assumption about the direction of the effect (<em>that the effect is positive</em>), <strong>the presence of an effect is 2.8 times more likely than the absence of an effect</strong>, given the observed data (or that the data are 2.8 time more probable under (H_1) than (H_0)). This indicates that, given the observed data, and a priori hypothesis, the posterior mass has shifted away from the null value, giving some evidence against the null (note that a Bayes factor of 2.8 is still considered quite <a href="https://easystats.github.io/report/articles/interpret_metrics.html#bayes-factor-bf">weak evidence</a>).</p>
<p><strong>NOTE</strong>: See the <em>Testing Contrasts</em> appendix below.</p>
<h3 id="support-intervals">Support intervals</h3>
<p>So far we’ve seen that Bayes factors quantify relative support between competing hypotheses. However, we can also ask:</p>
<blockquote>
<p><strong>Upon observing the data, the credibility of which of the parameter’s values has increased (or decreased)?</strong></p>
</blockquote>
<p>For example, we’ve seen that the point null has become somewhat less credible after observing the data, but we might also ask <em>which values have gained some credibility given the observed data?</em>. The resulting range of values is called <strong>the support interval</strong> as it indicates which values are supported by the data (Wagenmakers, Gronau, Dablander, &amp; Etz, 2018). We can do this by once again comparing the prior and posterior distributions and checking where the posterior densities are higher than the prior densities. This can be achieved with the <code>si()</code> function:</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" title="1">my_first_si &lt;-<span class="st"> </span><span class="kw">si</span>(model, <span class="dt">BF =</span> <span class="dv">1</span>)</a>
<a class="sourceLine" id="cb11-2" title="2">my_first_si</a></code></pre></div>
<pre><code>&gt; # Support Interval
&gt; 
&gt; Parameter |    BF = 1 SI
&gt; ------------------------
&gt; group2    | [0.24, 3.02]
</code></pre>
<p>The argument <code>BF = 1</code> indicates that we want the interval to contain values that have gained support by a factor of at least 1 (that is, any support at all).</p>
<p>Visually, we can see that the credibility of all the values within this interval has increased (and likewise the credibility of all the values outside this interval has decreased):</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" title="1"><span class="kw">plot</span>(my_first_si)</a></code></pre></div>
<p><img 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" /><!-- --></p>
<p>We can also see the this support interval (just barely) excludes the point null (0) - whose credibility we’ve already seen has decreased by the observed data. This emphasizes the relationship between the support interval and the Bayes factor:</p>
<blockquote>
<p>“The interpretation of such intervals would be analogous to how a frequentist confidence interval contains all the parameter values that would not have been rejected if tested at level (\alpha). For instance, a BF = 1/3 support interval encloses all values of theta for which the updating factor is not stronger than 3 against.” (Wagenmakers et al., 2018)</p>
</blockquote>
<p>Thus, the choice of BF (the level of support the interval should indicate) depends on what we want our interval to represent:</p>
<ul>
<li>A (BF = 1) contains values whose credibility has merely not decreased by observing the data.</li>
<li>A (BF &gt; 1) contains values who received more impressive support from the data.</li>
<li>A (BF &lt; 1) contains values whose credibility has <em>not</em> been impressively decreased by observing the data. Testing against values outside this interval will produce a Bayes factor larger than (1/BF) in support of the alternative.</li>
</ul>
<h2 id="comparing-models-using-bayes-factors">Comparing Models using Bayes Factors</h2>
<p>Bayes factors can also be used to compare statistical models, for which they answer the question:</p>
<blockquote>
<p><strong>Under which model are the the observed data more probable?</strong></p>
</blockquote>
<p>In other words, which model is more likely to have produced the observed data? This is usually done by comparing the marginal likelihoods of two models. In such a case, the Bayes factor is a measure of the <em>relative</em> evidence of one of the compared models over the other.</p>
<p>Let’s use Bayes factors for model comparison to find a model that best describes the length of an iris’ sepal using the <code>iris</code> data set.</p>
<h3 id="for-bayesian-models-brms-and-rstanarm">For Bayesian models (<code>brms</code> and <code>rstanarm</code>)</h3>
<p><strong>Note: In order to compute Bayes factors for Bayesian models, non-default arguments must be added upon fitting:</strong></p>
<ul>
<li><code>brmsfit</code> models <strong>must</strong> have been fitted with <code>save_all_pars = TRUE</code></li>
<li><code>stanreg</code> models <strong>must</strong> have been fitted with a defined <code>diagnostic_file</code>.</li>
</ul>
<p>Let’s first fit 5 Bayesian regressions with <code>brms</code> to predict <code>Sepal.Length</code>:</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb14-1" title="1"><span class="kw">library</span>(brms)</a>
<a class="sourceLine" id="cb14-2" title="2"></a>
<a class="sourceLine" id="cb14-3" title="3">m0 &lt;-<span class="st"> </span><span class="kw">brm</span>(Sepal.Length <span class="op">~</span><span class="st"> </span><span class="dv">1</span>, <span class="co"># intercept only model</span></a>
<a class="sourceLine" id="cb14-4" title="4">          <span class="dt">data =</span> iris, <span class="dt">save_all_pars =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb14-5" title="5">m1 &lt;-<span class="st"> </span><span class="kw">brm</span>(Sepal.Length <span class="op">~</span><span class="st"> </span>Petal.Length,</a>
<a class="sourceLine" id="cb14-6" title="6">          <span class="dt">data =</span> iris, <span class="dt">save_all_pars =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb14-7" title="7">m2 &lt;-<span class="st"> </span><span class="kw">brm</span>(Sepal.Length <span class="op">~</span><span class="st"> </span>Species,</a>
<a class="sourceLine" id="cb14-8" title="8">          <span class="dt">data =</span> iris, <span class="dt">save_all_pars =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb14-9" title="9">m3 &lt;-<span class="st"> </span><span class="kw">brm</span>(Sepal.Length <span class="op">~</span><span class="st"> </span>Species <span class="op">+</span><span class="st"> </span>Petal.Length,</a>
<a class="sourceLine" id="cb14-10" title="10">          <span class="dt">data =</span> iris, <span class="dt">save_all_pars =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb14-11" title="11">m4 &lt;-<span class="st"> </span><span class="kw">brm</span>(Sepal.Length <span class="op">~</span><span class="st"> </span>Species <span class="op">*</span><span class="st"> </span>Petal.Length,</a>
<a class="sourceLine" id="cb14-12" title="12">          <span class="dt">data =</span> iris, <span class="dt">save_all_pars =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
<p>We can now compare these models with the <code>bayesfactor_models()</code> function, using the <code>denominator</code> argument to specify which model all models will be compared against (in this case, the intercept-only model):</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb15-1" title="1"><span class="kw">library</span>(bayestestR)</a>
<a class="sourceLine" id="cb15-2" title="2">comparison &lt;-<span class="st"> </span><span class="kw">bayesfactor_models</span>(m1, m2, m3, m4, <span class="dt">denominator =</span> m0)</a>
<a class="sourceLine" id="cb15-3" title="3">comparison</a></code></pre></div>
<pre><code>&gt; # Bayes Factors for Model Comparison
&gt; 
&gt;   Model                            BF
&gt;   [1] Petal.Length           3.45e+44
&gt;   [2] Species                5.63e+29
&gt;   [3] Species + Petal.Length 7.12e+55
&gt;   [4] Species * Petal.Length 9.15e+55
&gt; 
&gt; * Against Denominator: [5] (Intercept only)
&gt; *   Bayes Factor Type: marginal likelihoods (bridgesampling)
</code></pre>
<p>We can see that the full model is the best model - with (BF_{\text{m0}}=9\times 10^{55}) compared to the null (intercept only).</p>
<p>Due to the transitive property of Bayes factors, we can easily change the reference model to the main effects model:</p>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb17-1" title="1"><span class="kw">update</span>(comparison, <span class="dt">reference =</span> <span class="dv">3</span>)</a></code></pre></div>
<pre><code>&gt; # Bayes Factors for Model Comparison
&gt; 
&gt;   Model                            BF
&gt;   [1] Petal.Length           4.84e-12
&gt;   [2] Species                7.90e-27
&gt;   [4] Species * Petal.Length     1.28
&gt;   [5] (Intercept only)       1.40e-56
&gt; 
&gt; * Against Denominator: [3] Species + Petal.Length
&gt; *   Bayes Factor Type: marginal likelihoods (bridgesampling)
</code></pre>
<p>As we can see, though the full model is the best, there is hardly any evidence that it is preferable to the main effects model.</p>
<p>We can also change the reference model to the <code>Species</code> model:</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb19-1" title="1"><span class="kw">update</span>(comparison, <span class="dt">reference =</span> <span class="dv">2</span>)</a></code></pre></div>
<pre><code>&gt; # Bayes Factors for Model Comparison
&gt; 
&gt;   Model                            BF
&gt;   [1] Petal.Length           6.12e+14
&gt;   [3] Species + Petal.Length 1.27e+26
&gt;   [4] Species * Petal.Length 1.63e+26
&gt;   [5] (Intercept only)       1.78e-30
&gt; 
&gt; * Against Denominator: [2] Species
&gt; *   Bayes Factor Type: marginal likelihoods (bridgesampling)
</code></pre>
<p>Notice that in the Bayesian framework the compared models <em>do not</em> need to be nested models, as happened here when we compared the <code>Petal.Length</code>-only model to the <code>Species</code>-only model (something that cannot be done in the frequentists framework, where compared models must be nested in one another).</p>
<p><strong>NOTE:</strong> In order to correctly and precisely estimate Bayes Factors, you always need the 4 P’s: <strong>P</strong>roper <strong>P</strong>riors,[2] and a <strong>P</strong>lentiful <strong>P</strong>osterior.[3]</p>
<h3 id="for-frequentist-models-via-the-bic-approximation">For Frequentist models via the BIC approximation</h3>
<p>It is also possible to compute Bayes factors for the comparison of frequentist models. This is done by comparing BIC measures, allowing a Bayesian comparison of non-nested frequentist models (Wagenmakers, 2007). Let’s try it out on some <strong>linear mixed models</strong>:</p>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb21-1" title="1"><span class="kw">library</span>(lme4)</a>
<a class="sourceLine" id="cb21-2" title="2"></a>
<a class="sourceLine" id="cb21-3" title="3">m0 &lt;-<span class="st"> </span><span class="kw">lmer</span>(Sepal.Length <span class="op">~</span><span class="st"> </span>(<span class="dv">1</span> <span class="op">|</span><span class="st"> </span>Species), <span class="dt">data =</span> iris)</a>
<a class="sourceLine" id="cb21-4" title="4">m1 &lt;-<span class="st"> </span><span class="kw">lmer</span>(Sepal.Length <span class="op">~</span><span class="st"> </span>Petal.Length <span class="op">+</span><span class="st"> </span>(<span class="dv">1</span> <span class="op">|</span><span class="st"> </span>Species), <span class="dt">data =</span> iris)</a>
<a class="sourceLine" id="cb21-5" title="5">m2 &lt;-<span class="st"> </span><span class="kw">lmer</span>(Sepal.Length <span class="op">~</span><span class="st"> </span>Petal.Length <span class="op">+</span><span class="st"> </span>(Petal.Length <span class="op">|</span><span class="st"> </span>Species), <span class="dt">data =</span> iris)</a>
<a class="sourceLine" id="cb21-6" title="6">m3 &lt;-<span class="st"> </span><span class="kw">lmer</span>(Sepal.Length <span class="op">~</span><span class="st"> </span>Petal.Length <span class="op">+</span><span class="st"> </span>Petal.Width <span class="op">+</span><span class="st"> </span>(Petal.Length <span class="op">|</span><span class="st"> </span>Species), <span class="dt">data =</span> iris)</a>
<a class="sourceLine" id="cb21-7" title="7">m4 &lt;-<span class="st"> </span><span class="kw">lmer</span>(Sepal.Length <span class="op">~</span><span class="st"> </span>Petal.Length <span class="op">*</span><span class="st"> </span>Petal.Width <span class="op">+</span><span class="st"> </span>(Petal.Length <span class="op">|</span><span class="st"> </span>Species), <span class="dt">data =</span> iris)</a>
<a class="sourceLine" id="cb21-8" title="8"></a>
<a class="sourceLine" id="cb21-9" title="9"><span class="kw">bayesfactor_models</span>(m1, m2, m3, m4, <span class="dt">denominator =</span> m0)</a></code></pre></div>
<pre><code>&gt; # Bayes Factors for Model Comparison
&gt; 
&gt;   Model                                                           BF
&gt;   [1] Petal.Length + (1 | Species)                          8.24e+24
&gt;   [2] Petal.Length + (Petal.Length | Species)               4.77e+23
&gt;   [3] Petal.Length + Petal.Width + (Petal.Length | Species) 1.52e+22
&gt;   [4] Petal.Length * Petal.Width + (Petal.Length | Species) 5.93e+20
&gt; 
&gt; * Against Denominator: [5] 1 + (1 | Species)
&gt; *   Bayes Factor Type: BIC approximation
</code></pre>
<h3 id="order-restricted-models">Order restricted models</h3>
<p>As stated above when discussing one-sided hypothesis tests, we can create new models by imposing order restrictions on a given model. For example, consider the following model, in which we predict the length of an iris’ sepal from the length of its petal, as well as from its species, with a prior of (b_{petal} \sim N(0,2)) (b_{versicolors}\ &amp;\ b_{virginica} \sim N(0,1.2)):</p>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb23-1" title="1">iris_model &lt;-<span class="st"> </span><span class="kw">stan_glm</span>(Sepal.Length <span class="op">~</span><span class="st"> </span>Species <span class="op">+</span><span class="st"> </span>Petal.Length,</a>
<a class="sourceLine" id="cb23-2" title="2">                       <span class="dt">data =</span> iris,</a>
<a class="sourceLine" id="cb23-3" title="3">                       <span class="dt">prior =</span> <span class="kw">normal</span>(<span class="dv">0</span>, <span class="kw">c</span>(<span class="dv">2</span>, <span class="fl">1.2</span>, <span class="fl">1.2</span>), <span class="dt">autoscale =</span> <span class="ot">FALSE</span>))</a></code></pre></div>
<p>These priors are <strong>unrestricted</strong> - that is, all values between (-\infty) and (\infty) of all parameters in the model have some non-zero credibility (no matter how small; this is true for both the prior and posterior distribution). Subsequently, <em>a priori</em> the ordering of the parameters relating to the iris species can have any ordering, such that (a priori) setosa can have larger sepals than virginica, but it is also possible for virginica to have larger sepals than setosa!</p>
<p>Does it make sense to let our priors cover all of these possibilities? That depends on our <em>prior</em> knowledge or hypotheses. For example, even a novice botanist will assume that it is unlikely that petal length will be <em>negatively</em> associated with sepal length - an iris with longer petals is likely larger, and thus will also have a longer sepal. And an expert botanist will perhaps assume that setosas have smaller sepals than both versicolors and virginica. These priors can be formulated as <strong>restricted</strong> priors (Morey, 2015; Morey &amp; Rouder, 2011):</p>
<ol>
<li>The novice botanist: (b_{petal} &gt; 0)</li>
<li>The expert botanist: (b_{versicolors} &gt; 0\ &amp;\ b_{virginica} &gt; 0)</li>
</ol>
<p>By testing these restrictions on prior and posterior samples, we can see how the probabilities of the restricted distributions change after observing the data. This can be achieved with <code>bayesfactor_restricted()</code>, that compute a Bayes factor for these restricted model vs the unrestricted model. Let’s first specify these restrictions as logical conditions:</p>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb24-1" title="1">botanist_hypotheses &lt;-<span class="st"> </span><span class="kw">c</span>(</a>
<a class="sourceLine" id="cb24-2" title="2">  <span class="st">&quot;Petal.Length &gt; 0&quot;</span>,</a>
<a class="sourceLine" id="cb24-3" title="3">  <span class="st">&quot;(Speciesversicolor &gt; 0) &amp; (Speciesvirginica &gt; 0)&quot;</span></a>
<a class="sourceLine" id="cb24-4" title="4">)</a></code></pre></div>
<p>Let’s test these hypotheses:</p>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb25-1" title="1">botanist_BFs &lt;-<span class="st"> </span><span class="kw">bayesfactor_restricted</span>(iris_model, <span class="dt">hypothesis =</span> botanist_hypotheses)</a>
<a class="sourceLine" id="cb25-2" title="2">botanist_BFs</a></code></pre></div>
<pre><code>&gt; # Bayes Factor (Order-Restriction)
&gt; 
&gt;                                        Hypothesis P(Prior) P(Posterior)
&gt;                                  Petal.Length &gt; 0     0.50            1
&gt;  (Speciesversicolor &gt; 0) &amp; (Speciesvirginica &gt; 0)     0.24            0
&gt;        BF
&gt;      2.02
&gt;  0.00e+00
&gt; 
&gt; * Bayes factors for the restricted model vs. the un-restricted model.
</code></pre>
<p>We can see that the novice botanist’s hypothesis gets a Bayes factor of ~2, indicating the data provides twice as much evidence for a model in which petal length is restricted to be positively associated with sepal length than for a model with not such restriction.</p>
<p>What about our expert botanist? He seems to have failed miserably, with a BF favoring the <em>unrestricted</em> model many many times over ((BF\gg1,000)). How is this possible? It seems that when <em>controlling for petal length</em>, versicolor and virginica actually have shorter sepals!</p>
<p><img 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" /><!-- --></p>
<p>Note that these Bayes factors compare the restricted model to the unrestricted model. If we wanted to compare the restricted model to the null model, we could use the transitive property of Bayes factors like so:</p>
<p>[ BF_{restricted / NULL} = \frac {BF_{restricted / un-restricted}} {BF_{un-restricted / NULL}} ]</p>
<p><strong>Because these restrictions are on the prior distribution, they are only appropriate for testing pre-planned (<em>a priori</em>) hypotheses, and should not be used for any post hoc comparisons (Morey, 2015).</strong></p>
<p><strong>NOTE</strong>: See the <em>Specifying Correct Priors for Factors with More Than 2 Levels</em> appendix below.</p>
<h2 id="bayesian-model-averaging">Bayesian Model Averaging</h2>
<p>In the previous section we discussed the direct comparison of two models to determine if an effect is supported by the data. However, in many cases there are too many models to consider or perhaps it is not straightforward which models we should compare to determine if an effect is supported by the data. For such cases we can use Bayesian model averaging (BMA) to determine the support provided by the data for a parameter or term across many models.</p>
<h3 id="inclusion-bayes-factors">Inclusion Bayes factors</h3>
<p>Inclusion Bayes factors answer the question:</p>
<blockquote>
<p><strong>Are the observed data more probable under models with a particular predictor, than they are under models without that particular predictor?</strong></p>
</blockquote>
<p>In other words, on average - are models with predictor (X) more likely to have produced the observed data than models without predictor (X)?[4]</p>
<p>Since each model has a prior probability, it is possible to sum the prior probability of all models that include a predictor of interest (the <em>prior inclusion probability</em>), and of all models that do not include that predictor (the <em>prior exclusion probability</em>). After the data are observed, and each model is assigned a posterior probability, we can similarly consider the sums of the posterior models’ probabilities to obtain the <em>posterior inclusion probability</em> and the <em>posterior exclusion probability</em>. Once again, the change from prior inclusion odds to the posterior inclusion odds is the Inclusion Bayes factor (“(BF_{Inclusion})”; Clyde, Ghosh, &amp; Littman, 2011).</p>
<p>Lets use the <code>brms</code> example from above:</p>
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb27-1" title="1"><span class="kw">bayesfactor_inclusion</span>(comparison)</a></code></pre></div>
<pre><code>&gt; # Inclusion Bayes Factors (Model Averaged)
&gt; 
&gt;                      Pr(prior) Pr(posterior) Inclusion BF
&gt; Petal.Length               0.6          1.00     1.93e+26
&gt; Species                    0.6          1.00     3.15e+11
&gt; Petal.Length:Species       0.2          0.56         5.14
&gt; 
&gt; * Compared among: all models
&gt; *    Priors odds: uniform-equal
</code></pre>
<p>If we examine the interaction term’s inclusion Bayes factor, we can see that across all 5 models, a model with the interaction term (<code>Species:Petal.Length</code>) is <em>on average</em> 5 times more likely than a model without the interaction term. <strong>Note</strong> that <code>Species</code>, a factor represented in the model with several parameters, gets a single Bayes factor - inclusion Bayes factors are given per predictor!</p>
<p>We can also compare only matched models - such that averaging is done only across models that (1) do not include any interactions with the predictor of interest; (2) for interaction predictors, averaging is done only across models that contain the main effect from which the interaction predictor is comprised (see explanation for why you might want to do this <a href="https://www.cogsci.nl/blog/interpreting-bayesian-repeated-measures-in-jasp">here</a>).</p>
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb29-1" title="1"><span class="kw">bayesfactor_inclusion</span>(comparison, <span class="dt">match_models =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
<pre><code>&gt; # Inclusion Bayes Factors (Model Averaged)
&gt; 
&gt;                      Pr(prior) Pr(posterior) Inclusion BF
&gt; Petal.Length               0.4          0.44     1.27e+26
&gt; Species                    0.4          0.44     2.07e+11
&gt; Petal.Length:Species       0.2          0.56         1.28
&gt; 
&gt; * Compared among: matched models only
&gt; *    Priors odds: uniform-equal
</code></pre>
<h4 id="comparison-with-jasp">Comparison with JASP</h4>
<p><code>bayesfactor_inclusion()</code> is meant to provide Bayes Factors per predictor, similar to JASP’s <em>Effects</em> option. Let’s compare the two:</p>
<ol>
<li>Across all models:</li>
</ol>
<!-- end list -->

<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb31-1" title="1"><span class="kw">library</span>(BayesFactor)</a>
<a class="sourceLine" id="cb31-2" title="2"><span class="kw">data</span>(ToothGrowth)</a>
<a class="sourceLine" id="cb31-3" title="3">ToothGrowth<span class="op">$</span>dose &lt;-<span class="st"> </span><span class="kw">as.factor</span>(ToothGrowth<span class="op">$</span>dose)</a>
<a class="sourceLine" id="cb31-4" title="4"></a>
<a class="sourceLine" id="cb31-5" title="5">BF_ToothGrowth &lt;-<span class="st"> </span><span class="kw">anovaBF</span>(len <span class="op">~</span><span class="st"> </span>dose<span class="op">*</span>supp, ToothGrowth, <span class="dt">progress =</span> <span class="ot">FALSE</span>)</a>
<a class="sourceLine" id="cb31-6" title="6"></a>
<a class="sourceLine" id="cb31-7" title="7"><span class="kw">bayesfactor_inclusion</span>(BF_ToothGrowth)</a></code></pre></div>
<pre><code>&gt; # Inclusion Bayes Factors (Model Averaged)
&gt; 
&gt;           Pr(prior) Pr(posterior) Inclusion BF
&gt; supp            0.6          1.00       140.99
&gt; dose            0.6          1.00     3.21e+14
&gt; dose:supp       0.2          0.72        10.12
&gt; 
&gt; * Compared among: all models
&gt; *    Priors odds: uniform-equal
</code></pre>
<p><img 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" /><!-- --></p>
<ol start="2">
<li>Across matched models:</li>
</ol>
<!-- end list -->

<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb33-1" title="1"><span class="kw">bayesfactor_inclusion</span>(BF_ToothGrowth, <span class="dt">match_models =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
<pre><code>&gt; # Inclusion Bayes Factors (Model Averaged)
&gt; 
&gt;           Pr(prior) Pr(posterior) Inclusion BF
&gt; supp            0.4          0.28        59.19
&gt; dose            0.4          0.28     1.36e+14
&gt; dose:supp       0.2          0.72         2.57
&gt; 
&gt; * Compared among: matched models only
&gt; *    Priors odds: uniform-equal
</code></pre>
<p><img 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" /><!-- --></p>
<ol start="3">
<li>With Nuisance Effects:</li>
</ol>
<p>We’ll add <code>dose</code> to the null model in JASP, and do the same in <code>R</code>:</p>
<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb35-1" title="1">BF_ToothGrowth_against_dose &lt;-<span class="st"> </span>BF_ToothGrowth[<span class="dv">3</span><span class="op">:</span><span class="dv">4</span>]<span class="op">/</span>BF_ToothGrowth[<span class="dv">2</span>] <span class="co"># OR: </span></a>
<a class="sourceLine" id="cb35-2" title="2"><span class="co"># update(bayesfactor_models(BF_ToothGrowth),</span></a>
<a class="sourceLine" id="cb35-3" title="3"><span class="co">#        subset = c(4, 5),</span></a>
<a class="sourceLine" id="cb35-4" title="4"><span class="co">#        reference = 3)</span></a>
<a class="sourceLine" id="cb35-5" title="5">BF_ToothGrowth_against_dose</a></code></pre></div>
<pre><code>&gt; Bayes factor analysis
&gt; --------------
&gt; [1] supp + dose             : 59  ±4.5%
&gt; [2] supp + dose + supp:dose : 152 ±1.5%
&gt; 
&gt; Against denominator:
&gt;   len ~ dose 
&gt; ---
&gt; Bayes factor type: BFlinearModel, JZS
</code></pre>
<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb37-1" title="1"><span class="kw">bayesfactor_inclusion</span>(BF_ToothGrowth_against_dose)</a></code></pre></div>
<pre><code>&gt; # Inclusion Bayes Factors (Model Averaged)
&gt; 
&gt;           Pr(prior) Pr(posterior) Inclusion BF
&gt; dose           1.00          1.00          NaN
&gt; supp           0.67          1.00       105.74
&gt; dose:supp      0.33          0.72         5.06
&gt; 
&gt; * Compared among: all models
&gt; *    Priors odds: uniform-equal
</code></pre>
<p><img 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" /><!-- --></p>
<h3 id="averaging-posteriors">Averaging posteriors</h3>
<p>Similar to how we can average evidence for a predictor across models, we can also average the posterior estimate across models. One situation in which this is useful in <strong>situations where Bayes factors seem to support a null effect, yet the <em>HDI</em> of the alternative excludes the null value</strong> (also see <code>si()</code> described above). For example, looking at Motor <em>Trend Car Road Tests</em> (<code>data(mtcars)</code>), we would naturally predict miles/gallon (<code>mpg</code>) from transition type (<code>am</code>) and weight (<code>wt</code>), but what about number of carburetors (<code>carb</code>)? Is this a good predictor?</p>
<p>We can determine this by comparing the following models:</p>
<div class="sourceCode" id="cb39"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb39-1" title="1">mod &lt;-<span class="st"> </span><span class="kw">stan_glm</span>(mpg <span class="op">~</span><span class="st"> </span>wt <span class="op">+</span><span class="st"> </span>am,            </a>
<a class="sourceLine" id="cb39-2" title="2">                <span class="dt">data =</span> mtcars,</a>
<a class="sourceLine" id="cb39-3" title="3">                <span class="dt">prior =</span> <span class="kw">normal</span>(<span class="dv">0</span>, <span class="kw">c</span>(<span class="dv">10</span>,<span class="dv">10</span>), <span class="dt">autoscale =</span> <span class="ot">FALSE</span>),</a>
<a class="sourceLine" id="cb39-4" title="4">                <span class="dt">diagnostic_file =</span> <span class="kw">file.path</span>(<span class="kw">tempdir</span>(), <span class="st">&quot;df1.csv&quot;</span>))</a>
<a class="sourceLine" id="cb39-5" title="5"></a>
<a class="sourceLine" id="cb39-6" title="6">mod_carb &lt;-<span class="st"> </span><span class="kw">stan_glm</span>(mpg <span class="op">~</span><span class="st"> </span>wt <span class="op">+</span><span class="st"> </span>am <span class="op">+</span><span class="st"> </span>carb,            </a>
<a class="sourceLine" id="cb39-7" title="7">                     <span class="dt">data =</span> mtcars,</a>
<a class="sourceLine" id="cb39-8" title="8">                     <span class="dt">prior =</span> <span class="kw">normal</span>(<span class="dv">0</span>, <span class="kw">c</span>(<span class="dv">10</span>,<span class="dv">10</span>,<span class="dv">20</span>), <span class="dt">autoscale =</span> <span class="ot">FALSE</span>),</a>
<a class="sourceLine" id="cb39-9" title="9">                     <span class="dt">diagnostic_file =</span> <span class="kw">file.path</span>(<span class="kw">tempdir</span>(), <span class="st">&quot;df0.csv&quot;</span>))</a>
<a class="sourceLine" id="cb39-10" title="10"></a>
<a class="sourceLine" id="cb39-11" title="11"><span class="kw">bayesfactor_models</span>(mod_carb, <span class="dt">denominator =</span> mod)</a></code></pre></div>
<pre><code>&gt; # Bayes Factors for Model Comparison
&gt; 
&gt;   Model                BF
&gt;   [1] wt + am + carb 0.82
&gt; 
&gt; * Against Denominator: [2] wt + am
&gt; *   Bayes Factor Type: marginal likelihoods (bridgesampling)
</code></pre>
<p>It seems that the model without <code>carb</code> as a predictor is (1/BF=1.2) times more likely than the model <em>with</em> <code>carb</code> as a predictor. We might then assume that in the latter model, the HDI will include the point-null value of 0 effect, to also indicate the credibility of the null in the posterior. However, this is not the case:</p>
<div class="sourceCode" id="cb41"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb41-1" title="1"><span class="kw">hdi</span>(mod_carb, <span class="dt">ci =</span> <span class="fl">.95</span>)</a></code></pre></div>
<pre><code>&gt; # Highest Density Interval
&gt; 
&gt; Parameter   |        95% HDI
&gt; ----------------------------
&gt; (Intercept) | [28.29, 40.52]
&gt; wt          | [-5.46, -1.61]
&gt; am          | [-0.93,  5.84]
&gt; carb        | [-2.01, -0.32]
</code></pre>
<p>How can this be? By estimating the HDI of the effect for <code>carb</code> in the full model, we are acting under the assumption that that model is correct. However, as we’ve just seen, both models are practically tied, and in fact it was the no-<code>carb</code> model, in which the effect for <code>carb</code> is fixed at 0, that was slightly more supported by the data. If this is the case <strong>why limit our estimation of the effect just to one model?</strong> (Bergh, Haaf, Ly, Rouder, &amp; Wagenmakers, 2019).</p>
<p>Using Bayesian model averaging, we can combine the posteriors samples from several models, weighted by the models’ marginal likelihood (done via the <code>bayesfactor_models()</code> function). If some parameter is part of some of the models but is missing from others, it is assumed to be fixed a 0 (which can also be seen as a method of applying shrinkage to our estimates). This results in a posterior distribution across several models, which we can now treat like any posterior distribution, and estimate the HDI. We can do this with the <code>weighted_posteriors()</code> function:</p>
<div class="sourceCode" id="cb43"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb43-1" title="1">BMA_draws &lt;-<span class="st"> </span><span class="kw">weighted_posteriors</span>(mod, mod_carb)</a>
<a class="sourceLine" id="cb43-2" title="2"></a>
<a class="sourceLine" id="cb43-3" title="3">BMA_hdi &lt;-<span class="st"> </span><span class="kw">hdi</span>(BMA_draws, <span class="dt">ci =</span> <span class="fl">.95</span>)</a>
<a class="sourceLine" id="cb43-4" title="4">BMA_hdi</a></code></pre></div>
<pre><code>&gt; # Highest Density Interval
&gt; 
&gt; Parameter   |        95% HDI
&gt; ----------------------------
&gt; (Intercept) | [28.99, 42.75]
&gt; wt          | [-6.62, -1.93]
&gt; am          | [-2.65,  5.32]
&gt; carb        | [-1.71,  0.00]
</code></pre>
<div class="sourceCode" id="cb45"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb45-1" title="1"><span class="kw">plot</span>(BMA_hdi)</a></code></pre></div>
<p><img 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" /><!-- --></p>
<p>We can see that across both models under consideration, the posterior of the <code>carb</code> effect is almost equally weighted between the alternative model and the null model - as represented by about half of the posterior mass concentrated at 0 - which makes sense as both models were almost equally supported by the data. We can also see that across both models, that now <strong>the HDI does contain 0</strong>. Thus we have resolved the conflict between the Bayes factor and the HDI (Rouder, Haaf, &amp; Vandekerckhove, 2018)!</p>
<p><strong>Note</strong> that parameters might play different roles across different models; For example, the parameter <code>A</code> plays a different role in the model <code>Y ~ A + B</code> (where it is a main effect) than it does in the model <code>Y ~ A + B + A:B</code> (where it is a simple effect). In many cases centering of predictors (mean subtracting for continuous variables, and effects coding via <code>contr.sum</code> or orthonormal coding via <a href="https://easystats.github.io/bayestestR/reference/contr.bayes.html"><code>contr.bayes</code></a> for factors) can in some cases reduce this issue.</p>
<h2 id="appendices">Appendices</h2>
<h3 id="testing-contrasts-with-emmeans--modelbased">Testing contrasts (with <code>emmeans</code> / <code>modelbased</code>)</h3>
<p>Besides testing parameter <code>bayesfactor_parameters()</code> can be used to test any estimate based on the prior and posterior distribution of the estimate. One way to achieve this is with a mix of <code>bayesfactor_parameters()</code> + <a href="https://cran.r-project.org/package=emmeans"><strong><code>emmeans</code></strong></a> to <a href="https://easystats.github.io/blog/posts/bayestestr_emmeans/">test Bayesian contrasts</a>.</p>
<p>For example, in the <code>sleep</code> example from above, we can estimate the group means and the difference between them:</p>
<div class="sourceCode" id="cb46"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb46-1" title="1"><span class="kw">library</span>(emmeans)</a>
<a class="sourceLine" id="cb46-2" title="2"></a>
<a class="sourceLine" id="cb46-3" title="3">groups &lt;-<span class="st"> </span><span class="kw">emmeans</span>(model, <span class="op">~</span><span class="st"> </span>group)</a>
<a class="sourceLine" id="cb46-4" title="4">group_diff &lt;-<span class="st"> </span><span class="kw">pairs</span>(groups)</a>
<a class="sourceLine" id="cb46-5" title="5"></a>
<a class="sourceLine" id="cb46-6" title="6">(groups_all &lt;-<span class="st"> </span><span class="kw">rbind</span>(groups, group_diff))</a></code></pre></div>
<pre><code>&gt;  group contrast emmean lower.HPD upper.HPD
&gt;  1     .          0.82      -0.4       2.1
&gt;  2     .          2.28       1.1       3.5
&gt;  .     1 - 2     -1.46      -3.2       0.2
&gt; 
&gt; Point estimate displayed: median 
&gt; HPD interval probability: 0.95
</code></pre>
<div class="sourceCode" id="cb48"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb48-1" title="1"><span class="co"># pass the original model via prior</span></a>
<a class="sourceLine" id="cb48-2" title="2"><span class="kw">bayesfactor_parameters</span>(groups_all, <span class="dt">prior =</span> model)</a></code></pre></div>
<pre><code>&gt; # Bayes Factor (Savage-Dickey density ratio)
&gt; 
&gt; Parameter |   BF
&gt; ----------------
&gt; 1, .      | 0.07
&gt; 2, .      | 8.63
&gt; ., 1 - 2  | 1.36
&gt; 
&gt; * Evidence Against The Null: [0]
</code></pre>
<p>That is strong evidence for the mean of group 1 being 0, and for group 2 for not being 0, but hardly any evidence for the difference between them being not 0. Conflict? Uncertainty? That is the Bayesian way!</p>
<p>We can also use the <code>easystats</code>’ <a href="https://cran.r-project.org/package=modelbased"><strong><code>modelbased</code></strong></a> package to compute Bayes factors for contrasts:</p>
<div class="sourceCode" id="cb50"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb50-1" title="1"><span class="kw">library</span>(modelbased)</a>
<a class="sourceLine" id="cb50-2" title="2"></a>
<a class="sourceLine" id="cb50-3" title="3"><span class="kw">estimate_contrasts</span>(model, <span class="dt">test =</span> <span class="st">&quot;bf&quot;</span>)</a></code></pre></div>
<pre><code>&gt; Level1 | Level2 | Median |         89% CI |   BF | Median (std.)
&gt; ----------------------------------------------------------------
&gt; 1      |      2 |  -1.46 | [-2.86, -0.13] | 1.36 |         -0.72
</code></pre>
<p><strong>NOTE</strong>: See the <em>Specifying Correct Priors for Factors with More Than 2 Levels</em> section below.</p>
<h3 id="specifying-correct-priors-for-factors">Specifying correct priors for factors</h3>
<p>This section introduces the biased priors obtained when using the common <em>effects</em> factor coding (<code>contr.sum</code>) or dummy factor coding (<code>contr.treatment</code>), and the solution of using orthonormal factor coding (<code>contr.bayes</code>) (as outlined in Rouder, Morey, Speckman, &amp; Province, 2012, sec. 7.2). Specifically, <em><strong>special care should be taken when working with factors which have 3 or more levels</strong></em>.</p>
<h4 id="contrasts-and-marginal-means">Contrasts (and marginal means)</h4>
<p>The <em>effects</em> factor coding commonly used in factorial analysis carries a hidden bias when it is applies to Bayesian priors. For example, if we want to test all pairwise differences between 3 levels of the same factor, we would expect all <em>a priori</em> differences to have the same distribution, but…</p>
<p>For our example, we will be test all <em><strong>prior</strong></em> pairwise differences between the 3 species in the <code>iris</code> data-set.</p>
<div class="sourceCode" id="cb52"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb52-1" title="1"><span class="kw">contrasts</span>(iris<span class="op">$</span>Species) &lt;-<span class="st"> </span>contr.sum</a>
<a class="sourceLine" id="cb52-2" title="2"></a>
<a class="sourceLine" id="cb52-3" title="3">fit_sum &lt;-<span class="st"> </span><span class="kw">stan_glm</span>(Sepal.Length <span class="op">~</span><span class="st"> </span>Species, <span class="dt">data =</span> iris,</a>
<a class="sourceLine" id="cb52-4" title="4">                    <span class="dt">prior =</span> <span class="kw">normal</span>(<span class="dv">0</span>, <span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">1</span>), <span class="dt">autoscale =</span> <span class="ot">FALSE</span>),</a>
<a class="sourceLine" id="cb52-5" title="5">                    <span class="dt">prior_PD =</span> <span class="ot">TRUE</span>, <span class="co"># sample priors</span></a>
<a class="sourceLine" id="cb52-6" title="6">                    <span class="dt">family =</span> <span class="kw">gaussian</span>())</a>
<a class="sourceLine" id="cb52-7" title="7"></a>
<a class="sourceLine" id="cb52-8" title="8">pairs_sum &lt;-<span class="st"> </span><span class="kw">pairs</span>(<span class="kw">emmeans</span>(fit_sum, <span class="op">~</span><span class="st"> </span>Species))</a>
<a class="sourceLine" id="cb52-9" title="9">pairs_sum</a></code></pre></div>
<pre><code>&gt;  contrast               estimate lower.HPD upper.HPD
&gt;  setosa - versicolor       0.031      -2.7       3.0
&gt;  setosa - virginica        0.016      -4.1       4.7
&gt;  versicolor - virginica    0.042      -4.1       4.4
&gt; 
&gt; Point estimate displayed: median 
&gt; HPD interval probability: 0.95
</code></pre>
<p><img 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" /><!-- --></p>
<p>We can see that the though the prior estimate for all 3 pairwise contrasts is ~0, the scale / HDI is much more narrow for the prior of the <code>setosa - versicolor</code> contrast!</p>
<p><em><strong>What happened???</strong></em><br />
This is caused by an inherent bias in the priors introduced by the <em>effects</em> coding (it’s even worse with the default treatment coding, because the prior for the intercept is usually drastically different from the effect’s parameters). <strong>And since it affects the priors, this bias will also bias the the Bayes factors over / understating evidence for some contrasts over others!</strong></p>
<p>The solution is to use <em>orthonormal</em> factor coding, a-la <code>contr.bayes</code>, which can either specify this factor coding per-factor:</p>
<div class="sourceCode" id="cb54"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb54-1" title="1"><span class="kw">contrasts</span>(iris<span class="op">$</span>Species) &lt;-<span class="st"> </span>contr.bayes</a></code></pre></div>
<p>Or you can set it globally:</p>
<div class="sourceCode" id="cb55"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb55-1" title="1"><span class="kw">options</span>(<span class="dt">contrasts =</span> <span class="kw">c</span>(<span class="st">&#39;contr.bayes&#39;</span>, <span class="st">&#39;contr.poly&#39;</span>))</a></code></pre></div>
<p>Let’s again estimate the <em><strong>prior</strong></em> differences:</p>
<div class="sourceCode" id="cb56"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb56-1" title="1"><span class="kw">contrasts</span>(iris<span class="op">$</span>Species) &lt;-<span class="st"> </span>contr.bayes</a>
<a class="sourceLine" id="cb56-2" title="2"></a>
<a class="sourceLine" id="cb56-3" title="3">fit_bayes &lt;-<span class="st"> </span><span class="kw">stan_glm</span>(Sepal.Length <span class="op">~</span><span class="st"> </span>Species, <span class="dt">data =</span> iris,</a>
<a class="sourceLine" id="cb56-4" title="4">                      <span class="dt">prior =</span> <span class="kw">normal</span>(<span class="dv">0</span>, <span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">1</span>), <span class="dt">autoscale =</span> <span class="ot">FALSE</span>),</a>
<a class="sourceLine" id="cb56-5" title="5">                      <span class="dt">prior_PD =</span> <span class="ot">TRUE</span>, <span class="co"># sample priors</span></a>
<a class="sourceLine" id="cb56-6" title="6">                      <span class="dt">family =</span> <span class="kw">gaussian</span>())</a>
<a class="sourceLine" id="cb56-7" title="7"></a>
<a class="sourceLine" id="cb56-8" title="8">pairs_bayes &lt;-<span class="st"> </span><span class="kw">pairs</span>(<span class="kw">emmeans</span>(fit_bayes, <span class="op">~</span><span class="st"> </span>Species))</a>
<a class="sourceLine" id="cb56-9" title="9">pairs_bayes</a></code></pre></div>
<pre><code>&gt;  contrast               estimate lower.HPD upper.HPD
&gt;  setosa - versicolor      0.0116     -2.65      2.92
&gt;  setosa - virginica      -0.0024     -2.67      2.66
&gt;  versicolor - virginica   0.0150     -2.77      2.62
&gt; 
&gt; Point estimate displayed: median 
&gt; HPD interval probability: 0.95
</code></pre>
<p><img 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QaVITw6D0pvJ11nxhbAmDICmUcXpbeQrmvdFMCQ0s8j8xii9PY579ee29i4xho9MlF5JDGptw5H3WACMo9RgzGhe3BiWlruMFUEg0krMo8UlN4+H1269NaETQi9o6g8MlB6gQi9k5jNIwept0f79BNDxzIPpSf07uFIG0zCAo4t+vtiJ+yQJfSuofKYgkfq7dBaPTB0kNAjQuYxJY/WC0LoHcKaDYog9bbgevQYoPIojNSbj+vRY4DJPGbjxekexhjHmbHoI/OYA603GdejRxeVx5w8j9gbiuvRI8RkHip4Y3SPbES4Hj2YzEMpam8erkcPrnQA1Ui9abgevevIPErBxN4kHHXjNiqPMlF7U3A9eocxm0cFSL0RuB69m2JvPd0lgHBM7O1A6MWh8qgYqddneJlijrpxCZmHHrReC0LvINZsoBGp16B3h6lVv5Z7WTNCL0TiTab7HQ93kXo9bi5zCQT5yDyMwbReBy6BIB+Zh1lYxKkcM3rhqDwMROrNQugtRuNhLlJvEkJvLTIPw9F6YxB6S5F52IHUm4DQ28fzqDwsQur1I/RWofGwESs4uhF6i5B5WIvUa0Xo7eCN0f3GBYphWq8RoTcejYcQpF4bQm8yGg9hSL0ehN5cZB4CMa3XgdAbicZDMFJfOUJvHBoP8ZjWV4zQm4TGwxWkvlKE3hgs18AtpL46hN4INB4uSox53W9C0Qi9fkQe7iL1lSD0WtF4OI9pfQUIvUZUHnh/JPXUvgyEXg+m8kACqS8TodeBygNjmNiXh9BXjMYDmUZb72QjykDoqzM+iHW/rQADEXv1CH01aDwwtfE5kUuxKAOhL13aoNX9RgKMR+0VIvTlSRuoNB4oIP1NJD0d6hH6EmQNTt1vGsBGWe8nkj+9Er5N7z5Wr9/31HuTHxT1M8ofjEQemMukN9g0dEdCI+Vf++7ZeuSB9yY9aGvoCw8v3W8RQAgVtXez/cq/0M364aeDCfxK/fFJD1oR+rkGke63BSCUosyP0p2b8qj+0j55sv5M+OtW/d5X8x80LvQKx4vutwHgCoVv2zG6k6SQ6q+l3/Lds922Zz84d+jL/AnPQPeAB5BL1Vt9rmxpovqTXqs/OPJB1oNzhl7VDw0AdJgjf8V7qfj51vrL8JuxPa+pDxJ6AC6bo3+Fe6n26XbP9pu+NWx66oOd+UKv+0cEAPOZvX/Fg6n26Yar8MnQjz/YYUYPwGVz9K9wL9U+XXWhBwBMx9alGwDAlKzdGQsAmI7q1m6mHUmZ+iChB4BKqD9hqjtnHzlhKuVBQg8AleASCAAgHBc1AwDhSr5M8Vr3V1GXKQYAu6hv7W78HiNr/dV58TceAQBTcStBABCO1gKAcIQeAIQj9AAgHKEHAOEIPQAIR+gBQDhCDwDCEXoAEI7QA4BwhB4AhCP0ACAc17oBAOEIPQAIR+gBQDhCDwDCEXoAEI7QA4BwhB4AhCP0ACAcoQcA4Qg9AAhH6AFAOEIPeEO6PxWgDIQe7vGmofuTBNQh9BBvqq4TewhG6CHV7H2n9RCG0EOgojXf20fqIRKhhyzFwp6N2kMQQg9JZu765Njr/tKA2RF6iKGq77Qe0hB6CKG88aQeYhB6iFBK49Nqr/sLBWZA6GG/cqbyWaln2MI6hB62q6Dyo63X/SUDxRB62K2yzCdTT+1hE0IPq1WZeVIPWxF6WKziytN6WIrQw1paMj+WeoYxzEfoYSttnU+pve7vBZCL0MNOmjM/lnpqD4MRetjIgMyntV73twVIR+hhIVM6H2FiD+MRetjHqM6HSD3MRuhhHcMq30ftYSxCD8uYNpuPIfUwFKGHXQzufBeth3kIPaxifOf3knqYh9DDJhZkvofUwyCEHhaxp/N7uYI9DELoYQ+rOh8i9TADoYctbFieH8O0HiYg9LCElZ3fS+phAkIPO9ja+RClh2aEHlawOPMhSg+tCD1sYHnnQ7Qe+hB6WEBA5yk9NCL0MJ7Ny/NxlB66EHqYTkrn97JXFroQehhOUOf3MquHHoQeZhOV+b2UHloQehhNWuf3DlKv+zsLlxB6mExg5/f2U6/7ewuHEHoYTGbnmdSjaoQe5pLaeUqPihF6GEtu5yk91PndO51O+638bQg9TCW583tZqIca7ebClc4F31+8krcVoYehhHee0kOJdX/hyg0/sD9vK0IPI8k6TSodpcfc2k3/ROe8v3jRv+OdnM0IPUzkQudZqMf8Wo2FK62GfzL8NWczQg8DudF5So+5hYG/udz9L2czQg/zOJL5vZQe82o3/Xt+4O8PJvUs3cAu7nR+Lwv1mNO67/u1M61G7WTeVoQepnGq80zqMZ/2Bd8/1Gl9/+e5WxF6GMaxzlN6zKk9xTaEHmZxrvOUHnN689KllydsQuhhFAc7v5eFeszh9eXwdKnaidyNCD1M4mbnKT1mFp0VG2JnLGzhaucpPWbUbvr7324svHyewythC3c7T+kxm+6Zsd3/cjYj9DCGy52n9JhJP/KcGQtLuN15So9ZtJv+HW83Fn50lKtXwgqud57SYxY3/IXLjejs2LytShhW7z5Wr9/31HuJx/741yvjDzKmMUTnKT1mce3rYegPVnxm7O7ZeuSBeNQ3u4/V73818doMafTR+RCnTmEWH21M2kL5mNqsH346mNWv1B8fPnZrpf7Vf40efDDx2gxo9ND5LkqP2bRWD1a5M/aTJ+vPhL9u1e8dzt7XeoHfqh9+Pv7ajGdEPDrfR+lRTPva6ScC31yufelYdupVj6h+4HfPdoOf+Dj+YIfQo4fOx1F6FHHeHzqUuZXqAdWfvA8/iCH0SEHnkyg9ptdqxEKffXKs+tD31uY3k7tjI7dWWLrBKDI/itBjav0zYqs9MzaYs/dCv5US+rXkYwxm0Pk0lB7TajcPRgfQV7szdrg4kxL6zXpi5YbQg86nY/UGRWxUfXhlXuiDzj+eeICRDDqfimNvMLXtU+Hy/N15166scukm6PxXR16bgew6Op+B0mNK/b2xuUv0le2M3f3F6Hye0IPOZ6P0mMq6Xzv+8psXl6u9qNlm+uGVwUw/PGF25LUZxm6j83koPabQbnZvLXWj6I1HPv7T//DS7C/bX7FJHjIfdv75sW0ZxW6j8/koPSab+fDKjx/yvD13vjTjy6ZeAiHo/L2vpLw2g9hhnCY1EaXHRK1G9/rEhW88svPiZ8PhNWvr0y5qthmvfuy1GcPuovNToPSY6Lxfe25j49osa/Q7f/vlmVufvExxdIpUMMsfiC/cM4TdReenQukxyXxH3ey81mv9T4u+7m78xiNR6G+tEHokkPkpUXpMsn007PzizDce2Xntz6LW/8VvFX9ig9dmADuKzk+N0mOijy5demvCJnlD6FfPLkWjbM8jKj+p2Gszft1E5wvg1ClM8Lt3Op32hNJnDqDXnu1O53/9YlD7R1V/at3XZvQ6ic4XQumRp91cuNK54PuLM6zR9xZtejtjr3q3l7J6w+B1Ep0viNIjx7q/cOVGuEpf9KibbuW94W7Yjx/61N+p//wIvZvofHGUHlnaTf9E57y/eLHwmbEPBWPq899NPELooQidnwWlR4bwjNhWwz85w5mxn/5ucqXmwx8XPsZyKoxc99D52VB6pAsDH54VW/jM2OowcF3DaVIzY6EeqdpN/54f+PuDSX3BpZuknV+r/KRGXpth6xY6PwdKj1Trvu/XzrQatZN5W6Ut3cRW5D9YKmd5vvvajFq30Pl5UHqkaV/w/UOd1veLnhmbCH1Z+2G7r82gdQqZnxOlR5r2FNskh82LDz/88FeW9vzJw32f9Qg91KDzc2NSj3HtS5fy7xcbSg6aD5a8UXeV9ekRerewbqMApceom8u+XzvZ+tq3crcaGTMvfO5zn/W82z7X9/nSrmjWIfROofNKUHoktZvRNYovN/xDeZtNWKMvFePVGRxvowylR1yr4Z9oN2tnXi96eOXOf/nzEmfxiddmuDqCzitE6RETXdNs3T9U+MzY6jBaHUHnlaL0iFn3T3RuLu8vEvqdf/nJb4P/+XHcT8qb3jNY3UDmFWOhHkPbR/19XwzX6adfuomW56OLmg1xeCXmwnRePUqPvv4tY/0CO2MJPVSj82Wg9OhpNfYdiBQ5vPIPvwn/59dxvynvU2SgykfnS0Lp0TXpbrFd7IxFich8aSg9CiD0KA+dLxGlx/RyBsrOv/z4J+W+NqNUNjpfKhbqMbXUYfLaf/273i0F93yvzNdmjErG8nzZKD2mlTZKzoWH2ux8u+yDbgi9aHS+fJQeU0oZJNejvH+w5H2m8+G3uXolZkLnK0HpMZWUMXIuivvVaNnmg6XbOTMWxZH5ijCpR6vh517+IJR29cqw8Dvf9sLEc4cpzIDpfHUovfNmDX3Y9g+Wonk9oUdxdL5KlN5184S+u3LD0g2Ko/MVo/Rumy30O+EO2N7KTfDLZ8r53KLXZmxKROYrR+md1lo9cLB46MOjbm5bCm8Wu/PiUqkH0jM0BWI6rwOlR760wfFCOGiCCX14ylSJR1cSenk8Oq8HC/XIlX5m7MMPfzc85OZP73yp1NdmXApD57Wh9MjDRc2gDJ3XiNIjB6GHKlReL0qPTFnjghuPoCA6rxuTepdd+8bUNwfv+fBZb4gTpjAVOq8fpXfV9ik//1j6lEHxwZJH6FEMq/NGoPQual9cDm8Ofsc7OdukXtTMu/M7P+77CWfGYhL2whqD0rvm2tGw8vuO52U+/RIIpR48H39txqMMdN4glN4trYbvL/4wv/Kd7GvdVIHhKAKZNwuld0oYen/xuUmlJ/SYD5k3Tf9fXt0jA5XordBPaH3qGv2jZX1OI6/NULQfnTcPpXfM9qmw9cWPuinx0sSJ12Yk2o7MG4nSO+fN1cKh/8MvvT13ctQNJqPzpqL0zmm/UezwyvCalRxHj8nYC2sySo8YQo/ZkHnDMal3Qvv0Ez3Hfn/6WOakPm3p5tdxXOsGaei88Si9C6LDKyMLlxvZy/RcvRIzIPM2YJ+sA1qrB3oOXl7NvqMgoUdhZN4WpB5d6UPgw2c/Gy7Of/xvXyr1tRl/VqLz9qD0iKSNgJ0XenthP36o1HOnGH42IvN2ofSyxdboi16P/pznffrfLwWh3/m2t+d7ZX2ChN5GZN4+lF6y2UN/Pax794o3QelLvJIlg886dN5GTOoF6x1euerX7i58Pfq7Bpc2u+6VeDkEhp5t6LydWKmX7+ZywRuPfPxQuFzTC32pl7Jk4FmFytuL0ovXyjmIvpN9mWJCjwSPzluN0gs334z+gyVCjw6dF4DSi9Xe2Ch+45HEGv057zNlfGK912bUWYLMS8D6jTjtCwtXOjejm8YunsndMvWoG++RbujDA+pLPJCeIWcHKi8EpZdm3V+4cqN/eOXJvC0zjqO/7StLe/7bV4IxUeKEntDbgc6LQellaTf9hX9o+rXjGxsXl/2Ca/SBFwb/V/0LMxxc+e5j9fp9T7039vhW/ZmR12bAWYDOS0LqJWk1amfC/8KPby4XPjO20/nDi58NBsNtd75U/LV3z9YjD4yW/tYKobcPlZeG1MsRHlLZbnYD32oUn9HPY7N++OlgVr9Sfzz5eNB5Qm8dOi8PpRcjms3f6C7O3/AP5W2q+sf9yZPdnG/V73019vDuPwadJ/SWYdVGJlIvRLvp73vih6f8L/3Vz77p78u+vVRnNPTJm0vNcoepfuB3z8azfuuxev0vzxJ6q5B5uSi9DIMjbopd1GzkdrGRgidMrdUfHPkgtFW//5VdQm8VOi8ZpZfh2qq20PfW5jfju2P/+E+jc/zotRlnxiLz0rF+45S0pZu/8bw7vxP8+i/PLu15pNjSTVDzXui3Ro+7IfQWIfPyUXqXpPyYP1ga3G3kw4cK3nhkWHNCbzE67wJKL0HvevSBY78/nb0/NvVaN8PLHlwveGosoReAzLvCo/XWi91h6nLOlYqzLlOc9pspsHRjPzrvEEpvu9bqgZ6Dl1cPVhX6jJ2xIUJvBfbCuoXSu2H8B7zz7djSzdWitxLcTD28MkTobUDmnUPqXZDy473q7Xlk8GHRm4P3V2zGs07oLUDnXUTqLRbbGVvwWjfBlN7b8/B3fvy3X1nyCt8bPOMSCB1CbwMy7yZKb6/YztiiV6/ceXawN774ZYqzLmpG6I3HdN5dpN5WvZ2xQeiz98R2si5q9uGz0WWK//yl4q+bvEzx2nCXLKE3G3th3cahllZr/33xO0zNZzd+4xFCbws67zpKb7Wqr0dfAEPKHHQerN/YbKY7TFWDEWUMKo8QqbdO/6ibLxbfGVsVxpMhmM2jj9RbZnjUzf68zQg96DyGKL1dBqG/J2+JntCDziOB1AtE6B1H5jGG0otD6N1G55GC0ltj9uvRV4aBpBsHVSID6zeWmPl69NVhFGlG5pGJ0tth5uvRV4dBpBedRw5KLwmhdxaZRz4uimCJ9sZG7rGVHULvLKbzmIzUW2D7aLhCv/jz3I0IvZvoPKZB6Y13c7m7L7Z2Jm8rQu8kOo8pUXrDnfdrxzc2fnaUSyBgBJlHEbTeYK1GdyrP1Ssxgs6jGEpvrlbv4PlWzkH0HULvHk6SQmGU3ljtZnQ5s+2mz41HMETmMQv2yppq3Q8Tf973D+VtRejdQucxI1JvpmBKf7LTueHfk7sVoXcJmcfsKL2h3gjXbCacMUXoHULnMRdKb6L2pUuTzosl9A5hLyzmR+tNE54wVTvZ+tq3crci9I4g81CCBRyztJu9SxSzMxZ0HspQeqO0Gv6JdrN25nUOr/IPZg8AABrCSURBVASZh0KU3iDt5sKVzrp/iBOmnEfmoRqtN8a6f6Jzc3k/oXcdnYd6LOCYYvuov++L4To9SzcuI/MohefRehMM7xnLzlh3kXmUhtSboNXY171lLIdXOsuj8ygVqdfvram2IvRy0XmUjtTbgdBLReVRDUpvAUIvE5lHdUi98Qi9QMP/O627AHAECziGI/TikHlUj7V6sxF6Ycg89CD1JiP0knhkHvpwYL25CL0cVB66kXpDEXohmMzDCKTeSIReBDIPY7CCYyBCLwCVh1lIvWkIvd28ON1vb6AvMTB1v0tA6G1G5WEuUm8SQm8tMg/D0XpjEHobeSN0v6GBdKTeEITePjQeViH1+hF6qzCTh42Y2OtG6C1C5mEtWq8VobcDjYftSL1GhN54o3teyTwsNTqQdb+1HELoDUfjIQu114HQm4upPGQi9ZUj9Eai8RBtfIDrfssJR+iNQ+PhBmJfHUJvkvF5DpmHbNS+EoTeGDQeTkqb3tAGxQi9EWg83JZee4KvCqHXjYk8ECH25SH0GtF4YERm7Un+PAi9DumDWPdbDDAGvVeL0FcsY+Tqfl8BRqL3ahD6qmSOVt1vJcBs+bN7kj+NEr5D7z5Wr9/31HuTH5T/45k0PHW/gwCrUPxZKf/G7J6tRx54b9KDIkM/xeSDwgNzmfJdRveHlH8nNuuHnw4m8Cv1xyc9aH/opx9vNB5Qbab3n6P/Eqj+Qj95sv5M+OtW/d5X8x+0MfRzDSndbwtAKhXJT6W7OMqo/kr6Ld8922179oPGh17FONH9BgCcouJNOzXdhSpC9Se7Vn9w5IOsB+cOfaU/06npHukA+nTXYNRcwZuvloqfb62/DL8Z2/Oa+uCcodf9IwOAecyRv+K9VPt0u2f7Td8aNj31wQ6hB+CyOfpXuJdqn264Cp8M/fiDnflCr/tHBADzmb1/xYOp9ukqCz2lB2C3OfpXuJdqn666pRsAwHSs3RkLAJiO6tZuph1JmfogoQeASqg/Yao7Zx85YSrlQUIPAJXgEggAIBwXNQMA4Uq+TPFa91d3LlMMAMZR39rd+D1G1vqr827eeAQADMCtBAFAOFoLAMIRegAQjtADgHCEHgCEI/QAIByhBwDhCD0ACEfoAUA4Qg8AwhF6ABCO0AOAcFzrBgCEI/QAIByhBwDhCD0ACEfoAUA4Qg8AwhF6ABCO0AOAcIQeAIQj9AAgHKGHw7wcuj83QB1CDxflFZ7sQxxCD8cUbjzZh/UIPZyiIvMkH7Yh9HDIWKePZCL2EITQwxHTFn7G7Ov+8oAchB4umKPx02df9xcJZCH0cIDSzOckX/fXCaQj9BCvtMynxF731wqkIfQQrszGp9Ze9xcMjCH0kKzcuXxW63V/1cAIQg+5Sl6zIfWwBaGHVBVXPpl63V88EEfoIZSGzCdar/vrB4YIPUTSVPl46nV/C4ABQg+BdGae0sM8hB7yaO48qYdpCD2k0bU4T+lhLEIPWUyofKz0jHGYgNBDFFM6f4RJPQxC6CGJQZ2n9DAHoYccJlU+QuphBkIPMYzrPKWHIQg9hDBq1WaA0sMEhB4yGJn5EKWHfoQeIhjbeUoPAxB6CGBw5o9QeuhH6GE/sztP6aEdoYftzNwLm0DpUbZr37iS86eEHpYzP/NHKD3KtX3K9xcIPcSyYDofofQoS/vish+4452cbQg9LGZL5o9QepTk2tGw8vuO52We0MNmFnWeU6dQilbD9xd/mF/5DqGHxazqPKVHGcLQ+4vPTSo9oYel7Kp8iNJDvd4K/YTWE3rYyb7OH2GhHqXYPhW2nqNuII5lqzYDlB6leHOV0EMaWzN/hNKjLO03OLwSoljceUoP9d68dOnlCZsQeljG6swfofRQ7PVoZ2ztRO5GhB5WsT3zRyg9lLrh95zM24rQwyKegM5TeijUbvr7324svHyeSyBAChGZP0LpoU6rsXCl/1/OZoQetpCS+SOcOgVl+pG/uUzoIYCgzB+h9FCl3fTveLux8KOj/v68zQg9bCBjcT6G0kONG/7C5fB6N7UzeVsRelhAWuZDlB5KXPt6GPqDP8/diNDDeBIzf4TSQ5mPNiZtQehhOKGZP9Irve5vL5xA6GE2uZ2n9JhfdD36CEfdwFqSM3+E1RvMjdDDesIzz7E3mFv79BOhVb92N2fGwkbiDqlMQemhxs1lLoEAGzmQ+RClhwpcAgE2ciTzR5jUQwlm9LCPO5k/Qukxp98FhW+/lb8NoYd5nOo8pcc82s2FK50Lvr+Yt3JD6GEcxzIfovSY1bq/cCW6+QgXNYNN3Mv8EUqPWbWb/onOeX/xIjcegU2c7Dylx4y616P3T3LUDSziaOaPsFCP2fRvOlL9jUfefaxev++p9xKP/fGvV8YfZFRjhLudp/SYSbvp3/MDf38wqa926Wb3bD3yQDzqm93H6ve/mnhtBjXiHNwLG0fpMYP16KYjrUbtZN5WyofVZv3w08GsfqX++PCxWyv1r/5r9OCDiddmTCPG7cyHKD0Ka1/w/UOd1vervfHIJ0/Wnwl/3arfO5y9r/UCv1U//Hz8tRnSGKLzTOoxi/YU26geU/3A757tBj/xcfzBDqFHHJkPUXqUQvWQ6k/ehx/EEHpkYDrf45F6FNG7THHg2O9PH8vcH6s+9L21+c3k7tjIrRWWbpDC8b2wSZQeBcRuPHI551B6xeMpmLP3Qr+VEvq15GMMZkTofAKlx/Raqwd6Dl5ePVhh6HuLMymh36wnVm4IPSJ0fhSph2IVhj7o/OOJBxjJ6LAXNg2lRwHtjY28k6VC1S3dBJ3/6shrM5DBbD4Vpce0to+GK/SL1R5Hn7UzdvcXo/N5Qg86n43SYyo3l7v7Ymtn8rZSPZI20w+vDGb64QmzI6/NMHYdnc9G6TGN837t+MbGz45Wez36/opN8pD5sPPPj23LKHYdmc9D6TFZq9Gdyld89crUSyAEnb/3lZTXZhC7jc7nY6EeE/WvQ1/19ejTLmq2Ga9+7LUZwk6j85NQekwSXqb4nU5nu6n1MsXRKVLBLH8gvnDPCHYZy/NToPSYZN0PE38+vIRlDvVjaDd+45Eo9LdWCD1G0PnpUHrkC6b0JzudG/49uVtxK0HoQOenxaQeE7wRrtlMOGOK0EMDMj89So9JNjYmbUHoUTmm84V4pB45tk+F50vdnT+lJ/SoGp0vitIjU/86xblHVxJ6VI3OF0fpkWXdrx1/+c2Ly9WeGVsEA9dFZH4mpB6pugfdhIfdVHscfQEMWwfR+RlReqTRdWZsAYxa99D5mVF6pNB0rZsiGLSuYXl+LpQe4877tec2Nq6xRg9T0Pk5ManHGI66gVno/NwoPcZoucNUEQxYp5B5BSg9xn106dJbEzYh9KgE03lFKD1mQOhRAY/OK0PpEdM+/cTQscxD6Qk9ykfnVaL0GOrvi52wQ5bQo3R0Xi0W6jHQWj0wdJDQQxsqrxqlR0GEHuViNl8CSo84rkcPzeh8OSg9ergePbSj82Wh9IhwZix0I/MlIvQIcT16aEbnS0XpwfXooRsHVZaN1RtwPXroReZLx7E34Hr00InpfBUoPbgePbQh81Wh9M7jqBtoQuerw6TeeVyPHlrQ+SpRenA9elSOyleM0mMyQg+l6Hz1KL3Dhpcp5qgbVIVVGy0ovbsIPapG5nWh9M7q3WFq1a/lXtaM0EMVOq8PpXfdzWUugYAKkHmtKL3juAQCKkDmdaP0bmNGj/LRef0oPbIResyPzpuA0iMTocfcqLwZOHUKWQg95sRs3hiUHhkIPebCqo1JKD3SEXrMg84bhtIjDaHH7Mi8eSi9c1qNCRej7xB6zI7MG4nSu4bQo0R03lAs1DuG0KM8dN5YlN4thB6lofIGo/ROaa0eOEjoUQJm84aj9Egg9CiOzhuP0iOO0KMwOm8BSo8YQo+CyLwdWKjHEKFHMXTeFpQeA4QeRXBQpUUovUuufYM7TEERMm8XSu+I7VN+/rH0hB7To/O2YVLvgPbFZT/ArQShBpm3D6WX7trRsPL7judlntBjenTeRpRetlbD9xd/mF/5DqHH1Oi8pSi9ZGHo/cXnJpWe0GMqLM/bi9JL1luhn9B6Qo9p0HmbUXrZtk+FreeoG8yLztutf/qD7nGEsry5SugxLzJvO0ovXvsNDq/EPJjOC0Dp3UboMQGdl4HUi9Q+/UTPsd+fPpY5qS/hx/7uY/X6fU+9N/b4Vv2ZkddmzFmAzotB6QWKDq+MLFxuZC/TK/+p756tRx4YLf2tFUJvITIvCJN6eVqrB3oOXl7NvqOg8p/5Zv3w08GsfqX+ePLxoPOE3j50XhZK7yjVP/JPnuzmfKt+76uxh3f/Meg8obcOnZeG0rtJ9U+8H/jds/Gs33qsXv/Ls4TeMizPS8T6jSixNfoqj6Nfqz848kFoq37/K7uE3jJ0XiZKL4m20PfW5jfju2P/+E+jc/zotRlqJqPzUlF6QXqHV676tbsrPGEqqHkv9Fujx90QeruQecFIvTg3l6u88ciw5oTeakznhSP1wrRyDqLvEHqkovPiUXpZKp/Rs3RjPzrvAlIvRXtjo+obj6TvjA0RemuQeUd4tN5u7QsLVzo3o5vGLp7J3VL1j3gz9fDKEKG3BZ13BqW327q/cOVG//DKk3lbqj9hqjuRH886obcEnXcJpbdYu+kv/EPTrx3f2Li47Fe5Rp91CYQOobcEy/OuIfXWajVqZ8L/wo9vLld7h6msi5oReht4dN5BpN5S4SGV7WY38K1GpTP6kcsUrw13yRJ689F5N1F6O0Wz+Rvdxfkb/qG8TdX/bHfjNx4h9DYh884i9TZqN/19T/zwlP+lv/rZN/192beX6nArQQyReZdxAI6FBkfcVH5RsyIYUyZhOu84Sm+ha6uEHkXQeVB6uQg9OuyFRRepl4rQg8xjgNRbpnc9+sCx35/O3h9L6EHnMUTp7RK7w9TlnCsVE3rn0XkkkHqbtFYP9By8vHqQ0CMDlccoSi8OoXcbs3mkoPTSEHqXsWqDDKzfWCK2M5YzY5GKziMTpbdDbGcsJ0whhUfnkYPSW6G3MzYIffae2A6hdxaVxyRcFMEe7b+v9g5TRTCAtGE2j2lQemtUfT36Ahg/utB5TIfS26LqO0wVwPDRg8pjeqTebP2jbr7IzljEkXkUQ+pNNjzqZn/eZoTeLSzaoDBKb7BB6O/JW6In9G6h85gFqbcdoXcIlcesONTSboTeGWQec6D0ZuJ69IgZvE3pPGZE6U3E9egx4NF5KEDqzcP16NFD5aEKCziWIvTCMZmHSpTeQO2NjdxjKzuEXjgqD9UovWG2j4Yr9Is/z92I0MvFZB6lIPUmubnc3RdbO5O3FaGXisyjNCzgmOO8Xzu+sfGzo1wCwUVUHmXySL0hWo3uVJ6rV7qHyTxK59F6I7R6B8+3cg6i7xB6gag8KkHqTdBuRpcz22763HjEGV6c7g5APlKv37ofJv687x/K24rQy0HmUTmm9boFU/qTnc4N/57crQi9EFQempB6zd4I12wmnDFF6EUg89CIab1O7UuXJp0XS+hFoPHQjNRrE54wVTvZ+tq3crci9LZjKg8jJP5fpe53hTvazd4litkZKxYLNjAJqdeg1fBPtJu1M69zeKVQZB7G8TxqX612c+FKZ90/xAlTMpF5GInUV2zdP9G5ubyf0AtE5WE0al+d7aP+vi+G6/Qs3QhD42E8Ul+R4T1j2RkrB1N5WITal6/V2Ne9ZSyHV4pB5mEZUl+6t6baitDbgsrDUtReP0JvBzIPi3m0XjNCbwMaD/sRe40IvfGYykMIJvbaEHrDUXnIQup1IPQmYzIPiZjYV47Qm4rdr5CM1FeK0BuJykM+JvbVIfQGIvNwA6mvCqE3DpWHU6h9BQi9SZjKw0UerS8boTcGlYfLiH2ZCL0RRga57vccoAET+/IQev1Gx7fu9xugD7UvBaHXisYDo0bfFXRifoRem7HhrPv9BRhj7N1B7+dC6KuXNoZ1v68A06S9T1xtxtwIfXXSBy6VBzJlvmlcq8ecSvhuvftYvX7fU+9NftCRH1XOUKXxwES57yCiPxXl36Hds/XIA+9NelBu6KcYmEQeKGbKtxX1T6P8m7FZP/x0MIFfqT8+6UFBoS84AHW/YwBbFa49+Y+o/rI/ebL+TPjrVv3eV/MftC/0cw8x3W8SQI75kz+guywVUP019lu+e7bb9uwHzQq9wlEzRvc7AhCujLet7iSppfrLWas/OPJB1oPVh76M0ZBO97gHUN37vZBqozeIn+LnW+svw2/G9rymPjhv6HX/vABgDvPkr3Au1T7d7tl+07eGTU99sDNn6HX/kABgLnP0r3Av1T7dcBU+GfrxBzuEHoDL5uhf4V6qfTpCDwBTmaN/hXup9umqW7oBAEzH3p2xAICpqG7tZtqRlKkPEnoAqIT6E6a6c/aRE6ZSHiT0AFAJLoEAAMJxUTMAEK7kyxSvdX917DLFAGAS9a3djd9jZK2/Ou/wjUcAQC+9txIEABRWOLZlFHzqFwcAFFa4tWUEHABgDkIPAMIRegAQjtADgHCEHgCEI/QAIByhBwDhCD0ACEfoAUA4Qg8AwhF6ABCO0AOAcITeLru/WKnX//P/0P1pyJR2LW2owtDVidBb5ZMnu3dweZAaKZd+dxwowtDVitDbJGjR/a+M35MRKqTf7xJqMHT1IvQ26d9dfZNpp3IZd7CHGgxdvQi9TTbrD0a/EiP1+t/TYOr5jO7PRSCGrl6E3kZMi9Rb65Vo+AFKwNDVg9DbZ/cX9cPP6/4kxFnrLx6TovIwdHUh9LbZqtej3VpQavdsP/RbhL4kDF19CL1ttu77T8HbhWmRYsOleUJfFoauPoTeQvwfYPUIfSUYupoQehsFVWKHoVos3VSDoasHobfArZXwlML4TIgdhsqxM7YafHu1IPQWGA89s07lNjm8shIMXS0IvUWGywtMi5TrB4gTpsrA0NWM0Ntks3da4SdPcsUQ1bgEQqkYunoRepsE75LwOOT/u8KsSD0ualYmhq5ehN4q3dX6ev1+Jp3KcZniUjF0tSL0dvl/4d0buDlGKXa58UiZGLo6EXoAEI7QA4BwhB4AhCP0ACAcoQcA4Qg9AAhH6AFAOEIPAMIRegAQjtADgHCEHgCEI/QAIByhBwDhCD0ACEfoAUA4Qg8AwhF6ABCO0AOAcIQeAIQj9AAgHKEHAOEIPQAIR+gBQDhCDwDCEXoAEI7QA4BwhB4AhCP0ACAcoQcA4Qg9AAhH6AFAOEIPAMIRegAQjtADgHCEHgCEI/QAIByhBwDhCD0ACEfoAUA4Qg8AwhF6ABCO0AOAcIQeAIQj9AAgHKEHAOEIPQAIR+gBQDhCD4t8sOQ9WmDz696e7w3/0mtf9rzgo94vWhT8/AFFCD0sMk/orwd9D37f+6WszzAfoYcehB5yXY8X/Zz3qe8Nf9GD0EMPQg+5RkL/mdgvehB66EHoIVc89Dvfjgrf+0UTQg89CD0M9PFD3qM7z3reni/8NvztB0uf+j8veN6nvzcI5Wt/5nne57/bSfzp8O/vvPjZ4IGXhmv0Qd/jwif5VcZTJB/3Hv0w/P2dL/We+ZfBE+/5i9/2fhfftuecd/tv+x+F/6T84cXPhTsFuk/Q/fwHX8XgH6Lk80S/u23wIsDcCD0MFIT+Pz7kDXabBh3+d8HHn/q7XiJ3XugFO/p3YPCnw7/+5e4fP5wT+v5T3D7yFCOPew8vDT+PTv9pei1PbNsziHf3c72+FP/HJSP0yee52vtd7AsC5kPoYaCPw8r/m992frkU5S4oY/C7nZ/0E3kuSnyY+3DOPPjTgXPenkeCWf+SFzvqZmTp5mr0/Dv/vDTyFOOPf/qnnZ2/8foL/NFn5Xl3jT/H8FOP/jD40+BTD353+0vBqw6/kJTQj71m+O/Xa0s615ggDKGHgcLQRy0MqndX9L+399ZweqHst7QX8tsTyxzXh385K/TBY4/0tk08xfjj3Xn1uX6ne4Xv/S6+bV9v7ab7Qlf78/KrsU9lJPQjz3N1MLFnSg9VCD0MFE6Eu+2OsjkoezeRgwR2Z8+DP+07N/zLWaG/mlxJj/3Tkf749egl+y/c/zSSq/E91/vLTYn9rtezQz/yPAQe6hF6GGiw/tHN3qCM0QexA2f6/wwkDmUZ/vn1rNAH/9v/t+Fq/CmyHu89UyLoo9uOfO6xXu/86n99xcsM/ejzXA93xP5mtu8bkI7Qw0DhUTfdj6IWjoU+vcbDv3xX7C+nhf7jh2J7ZmP/kmQ9PkhycjE+se1A9K/PYNvo+KD+7ty00I89z7lo6zt/qvIbCscRehgoN/TDkI/M94d/uffnHyypDv1diZdJD/31+N/sVvtPvvvL6UMfHcIZ/q1HVH5L4TRCDwMpmtHnhT6xrB8LferjGTP65K6BxCffW7m56nndqXn2Gn3a8+z87Z/1j8gEFCD0MFAFa/SJYxdja/Spj4+s0UebZZ9jG2w2dh5u9lE3Wc/zy9GDiYCZEXoYKAh9L379g19ioU856iY59b06+aibc8lDWwZPkfV4//iY7p92X/dc1uEx171P/e/uXxz8gxW8bEroz/X++Yg/T/zfBg6/gSKEHgYKAtk9NL17SPxI6MePo0+Gvv/n+cfRxybniRdIfXzw/w2Sx9HHt0189l/pRnrwJ9fjO2P7/4z1vsqR5xn0/RwzeqhC6GGgcAdluC+yt3wxEvrxM2NHFrO7J7Dmnhl7rnv9ml99eeTfiozHewfHn+t/VneNb5t8/V67u59J95INj8Y///Dz+7LXf9b48wRf+6d/2r3MA2v0UITQw0BB7D63lLjkTCL0o9e6GS1i75I0ex7ODv3gKfY8mniKjMd7oU9e6ya5bcz1wa1N+ofUfPq/L8VWmT7sPvqp/9l71uTzDC6P8wW131Q4jNDDQOHadnjVyNseiV35IP5B9+qVP00+GPPLkatXplymOHqK277wm9GnSH18cJWD8IlveyRt2+Sn31912XlhKboSZXIp6MNnl8Ircw6eNfk84Z9yID1UIvQwUOahiwBmQOhhIEIPqEToYSBCD6hE6GEgQg+oROhhIEIPqEToAUA4Qg8AwhF6ABCO0AOAcIQeAIQj9AAgHKEHAOEIPQAIR+gBQDhCDwDCEXoAEO7/A67FWTbEyFJ5AAAAAElFTkSuQmCC" /><!-- --></p>
<p>We can see that using this coding scheme, we have equal priors on all pairwise contrasts.</p>
<h4 id="order-restrictions">Order restrictions</h4>
<p>This bias also affect order restrictions involving 3 or more levels. For example, if we want to test an order restriction among A, B, and C, the <em>a priori</em> probability of obtaining the order A &gt; C &gt; B is 1/6 (reach back to <em>intro to stats</em> year 1), but…</p>
<p>For our example, we will be interested in the following order restrictions in the <code>iris</code> data-set (each line is a separate restriction):</p>
<div class="sourceCode" id="cb58"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb58-1" title="1">hyp &lt;-<span class="st"> </span><span class="kw">c</span>(</a>
<a class="sourceLine" id="cb58-2" title="2">  <span class="co"># comparing 2 levels</span></a>
<a class="sourceLine" id="cb58-3" title="3">  <span class="st">&quot;setosa &lt; versicolor&quot;</span>,</a>
<a class="sourceLine" id="cb58-4" title="4">  <span class="st">&quot;setosa &lt; virginica&quot;</span>,</a>
<a class="sourceLine" id="cb58-5" title="5">  <span class="st">&quot;versicolor &lt; virginica&quot;</span>,</a>
<a class="sourceLine" id="cb58-6" title="6">  </a>
<a class="sourceLine" id="cb58-7" title="7">  <span class="co"># comparing 3 (or more) levels</span></a>
<a class="sourceLine" id="cb58-8" title="8">  <span class="st">&quot;setosa    &lt; virginica  &amp; virginica  &lt; versicolor&quot;</span>,</a>
<a class="sourceLine" id="cb58-9" title="9">  <span class="st">&quot;virginica &lt; setosa     &amp; setosa     &lt; versicolor&quot;</span>,</a>
<a class="sourceLine" id="cb58-10" title="10">  <span class="st">&quot;setosa    &lt; versicolor &amp; versicolor &lt; virginica&quot;</span></a>
<a class="sourceLine" id="cb58-11" title="11">)</a></code></pre></div>
<p>With the default factor coding, this looks like this:</p>
<div class="sourceCode" id="cb59"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb59-1" title="1"><span class="kw">contrasts</span>(iris<span class="op">$</span>Species) &lt;-<span class="st"> </span>contr.sum</a>
<a class="sourceLine" id="cb59-2" title="2"></a>
<a class="sourceLine" id="cb59-3" title="3">fit_sum &lt;-<span class="st"> </span><span class="kw">stan_glm</span>(Sepal.Length <span class="op">~</span><span class="st"> </span>Species, <span class="dt">data =</span> iris,</a>
<a class="sourceLine" id="cb59-4" title="4">                    <span class="dt">prior =</span> <span class="kw">normal</span>(<span class="dv">0</span>, <span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">1</span>), <span class="dt">autoscale =</span> <span class="ot">FALSE</span>),</a>
<a class="sourceLine" id="cb59-5" title="5">                    <span class="dt">family =</span> <span class="kw">gaussian</span>())</a>
<a class="sourceLine" id="cb59-6" title="6"></a>
<a class="sourceLine" id="cb59-7" title="7">em_sum &lt;-<span class="st"> </span><span class="kw">emmeans</span>(fit_sum, <span class="op">~</span><span class="st"> </span>Species) <span class="co"># the posterior marginal means</span></a>
<a class="sourceLine" id="cb59-8" title="8">  </a>
<a class="sourceLine" id="cb59-9" title="9"><span class="kw">bayesfactor_restricted</span>(em_sum, fit_sum, <span class="dt">hypothesis =</span> hyp)</a></code></pre></div>
<pre><code>&gt; # Bayes Factor (Order-Restriction)
&gt; 
&gt;                                        Hypothesis P(Prior) P(Posterior)
&gt;                               setosa &lt; versicolor     0.51            1
&gt;                                setosa &lt; virginica     0.51            1
&gt;                            versicolor &lt; virginica     0.50            1
&gt;  setosa    &lt; virginica  &amp; virginica  &lt; versicolor     0.10            0
&gt;  virginica &lt; setosa     &amp; setosa     &lt; versicolor     0.20            0
&gt;   setosa    &lt; versicolor &amp; versicolor &lt; virginica     0.20            1
&gt;        BF
&gt;      1.97
&gt;      1.97
&gt;      1.99
&gt;  0.00e+00
&gt;  0.00e+00
&gt;      4.94
&gt; 
&gt; * Bayes factors for the restricted model vs. the un-restricted model.
</code></pre>
<p><em><strong>What happened???</strong></em></p>
<ol>
<li>The comparison of 2 levels all have a prior of ~0.5, as expected.</li>
<li>The comparison of 3 levels has different priors, depending on the order restriction - i.e. <strong>some orders are <em>a priori</em> more likely than others!!!</strong></li>
</ol>
<p>Again, this is solved by using the <em>orthonormal</em> factor coding (from above).</p>
<div class="sourceCode" id="cb61"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb61-1" title="1"><span class="kw">contrasts</span>(iris<span class="op">$</span>Species) &lt;-<span class="st"> </span>contr.bayes</a>
<a class="sourceLine" id="cb61-2" title="2"></a>
<a class="sourceLine" id="cb61-3" title="3">fit_bayes &lt;-<span class="st"> </span><span class="kw">stan_glm</span>(Sepal.Length <span class="op">~</span><span class="st"> </span>Species, <span class="dt">data =</span> iris,</a>
<a class="sourceLine" id="cb61-4" title="4">                      <span class="dt">prior =</span> <span class="kw">normal</span>(<span class="dv">0</span>, <span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">1</span>), <span class="dt">autoscale =</span> <span class="ot">FALSE</span>),</a>
<a class="sourceLine" id="cb61-5" title="5">                      <span class="dt">family =</span> <span class="kw">gaussian</span>())</a>
<a class="sourceLine" id="cb61-6" title="6"></a>
<a class="sourceLine" id="cb61-7" title="7">em_bayes &lt;-<span class="st"> </span><span class="kw">emmeans</span>(fit_sum, <span class="op">~</span><span class="st"> </span>Species) <span class="co"># the posterior marginal means</span></a>
<a class="sourceLine" id="cb61-8" title="8"></a>
<a class="sourceLine" id="cb61-9" title="9"><span class="kw">bayesfactor_restricted</span>(em_bayes, fit_sum, <span class="dt">hypothesis =</span> hyp)</a></code></pre></div>
<pre><code>&gt; # Bayes Factor (Order-Restriction)
&gt; 
&gt;                                        Hypothesis P(Prior) P(Posterior)
&gt;                               setosa &lt; versicolor     0.50            1
&gt;                                setosa &lt; virginica     0.50            1
&gt;                            versicolor &lt; virginica     0.50            1
&gt;  setosa    &lt; virginica  &amp; virginica  &lt; versicolor     0.17            0
&gt;  virginica &lt; setosa     &amp; setosa     &lt; versicolor     0.16            0
&gt;   setosa    &lt; versicolor &amp; versicolor &lt; virginica     0.16            1
&gt;        BF
&gt;      2.02
&gt;      2.01
&gt;      1.98
&gt;  0.00e+00
&gt;  0.00e+00
&gt;      6.11
&gt; 
&gt; * Bayes factors for the restricted model vs. the un-restricted model.
</code></pre>
<h4 id="conclusion">Conclusion</h4>
<p>When comparing the results from the two factor coding schemes, we find:<br />
1. In both cases, the estimated (posterior) means are quite similar (if not identical).<br />
2. The priors and Bayes factors differ between the two schemes.<br />
3. Only with <code>contr.bayes</code>, the prior distribution of the difference or the order of 3 (or more) means is balanced.</p>
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</div>

<ol>
<li><p>Note that as the width of null interval shrinks to zero, the prior probability and posterior probability of the alternative tends towards 1.00.</p></li>
<li><p><a href="https://doi.org/10.1016/j.jmp.2015.08.002">Robert, 2016</a>; <a href="https://doi.org/10.1080/01621459.1995.10476572">Kass &amp; Raftery, 1993</a>; <a href="https://doi.org/10.1016/S0304-4076(00)00076-2">Fernández, Ley, &amp; Steel, 2001</a></p></li>
<li><p><a href="https://doi.org/10.1007/s11336-018-9648-3">Gronau, Wagenmakers, Heck, &amp; Matzke, 2019</a></p></li>
<li><p>A model without predictor (X) can be thought of as a model in which the parameter(s) of the predictor have been restricted to a null-point of 0.</p></li>
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