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Tip revision: 23ea3229abe72a5f23dcf3a4cfcd3478d744b536 authored by Dominique Makowski on 20 June 2019, 11:50:03 UTC
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<body>

<h1 id="in-depth-2-comparison-of-indices-of-effect-existence-and-significance">In-Depth 2: Comparison of Indices of Effect Existence and Significance</h1>
<ul>
<li><a href="#indices-of-effect-existence-and-significance-in-the-bayesian-framework">Indices of Effect <em>Existence</em> and <em>Significance</em> in the Bayesian Framework</a>
<ul>
<li><a href="#introduction">Introduction</a></li>
<li><a href="#study-1-relationship-with-noise-and-sample-size">Study 1: Relationship with Noise and Sample Size</a>
<ul>
<li><a href="#methods">Methods</a></li>
<li><a href="#results">Results</a></li>
</ul></li>
<li><a href="#study-2-relationship-with-sampling-characteristics">Study 2: Relationship with Sampling Characteristics</a></li>
<li><a href="#study-3-relationship-with-priors-specification">Study 3: Relationship with Priors Specification</a></li>
<li><a href="#discussion">Discussion</a></li>
</ul></li>
<li><a href="#references">References</a></li>
</ul>
<p>This vignette can be referred to by citing the package:</p>
<ul>
<li>Makowski, D., Ben-Shachar M. S. &amp; Lüdecke, D. (2019). <em>Understand and Describe Bayesian Models and Posterior Distributions using bayestestR</em>. Available from <a href="https://github.com/easystats/bayestestR">https://github.com/easystats/bayestestR</a>. DOI: <a href="https://zenodo.org/record/2556486">10.5281/zenodo.2556486</a>.</li>
</ul>
<hr />
<h1 id="indices-of-effect-existence-and-significance-in-the-bayesian-framework">Indices of Effect <em>Existence</em> and <em>Significance</em> in the Bayesian Framework</h1>
<p>There is now a general agreement that the Bayesian statistical framework is the right way to go for psychological science. Nevertheless, its flexible nature is its power and weakness, for there is no agreement about what indices should be computed or reported. Moreover, the lack of a consensual index of effect existence, such as the frequentist <em>p</em>-value, possibly contributes to the unnecessary murkiness that many non-familiar readers perceive in Bayesian statistics. Thus, this study describes and compares several indices of effect existence, provide intuitive visual representation of the “behaviour” of such indices in relationship with traditional metrics such as sample size and frequentist significance. The results contribute to develop the intuitive understanding of the values that researchers report and allow to draw recommendations for Bayesian statistics description, critical for the standardization of scientific reporting.</p>
<h2 id="introduction">Introduction</h2>
<p>The Bayesian framework is quickly gaining popularity among psychologists and neuroscientists (Andrews and Baguley 2013). Reasons to prefer this approach are reliability, better accuracy in noisy data, better estimation for small samples, less proneness to type I error, the possibility of introducing prior knowledge into the analysis and, critically, results intuitiveness and their straightforward interpretation (Dienes and Mclatchie 2018; Etz and Vandekerckhove 2016; Kruschke 2010; Kruschke, Aguinis, and Joo 2012; Wagenmakers et al. 2018; Wagenmakers, Morey, and Lee 2016). The frequentist approach has been associated with the focus on null hypothesis testing, and the misuse of <em>p</em>-values has been shown to critically contribute to the reproducibility crisis of psychological science (Chambers et al. 2014; Szucs and Ioannidis 2016). There is a general agreement that the generalization of the Bayesian approach is a way of overcoming this issue (Benjamin et al. 2018; Etz and Vandekerckhove 2016; Maxwell, Lau, and Howard 2015; Wagenmakers et al. 2017).</p>
<p>While the probabilistic reasoning promoted by the Bayesian framework is pervading most of data science aspects, it is already well established for statistical modelling. This facet, on which psychology massively rely, could roughly be grouped into two soft-edged categories; predictive and structural modelling. Although a statistical model can (often) serve both purposes, predictive modelling is devoted to build and find the best model that accurately predicts a given outcome. It is centred around the concepts such as fitting metrics, predictive accuracy and model comparison. At the extrema of this dimension lie machine and deep learning models, used for their strong predictive power, often at the expense of Human readability (these models has been often refer to as “black-boxes”, emphasising the difficulty to appraise their internal functioning; Burrell 2016; Castelvecchi 2016; Snoek, Larochelle, and Adams 2012). On the other side, psychologists are often using more simple models (for instance related to the general linear framework) to explore their data. Within this framework, the goal switches from building the best model to understanding the parameters inside the model. In reality, the methodological pipeline often starts with predictive modelling involving model comparison (“what is the best model of the world (<em>i.e.</em>, the observed variable)”) and then seemingly transit to structural modelling: “given this model of the world, how the effects (<em>i.e.</em>, model parameters) are influencing the outcome”. For this last part, they often rely on an index of effect “existence”.</p>
<p>Indeed, while one of the strengths of the Bayesian framework is its probabilistic parameter estimation, allowing to quantify the inherent uncertainty associated with each estimation, psychologists are also interested in parameter <em>existence</em>: A decision criterion that allows them to conclude if an effect is “different from 0” (statistically corresponding to either “not negligible” or “not of the opposite direction”). In other words, to know, before taking interest in the importance, relevance or strength of the effect, whether it is related to the outcome in a given direction. This need has led to the wide adoption of the frequentist <em>p</em>-value, used as an index of effect existence, and its acceptance was accompanied with the creation of arbitrary clusters for its classification (.05, .01 and .001). Unfortunately, these heuristics have severely rigidify, becoming a goal and threshold to reach rather than a tool for understanding the data (Cohen 2016; Kirk 1996).</p>
<p>Thus, the ability of the Bayesian framework to answer psychological questions without the need of such null-hypothesis testing indices is often promoted as the promise of a “a new world without <em>p</em>-value” as opposed to the old and flawed frequentist one. Nonetheless, it seems that “effect existence” indices and criteria are useful for Humans to gain an intuitive understanding of the interactions and structure of their data. It is thus unsurprising that the development of Bayesian user-friendly implementations was accompanied with the promotion of the Bayes Factor (BF), an index reflecting the predictive performance of a model against another (<em>e.g.</em>, the null vs. the alternative hypothesis; Jeffreys 1998; Ly, Verhagen, and Wagenmakers 2016). It provides many advantages over the <em>p</em> value, having a straightforward interpretation (“the data were 3 times (<em>BF</em> = 3) more likely to occur under the alternative than the null hypothesis”) and allowing to make statements about the alternative, rather than just the null hypothesis (Dienes 2014; Jarosz and Wiley 2014). Moreover, recent mathematical developments allow its computation for complex models (Gronau, Van Erp, et al. 2017; Gronau, Wagenmakers, et al. 2017). Although the BF lives up to the expectations of a solid, valid, intuitive and better index compared to the p value, its use for model selection is still a matter of debate (Piironen and Vehtari 2017). Indeed, as the predictions used for its computation are generated from the prior distributions on the model parameters, it is highly dependent on priors specification (Etz et al. 2018; Kruschke and Liddell 2018). Importantly for the aim of this paper, its use for estimating effect existence of parameters <em>within</em> a larger model remains limited, its computation being technically difficult and its interpretation not as straightforward as for “simple” tests such as t-tests or correlations.</p>
<p>Nevertheless, reflecting the need for such information, researchers have developed other indices based on characteristics of the posterior distribution, which represents the probability distribution of different parameter values given the observed data. This uncertainty can be summarized, for example, by presenting point-estimates of centrality (mean, median, …) and of dispersion (standard deviation, median absolute deviance, …), often accompanied with a percentage (89%, 90% or 95%) of the Highest Density Interval (HDI; referred to as the <em>Credible Interval</em> - CI). Although the Bayesian framework gives the possibility of computing many effect existence indices, no consensus has yet emerged on the ones to use, as no comparison has ever been done. This might be a rebuttal for scientists interested in adopting the Bayesian framework. Moreover, this grey area can increase the difficulty of readers or reviewers unfamiliar with the Bayesian framework to follow the assumptions and conclusions, which could in turn generate unnecessary doubt upon the entire study. While we think that such indices and their interpretation guidelines (in the form of rules of thumb) are useful in practice, we also strongly believe that such indices should be accompanied with the knowledge of the “behaviour” in relationship with sample size and effect size. This knowledge is important for people to implicitly and intuitively appraise the meaning and implication of the mathematical values they report. This could, in turn, prevent the crystallization of the possible heuristics and categories derived from such indices.</p>
<p>Thus, based on the simulation of multiple linear regressions (one of the most widely used models), the present work aims at comparing several indices of effect existence solely derived from the posterior distribution, provide visual representations of the “behaviour” of such indices in relationship with sample size, noise, priors and also the frequentist <em>p</em>-value (an index which, beyond its many flaws, is well known and could be used as a reference for Bayesian neophytes), and draw recommendations for Bayesian statistics reporting.</p>
<p>For all of the simulated models, we computed the following indices:</p>
<ul>
<li><strong><em>p</em>-value</strong>: Based on the frequentist regression, this index represents the probability for a given statistical model that, when the null hypothesis is true, the statistical summary (such as the sample mean difference between two compared groups) would be greater than or equal to the actual observed results (Wasserstein, Lazar, and others 2016).</li>
<li><strong><em>p</em>-direction</strong>: the “Probability of Direction” (pd) corresponds to the probability (expressed in percentage) that the effect is stricly positive or negative (consistently with the median’s sign).</li>
<li><strong><em>p</em>-MAP</strong>: the MAP-based <em>p</em>-value is related to the odds that a parameter has against the null hypothesis (Mills and Parent 2014; Mills 2017).</li>
<li><strong><em>p</em>-ROPE</strong>: the ROPE-based <em>p</em>-value that represents the maximum percentage of HDI that does not contain (positive values) or is entirely contained (negative values) in the negligible values space defined by the ROPE.</li>
<li><strong>ROPE (89%)</strong>: this index refers to the percentage of the 89% HDI that lies within the ROPE, which is an interval considered as more stable than the frequentist-based 95% (McElreath 2014, 2018).</li>
<li><strong>ROPE (95%)</strong>: this index refers to the percentage of the 95% HDI that lies within the ROPE. This interval corresponds to the one originally suggested by Kruschke (2014).</li>
<li><strong>ROPE (full)</strong>: this index refers to the percentage of the whole posterior distribution that lies within the ROPE.</li>
<li><strong>Bayes factor</strong>: TODO.</li>
</ul>
<p>The Region of Practical Equivalence (ROPE) was defined as ranging from -0.1 to 0.1.</p>
<h2 id="study-1-relationship-with-noise-and-sample-size">Study 1: Relationship with Noise and Sample Size</h2>
<h3 id="methods">Methods</h3>
<p>The simulation aimed at modulating the following characteristics:</p>
<ul>
<li><strong>Model type</strong>: linear or logistic.</li>
<li><strong>“True” effect</strong> (original regression coefficient from which data is drawn): Can be 1 or 0 (no effect).</li>
<li><strong>Sample size</strong>: From 20 to 100 by steps of 10.</li>
<li><strong>Error</strong>: Gaussian noise applied to the predictor with SD uniformly spread between 0.33 and 6.66 (with 1000 different values).</li>
</ul>
<p>We generated a dataset for each combination of these characteristics, resulting in a total of <code>2 * 2 * 9 * 1000 = 36000</code> Bayesian and frequentist models. The code used for generation is avaible <a href="https://easystats.github.io/circus/articles/bayesian_indices.html">here</a> (please note that it takes usually several days/weeks to complete).</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>(ggplot2)</a>
<a class="sourceLine" id="cb1-2" title="2"><span class="kw">library</span>(dplyr)</a>
<a class="sourceLine" id="cb1-3" title="3"><span class="kw">library</span>(tidyr)</a>
<a class="sourceLine" id="cb1-4" title="4"><span class="kw">library</span>(see)</a>
<a class="sourceLine" id="cb1-5" title="5"></a>
<a class="sourceLine" id="cb1-6" title="6">df &lt;-<span class="st"> </span><span class="kw">read.csv</span>(<span class="st">&quot;https://raw.github.com/easystats/circus/master/data/bayesSim_study1.csv&quot;</span>)</a></code></pre></div>
<h3 id="results">Results</h3>
<h4 id="effect-detection">Effect Detection</h4>
<h5 id="sensitivity-to-noise">Sensitivity to Noise</h5>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1">df <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb2-2" title="2"><span class="st">  </span><span class="kw">select</span>(outcome_type, true_effect, error, sample_size, p_value, p_direction, p_MAP, p_ROPE, ROPE_<span class="dv">89</span>, ROPE_<span class="dv">95</span>, ROPE_full, BF_log) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb2-3" title="3"><span class="st">  </span><span class="kw">gather</span>(index, value, <span class="op">-</span>error, <span class="op">-</span>sample_size, <span class="op">-</span>true_effect, <span class="op">-</span>outcome_type) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb2-4" title="4"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">true_effect =</span> <span class="kw">as.factor</span>(true_effect),</a>
<a class="sourceLine" id="cb2-5" title="5">         <span class="dt">index =</span> <span class="kw">factor</span>(index, <span class="dt">levels=</span><span class="kw">c</span>(<span class="st">&quot;p_value&quot;</span>, <span class="st">&quot;p_direction&quot;</span>, <span class="st">&quot;p_MAP&quot;</span>, <span class="st">&quot;p_ROPE&quot;</span>, <span class="st">&quot;ROPE_89&quot;</span>, <span class="st">&quot;ROPE_95&quot;</span>, <span class="st">&quot;ROPE_full&quot;</span>, <span class="st">&quot;BF_log&quot;</span>))) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb2-6" title="6"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">temp =</span> <span class="kw">as.factor</span>(<span class="kw">cut</span>(error, <span class="dv">10</span>, <span class="dt">labels =</span> <span class="ot">FALSE</span>))) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-7" title="7"><span class="st">  </span><span class="kw">group_by</span>(temp) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-8" title="8"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">error_group =</span> <span class="kw">round</span>(<span class="kw">mean</span>(error), <span class="dv">1</span>)) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-9" title="9"><span class="st">  </span><span class="kw">ungroup</span>() <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb2-10" title="10"><span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x =</span> error_group, <span class="dt">y =</span> value, <span class="dt">fill =</span> index, <span class="dt">colour=</span>true_effect, <span class="dt">group =</span> <span class="kw">interaction</span>(index, true_effect, error_group))) <span class="op">+</span></a>
<a class="sourceLine" id="cb2-11" title="11"><span class="st">  </span><span class="co"># geom_jitter(shape=16, alpha=0.02) +</span></a>
<a class="sourceLine" id="cb2-12" title="12"><span class="st">  </span><span class="kw">geom_boxplot</span>(<span class="dt">outlier.shape =</span> <span class="ot">NA</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb2-13" title="13"><span class="st">  </span><span class="kw">facet_wrap</span>(<span class="op">~</span>index <span class="op">*</span><span class="st"> </span>outcome_type, <span class="dt">scales =</span> <span class="st">&quot;free&quot;</span>, <span class="dt">ncol=</span><span class="dv">2</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb2-14" title="14"><span class="st">  </span><span class="kw">theme_modern</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb2-15" title="15"><span class="st">  </span><span class="kw">scale_color_manual</span>(<span class="dt">values =</span> <span class="kw">c</span>(<span class="st">`</span><span class="dt">0</span><span class="st">`</span> =<span class="st"> &quot;#f44336&quot;</span>, <span class="st">`</span><span class="dt">1</span><span class="st">`</span> =<span class="st"> &quot;#8BC34A&quot;</span>, <span class="dt">name=</span><span class="st">&quot;Effect&quot;</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb2-16" title="16"><span class="st">  </span><span class="kw">scale_fill_manual</span>(<span class="dt">values =</span> <span class="kw">c</span>(<span class="st">&quot;p_value&quot;</span>=<span class="st">&quot;#607D8B&quot;</span>, <span class="st">&quot;p_MAP&quot;</span> =<span class="st"> &quot;#4CAF50&quot;</span>, <span class="st">&quot;p_direction&quot;</span> =<span class="st"> &quot;#2196F3&quot;</span>,</a>
<a class="sourceLine" id="cb2-17" title="17">                               <span class="st">&quot;ROPE_89&quot;</span> =<span class="st"> &quot;#FFC107&quot;</span>, <span class="st">&quot;ROPE_95&quot;</span> =<span class="st"> &quot;#FF9800&quot;</span>, <span class="st">&quot;ROPE_full&quot;</span> =<span class="st"> &quot;#FF5722&quot;</span>,</a>
<a class="sourceLine" id="cb2-18" title="18">                               <span class="st">&quot;p_ROPE&quot;</span>=<span class="st">&quot;#E91E63&quot;</span>, <span class="st">&quot;BF_log&quot;</span>=<span class="st">&quot;#9C27B0&quot;</span>), <span class="dt">guide=</span><span class="ot">FALSE</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb2-19" title="19"><span class="st">  </span><span class="kw">ylab</span>(<span class="st">&quot;Index Value&quot;</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb2-20" title="20"><span class="st">  </span><span class="kw">xlab</span>(<span class="st">&quot;Noise&quot;</span>)</a></code></pre></div>
<p><img 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" /><!-- --></p>
<h5 id="sensitivity-to-sample-size">Sensitivity to Sample Size</h5>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" title="1">df <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb3-2" title="2"><span class="st">  </span><span class="kw">select</span>(outcome_type, true_effect, error, sample_size, p_value, p_direction, p_MAP, p_ROPE, ROPE_<span class="dv">89</span>, ROPE_<span class="dv">95</span>, ROPE_full, BF_log) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb3-3" title="3"><span class="st">  </span><span class="kw">gather</span>(index, value, <span class="op">-</span>error, <span class="op">-</span>sample_size, <span class="op">-</span>true_effect, <span class="op">-</span>outcome_type) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb3-4" title="4"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">true_effect =</span> <span class="kw">as.factor</span>(true_effect),</a>
<a class="sourceLine" id="cb3-5" title="5">         <span class="dt">index =</span> <span class="kw">factor</span>(index, <span class="dt">levels=</span><span class="kw">c</span>(<span class="st">&quot;p_value&quot;</span>, <span class="st">&quot;p_direction&quot;</span>, <span class="st">&quot;p_MAP&quot;</span>, <span class="st">&quot;p_ROPE&quot;</span>, <span class="st">&quot;ROPE_89&quot;</span>, <span class="st">&quot;ROPE_95&quot;</span>, <span class="st">&quot;ROPE_full&quot;</span>, <span class="st">&quot;BF_log&quot;</span>))) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb3-6" title="6"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">temp =</span> <span class="kw">as.factor</span>(<span class="kw">cut</span>(sample_size, <span class="dv">10</span>, <span class="dt">labels =</span> <span class="ot">FALSE</span>))) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb3-7" title="7"><span class="st">  </span><span class="kw">group_by</span>(temp) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb3-8" title="8"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">size_group =</span> <span class="kw">round</span>(<span class="kw">mean</span>(sample_size))) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb3-9" title="9"><span class="st">  </span><span class="kw">ungroup</span>() <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb3-10" title="10"><span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x =</span> size_group, <span class="dt">y =</span> value, <span class="dt">fill =</span> index, <span class="dt">colour=</span>true_effect, <span class="dt">group =</span> <span class="kw">interaction</span>(index, true_effect, size_group))) <span class="op">+</span></a>
<a class="sourceLine" id="cb3-11" title="11"><span class="st">  </span><span class="co"># geom_jitter(shape=16, alpha=0.02) +</span></a>
<a class="sourceLine" id="cb3-12" title="12"><span class="st">  </span><span class="kw">geom_boxplot</span>(<span class="dt">outlier.shape =</span> <span class="ot">NA</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb3-13" title="13"><span class="st">  </span><span class="kw">facet_wrap</span>(<span class="op">~</span>index <span class="op">*</span><span class="st"> </span>outcome_type, <span class="dt">scales =</span> <span class="st">&quot;free&quot;</span>, <span class="dt">ncol=</span><span class="dv">2</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb3-14" title="14"><span class="st">  </span><span class="kw">theme_modern</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb3-15" title="15"><span class="st">  </span><span class="kw">scale_color_manual</span>(<span class="dt">values =</span> <span class="kw">c</span>(<span class="st">`</span><span class="dt">0</span><span class="st">`</span> =<span class="st"> &quot;#f44336&quot;</span>, <span class="st">`</span><span class="dt">1</span><span class="st">`</span> =<span class="st"> &quot;#8BC34A&quot;</span>, <span class="dt">name=</span><span class="st">&quot;Effect&quot;</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb3-16" title="16"><span class="st">  </span><span class="kw">scale_fill_manual</span>(<span class="dt">values =</span> <span class="kw">c</span>(<span class="st">&quot;p_value&quot;</span>=<span class="st">&quot;#607D8B&quot;</span>, <span class="st">&quot;p_MAP&quot;</span> =<span class="st"> &quot;#4CAF50&quot;</span>, <span class="st">&quot;p_direction&quot;</span> =<span class="st"> &quot;#2196F3&quot;</span>,</a>
<a class="sourceLine" id="cb3-17" title="17">                               <span class="st">&quot;ROPE_89&quot;</span> =<span class="st"> &quot;#FFC107&quot;</span>, <span class="st">&quot;ROPE_95&quot;</span> =<span class="st"> &quot;#FF9800&quot;</span>, <span class="st">&quot;ROPE_full&quot;</span> =<span class="st"> &quot;#FF5722&quot;</span>,</a>
<a class="sourceLine" id="cb3-18" title="18">                               <span class="st">&quot;p_ROPE&quot;</span>=<span class="st">&quot;#E91E63&quot;</span>, <span class="st">&quot;BF_log&quot;</span>=<span class="st">&quot;#9C27B0&quot;</span>), <span class="dt">guide=</span><span class="ot">FALSE</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb3-19" title="19"><span class="st">  </span><span class="kw">ylab</span>(<span class="st">&quot;Index Value&quot;</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb3-20" title="20"><span class="st">  </span><span class="kw">xlab</span>(<span class="st">&quot;Sample Size&quot;</span>)</a></code></pre></div>
<p><img 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" /><!-- --></p>
<h5 id="statistical-modelling">Statistical Modelling</h5>
<p>We fitted a (frequentist) multiple linear regression to predict the presence or absence of effect with the different indices as well as their interaction with noise and sample size.</p>
<!-- THIS MUST WAIT UNTIL PARAMETERS Is ON CRAN SO WE CAN USE ITS NORMALIZE FUNCTION -->

<!-- ```{r, message=FALSE, warning=FALSE} -->

<!-- df %>% -->

<!--   select(outcome_type, true_effect, error, sample_size, p_value, p_direction, p_MAP, p_ROPE, ROPE_90, ROPE_95, ROPE_full) %>% -->

<!--   gather(index, value, -error, -sample_size, -true_effect, -outcome_type) %>% -->

<!--   group_by(index) %>% -->

<!--   parameters::normalize(select="value") %>% -->

<!--   ungroup() %>% -->

<!--   glm(true_effect ~ outcome_type / index / value  * sample_size * error, data=., family="binomial") %>% -->

<!--    broom::tidy() %>% -->

<!--    select(term, index, p=p.value) %>% -->

<!--    filter(stringr::str_detect(term, 'outcome_type'), -->

<!--           stringr::str_detect(term, ':value')) %>% -->

<!--    mutate( -->

<!--      sample_size = stringr::str_detect(term, 'sample_size'), -->

<!--      error = stringr::str_detect(term, 'error'), -->

<!--      term = stringr::str_remove(term, "index"), -->

<!--      term = stringr::str_remove(term, "outcome_type"), -->

<!--      p = paste0(sprintf("%.2f", p), ifelse(p < .001, "***", ifelse(p < .01, "**", ifelse(p < .05, "*", ""))))) %>% -->

<!--    arrange(sample_size, error, term) %>% -->

<!--    select(-sample_size, -error) %>% -->

<!--    knitr::kable(digits=2) -->

<!-- ``` -->

<!-- This suggests that, in order to delineate between the presence and the absence of an effect: -->

<!-- - For linear Models; -->

<!--   - The **pd**, followed by the **p (ROPE)** and the 3 **ROPE percentage**, indices had a significamtly superior performance. However, **p (MAP)** performed not significantly worse. -->

<!--   - The **mean**, followed closely by the **median**, and the **MAP** estimate had a superior performance, altough not significantly. -->

<!--   - The **mean**, followed closely by the **median**, and the **MAP** estimate, were less affected by noise, altough not significantly. -->

<!--   - No difference for the sensitivity to sample size was found. -->

<!-- - For logistic models: -->

<!--   - The **pd**, followed by the **p (ROPE)** and the 3 **ROPE percentage**, indices had a significamtly superior performance. However, **p (MAP)** performed not significantly worse. -->

<!--   - The **MAP** estimate, followed by the **median**, and the **mean**, were less affected by noise, altough not significantly. -->

<!--   - The **MAP** estimate, followed by the **mean**, and the **median**, were less affected by sample size, altough not significantly. -->

<h4 id="relationship-with-the-frequentist-p-value">Relationship with the frequentist <em>p</em> value</h4>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1">df <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb4-2" title="2"><span class="st">  </span><span class="kw">select</span>(outcome_type, true_effect, error, sample_size, p_value, p_direction, p_MAP, p_ROPE, ROPE_<span class="dv">89</span>, ROPE_<span class="dv">95</span>, ROPE_full, BF_log) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb4-3" title="3"><span class="st">  </span><span class="kw">gather</span>(index, value, <span class="op">-</span>error, <span class="op">-</span>sample_size, <span class="op">-</span>true_effect, <span class="op">-</span>outcome_type, <span class="op">-</span>p_value) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb4-4" title="4"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">true_effect =</span> <span class="kw">as.factor</span>(true_effect),</a>
<a class="sourceLine" id="cb4-5" title="5">         <span class="dt">index =</span> <span class="kw">factor</span>(index, <span class="dt">levels=</span><span class="kw">c</span>(<span class="st">&quot;p_direction&quot;</span>, <span class="st">&quot;p_MAP&quot;</span>, <span class="st">&quot;p_ROPE&quot;</span>, <span class="st">&quot;ROPE_89&quot;</span>, <span class="st">&quot;ROPE_95&quot;</span>, <span class="st">&quot;ROPE_full&quot;</span>, <span class="st">&quot;BF_log&quot;</span>))) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb4-6" title="6"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">temp =</span> <span class="kw">as.factor</span>(<span class="kw">cut</span>(sample_size, <span class="dv">3</span>, <span class="dt">labels =</span> <span class="ot">FALSE</span>))) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb4-7" title="7"><span class="st">  </span><span class="kw">group_by</span>(temp) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb4-8" title="8"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">size_group =</span> <span class="kw">as.character</span>(<span class="kw">round</span>(<span class="kw">mean</span>(sample_size)))) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb4-9" title="9"><span class="st">  </span><span class="kw">ungroup</span>() <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb4-10" title="10"><span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x =</span> p_value, <span class="dt">y =</span> value, <span class="dt">color =</span> true_effect, <span class="dt">shape=</span>size_group)) <span class="op">+</span></a>
<a class="sourceLine" id="cb4-11" title="11"><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">alpha=</span><span class="fl">0.025</span>, <span class="dt">stroke =</span> <span class="dv">0</span>, <span class="dt">shape=</span><span class="dv">16</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb4-12" title="12"><span class="st">  </span><span class="kw">facet_wrap</span>(<span class="op">~</span>index <span class="op">*</span><span class="st"> </span>outcome_type, <span class="dt">scales =</span> <span class="st">&quot;free&quot;</span>, <span class="dt">ncol=</span><span class="dv">2</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb4-13" title="13"><span class="st">  </span><span class="kw">theme_modern</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb4-14" title="14"><span class="st">  </span><span class="kw">scale_color_manual</span>(<span class="dt">values =</span> <span class="kw">c</span>(<span class="st">`</span><span class="dt">0</span><span class="st">`</span> =<span class="st"> &quot;#f44336&quot;</span>, <span class="st">`</span><span class="dt">1</span><span class="st">`</span> =<span class="st"> &quot;#8BC34A&quot;</span>), <span class="dt">name=</span><span class="st">&quot;Effect&quot;</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb4-15" title="15"><span class="st">  </span><span class="kw">guides</span>(<span class="dt">colour =</span> <span class="kw">guide_legend</span>(<span class="dt">override.aes =</span> <span class="kw">list</span>(<span class="dt">alpha =</span> <span class="dv">1</span>)),</a>
<a class="sourceLine" id="cb4-16" title="16">         <span class="dt">shape =</span> <span class="kw">guide_legend</span>(<span class="dt">override.aes =</span> <span class="kw">list</span>(<span class="dt">alpha =</span> <span class="dv">1</span>), <span class="dt">title=</span><span class="st">&quot;Sample Size&quot;</span>))</a></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<h4 id="relationship-with-frequentist-p-based-arbitrary-clusters">Relationship with frequentist <em>p</em>-based arbitrary clusters</h4>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1">df<span class="op">$</span>sig_<span class="dv">1</span> &lt;-<span class="st"> </span><span class="kw">factor</span>(<span class="kw">ifelse</span>(df<span class="op">$</span>p_value <span class="op">&gt;=</span><span class="st"> </span><span class="fl">.1</span>, <span class="st">&quot;n.s.&quot;</span>, <span class="st">&quot;-&quot;</span>), <span class="dt">levels=</span><span class="kw">c</span>(<span class="st">&quot;n.s.&quot;</span>, <span class="st">&quot;-&quot;</span>))</a>
<a class="sourceLine" id="cb5-2" title="2">df<span class="op">$</span>sig_<span class="dv">05</span> &lt;-<span class="st"> </span><span class="kw">factor</span>(<span class="kw">ifelse</span>(df<span class="op">$</span>p_value <span class="op">&gt;=</span><span class="st"> </span><span class="fl">.05</span>, <span class="st">&quot;n.s.&quot;</span>, <span class="st">&quot;*&quot;</span>), <span class="dt">levels=</span><span class="kw">c</span>(<span class="st">&quot;n.s.&quot;</span>, <span class="st">&quot;*&quot;</span>))</a>
<a class="sourceLine" id="cb5-3" title="3">df<span class="op">$</span>sig_<span class="dv">01</span> &lt;-<span class="st"> </span><span class="kw">factor</span>(<span class="kw">ifelse</span>(df<span class="op">$</span>p_value <span class="op">&gt;=</span><span class="st"> </span><span class="fl">.01</span>, <span class="st">&quot;n.s.&quot;</span>, <span class="st">&quot;**&quot;</span>), <span class="dt">levels=</span><span class="kw">c</span>(<span class="st">&quot;n.s.&quot;</span>, <span class="st">&quot;**&quot;</span>))</a>
<a class="sourceLine" id="cb5-4" title="4">df<span class="op">$</span>sig_<span class="dv">001</span> &lt;-<span class="st"> </span><span class="kw">factor</span>(<span class="kw">ifelse</span>(df<span class="op">$</span>p_value <span class="op">&gt;=</span><span class="st"> </span><span class="fl">.001</span>, <span class="st">&quot;n.s.&quot;</span>, <span class="st">&quot;***&quot;</span>), <span class="dt">levels=</span><span class="kw">c</span>(<span class="st">&quot;n.s.&quot;</span>, <span class="st">&quot;***&quot;</span>))</a>
<a class="sourceLine" id="cb5-5" title="5"></a>
<a class="sourceLine" id="cb5-6" title="6"></a>
<a class="sourceLine" id="cb5-7" title="7">get_data &lt;-<span class="st"> </span><span class="cf">function</span>(predictor, outcome, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="fl">0.3</span>){</a>
<a class="sourceLine" id="cb5-8" title="8">  fit &lt;-<span class="st"> </span><span class="kw">suppressWarnings</span>(<span class="kw">glm</span>(<span class="kw">paste</span>(outcome, <span class="st">&quot;~ outcome_type * &quot;</span>, predictor), <span class="dt">data=</span>df, <span class="dt">family =</span> <span class="st">&quot;binomial&quot;</span>))</a>
<a class="sourceLine" id="cb5-9" title="9">  <span class="co"># data &lt;- data.frame(x=rep(1:100, 2))</span></a>
<a class="sourceLine" id="cb5-10" title="10">  data &lt;-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">outcome_type=</span><span class="kw">rep</span>(<span class="kw">c</span>(<span class="st">&quot;linear&quot;</span>, <span class="st">&quot;binary&quot;</span>), <span class="dt">each=</span><span class="dv">100</span>))</a>
<a class="sourceLine" id="cb5-11" title="11">  data[predictor] &lt;-<span class="st"> </span><span class="kw">rep</span>(<span class="kw">seq</span>(lbound, ubound, <span class="dt">length.out =</span> <span class="dv">100</span>), <span class="dv">2</span>)</a>
<a class="sourceLine" id="cb5-12" title="12">  data<span class="op">$</span>index &lt;-<span class="st"> </span>predictor</a>
<a class="sourceLine" id="cb5-13" title="13">  predict_fit &lt;-<span class="st"> </span><span class="kw">predict</span>(fit, <span class="dt">newdata=</span>data, <span class="dt">type=</span><span class="st">&quot;response&quot;</span>, <span class="dt">se.fit =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb5-14" title="14">  data[outcome] &lt;-<span class="st"> </span>predict_fit<span class="op">$</span>fit </a>
<a class="sourceLine" id="cb5-15" title="15">  data<span class="op">$</span>CI_lower &lt;-<span class="st"> </span>predict_fit<span class="op">$</span>fit <span class="op">-</span><span class="st"> </span>(<span class="kw">qnorm</span>(<span class="fl">0.99</span>) <span class="op">*</span><span class="st"> </span>predict_fit<span class="op">$</span>se.fit)</a>
<a class="sourceLine" id="cb5-16" title="16">  data<span class="op">$</span>CI_upper &lt;-<span class="st"> </span>predict_fit<span class="op">$</span>fit <span class="op">+</span><span class="st"> </span>(<span class="kw">qnorm</span>(<span class="fl">0.99</span>) <span class="op">*</span><span class="st"> </span>predict_fit<span class="op">$</span>se.fit)</a>
<a class="sourceLine" id="cb5-17" title="17">  data &lt;-</a>
<a class="sourceLine" id="cb5-18" title="18"><span class="st">    </span><span class="kw">select</span>(</a>
<a class="sourceLine" id="cb5-19" title="19">      data,</a>
<a class="sourceLine" id="cb5-20" title="20">      <span class="st">&quot;value&quot;</span> <span class="op">:</span><span class="er">=</span><span class="st"> </span><span class="op">!!</span>predictor,</a>
<a class="sourceLine" id="cb5-21" title="21">      <span class="op">!!</span>outcome,</a>
<a class="sourceLine" id="cb5-22" title="22">      .data<span class="op">$</span>outcome_type,</a>
<a class="sourceLine" id="cb5-23" title="23">      .data<span class="op">$</span>index,</a>
<a class="sourceLine" id="cb5-24" title="24">      .data<span class="op">$</span>CI_lower,</a>
<a class="sourceLine" id="cb5-25" title="25">      .data<span class="op">$</span>CI_upper</a>
<a class="sourceLine" id="cb5-26" title="26">    )</a>
<a class="sourceLine" id="cb5-27" title="27">  <span class="kw">return</span>(data)</a>
<a class="sourceLine" id="cb5-28" title="28">}</a>
<a class="sourceLine" id="cb5-29" title="29"></a>
<a class="sourceLine" id="cb5-30" title="30"></a>
<a class="sourceLine" id="cb5-31" title="31"></a>
<a class="sourceLine" id="cb5-32" title="32"><span class="kw">rbind</span>(</a>
<a class="sourceLine" id="cb5-33" title="33">  <span class="kw">rbind</span>(</a>
<a class="sourceLine" id="cb5-34" title="34">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;p_direction&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_001&quot;</span>, <span class="dt">lbound=</span><span class="fl">99.5</span>, <span class="dt">ubound=</span><span class="dv">100</span>),</a>
<a class="sourceLine" id="cb5-35" title="35">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;p_MAP&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_001&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="fl">0.01</span>),</a>
<a class="sourceLine" id="cb5-36" title="36">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;p_ROPE&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_001&quot;</span>, <span class="dt">lbound=</span><span class="dv">97</span>, <span class="dt">ubound=</span><span class="dv">100</span>),</a>
<a class="sourceLine" id="cb5-37" title="37">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;ROPE_89&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_001&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="fl">0.5</span>),</a>
<a class="sourceLine" id="cb5-38" title="38">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;ROPE_95&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_001&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="fl">0.5</span>),</a>
<a class="sourceLine" id="cb5-39" title="39">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;ROPE_full&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_001&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="fl">0.5</span>),</a>
<a class="sourceLine" id="cb5-40" title="40">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;BF_log&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_001&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="dv">10</span>)</a>
<a class="sourceLine" id="cb5-41" title="41">    ) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-42" title="42"><span class="st">    </span><span class="kw">rename</span>(<span class="st">&quot;sig&quot;</span>=sig_<span class="dv">001</span>) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-43" title="43"><span class="st">    </span><span class="kw">mutate</span>(<span class="dt">threshold=</span><span class="st">&quot;p &lt; .001&quot;</span>),</a>
<a class="sourceLine" id="cb5-44" title="44">  <span class="kw">rbind</span>(</a>
<a class="sourceLine" id="cb5-45" title="45">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;p_direction&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_01&quot;</span>, <span class="dt">lbound=</span><span class="dv">98</span>, <span class="dt">ubound=</span><span class="dv">100</span>),</a>
<a class="sourceLine" id="cb5-46" title="46">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;p_MAP&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_01&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="fl">0.1</span>),</a>
<a class="sourceLine" id="cb5-47" title="47">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;p_ROPE&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_01&quot;</span>, <span class="dt">lbound=</span><span class="dv">85</span>, <span class="dt">ubound=</span><span class="dv">100</span>),</a>
<a class="sourceLine" id="cb5-48" title="48">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;ROPE_89&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_01&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="dv">2</span>),</a>
<a class="sourceLine" id="cb5-49" title="49">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;ROPE_95&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_01&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="dv">2</span>),</a>
<a class="sourceLine" id="cb5-50" title="50">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;ROPE_full&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_01&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="dv">2</span>),</a>
<a class="sourceLine" id="cb5-51" title="51">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;BF_log&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_01&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="dv">5</span>)</a>
<a class="sourceLine" id="cb5-52" title="52">    ) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-53" title="53"><span class="st">    </span><span class="kw">rename</span>(<span class="st">&quot;sig&quot;</span>=sig_<span class="dv">01</span>) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-54" title="54"><span class="st">    </span><span class="kw">mutate</span>(<span class="dt">threshold=</span><span class="st">&quot;p &lt; .01&quot;</span>),</a>
<a class="sourceLine" id="cb5-55" title="55">  <span class="kw">rbind</span>(</a>
<a class="sourceLine" id="cb5-56" title="56">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;p_direction&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_05&quot;</span>, <span class="dt">lbound=</span><span class="dv">95</span>, <span class="dt">ubound=</span><span class="dv">100</span>),</a>
<a class="sourceLine" id="cb5-57" title="57">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;p_MAP&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_05&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="fl">0.3</span>),</a>
<a class="sourceLine" id="cb5-58" title="58">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;p_ROPE&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_05&quot;</span>, <span class="dt">lbound=</span><span class="dv">50</span>, <span class="dt">ubound=</span><span class="dv">100</span>),</a>
<a class="sourceLine" id="cb5-59" title="59">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;ROPE_89&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_05&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="dv">10</span>),</a>
<a class="sourceLine" id="cb5-60" title="60">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;ROPE_95&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_05&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="dv">10</span>),</a>
<a class="sourceLine" id="cb5-61" title="61">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;ROPE_full&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_05&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="dv">10</span>),</a>
<a class="sourceLine" id="cb5-62" title="62">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;BF_log&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_05&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="dv">2</span>)</a>
<a class="sourceLine" id="cb5-63" title="63">    ) <span class="op">%&gt;%</span><span class="st">  </span></a>
<a class="sourceLine" id="cb5-64" title="64"><span class="st">    </span><span class="kw">rename</span>(<span class="st">&quot;sig&quot;</span>=sig_<span class="dv">05</span>) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-65" title="65"><span class="st">    </span><span class="kw">mutate</span>(<span class="dt">threshold=</span><span class="st">&quot;p &lt; .05&quot;</span>),</a>
<a class="sourceLine" id="cb5-66" title="66">  <span class="kw">rbind</span>(</a>
<a class="sourceLine" id="cb5-67" title="67">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;p_direction&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_1&quot;</span>, <span class="dt">lbound=</span><span class="dv">90</span>, <span class="dt">ubound=</span><span class="dv">100</span>),</a>
<a class="sourceLine" id="cb5-68" title="68">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;p_MAP&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_1&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="fl">0.5</span>),</a>
<a class="sourceLine" id="cb5-69" title="69">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;p_ROPE&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_1&quot;</span>, <span class="dt">lbound=</span><span class="dv">25</span>, <span class="dt">ubound=</span><span class="dv">100</span>),</a>
<a class="sourceLine" id="cb5-70" title="70">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;ROPE_89&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_1&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="dv">20</span>),</a>
<a class="sourceLine" id="cb5-71" title="71">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;ROPE_95&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_1&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="dv">20</span>),</a>
<a class="sourceLine" id="cb5-72" title="72">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;ROPE_full&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_1&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="dv">20</span>),</a>
<a class="sourceLine" id="cb5-73" title="73">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;BF_log&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;sig_1&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="dv">1</span>)</a>
<a class="sourceLine" id="cb5-74" title="74">    ) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-75" title="75"><span class="st">    </span><span class="kw">rename</span>(<span class="st">&quot;sig&quot;</span>=sig_<span class="dv">1</span>) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-76" title="76"><span class="st">    </span><span class="kw">mutate</span>(<span class="dt">threshold=</span><span class="st">&quot;p &lt; .1&quot;</span>)</a>
<a class="sourceLine" id="cb5-77" title="77">) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-78" title="78"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">index =</span> <span class="kw">as.factor</span>(index)) <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb5-79" title="79"><span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x=</span>value, <span class="dt">y=</span>sig)) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-80" title="80"><span class="st">  </span><span class="kw">geom_ribbon</span>(<span class="kw">aes</span>(<span class="dt">ymin=</span>CI_lower, <span class="dt">ymax=</span>CI_upper), <span class="dt">alpha=</span><span class="fl">0.1</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-81" title="81"><span class="st">  </span><span class="kw">geom_line</span>(<span class="kw">aes</span>(<span class="dt">color=</span>index), <span class="dt">size=</span><span class="dv">1</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-82" title="82"><span class="st">  </span><span class="kw">facet_wrap</span>(<span class="op">~</span><span class="st"> </span>index <span class="op">*</span><span class="st"> </span>threshold <span class="op">*</span><span class="st"> </span>outcome_type, <span class="dt">scales =</span> <span class="st">&quot;free&quot;</span>, <span class="dt">ncol=</span><span class="dv">8</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-83" title="83"><span class="st">  </span><span class="kw">theme_modern</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb5-84" title="84"><span class="st">  </span><span class="kw">scale_color_manual</span>(<span class="dt">values =</span> <span class="kw">c</span>(<span class="st">&quot;p_value&quot;</span>=<span class="st">&quot;#607D8B&quot;</span>, <span class="st">&quot;p_MAP&quot;</span> =<span class="st"> &quot;#4CAF50&quot;</span>, <span class="st">&quot;p_direction&quot;</span> =<span class="st"> &quot;#2196F3&quot;</span>,</a>
<a class="sourceLine" id="cb5-85" title="85">                               <span class="st">&quot;ROPE_89&quot;</span> =<span class="st"> &quot;#FFC107&quot;</span>, <span class="st">&quot;ROPE_95&quot;</span> =<span class="st"> &quot;#FF9800&quot;</span>, <span class="st">&quot;ROPE_full&quot;</span> =<span class="st"> &quot;#FF5722&quot;</span>,</a>
<a class="sourceLine" id="cb5-86" title="86">                               <span class="st">&quot;p_ROPE&quot;</span>=<span class="st">&quot;#E91E63&quot;</span>, <span class="st">&quot;BF_log&quot;</span>=<span class="st">&quot;#9C27B0&quot;</span>), <span class="dt">guide=</span><span class="ot">FALSE</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-87" title="87"><span class="st">  </span><span class="kw">ylab</span>(<span class="st">&quot;Probability of being significant&quot;</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-88" title="88"><span class="st">  </span><span class="kw">xlab</span>(<span class="st">&quot;Index Value&quot;</span>)</a></code></pre></div>
<p><img 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" /><!-- --></p>
<h4 id="relationship-with-equivalence-test">Relationship with Equivalence test</h4>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" title="1">df<span class="op">$</span>equivalence_<span class="dv">95</span> &lt;-<span class="st"> </span><span class="kw">factor</span>(<span class="kw">ifelse</span>(df<span class="op">$</span>ROPE_<span class="dv">95</span> <span class="op">==</span><span class="st"> </span><span class="dv">0</span>, <span class="st">&quot;significant&quot;</span>, <span class="st">&quot;n.s.&quot;</span>), <span class="dt">levels=</span><span class="kw">c</span>(<span class="st">&quot;n.s.&quot;</span>, <span class="st">&quot;significant&quot;</span>))</a>
<a class="sourceLine" id="cb6-2" title="2">df<span class="op">$</span>equivalence_<span class="dv">89</span> &lt;-<span class="st"> </span><span class="kw">factor</span>(<span class="kw">ifelse</span>(df<span class="op">$</span>ROPE_<span class="dv">89</span> <span class="op">==</span><span class="st"> </span><span class="dv">0</span>, <span class="st">&quot;significant&quot;</span>, <span class="st">&quot;n.s.&quot;</span>), <span class="dt">levels=</span><span class="kw">c</span>(<span class="st">&quot;n.s.&quot;</span>, <span class="st">&quot;significant&quot;</span>))</a>
<a class="sourceLine" id="cb6-3" title="3"></a>
<a class="sourceLine" id="cb6-4" title="4"></a>
<a class="sourceLine" id="cb6-5" title="5"><span class="kw">rbind</span>(</a>
<a class="sourceLine" id="cb6-6" title="6">  <span class="kw">rbind</span>(</a>
<a class="sourceLine" id="cb6-7" title="7">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;p_direction&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;equivalence_95&quot;</span>, <span class="dt">lbound=</span><span class="fl">97.5</span>, <span class="dt">ubound=</span><span class="dv">100</span>),</a>
<a class="sourceLine" id="cb6-8" title="8">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;p_MAP&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;equivalence_95&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="fl">0.2</span>),</a>
<a class="sourceLine" id="cb6-9" title="9">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;p_ROPE&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;equivalence_95&quot;</span>, <span class="dt">lbound=</span><span class="fl">92.5</span>, <span class="dt">ubound=</span><span class="fl">97.5</span>),</a>
<a class="sourceLine" id="cb6-10" title="10">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;ROPE_89&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;equivalence_95&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="dv">3</span>),</a>
<a class="sourceLine" id="cb6-11" title="11">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;ROPE_full&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;equivalence_95&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="dv">3</span>),</a>
<a class="sourceLine" id="cb6-12" title="12">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;BF_log&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;equivalence_95&quot;</span>, <span class="dt">lbound=</span><span class="fl">0.5</span>, <span class="dt">ubound=</span><span class="fl">2.5</span>)</a>
<a class="sourceLine" id="cb6-13" title="13">    ) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb6-14" title="14"><span class="st">    </span><span class="kw">rename</span>(<span class="st">&quot;equivalence&quot;</span>=equivalence_<span class="dv">95</span>) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb6-15" title="15"><span class="st">    </span><span class="kw">mutate</span>(<span class="dt">level=</span><span class="st">&quot;95 HDI&quot;</span>),</a>
<a class="sourceLine" id="cb6-16" title="16">  <span class="kw">rbind</span>(</a>
<a class="sourceLine" id="cb6-17" title="17">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;p_direction&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;equivalence_89&quot;</span>, <span class="dt">lbound=</span><span class="fl">99.5</span>, <span class="dt">ubound=</span><span class="dv">100</span>),</a>
<a class="sourceLine" id="cb6-18" title="18">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;p_MAP&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;equivalence_89&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="fl">0.02</span>),</a>
<a class="sourceLine" id="cb6-19" title="19">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;p_ROPE&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;equivalence_89&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="dv">100</span>),</a>
<a class="sourceLine" id="cb6-20" title="20">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;ROPE_95&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;equivalence_89&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="fl">0.5</span>),</a>
<a class="sourceLine" id="cb6-21" title="21">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;ROPE_full&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;equivalence_89&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="fl">0.5</span>),</a>
<a class="sourceLine" id="cb6-22" title="22">    <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">&quot;BF_log&quot;</span>, <span class="dt">outcome=</span><span class="st">&quot;equivalence_89&quot;</span>, <span class="dt">lbound=</span><span class="dv">0</span>, <span class="dt">ubound=</span><span class="dv">3</span>)</a>
<a class="sourceLine" id="cb6-23" title="23">    ) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb6-24" title="24"><span class="st">    </span><span class="kw">rename</span>(<span class="st">&quot;equivalence&quot;</span>=equivalence_<span class="dv">89</span>) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb6-25" title="25"><span class="st">    </span><span class="kw">mutate</span>(<span class="dt">level=</span><span class="st">&quot;90 HDI&quot;</span>)</a>
<a class="sourceLine" id="cb6-26" title="26">) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb6-27" title="27"><span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x=</span>value, <span class="dt">y=</span>equivalence)) <span class="op">+</span></a>
<a class="sourceLine" id="cb6-28" title="28"><span class="st">  </span><span class="kw">geom_ribbon</span>(<span class="kw">aes</span>(<span class="dt">ymin=</span>CI_lower, <span class="dt">ymax=</span>CI_upper), <span class="dt">alpha=</span><span class="fl">0.1</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb6-29" title="29"><span class="st">  </span><span class="kw">geom_line</span>(<span class="kw">aes</span>(<span class="dt">color=</span>index), <span class="dt">size=</span><span class="dv">1</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb6-30" title="30"><span class="st">  </span><span class="kw">facet_wrap</span>(<span class="op">~</span><span class="st"> </span>index <span class="op">*</span><span class="st"> </span>level <span class="op">*</span><span class="st"> </span>outcome_type, <span class="dt">scales =</span> <span class="st">&quot;free&quot;</span>, <span class="dt">nrow=</span><span class="dv">7</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb6-31" title="31"><span class="st">  </span><span class="kw">theme_modern</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb6-32" title="32"><span class="st">  </span><span class="kw">scale_color_manual</span>(<span class="dt">values =</span> <span class="kw">c</span>(<span class="st">&quot;p_value&quot;</span>=<span class="st">&quot;#607D8B&quot;</span>, <span class="st">&quot;p_MAP&quot;</span> =<span class="st"> &quot;#4CAF50&quot;</span>, <span class="st">&quot;p_direction&quot;</span> =<span class="st"> &quot;#2196F3&quot;</span>,</a>
<a class="sourceLine" id="cb6-33" title="33">                               <span class="st">&quot;ROPE_89&quot;</span> =<span class="st"> &quot;#FFC107&quot;</span>, <span class="st">&quot;ROPE_95&quot;</span> =<span class="st"> &quot;#FF9800&quot;</span>, <span class="st">&quot;ROPE_full&quot;</span> =<span class="st"> &quot;#FF5722&quot;</span>,</a>
<a class="sourceLine" id="cb6-34" title="34">                               <span class="st">&quot;p_ROPE&quot;</span>=<span class="st">&quot;#E91E63&quot;</span>, <span class="st">&quot;BF_log&quot;</span>=<span class="st">&quot;#9C27B0&quot;</span>), <span class="dt">guide=</span><span class="ot">FALSE</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb6-35" title="35"><span class="st">  </span><span class="kw">ylab</span>(<span class="st">&quot;Probability of rejecting H0 with the equivalence test&quot;</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb6-36" title="36"><span class="st">  </span><span class="kw">xlab</span>(<span class="st">&quot;Index Value&quot;</span>)</a></code></pre></div>
<p><img 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Azuic0PjYYILvBEC7gX7jqxe/NKpOF07/MCc9+DhsBaiLquXmCft0GXmNdeuG7YvhLJYLr7JgqhPpxAPDfXfxGFi8C+ahcaCOzOVAO4l+4ocP2jtz/XF3icxLgKERm+zXy/tRRYjO2xhkHxLHA6f9Xr4ZQCeyjddRe6+tG9h/q70Kz8HZ6buzqBwEbM+BurISbWw6EFPp9oAg/Fu57Eqn707sfaJ7HGQYxrEJFhrN2dP0JgE8TRm8rfqfVwQoF9FO94Gal9JZL2ZSRmAos7Fvm3tsfATex1CTyx9YnZDFPr4cgC+47veiNH+0ok3Rs5eAksHtoXaylLgQexPVczGD4FTngNeGo9HFVg73vQVrUfvSjqYe+nM+VKkljUIBqiwMVtShDYACL+Tq6H4wrsPX7shxl4+Tt4tXvxDQQ2gKbA4no4sMDn6jbwUn5kgSVJAlXAD8LB025bUT9rZyHwH9Yl8MTgjdsIU+vhqAL7j59GYDYPIg3PzTlugbn56zY8RF2TCjyxHg4r8EQT+PmAVAIn6EUrhhcQIbA+dASeWg/HFDhA/NQCB/p4fwhn7DI3gdntQXsUOO1NlBPr4aACTzWBn0+AwNaYNN0gdrA6+cZleMz4G7cR1OvhaAIPm8DPJ8QVeBwl0MfHAAJPIwqc1t8pAgo8tQ7z9BFpBKbXixbYCxysSt5xGB5Tg5dUG4QT+HyqDTx9RlSBx0kCfXoUDJquyb0igWf8JdQGEFi73EX5ayMwzdjCo3Z6N9TNMhi7hP0NJ3AMf6MKPE4S6MPjsBSBxUnt3v5cd27CKSbHLrEWiCVwmBaAwNboN90weMBKmTOYVvZ+5uPVKpNjl1gLhFJgciXm7VMiCiyJEujD42AtcMA6WTB+vtu3wKSPgKMJHKgBkgoc6LMjsRSBRzOs7Jx3oTn5G0fgMBeBs5gCS6IE+uxIaDfdMHjISpkznM1s7z4v9OS2h1wLBFIgjr9Wte+ftXz9pJ4IvHvHiqLccX/a1ZgMCxW46FPbmTcrBv4S3wAHEni6Dfx9juOMHK+f5F/sCm8H73ZXCryYDbB207XBaQosmaRQa3ZgJcz8DSPwdCN4/CAvc2KV1x76I0BSbpuFaj8aYywwzdyySQpdXi86sJX6DvT6BJZ0eLHG3t0VllIJvJwN8FIElk9SWP/SWWD6G+AgAg8bgZTA412u0/yb5h0rinLHK2Sr6lJiIQIPJil0fjvhYOyS9zeEwDP+phV4fNJjn4vbvmNFUe54hWxbYzJoNt0oedhaWSCfpLDEfHgMxy4EHvtLTOB9d+5Zue+1QH9NBWYZ3Hh48PM3gMAR/fWwC73v7Tgrz36sV+BR8sC18ovp8GDor3eBz6M2gvNJrF3P3961JLFcSRir2pLCRGCuwc2Gx2jorlHgeX8TCyxOELarD5gmT1/K0thXmQpaTcc8uKPAHPz1LPD8OsxvIzjeyPHyXrP9bd+xIil3kf5C4CHz/pIM71Xgkb8EBe6ftazn2C2+VZ++lPWoc8XTo9N03Fdd5tMG9cYuD399CjxahUnawHMjxHiYYZn+6jQd+1WX8aQFvbHLYwfaowJjfWX+8hNYtjoK9KlR0RfY+0tho2H6wEZv8IYeud7wpcDI3hj+RhB4qf5qNJ3MX17Z7QUm5q8w79fez7xfApr68hNY2qWBPjQuc03X60220Q1vF53yN2ly4XZR3VdZ6zNyV2Wv/1YILbC0TwN9ZhDUq+6ZppP7yyq74c0qisQEkguXPocvSnJTYCjutL58Bf4D0/OwE6vu6abr9SuVYWyByZn2/kim5a9489Hwcbom4x/8oHaXs8Bcj4CnVt0TTdcfy+GeJIuAwZl21RqLQu7x+4El9xyFFZe3wH/gehg4teqWNd1sv0aqty9mh4emvolzCw/gDJ/G8bMLrYtbkDFRBGbr7+Squ206k7Vy/AhOaB7n9/Sl6G9IgSuS+RtF4D/wPY0z1fNSgbOZzkyQwYWp4THKpt6RjFZfBVq70K4sVmDG/uoJPGYFAkvkjXftxBStk1geiO9vBIE5+6u3Cy2F5kiWYn6Tg9Reuv6GvIw0YHECD/s10MeFwvQkVh+SQ1mC2U0OCnenTsJGS6Im8I0cPSL7G0NgYn1phOVlpBqSY3mI/tZJbu7sJZSoaVQI8355eoWqgmUJTLEzTbC+kaOE5Fge4HiTw6S7VDMLBJ4TK3BT2NRefswkP3hi1plj1Ktukwf6Cee3vclh1ly6kUWCTysbtCkcZ+TovlEcWDDrTBPM7xMOXCE7tK6RatrK0d8wO6GEBZYfM6kOnph1pglm08qSbQCTmxwWqG+wo0iyAsuPmVQHT8w60wTzR+2CVscWm5scFqRvuNNAcZrDcV7o9htVz3PrTQPMH3YPWRtrHG5yWIS/wQSOc/xoXnv5LtfMwZOPqlJDv+lIN4LzTQ689Q0pcIy9rxgC+6gnQUxnbAxWEUc83eTA094srMBNs4QrP/Qu9IJZTMSINzlQhHfG0CexFswKIiIjeUJfRlowK4iIjOQJfSPHgllBRGQkj03t5cdMqzt4WkFEZCRP+IndF8sKIiIjeYIJzJIVRFxDRqOIzDPyXv0AsHIgMACMgcAAMAYCA8AYCAwAYyAwAIyBwAAwJqjA4jx3Jrx873Pjv3n9ZLvd3jf/rJ11LQusM3KI2A93dZJ/9vauatFenJkm6ZacKlKMOlWksORMLSUYZNQPGS9jSIEHt0cbcHXSPtakzesn+UftjPqupHiCyq6WBdYZOUQUwlUPjKroxZlpkt6SE0WKUaeKFJecrqUEg4z6ISNmDCjw8AElffJ1kfnoHj7QqMnVyf3mtm4LrDNyiCiG2099bC/OTJP0g08UKUSdLFJslMlaSjDIqB8yZsaAAlsOtyL/fdN+6P7WaltqLbBtRhYRxXC7ib/vx5luEiH4VJHV0lVUjVZuGmW2yAH6GfVDRs0YUuDBJB0mWI/uU6s/3NnuQjtkpB9RDHf6q8nj725szzVJ96uZItuoGq18qlfLEUYZ9UPGyxhQ4OE0WUZ/azm69zanePZWJ4aqP7XPSD+iEO7q5M6X+dhRltLGmW2Sdsm5Ituo863cLDlb5OgPTTLqh4yXcVkC781P8JS8flK0ic0HRhc4YkRJOI1tjr7As0X2z+9MFyk2ikHTGmW0EHiuSOeMi9qFtto4VX9pe7kr9i50zIiScNWRmrR4i13o6SK7qLM7rINGUddyhFFGm13o6SLdM1I8iZVZju6d9eA26XLJ31llpB9REk59UUTzJFY2HtyKIntRZ4ocNYrBtSSjjPoh42UkeRnJbnTvtjYSVg1ne0DqkJF+RCHcXCHtb2abZGCBosh+1Oki+0saRzXKqB8yXkaaN3LYjO6X9+w2Tqf5sWHVejbYZ2QQUQhXji2Nk1izTdI7Q6suUow6VaS45EwtJbUxyKgfMl7GoLdS2t+kaDG6d9sS8w88zf/Kbge6/tho66joEYXpC6cLKePI5oafWlJdZBt1tsjBksZRDTLqh4yXEQ8zAMAYCAwAYyAwAIyBwAAwBgIDwBgIDABjIDAAjIHAADAGAgPAGAgMAGMgMACMgcAAMAYCA8AYCAwAYyAwAIyBwAAwBgIDwBgIDABjIDAAjKEq8OXhZrM5+CDLrv98uDn4lxfZq+PqB9UvHjfLXRxubj6u/vk0YXVtWEFEo4x5uAvhh0xIm5GuwEdZ9uc86NP8/5eHt/NWuZ1d59+Uv2jJf3r99Maz/IfXT2+9SFZbK1YQUTdjEy7Pmayq1qTNSFrgi82DqhHODh4XrVL8dNQqxS/zBbMLbqvuFUTUzViHu37Ebg2Vpc5IWuB8LXZWjtn8O3mr1JunsnHy/7NiBRF1M9bhin3Pg9+mqqstaTPSFTg/VPjeR5nQKtV+Sf6LbiX2n/lhx4Nyx+Upt9G9goi6Getwl8dH9ZKcSJuRrsB5O+RHC71W2ZSrLun+ZXGC4CeHzEb3CiLqZuyFG/yCAWkzEhY43+G4nYkHFtkw/Fm3Y8nuAHEFEXUzFtThWAqcMCNlga8flaf2Psr+qz611/xCXCxvssviOJHbCZAVRNTOWIVrdjZ4kTYjZYHL9dr1vzYX15pWyfdPumPB4gDxt1n218PNTzkO7oVH1M5Yh/trdcmbF2kzUhUYAKABU4GrlRu/u3YMWEFEZHSHqcAAgAIIDABjIDAAjIHAADAGAgPAGAgMAGMgMACMgcAAMAYCA8AYCAwAYyAwAIyBwAAwBgIDwBgIDABj6As8nA+7fH76rJ4yLP8Nv6kaR6wgYpPx8v36cXZk9AN5gUfzYXetUsxkUs23ypsVRGwyvmonBkJGP5AXeDQfdr9Vit/y7/kVRGwydtPTI6MfeAjcnw9baJX8P/x7fgURm4zd9PTI6AfyAo/mw66mKKlbJd9d4d/zK4jYZDzrDW5k9AF5gUfzYS9x1b38iHXGRW+Bk2SkL3A2mA970CpLOHjKVhFx4vgQGa0hL/BoPmzh1N7TJZy+XEHEJqPiDC0yWkNe4NF82Au8gLiCiE3GRV8HTpGRvsAAACUQGADGQGAAGAOBAWAMBAaAMRAYAMZAYAAYA4EBYAwEBoAxEBgAxkBgABgDgQFgDAQGgDEQGADGQGAAGENe4IvNUfUP/8dF1SxjVsYpVtGNKfqRi8DLZuHjOltJN6boRwYCv3WYd37eNJeHb71/8EE570H+z6vj7x0eHBYTmCxgZFRr7iZgPaNDl/PGp6kr6MwqujFFPzIQ+NaLs7pdbv3t0Y1nlz96XLzD4NXxjWfZ2SLmUuo6vgpYTPAv5uTPKroxRT8yEPionpGzmGGoePvI9V9+vTl4/Or4djnT39kSxnfT8WXAYmK7Yjz3cvJnFd2Yoh8ZCPygnhO7bpfLw5sfPa0b5PrR7adLGN9ixxezgW+O+jn5s4puTNGPDAQerLqLFVu5S1I0yNnBj5d07NQGzMrZDLuc7FlFN6boRwYC5wdP5WzZ7YrtwT+OmzXaRfU+Tu4IHV/PDy3kZM8qujFFPzIQuDt9WbbL9aPNwW+KV4EVDfLquHmjH2uEji/e0HGQHzr1c7JnFd2Yoh/JCzzNQob32kE3WsNb4LPNzSWcvFw76EZ7eAsMwMpxFni33W4fFl/st9uff+xeIQCAPq4C73Jp94XB++ILGAxAVBwFfv3kbv7f07v5F/erLwAA8fAl8Mt7xX707u3PPdQJAKCJr13ol+8We8/7VuAVnBxbQURkJI9z7etzV9Xhb3cQzLtVtFhBRGQkj2vtT/Nt7st79yHwMkFG6jjWvj30xS70IkFG6jjWvt3wDk9i8W4VLVYQERnJ42ULnG94h5eReLeKFiuIiIzk8XUMPLyRo1/uueNnEKWLuNCAGYfBfd7DrgT6GTvGGZ1rf7rdbouNb3FPZe9GLAi8CKgO7nMpdmVRzShBkjFU7Xvl2jYsdSBwXOTSrkZgRUYIbA0EDs+8tGsRWJURAlsDgQNhZu0KBJ7KGF5g64alDgT2iLW0KxB4MiMEtgYCu+HF2aULPJsRAlsDgc3xL+2iBdbICIGtgcA6hFV2yQLrZYTA87x+0lzrFqYNgsBSYho7M7iDZYyAbkYIPMvrJ7m0u+3d4d1mELghoqOThMwYFYOMEHiW9jmNwf3eKxY4lpGG6Od6+d7ncxmTYZYRAmsyfuJqTQJH8c8Z7VhXJ725n2gJbJoRAmtyOnrmGQJTQzfVfrulKbBFRgisx347mnUEAlNDM1Tel3t6AltmhMBa7JtzWBCYMPq5qAlsnxEC67AvryJhF5o4+rlICeyUEQJrsKuuAqtPYrFPqAICB8Y5IwSeZ1e9/Gn49gkITA39XDQE9pERAs9SzhhUoryRg3lCNRA4GJ4yQuBZivcvbqt7KHeKWymZJ1QDgYPgMaOP2p/e+TIbvl50QQKrgMDU0M+VUmC/GT3Ufr8tBFbOSmnUsJyAwNTQz5VMYO8Z3Wt/dVIIrJ4X2qhhOcFf4N4dwe3+k3xHyl2uGNi1QiyBw2R0r/3uzh9zgdVvZrBuWOqwF7h3R3C7/6TYkfIz+EJj1wwxBA6X0bn2L9/9uDgGVr8bybphqcNd4N4dwe3+k2pHyt8ADIldOwQWOHBG19oXPV4IrH47oXXDUoe5wP07grt31Cl2pDyPwkDYNUQ4gWNkdK39LpcXAjNN2Anc7D+pdqRCDEX/2LVCIIEjZXSsfdnh2IVmmrDtrnb1q1oPBxqNnrFrBf8Cx8zoWPv6JoftQ5zEYggE7mf0QvSMvm7kmLyMxHR8z7AggbEL7YE0Gb3diaW6kcOhZQlRXzFVzUrJNCBOYvUzWpM0o79bKRWvF3VoWTrUV0yX9jDDHpeRMjcF0mcM/jCDQ8uSob5iurjHCfe4kSOzVIBMRgg8T3PFdHEP9O+r1VKxRmr3n+Q7UsnGqxF2rWCoALGMEFiLSmDllDoLSCgHAnekTldhW3tTliiwelK7BSSUA4GphTeuvSUQeBGsWuDUcaRo196RJQqMXWjiuGVsSJ1imrna+0lFxSgAACAASURBVEIUmP34njuJxT6ggibj/yeQdARP4JYxde31UNXeN0sUeOIyEvuACuQCU9XZLWPq2uuhqr1vlijwxI0c7AMqkA5usi57zEgWVe19s0iB1bNSsg+oQGNwE5I5WEZCqGrvm4UJLAMCD0m9ZY6RMTWq2vsGAi8C+8GdQuXYGVOgqr1v1ibwIhP6GtyxVE6ZMRaq2vsGAi8C34M7rMr6uXoT6kJgKRB4EYQb3CFE1o7Vm1AXAssZCLzE8Q2B/eDPZN1UvQl1I2X0h6qHfAOBF0HMwe0usmao/oS6kTO6o+oh30DgRZBkcFuLrJ8LAs8xFHiBAxwCB8bYY/1cMoGlJ9iS3102RNVDvoHAi4DE1knbKP1cBgKbE7QxVD3kjuL9wOaNywUIHJd5TfRz6e9Ce9Pak+KqHnJmclpZo8YljHJa2YXkG0FJ4JoJFfRzhTwGDuu1qodcUU7sbtG6ZJl4GmkR+cYQFLhGNvT1c6U9ieUitaqHXFHNCP5/Cgxblyqq54GrgGnqFBi6AleIg10/F8mz0FpCq3rIFdU7Of4PCzQzKmbkSF17Ley6lczgnmYZAsuIJrDy9aJ5JVKP3Xn0MirnxEpdfR3supX04B6xxowxBJZ/Mj+mZqVcQj4JvAf3GjKG3oWGwKzhPbjXkDH0SawlXUaamlZ2EQHH8B7ca8gY7TKSp89JydS0skvIJ4H34F5DRtzIoc/UtLIM8wl3pfRfbrZ92C7De3DrwTujv1spp94PLPlgjkzdyJGmRg4IYU7zL8odjF3x085g3oNbD94Z8TihCeppZdkFFHYnrk6Kb3Z3vqzeNbrWF3ynrr8O6tr7ZZkCC/AWWDigb8+vr1vghtQ5ppivvR8gMHGEU+q1wPmus2IXOqM9qmvsWkKpQOo4UrRr7wgmtSOOcFG73hwX4iqeCq1JOXTnsWuJOQVSpxIxrb0tEJg44l0p1UmsXODTfJP88t79Zinp8Eg5fiexawktBVJHa7GqvQUQmDiDu1JOt9u3P/vdx8r7cUYkHMNK7FrCQIHUAc8hsEeYCzxQNStnO1fe0i4l5UCWYdcSpgrQygiBrREETlgPO4a3zmXlZaRKa8kt7WqSDmcRu5awUoBMRghsDW+BhRs5rk7u1ttdnWNgCcnGs4BdQ9grQCEjBLaGucDNXSnlpd+rk+bkc34wvG39ZX58qIejAokzQmBruAusA+/jwzAZ08YOUHspCxO4fpudelbK+FWKAu/jw4AZk8UOVXtluQ4tS4f6bXYTDzOkqFUEbIdHnME8N7jDZkwTO2Dt5eU6tCwZ6rfZTT1OmKJaEXAZHhFG88zgDp9RRtyMEHie5m12Uw/0p6hXBJyHR9jRPDO4I2UcEzEjBNaiEnhiSp0UlYqAj+ERcDTPDO54GSVEygiBtSitnZrULkmtwuNpeAQazHODO2pGKeEzQmAt5gROUqkI+Bwe/gfz3OBWU19V8J5RQuCMEFiLuV3oFHWKAe8TPErqqwphMkoImDG4wCzv9G/YNbclzZ3Eil+1OPA+waOivqoQLqOEUBl91P70zpeZ+klwk5Yly37mMlKKOsWA9wkeBc1VhZo4AmeeUg8L9VD7/bYQWDWt7DLO8OxnbuRIUacYhBvcXkbzzOBWk0RgL6GHRbrX/uqkEFg5sfuSBFbPShm/RnEIOrg9DOfpwa0mkcCZe+hhee613935Yy6weioHo4blBAT2gAdt1YNbTTqBC3xmdK79y3c/Lo6BlS83g8CciTG4PdlrNM7SCpw5hB4W5Fr7Yte5EFg9FwsEZkykwb0+ga0zD4txrf0ulxcCLzQh9xM8aggInNlFHpbhWPtyzxm70AtNyP0EjxoaAmcWkYcF2Ne+vMmh+E/BQ5zEWiTRB/faBDZOPPxzXzdyLPwykgwIHIgIAvdJLHBmFnj4t97uxFr2jRwyIHA41iWwSd7hX/q7lVLxfmAIzBneJ3j0oCBwph14+GdRHmYI9BmJgcCBWZfAmnmHfwSB53n9pH0oCbdSxmVVAmvFHf4JBJ7l9ZNc2t327sTDDGkqFgEKg3tFAmfzaYfLQ+BZ2itk6scJU1QrCiQG96oEnos7XBoCa5JvedUP9CepUQzIDO4VCTyddbgwBNbk9O3P1VPqJKmRK8IBfXsN4eW97bZ7ZSGlwb0agQsgsGf22/sTk9qlqZMbwgH9af5FuYOxv/Nl9a7CClqDe0UCK7MOl4PAWuybc1iLEVg4oL86Kb7Z3fmy+qnsjlgirEZgVdThUhBYh315FWlRu9DCAX27aqoidlAc3GsRWJ50uAwEnqKelXJXXQVe1EksYW1UC7x9uH/7sxPL9wNHZC0CZ5KowwUg8Dy77cPy30VdRhKOB+pV0/bhrphvtXzldwXdwb0SgUdBh7+GwLO8vNdskZZ0I4d4QF+dxMoF/rnqsW5yrEXgQdLhLyHwLPUzz8XQVs1KmaRebgwO6E+327c/+93H1fFBtUEuID641yFwllbgxd4pzFzgwQwMWfnCoEro7lQW9cG9DoH7OYe/gcDW8BZ4OANDVl5GujoptGaxC92yBoG7lMOfQ2BreAssHNCXt26U3+za2VVKWAzuVQicpROY5/ieh7nAzQF9ec756qQ5ut9v6V9GkrB8gRUZIbA13AXWgc3gdniHHp+MshPCMQReKCuIiIzkgcDWrCAiMpLHufbFVdLyqqHq/cCLZQURkZE8rrUvbt3ZFwYrp5VdLCuIiIzkcax9ddvs6d2Jid0XywoiIiN5fAmsfrXKYllBRGQkj69d6NHLzVhi1nQ8WUFGo4jMMzqvfupzV8PXiwIAIuAq8Gm+zS2et4PAACTAUeD20He4Cw0AiIC9wOV0M91kSuOn0wAAofGyBd6P3loAAIiBr2Pg4Y0cAIAIOJ+FPm0ePxPfDwwAiADvq9gArJygAu+tN8ov3/vc+G+61/ia4bbrYJ2RQ0QhnCzp4GfjTP0FpLWXv6FJ/RHZ6Z0vJ5YoZibov9xpnggZPYRUZgwpsP1h8dWJ+ens7jW+ZhRnzu0P3q0zcogohJMlHfxsnKm/gLT2Qgmyeg4/dr8dje3+EsNXS8wSIaOHkOqMAQW2PzGdr27MR7flhazytUC9qczNsM7IIaIQTpZ08LNxJmEBWe3Hb2ga1HP4sfnWZzC2xSVM70SIkNFDyImMAQW2vjS83963viPEbltqLbBtRhYRhXCypOLPJJkkfyTWfrzAoJ7DBXZ3/jgQWFxiZ5gyQkYPIScyhhTY4eYs69F9avWHO9tdaIeM9CMK4WRJRz8bDe7xH51OlzCs52CB/Nvh4aG4xOmvzM4SRMjoIeRExoACu9webTu69zanePZWJ4aqP7XPSD+iEE6WdPSzYabxHw1qP1xgVE9xgWJXciiwsMTVSfHbU/2sETJ6CDmRcVkC781P8JS8fjI6tan5gdEFjhgxwOAe1n5c6qCe4gLFrNWTAsurMUGEjB5CTmRc1C601cap+kvLfejou9AxI/rfvRzVXlLq4CB5VIfpXejqR+27nWaJkNFDyImMFE9iZZaje2c9uE26XPJ3VhnpRzQ8wZNJBre4wLj2sjc0CfUUFqhfM7cVgsiK0L+WFCGjh5ATGUleRrIb3butjYTt4xgWf+uUkX5E00ss488QF5DUXnINRixj/LHDLfBsEZNEyOgh5EQJNG/ksBnd3Wt8zTgV3wZkiH1GBhFNb3IYZ+ovIK29UIKsnqOPHd+kJBRRztFmEDVCRg8h1RmD3kppf5OixejuvcbXjNPhTpnpx0ZbR0WP2H99kjSpsIAsU28Bee2FEmT1HHyE5FbK2SJSZ/QQUlkCHmYAgDEQGADGQGAAGAOBAWAMBAaAMRAYAMZAYAAYA4EBYAwEBoAxEBgAxkBgABgDgQFgDAQGgDEQGADGQGAAGAOBAWAMBAaAMRAYAMZAYAAYQ1Tgy8PNZnPwQZZd//lwc/AvL7JXx9UPql88bpb71/bri82D/H+JqmvDCiIiY/iMZAU+yrI/54mf5v+/PLydt8rt7Dr/pvxFy6vjWy/qL9n1/AoiImNHqIyUBc5TVo1wdvC4aJXip0KrXD/abG782+Yoe3rjGbueX0FEZGwJlpGywPla7Kzc7ci/k7ZKuVq7YNrzK4iIjB2hMpIVOD9++N5HmdAq1X5J/ot2Z4Rzz68gIjKGz0hW4Lwdbjzrt0pxRuC32XJW3SuIiIwd6xM4P664nYkHFvUvOjj3/AoiImPHCgW+flSe2vso+6/61F7zi46iVeo1ILueX0FEZOwIlZGwwOV67fpfm4trTavk+ydt+vLc/J83N/83w55fQURkDJ+RqMAAAB14Clyt3Hq3uSyPFURERg/wFBgAUAKBAWAMBAaAMRAYAMZAYAAYA4EBYAwEBoAxEBgAxkBgABgDgQFgDAQGgDEQGADGQGAAGENf4IvDzc1Ps+zy/c3N8omO8vnLs3rKofw3zB4flbGCiE3Gi+bJHGT0A3mB80a4fnrrxavj28U/Wb9VipkQysejU9fRkRVEbDIWM8q0P0BGD5AXuAh9cfC4/icTW4XhPOASVhCxyXj9qJmmERn9wEPgKnuVX2iV/D/8e34FEZuMxYyNB78tfoCMfiAvcPGyiqebB2e90V3NcFC2Sr7byb/nVxCxyXh5fNTOv4qMPiAvcHFm4CeHC988LT9inbH4qpqtERn9QF/gHPUB4uXhEg6eslVErMPJBzcyWkJe4Mvq3KziFO3TJZy+XEHEJmOR6Gy0kkJGe8gLnP31cPPTF8u+SLqCiE3G/B9k9Al9gQEASiAwAIyBwAAwBgIDwBgIDABjIDAAjIHAADAGAgPAGAgMAGMgMACMgcAAMAYCA8AYCAwAYyAwAIyBwAAwBgIDwBjyAl9sjqp/+D/vrWYZ06pOsYpuTNGPXAReNgsf19lKujFFPzIQ+K3DvPPzprk8fOv9gw/KGUvyf14df+/w4LCYSWoBI6NaczcB66l1upw3Pk1dQWdW0Y0p+pGBwLdenNXtcutvj248u/zR4+LlFa+ObzzLzhYxGVrX8VXA4g0dYk7+rKIbU/QjA4GP6il1iynCitfOXP/l15uDx6+Ob5dTdZ4tYXw3HV8GLGY0LMZzLyd/VtGNKfqxL/A3v/h9+e/zf/pTkM+yomiCalL7ul0uD29+9LRukOtHt58uYXyLHV9M57856ufkzyq6MUU/MhB4sOouVmzlLknRIGcHP17SsVMbMCunI+1ysmcV3ZiiH1uBv/3wjZYfBvkoO8qDp/K1Be2K7cE/jps12kX1IlbuCB1fTwwu5GTPKroxRT/KtsCk6J++LNvl+tHm4DfFO+CKBnl13LzKkTVCxxev2DnID536Odmzim5M0Y/kT2JNs5DhvXbQjdYIAn/74Xez52+88eYXqWpjytnm5hJOXq4ddKM9gsCf/NOfvv7BdwuNAQAc6Av87Yc/zL5645e0zkIDANQIJ7He+WH2/Du/h8AAcGEocL4XnT3ncxAMwLoRj4Hf/Pd3vlt67LfcZbKCiEvK+PK9z+uvXj/Zbrf321/wzjg4C/3GP/1J5xxW1xjZfrv9+cejnzJvFS1WEHFBGa9O3v68+ur1k3zA7rZ3m9/wzmhV+64xsn3eGPvK4N5PubeKFiuIuJyM+XamGZ0v7z3M/7trByvvjDa17zXG6yfFrsjpXfGnluUyYwURF5Nxv72/743OLGu2Ohn3jGLtv3nnjTf/48OZQ+B+Y3Rrs0ET8W4VLVYQcUkZBwKfLnEL/NV3fv/8zS++/sHsSaxO4Hc/7r6FwItjQRlFgffdWSzeGYc3chSXkOYvI7WNUe2I1LsjTAU+Pz+3+jtGEa2xzXhu26jhEATed+ewIvfjueemGd3IUQg8eyPHAgQ+72NVAvmIHdZjxiqjU6sGoz86e9vfyP3oOObGiFvg75YCf6K/Bea2C30ux6osohHHOIwXi4zO7RqI3ujc9f2N148+Bt0I8Rj4jV/mAj9/45dzfyU5iZWRFVgh7UoEdhsuphn9tGwQutG52z7s/yJWP3oadgNGZ6HfeOM784/17yWXkYgJrKHtCgR2HSxmGb01bQh6G537wi/i9KO/cSdiV/tO1d6NHOkFNpJ2+QJ7GCsGGX02bQjK0fn6yd18A1wS8zqw34En4CBw0RjF/kjbFIkEttZ22QJ7GSq6GX23bUyC96P3kdcn1KyUgVvF3Vn3ZqQssK+RopExTONGJOlQdS6ej8CWbmphVSGyAnscKU3GvzdMf4jHT45HyH4M3zqhppX10yr6o8SNhBE943mgjASWEaR14xGqH0OOvY5Q08pat4quc16JGzEgniOOMlp57JwqLEH6MfTgawk1Ct1PX8YkdMQoBIg4mbEped5hu8+ORfKjPafPSiGwYb4I6OfqTVpASmCPEfvMZuw+YEJhu8+Ohdd+DDz8xkQR2CpVTLRj9SctICOw34gCZmehlQrbfXgkfPRjpOEXqPZT5boli4VuKmHSAhoC+44oopex+xSFwXYfHgmtE3VmhBp/E7X3zSIFFictICCw/4gDzG/kkI5fuw+PRACBjUV2r71vFilwJtxtlljgYBH7aGfsPkg2cu0+PBL+d6HNLfZVe60pdYzKNRxmidDPRUTgkBH76GfsfZRk0Np9ehw89uMgtYHEfmqvPaWOfrnmIy0F+rkoCBw4Yh/Lq4GsNsF++3HU8JoO236e3ZQ6+uVaDbbo6OdKLXCEiH2MMvY+jZPBEa4Dayhs+1l2U+rol2s94KKinyuhwLEi9rF9Hng0XO0+PgoB+lHS/qEMtptSR79ct1EXC/1cqQSOGLGP9QP9w+Fq9/FRiHUr5YzClh9kN6WOfrnOAy8K+rkSCBw7Yh/DjL3P42NwoH4cd8H0frTlx9hNqaNfrp/RFxr9XLEFThCxj8OcWGsXWLERTt1DpuV6GXzBcYoYCgIRzTP2PpGLweH6UdIN3g22q73s7YS91xRmENiRqBGFnitmjOombTTP2PvI1Qus2I+26CI1Yu2L01dfzR8Cy95O2J/dLoPA9kSPKPTcrvimM9hpXmhxqJqX5BOvKykThv0wdRxsU75Q++r60eyNHLK3Ewrzy2YQ2IokEYWeqyYqHHejXQg6BnteSZkw6gm/Bg+vAxfM3MghfTuh6qWr/kZgSCwajsJzpF4iCj3nV+BzKgL7XkmZMO6K8ALrvxupfbWK8I6VDAIbkDii2HMetk69DxWGqkVRvvC9kjJj1Bc+DR7eyFFg8G6k9uVmwlvOMgisA42Ig56Tn4u0zdUfqjZFecL7SsqMUWd4nHxocCNHsQk2eTcSBLaDUESx54r3XvfePWKXsfexvaFqVZQf/K+kzBh2hvIw2LxoyY0cGndCYxd61HS6kIso9JzqVIZ1Rhqb4AArKTOGvaEy2Lxku9rjJJZ501GNKL5gUrEjZUj3sTQEDrCSMmPUHd4MdhQYl5F0SB2vQFk5oeeqwT3ekTKk+1giAgdYSZkx6g9fO9FC7Zu3M+ifhcaNHFOkjtVnojP7Pedp97L9WBrnoUOspAwZdYinTbBQ+090HwSWvZ1wh1spW1LnkTARpO65+vLKdtt7f72zwEQ2wSFWUoaMesTPJlh2HdgH6xQ4dY4J3LrRlN4HkxA4xErKlGGP+NkEQ+ACp4g8Qrp1ozHdB9PYBE+Q7LluLwbLbuTwwZoETl17Ldy60ZjeJ9O4FKwm2swqoz7xLbDOg0im5drMch0fp4gQWEb3ydQ3wQkF9mCwuAv9huZZaP1yPcxaHwGniBBYRvfJELhh1Cke3iQVb0YOTy+hCIJTRAgspf3kvxPfh444OeGoVzgJPKw6JZedIkJgKd1HE98EJxXY+VVwo3uh07xaJbHMThEhsJz2o4lvgmNODzzqFudNsHgSi8KrVVKo7BQRAsvpPhsCtwy7xXkTLF5GovZqlVgqO0WEwAraz4bALaN+cd0ED2/koPpqlbAqO0WEwAraz4bAHcN+cd0ED2/koP9qlRAqO0WEwAraz+51kEt5gUgrsOsmeHgjB6dXq/gzWT9Xb0ZsCDxD9+GkN8GpBXbbBEtm5GD4ahVXkbVj9WbEhsBzdB8OgTtGPeNmcLLrwGGw9Fg3VW9GbAg8T/vhpC8kRRZYz2Dt0hYmcIOhyJqh+jNiQ+B5uk+HwB2jrnHaBC9U4Bpdj/VzQWAT2k+nfBortsB+T0S3tW+fZPD7MEOY0WjKnMf6uWQCS8+LmxO2Cdy60ZJ+DzjWJBzRBR5KEX0LPHxRFJspdZTCGGQPJ3BY2y36OYPAYRiPS/sOs6i9OL9Q/kU5S1j7hViu8UCLwFgFg/DBdqEDK27ez0JGS/rpHKsSjPgCaxmsWZRQ+2Y3enIXWpjh7+qk+GZ358v2i0G5fgZ3CPqDXb/piRwDm/usH7GPN4EJb4IJCCy9m0OvKHFWyje/eP7d7Ot/nrwQLJ9jl+2rVXgKPA8NgTncTklCYOtN8PBe6K+Ke6Enp8YSZrmvvd0+bL8YlBtkcHpHv+nZCDxEP2IffwInNlh8HVK+B9nuK5IQ2N7gocBf/48/lf9XI2xr683x9mH7xaDcAEMxAPpND4HN6CqQVODBawdyn5MK7NHg4eOE3/zi9yYC1+eucm/bL8RyAwzFAJi2v9h0qWuvhVVED4O7rUDKeTmGL/65OlmkwOVzhJ/8cOZ54MF7CE+327c/+93HvS+Ecr0PxCAYNf6o6VLXXguriEsRePg+s92dP6YV2N/tWGLtP3nzi2/embmPY9gYWe8Rne5ZHQhMDKuIfgVOtw892Ojk36Y9Bg4msA7D3ZGsd/WI02WkPsaNIDRd6tprMREk6AmergYJN8HiYV8xglML7O1EtOONHFcnd6sftF8Myg0wFANg3gjZcgQOe4KnqwEZgYvNTHKBta4FaxRjU/v+i6KuTurVd/uFWG6AoRgAi0ZYjMChT/C0NUi4Dy2+4Lv4hqLAVgaLl5E8zKUzKDfIYPSOU0QeGZUxQp/g6aqQUOB+xnz7UzK8YBKbURdZGSwKXNxH6ef1SP1yQ4xHv7hFTF17LZQxgp/gaavQ2wR7KNaE8Xmb9FtgmcAWz8kNa/9V4bDHSe3U1aWEW8TUtddCGSP4CZ6uDuk2wcPjfJICWxksqf23H3p8HniuzjRwi5i69looYwQ/wdPVIeGFJOEF3xlRgW12ooe1f15sgT28JniiVfwPTmd8REydYQZljPAneNo60HymMJXAfm7HGh0De5mTcr5VQoxRezxGTB1FhTJG+BM8XSUgcJ9xJ1nczSEK7GHTKylXgf9Bao3/iKkTjVDWNMIJnrYS6U5jTZBMYC8Gh57Ubo4QY9WcUBFT5+qhrmT4EzxdLShugikJbG5waoFLvI9WU4JGTB2uYqKCwU/wdLWAwAKSfjI9DO7NSvnmv7/jf1ZKXfyPWBNiREybMNnDDGJ2iu/6piaw2aUkElvghgDjVo9oEZMlhMBKEgrsYRNMSuAC/0NXA0T0nVGVmKDBxAQ2NHh8K2WaXWgR36N3DkQMklEWFgILyLrKyGBJ7b/y4K/PyZQikCZi1JB2FQwgMLnTWAQFNjBYVvtPwt6JZYDvUawCEYNmVGyCPZXtCjmBjQyW1f558l3oPp4HspTEEWOEtKtXCIGpGUxPYJNT0fQFziIM7/QRC+hFDCEwuaPgpAIrDdbtQkntvdxR6btVPA5l3aaJHbGAWER/GXs1obYJpiiwvsGys9BzjwMr307Y3gO/itEdrOPpRAwiMLVNcFqB1QbrHQc7TmrXvZRwV/x0O3o7oXc8jmmbcdRNnBu041NG7BNGYFoGkxVYy2DHaWXblxJWN9J2z7QsdHRfnUherRKIRBEFwpyLhMB9VD0mNXjUYNIbOSbv5ZC/nTCmwJl3h3U/Nj94iCdw5jWmXQUCCUzKYKoC6+1FC7X/+gfFjHbPp+e1k7+dMNoudIe3sa09ivbb+7KXmwUlcsQBHjP2K0PqPFZqgScNHik8/ON+7YuXmxV8MnkWS/52wuH8/ksd3dEFLokasQ8EjoC60/4+Vnj4x8Iu9C+q6XSmrwOr3k6Yj+yX9+7Lyg2Kh7FNX+CCaBH7BLpZhdQ+dHKBp7r270OHh38rboGrfefpLbD87YTD6cGjtorj2OYhcOaW0+4Tgwkc22Bh/zDFzuIUkx33978LEg//Vqj9V+WMds+n57WTv51Q3C7HbxWXsc1GYJeYdp8X6nbR2PNTCpc+B3MIERf4vHO4aLTh34q1L89Dz9zHIX87YaX1Ps0WuMR+bDMSOLPOafdhwe73jmuwMGaHA5i+wHWDlQz/1vFGju6lhKmOgftYjm1eAmd2Oe0+KdwDG1ENFvYakx7tydHtRD8Cy99OWBwMb1t/KU1373V0kxC4IFzEPgGfSYl5GDyevD7pzuIIh34c7UK/+R/1xSQ3qM1T4mt0kxE4M8xp9xEhHyqLuAkWztCkPl0jw74fhyexnr/5xdc/8GAwzRtMvY5uAh0fOqLnjIMqxdsGr0Tg4kaOXODy/64kbxWDsc1X4Ew/p13pgQWOZTD1XWiD4Tr8Q/Fe6Fpgag/0W6LdKJwFzjRz2hXtOeOgTn+PdSaL+kksTwJ/++F3S4Gnb+TQg0SrZLoNY1U0lYiZVkq7gsMKHG0bTPwyUoGmvzPHwG/8Mhd45mEGPWi0SskKBC7gIPC4kpO3CfqD9o0cBV4E9viCURqtUrMKgWdS2pUZfmakv8fZjxbe/7QjditliZW+4WpPpFVaViBwgVXEmPcJywVO+nASkX6083c1Ak83kFWB9CJmEynVfxJ191Jat7QSE+lHK33XJHBmNbonIBmxwCxi3BM88vb/e5/YFlPpRxt9VyZwpmolq6KoRpSHVC4c9xKLeoD+3R27KpHpRxt/Vyew4eiegm7EAn2B497kMDlKHUW2qxGZfrTQd40CZ5KWsiqFdMRRSOVysW8znDXYoUusoNOPNm2xSoH1R/cUxCMOQiqXoiaw/0+cIAK8JQAAIABJREFUgVA/WrTIEp9G0mP5Amf9kMpFot8nTMjdEkL9aNEoC30aSYsVCNyFVC4Q/z5hSvZmtPrRvFUW+zSSHssXuMmo/HWC+4TpyFtAqR/N22XJTyNpsXyB5yImuE+Ykr/E+tG0XWyeRpK9nTD/WUnzagZarTLN0gUumIqY4D5hKvIWUOtHs5axeBpJ/nbCgnKKO0m5y2QFEZGRPOZPI8nfTlh+tyM0YVR4VhARGcljXnv52wmr36SflTIiK4iIjOSxEFj+dsKsmhravlx2rCAiMpJHqP23H869HDhTv52w3p2WlbtMVhARGckj1P4TnetHircT9m/AG5a7TFYQERnJM7wOPI/87YTFV/WprKpclpg1HU9WkNEoIvOMFgJL30442IMGAMRgeCPHPPK3E2bd1WAAQCyE3Y2vf6Azoaz87YTiITAAIAatwOU9HDUz57LkbycUdqoBADHgfQoOgJUDgQFgjHAW+hfVXdA+HicEAEQAAgPAmFbg5jbKAvcpdSbe1iH9WXklWb3E6yfb7XZ4mVn2WPLUZ4h3mgwXKM7Gbbd3M33cM5KPKP8QbSS9Oou0HXSQNY8+UTNah5RklG2B3Zma5EH2s6uT0Sns/hKvn+Rf7AZDTyiiOAU+8xl5Hw1Gt7BAdYOZAe4ZyUeUf4g2kl6dRdoOOsiaR5+oGa1DyjIGOYk1Oc2S5Gf56m/YDMISkpu/JI8lVzNKqD6j2P6Io1tcYG/YEe4ZyUeUf4g2kl6dR9YOOsiaR5+4GW1DSjMGEXhyosPxz/bb+6OxJfkrcd0zXmAQbbjA7s4fxdEtLrAz7D33jOQjKquphaxXtf/WblNqLXCKjJYh4wg8NdWw9Gdjgcd/dTpdRLYTG2SwQP7t4ABRXOD0V2aHJe4ZyUdUVlMX68F9aveHO9td6BQZLUMOMwYReHKyf+nPRs0w/qu9OPaGC+yHY1NcoNhJGoxuYYGrk+KXp/rD2z0j+Yiqaur/qeXg3ludxRo1j8Gfxs9oF3KckY3Ae8kJHrHY10+E0SsuUDxvMTW6FdVQE0BgahGVZWj/qd3gHraDNoPmMfjEFCspu5DDjFx2oUfrK0mxgyPIUSUm9y+rH+k/UeV/F5pcRGUZutgNbrvtb/WnlvvQ8Xeh7UMOMppPqaOB4QmeTCawuMRulFf2WLIwNoUFdoNpq1Ul6F9occ9IPqKyDF2sBve4HfSxfaQ1dkaXkIOM5lPqaGB6iUXSDOISu+2oYyQXYcRCxp97OnGNRVbCJO4ZyUdUfYg2NoNb0g46WIariZzRMqQ0o/mMHDqY3uQgaYb+Ev35auVFFAO36gX1Z4xuUxJLKHrP5AyPe0byERUfov+XNteBLTdNsubRJ2pG65CyjGEEnnpbh2QBaTP0lqj3DgeFCEWcDnYeJZ8xvs9wtoTAGclHFMowx2Jwy9tBB8tw3edGy2gfUpLRfEodAAAZLKbUAQBQwWZKHQAAETAjBwCMgcAAMAYzcgDAGAgMAGMCTakDAIhBmCl1AABRwEksABgDgQFgTO9Gjjf//R3cyAEAK7AFBoAxEBgAxoiPE2LnGQBWiDNy4BgYAFZIdqG/eQcCA8CDocBf/+CNN76D+zkA4MHggX7a9l5sNkdZdrbZPMiyy8NbL7Ls1fFmszn4IHXF/LGCiMjoE/EkFvF957xV8qZ4WrbK2ebgcdEqt7Prp8VXC2EFEZHRJ6N5oWk5fLF567BYlTXf/c8bz14d/8+8Va4f/V95g5Stkq/hjqbKIM4KIiJjuIzDY+DC4Te/8Pwh9lzk67GzYi1Wf/fW4YOLg/83/0HeEk9vPFtCz68gIjKGy0j8LPRFvk7rQl9s/u9Ht89u/FveKmcHjy/yfxaw77WCiMgYLuPgRg5q14Gb5M13R09vvn87/+H1o1svip9XZwZ+m7SOjqwgIjKGyzgQmNo56OFq7ag4u5e3yuVh3hqb4ijj9uTfM2AFEZExXEbi90KXBxbtbkfeKpeH5Q7JWXV278ECen4FEZExXEbyAr/1fnftLG+V60c3nl1sfpv/tzgjcHsBPb+CiMgYLiN5gVmfmdRhBRGRMRwsBK4OIza8z1KqWEFEZAwHcYEBAFNAYAAY0xO4mRmazlVgAMA0ncDP36jfLfoVJnYHgAm9NzO02j4ndDM0AGCC3rSy7cu9v8JONAA8wBYYAMb0joGb+6C//gGOgQHgQe8sdPMwEra/AHAB14EBYAwEBoAxvbPQ3Qu+cRYaAB6IW+Cvf/BLxXIAAIJAYAAYQ1/gi8PNzU+z7PL9zc3yGa1y2pKzg8flg1v5by7amQDZsoKITcaL5lk7ZPQDeYHzRrh+Wk4LVvyT9VvlKKsmPeDe8yuI2GTMiglWmx8gowfIC1yEvqgm5rwYrNaOyt/y7/kVRGwyFlM0Vj9ARj/wELjKXuUXWiX/D/+eX0HEJmMxt2o1tSoy+oG8wJeHt7On1dx+zeiu5iwpWyXf7eTf8yuI2GS8PD7Kzuq9DGT0AXmBizMDPzlc+OZp+RHrjMVX1eTJyOgHFjdyqA8QLw+XcPCUrSJiHU4+uJHREvK3Ul5W52YVp2ifLuH05QoiNhmLRGejlRQy2kNe4Oyvh5ufvlj2RdIVRGwy5v8go0/oCwwAUAKBAWAMBAaAMRAYAMZAYAAYM3jBdwO9+zkAAGOELXD1Tobnb/wSE1MCwIK+wN9++N3y30/e/AJTQwPAAXEXutruPv+nPz2ndjclAECCagsMgQFggPwYuFEZAEAa8TJSeR76n/707Yc4BAaAA7gODABjIDAAjBEE/vZDmg/0AwDkCAJ/AnMBYIXsOjApLjZH1T/8n/dWs4xpVadYRTem6EcuAi+bhY/rbCXdmKIfZTdykOJi89Zh3vl501wevvX+wQfljCX5P6+Ov3d4cFjMJLWAkVGtuZuA9dQ6Xc4bn6auoDOr6MYU/Ti4kYPeU0gXm1svzup2ufW3RzeeXf7ocfHyilfHN55lZ4uYDK3r+Cpg8YYOMSd/VtGNKfpR+jghpXNZxb5XNaVuMUVY8dqZ67/8enPw+NXx7XKqzrMljO+m48uAxYyGxXju5eTPKroxRT+Svw5cNEE1qX3dLpeHNz96WjfI9aPbT5cwvsWOL6bz3xz1c/JnFd2Yoh8ZCDxYdRcrtnKXpGiQs4MfL+nYqQ2YldORdjnZs4puTNGPDATOD57K1xa0K7YH/zhu1mgX1YtYuSN0fD0xuJCTPavoxhT92Hu1ypv/TvMYuDt9WbbL9aPNwW+Kd8AVDfLquHmVI2uEji9esXOQHzr1c7JnFd2Yoh/Jb4GnWcjwXjvoRmt4C3y2ubmEk5drB91oj3AZ6Re/L//FdBwAMAECA8CYVuDmUcICgrdEx+fle583X+63259/nP/7+sl2u72frkoADJFtgUHO1cnbn9df7nN7i/+/fpL/Z7e9m7JaAAiEOonF++RYudFtBH79pNjont7NXt57mH+xa37OPaIOyEgdsfafvPmFpycaeLdK7u/9fSPqwNt9uTedsY+oBTJSR6h9dfbKy3tVeLdKQSfwux/3vz3FFnhZ8M4ofTODh0llebdKwV7c5DYb3n17Fot/xHmQkTqKV6t4LZcncoH33Tks/hHnQUbqKF6t4rVcnkh3ofe9q0j8I87DP6PkamD3RQnvjPJXq9iV1WsrVq1yfi77qewk1q5/FZhNxPMCuz91zHhe41aKC5Krgd0XFYOMiStsivTVKnZF9dqK0egue0z24/3oMlLu78PeAgwinnfYFWCf8VzEuhw3ZFcDu/6s6GVMXl9z/I3CXlt5LTcoE921H626X94T7sKiHPF8hF05lhnHH59GCenVwMFlwTZj8tpa4W0U9tvKZ7lBmequMs3rJ8WKelcdM+X/FJC+DixTJ67AnmvgyvhUxuCyYJvxZz/7WfLamjPahX7zPz60vQzMS2DnsUUsolKcmAL7r4Er44sJ4mVBQWCGBosnsb7z++dvfmF9IwcjgX2MLRoRZ7yNJ3CoKjiiL/D5uahw9KpaIV5G+mF5E4ftjRxsBPYztBJH1DU3uMCh6+CG/i70eWXwz1LW1pzhjRylwJbnoXkI7G1kJYpoKK4fgX82BWmD9U9iNQonXd8YM7yRoxDY9kYOBgL7HFixI1qa6zQatQQ2MNlbY2gzvho4cxmJ2SZ4+GqVXGDrGzmIC+x7XMWK6Cau22iczliVbGSxXTUcGF8NnLiRo86TrLIWSG7k+I7tY/2UBQ4wrEJHdPLVkzkaGbuP0HHYrhoOjK8G9r4o6WesY6SqrAU+RyFZgcOM7jARHRz1H1E7Y/c5Mw7bVSMoQsYqAuHaDln8jBzhRrfXiNZi6mJXLe2M/Y+aVNiuHiERM1YByFZ2xLIFDjq6PUS0c9GKiVoID+eody9N0kwobN5KgZE8zNDUPU2FjJC+WsXDxJQEBPYzutVYR7Qz0BF1dYRzOqflPd/tMxtmGfufpzTYqslCMiEwvcqOUPSQ8zP9K7jLwTCisXJ+UdZLuKpydVJ8s7vzpV1GLYUNiwzOMGNVd5p1HaMS2PWZ/pQCexzdUxidoU2Oso7CfQ2q2wz16X0kE4OXJLDbwwzqcuPheXRPoYpoV4XQKGMIdxbWArfPPTs+jSQ32LzMoCxIYMeHGZTlxiHE6J5CuAWPPMoYwka33hz7EvicwyZ4lPGcq8CuDzOoyo1BoNE9RRvR7bMjoYwh7jVXJ7FcBB5ug03qkgS5wD8jWFMZPh9mUJUbnHCje4qFCDyc9nq7ffuz39kfAw8bhL7B44znjDbBPh9mUJUbksCje4qlCDx4OCfrz07oPqWO9DDYrtRALEdg14cZlOWGIcbonmIhAg8fzslcLiNJmoS8wQqBmexD+3yYQV2ud2KN7ikWIrBwI8fVyd1sYspVbfqfLDPYstggSDKet5vg+NUxxKdoceaFjju6J1iKwM3dk+UjO1cnfiY97390QoF7d4nuqykJtw/LjNvJN2yc89mHFo+Bnfadw88LHXV0z07pvxiBJ/AzL/TYYOtizRg8+VvvYVSn7VpUAvPYBA/PQtsTdl7o6KN7fkp/CDxJ/8MTbYLHh/flubr+Y6+ZPOM5m33o4XVga8LNC51kdGtM6Q+Bp+l9eKJN8OgEezU1/+6usNRyBM6+/men81f+BY40uMe9pDOlPwSeoffpaQwezj9Zv9v59Ff5IfD0S+rOWQr8TfM4IYFZKSMN64nRNDsfKQSeof/xSQQePppRPWt1dVJcJTttDVYKzMJgelPqxBnP86NpdkZwCDxD/+OTbIKHAvfPZ43nhRY457IJpiRwnJEsYzINBLal//kjg51K1mO4C33a3qCS9WYtgMAN1gJHGsNqJtNgF9qafgUSGDw4aVHtQTe/e3fyfu9zCKxDpME7y2QanMSypl+BBAIPLhvUG93qH51daA4GpxI40rDVZDINLiPZ06tAin1o8cJ9+8hk0YPTJ7H6T/UHr6UTkQWONFpNmU4zdyOHxUuC4mPXpc7Do6tAktNY/btEu32n0/KOyoaFCPzNOz/85hc+HmMYlFsRaZTaIkswO6X/WGDrl35FwEs3mtOrQYpNsA4QeKrcSMPTEaeIbUbHF3+Fxa0b7elVAQIHoKv9825SaI8zckQano64NZ2iUFoyu3WjPUKD+KiSd+QZz5mcxRLuxMIW2CaiVsbkKrt1oz1CI/iokneUArPYBId6bhcCq0misls32iMG91En30DgqXJDj0s/OEV0yBjRZLdudEDI66NOvlmSwF//wPeUOoGGo2ecInrJGFzkiSDCNAUv7/Umq/CyfhdSalcqHuornuwE/qp8p5nXSe08D8NAOEX0mzGUyOocwtXt/Z0vq2krBhkdENNpVioiE7csVNWNWx1DhtPKFvicVtbT8AuMU8QwGX17rIwh3F9WfbObvs3QFCGVZq0ishyBmyl1fE7s7jru4uAUMWxGXx4rYwh3eA+mi4LAVCqpBFvgAqeIUTI6a6yMIb7c7O3PTuZmqzCnH0O3WvFYjsA4BraKGDOjvcfKGMJTzrtiJrDq/lExoxP9+utWKx6qjOf8BHY8Cy2bF9p1xMbBremiV9fGYmUMUeCfi4/Ae5/ajOA+9JIEdkI6L7SXARscq7zJHyc08lgZQ9iFrg6FZ2arMKdfad16RWNWYAJ1VONPYPm80N5Ga1CsAicXuETXYmUM4SRW5fHMbBXmCPXVrJcX+pe4uxcy7LXePnG+LoEV80L7Hq9hsEpMQ+CaWYuVMYTLSFcnGrNVmCPUVLNePhAucberpcH7GiBwAwROzJTDE93WH8/FiwkrpcWMboiV1KyXO+IUKs3wHL6vAQI3yAQe3VikINSY1sMqLj2BSxRNOhFEmLZiPzvpuQ1CBXXr5Yw4iVnzQobh+xqUGc85nMWiIrAV3ga9VVyx6bzVxQOyJrLr0iACRzNYnEa0eSHDcLLZeYEpG0xkFzqx5VZxJU2nmTYKgxawiuhveAg1E+rp6xMkCFfI2hcyDKd7X47A37zzhtM9WLGPgf3ZbRV34tiJDF1Sq4iBBI5l8FDVrByjyxW4mlbH3mGSJ7G0PLaKOzm4U8fuESyjCWKNhO98fcSY0cvNygPghe9COzhMUuBJwglMLXrqLfDEJtjbR4wYvV60dNrkJBb9s1iS2n+SK/xdm7L4CdwgydK/1t+fVrabUFhrcKdO1mLToaEEjmWwcMGofSGD7mUkHqehh7UvNsC/zL6yM1hSbrwR6sKo+sK10dP8i7L7izuF91vT2wxTh6tw60YP9CsTaxMsdmPzQgbNGzl47EMLtS+eZajMdX4kmLfAg7uTyofc73xZXSgdv1pFh9QJKQscUA/hEnf7Qoad1q2U/AT+5p32QaSVCyzeH9yctXQROHlbuHWjB/qVITWzzozAxPehMStlwbD24kPulcD5yttuF7oPnYh6+BwevcqQeqhwUmDyB8EQuGBYe+FSYb05LsTdS9+NZAqJiHoEEpjUJng5AjdvZsCcWOK1/uokVi7wab5Jfnnv/iCiDekj6hFK4Ig3RM8ykfGc/kEwBC4Y1n5wrf90u337s98NLyC6Du60EfUIJ3Ck89AaLETgbz/sXm72Q3/lRhugTgxrL7sD4L3hPXgeBnfCiHoEE5jQJnhOYNr70LItsM9yY41PN4a1H17rz8rLSO2tAOOmsydVRD28niIR6kNnEzwtMPWDYJzEKhhVv3+tv3xRQfmNv2NgkSQRtQgnMJ1NMASeKjfG2HRnXP/+HQDFZEqVzKf9p90DDu4oEVvkt436z0h0E6wlMFmD29p/886b//6O/xd8lwQfna64R/RBoojilDr5TobWbYbuAakYvBCBPSCbF7pP0AHqhFVc/zsvSSKObxv1PrG7PB8DgRnsQ4eeF1pCyGFqiVXeMEcf0SOOz7gHE3hqHzqdIRC4Qj4vtJJQA9UKq8ChTh+EaRrlp42fet+F2oWGwP7pHQN314FtjoEV80JPE2SoWmAeVzuiHREjDmeY2YeYlVIWi8oN0QsR2APmAtf4H66GWMUNKbD/NlF+0HjiqNdPiunfgmQUqhRhE9w/wf76SX0NoXtFQwkEbrAWuMT3iDXBKm5YgTPPLaL8FMnEURoTvnmJFPw0Vv8E++sn+Re7wtvBS5AhcIObwCVex6w+VnUNLrDX5lB+huy2Ud8vN1MFCrwJlkypU14m62fVFZiqwT6vA3sQuMTjsNXEqpoRBM78NYbyA+QTR9W/DHytO/AmWLJuKrbHu7vCUgsR2AO+BC7xNXC1sKphHIF9tYS6fHHiqCDvRlKGCWuw5OiguBu2eUVDzbzAlPehyQpc4mfszmNVuVgCZ17aYaJ0+cRRJcHvNhMM9v1h4/Nz+1zc9hUNNXMC094Ei7UvryVZz+zuX+ACD4PXZXSriSiwh1aw+9i4Avt2ZCTwvjv3rHeY0K+j58p5Qqj9V+WTwM+/Y/lUYRiBS5zHb4DRHVXgzLUN7D4zssCeJRnuQu97O856J+r6dfRbN1/0a//th9Wcsp84vSFpXK4f3MZvgNEdW2C3JrD7xODdGPRujsFJrF3P3961JA2BCR8Ei9PKVlNx+JxSxzMuQ9j36I4vcObQAHYfF349HHITLM7LsKuP7Y3OtPfr6LVuvuCzBW6wHsK+R3cSga3z231YgIzDioU8Cu6fYO9mYmhf0VCxHIGzr3/gdAysLNc7loPY7+hOJLBleruPiiBwUIN7J9iL91ptq28NzrTXNaS7D+3tYQZVueGwGsceR3cygTOb7HafE0fgtA8GzwtMeRMcekqdsJiPY4+jO6XA5tHtPiVExlHVEj9XCIFjljvCQldPozutwKbB7T4kisCJDdYSmOw+tFD7Zjeaxy50h42z7kMmscAFoSOGyTiuXFKDpzMKFYxUIxOE2n/y5hfPv5t9/c/kT2KNsTRXOWLkMzW+vKf5HGksXCLqEE3ghIfBegJT3QQPrwN/9eYX2XPXt3tnSUa3vb/T80J3L/je3/mymiW6hILA+qntSo8kcCFIMoV1BKb7UPBQ4K//x5/K//ssNyKeBFa94Lv8wu+bGdxhJ7DCYA/VtWI5An/74Q/L16swFjizc3hYhvwF3wYzOURlCQInPA42EJigwULti3soP/lh9tzyTqy5eaGj4S6w9AXf+7c/Own1ZgZXWAmsMjjNXvSCBC7OYn3zju1JaN15oWPgKLD8Bd+7Yt7cbtJkUgLPRrYrNLLASRSeyUjc4GTzQofHl8DtC76r+ZI9v53QH9wFrhWO7rCewFSPgr31kNW80KGxFVj+gu/qmLh9kJRGxD6WAguXzPYBX242X9MUCmsKfE5zE0x/Rg43LAWWv+C7Erg5lUUlYh8bgYVLZsI38QVuHe5bHKgODdoCkzSYyYwcLlgILH/B99VJgBd8e8ZcYCHrMHgCgTuFBZEDVSSbz0jbYJ/PAxMVOJt3ePQH8hd874QpG2lFbDEVWNjbGO56JBFYYrEe48+RHx2YHSb060RbYNcZOegKbH6GR/6C732wF3z7xFDg/vH+cBapYBnnDbYQefQp8qMDw8MEsS7UFF7HFrjESGANCEasMYoonHEfzuOYXmCXTpIfHZgeJozWJqQU9jkjB3GBbQ4QpyAZsYa8wOEfpMpURwemhwlCXWz27L0yrJ3PGTnIC5ypxo1VUVQjVmhHTLMLHfQO7gZ5NOOMgzoRFdgDHAQ23L+cgm7EEm2BU5zEGtfPu72ZaufCeC8jSN18wX5GDhtWIfAgpnKpJJeRRtULYkgogX3UzRdi7b/+Qb4D7WNSSuqjexUCCzHVC6W4kWNQuUCCBNmF9lU5T8hu5Hjjl57LpcgqBM60IgovN9tFuZVyULlAivg5iZUFqZsv+E3s7o8VCNyFtPvrBAJ7/AA/l5H09mNS4fNGDlW5dFmBwE1Iu7+NPT+/5w/wciNHr56eq+eDNW+Bs7ZrrP6WScQ6pN2fBs4YVt9MdXRgephA2N/1HgN3LF5g+4jhMxLYPdXISFbf1Z6FFli+wHlGuz8LnzH9xk1P4PD1sGOV14H9sIKIyEge8RjYw76zpNyFsoKIyEge2Vlo3+UulBVEREbyDOeFDlHuQllBRGQkj/g4oY+3IknKXSYriIiM5BF3oVm94Ds1K4iIjOQJdhaaJSuIuIaMRhGZZ+S9+gFg5fQEfp577e86EgAgPJ3AxVRY1aRYAAAmtAJX15BsX0wIAEhBb1K7QuCvPJyABgDEYiiwlycZAABxgMAAMMavwDOvoun9enrJ4mUm2+1d9YLl2wK1imyWnCvy9ZPmtSnim3PsM/KNqBFzZsFeVRRLyuJNLjhbpFE8s4z6IaNn9CrwzAwmvV/PLNm+wlM5ZeLVSTkp2WyR7ZIzRb5+kv9nt707mnDFOiPfiBox5xbsqqJYUhZvesG5Io3imWXUDxk/o883M8zMIdb79dxsY+2cn6pJi/P1T7HIbJHtknNFtlMVSt4sapWRb0SNmHMLCnP8y5aUxZtecLZIk3hmGfVDJsjo806smVk8e7+em+9zd3eyyDzw/f2gnJklZ4ssyddpknd7W2XkG1Ej5tyCXVUUS8riTS84X6RBPLOM+iETZPQq8PQ82r1fz824ffqreldf/eKeYTkzS+oUmS80nvbbNiPfiBox5xbsqqJYUhZvekGtInXjmWXUD5kgo9d3I7XHfv1vZK+0mFny6uTOl3k976sWbBPPFtkuqVNksSYc/MQ+I9+IGjHnFuyqolhSFm96Qa0ideOZZdQPmSAjSYG7TN5Gt1aRzdmBCAITjqgRc27BriqKJWXxphfUK1IznllG/ZAJMtLcha6Ky/fz/e1fzhe5H+9+OmTkG1Ej5tyCXVUUS8riTS+oU6R2PLOM+iETZKR5Equtuvp0zH5QzsyS80XuqoOPWCex6EbUiDm3YFcVxZKyeNMLahSpH88so37IBBl9CuztMlJVvSKT+t2XZWKNayxCI04Uuds+HP+pQ0a+ETVizi3YVUWxpCze9ILzRRrEM8uoHzJBRq8P9HfHfv1vxl/MLlnWtzyYly/Yxpwtsl1ypsiX95pTB8KvHTLyjagRc27BriqKJWXxphecK9IonllG/ZDxM/qdkWPmVTS9d9LMLHm63dbrGtW7L+v11GyR7ZLTRe62JfWXU72vnZFvRI3azy3YVUWxpCze9IIzRZrFM8uoHzJ6RkypAwBjIDAAjIHAADAGAgPAGAgMAGMgMACMgcAAMAYCA8AYCAwAYyAwAIyBwAAwBgIDwBgIDABjIDAAjIHAADAGAgPAGAgMAGMgMACMgcAAMIaPwJeHm83m4IMsu/7z4ebgX15kr46rH1S/eJy6fh5YQURk9JyRk8BHWfbnPP3T/P+Xh7fzVrmdXefflL9YBCuIiIyeoSzwxeatw02buAx/sXlQNcLZweOiVYqfcu75FURExqAZaQt868XZ5kH9XRk+X4udlbsg+XdL6PkVRETGoBlpC3yUdZFrjkCzAAAgAElEQVTL44fvfZQJrVLtl+S/uPUiZU2tWUFEZAyakbbAD8qjh4qyHW4867dKcUbgtxnnVfcKIiJj0Iy0BRZXa0f5T25n4oFF/QuurCAiMgbNSFvg/MCiPelehL9+VJ7a+yj7r/rUXvMLrqwgIjIGzUhb4LfeL66mVdSn9vKDiX9tLq41rZLvn7QnEHixgojIGDQjbYEZr5P1WEFEZAwKfYGrtdZC7tEZsoKIyBgUygIDAGaAwAAwBgIDwBgIDABjIDAAjIHAADAGAgPAGAgMAGMgMACMgcAAMAYCA8AYCAwAYyAwAIyBwAAwhr7AF4ebm59m2eX7m5vlY1rl09Jn9QRh+W8uuD4E3rGCiE3Gi+ZxO2T0A3mB80a4fnrrxavj28U/Wb9VinlLbjzj3/MriNhkzJ7eeNb+ABk9QF7gIvTFweP6n0xsleK3/Ht+BRGbjNePmklVkdEPPASuslf5hVbJ/8O/51cQsclYzK968NviB8joB/ICF6+Webp5cNYb3dW0JWWr5Lud/Ht+BRGbjJfHR+1sycjoA/ICF2cGfnK48M3T8iPWGYuvqrlVkdEP9AXOUR8gXh4u4eApW0XEOpx8cCOjJeQFvqzOzSpO0T5dwunLFURsMhaJzkYrKWS0h7zA2V8PNz99seyLpCuI2GTM/0FGn9AXGACgBAIDwBgIDABjIDAAjIHAADAGAgPAGAgMAGMgMACMgcAAMAYCA8AYCAwAYyAwAIyBwAAwBgIDwBgIDABjyAt8sTmq/uH/uKiaZczKOMUqujFFP3IReNksfFxnK+nGFP3IQOC3DvPOz5vm8vCt9w8+KCc8yP95dfy9w4PDYiKaBYyMas3dBKxn5uhy3vg0dQWdWUU3puhHBgLfenFWt8utvz268ezyR4+Lue9fHd94lp0tYi6lruOrgMUE/2JO/qyiG1P0IwOBj+oZOYsZhoq3Vlz/5debg8evjm+XM/2dLWF8Nx1fBiwmRCvGcy8nf1bRjSn6kYHAD+o5set2uTy8+dHTukGuH91+uoTxLXZ8MRv45qifkz+r6MYU/chA4MGqu1ixlbskRYOcHfx4ScdObcCsnM2wy8meVXRjin5kIHB+8FTOet6u2B7847hZo11U73HkjtDx9bzCQk72rKIbU/QjA4G705dlu1w/2hz8pniFVNEgr46bN8GxRuj44g0dB/mhUz8ne1bRjSn6kbzA0yxkeK8ddKM1vAU+29xcwsnLtYNutIe3wACsHAgMAGMgMACMgcAAMAYCA8CYUAKvYMWwgojISB4IbM0KIiIjeSCwNSuIiIzkgcDWrCAiMpIHAluzgojISB4IbM0KIobOeH4etnwtePfjKgU+9zJwKEf0JUbIjOc1AT9Ci+D9GDThWgX20KiUIzIQ+Lwj3IfoEF7gkAFXK7B7o1KOSF/g83MqBkcQOGBACGwN5YjUBT4fEuhzdIDAMcv1gp8xQzkiO4HDjfHXT7bb7f3iq/12+/OPhS9KIHDMcr3QDpr/rrEqhXJE4gJL/A01yF8/yV3dbe/m2uZfFP/vvqiAwDHL9cFQXwisJKbAYYb5y3sP8//u3v789ZNiM3x6N2u/qIHAMcv1QU9fl2JIR/RUTpiMCn8DDvR8g9ua3H5R/w4CxyzXB62/bu1KOqKncoJkVPsbbKSf5t6+W+w073tf1L+DwDHL9UEtsGurko7oqZwQGaf8DTTS99v79VFv/t/2i/qXEDhmuT6o/V20wJ6GTICMk/6GGev75hwWBE5erg9KgRd+GYmvwP5H+768ioRdaBLl+sDLDjT1iH7K8Z9x3l/fw31XXQXGSSwS5frAyw406Yjehkwagb0O+N32YfkvLiORKNcD5xBYG+8Z9fz16PDLe/frr3AjB4VyPQCB9UkosKcIu21J4euuuYNyh1spk5XrAQisT1KBIz3iELofwwZZpcBm93C8fO/z5kvhLnjSEYkKbOZvHIchcMxyPWC4Ab46ac9XigdPpCOaDRnVOoqAwOEdhsAxy/WAmcD5gG4EHpy+JB3RaMgo11G+M1r5G1piCByzXA8YCbzf3m8v+Q8uIJKO2HvUavaBK/U6io7AATWGwDHLdac6BDZo0k5g8RYeuhHNBJ5YR3nO6OpvGI9XLLBwvFScr3/op9ygGJ+Dbkf34CZauhHLjCbLq9ZRpAQerpLG2NVqvQILx0u74pvOYLqjeyUCGy2viug3YxBpIbA1wvHS6yd3s6j3p1ljLzCjXWjDwUxQYImfwu/9VWu1AgvHS6sQmM1JLNNnNaLsQlvJO/6lxxpVrFdgobNXsAvN5jKS8cNWUU5iGdor+53H2nSsVuDB7lbkuTqtMX6WcK+4SEo4orXAIS8jGegr/ZXHqghA4PK703wMdE9+UB7dVgJXhwg7HrdS2gsc8kaOdPbWN5olmhearMDCLnT0p6St8fM0f0Y6opXAknWUz4z2+rp+cn2jWarHCekK3Hc2+kRD1kBgAyIKHGzXub7RLNkD/WQFFhqisjniREO2nENgA6IJHO7It7nRLNmUOmQFFndFuBwDQ2AT/GWc1TfgaedK4EST2nnMIcOp9vXxUn0NuHmHlHu5IYHAJsQTOMzmt6KUNdW0spQFTlCuM978pRuRosAz/obUl4LA4QyGwNaQjchM4PDXfdPvQkNgb0BgE3xlnPY39F1XSU9iQWC/rERgTyWFFzjGfVf7lJeRILBfILAJnjKm9bfeXU50IwcE9gsENiG0wHHue66Pd9PMCw2B/QKBTQgscOznFqRA4JjlunJuOtuMGqoR+QhMwl8IHLVcVyCwEX4yGvq7RIHZPQxJdXRDYCMCC0zBXwgctVxX/B0Ck41ITmDa/kLgqOW6AoGNCCowDX8hcNRyXYHARvjIaLYB9vCBhkDgmOW6AoGNCCcwGX8hcNRyXVmFwN4i+sho5C8ENgUCW0M1IgeBiVxBKoHAMct1BQIb4Z6RvL+B+zF0MghsDdWIhcC+SgomMB1/IwkcKhsEtoZqRL4Ce6iwBRA4ZrmumL5XZQKqEekLnPoe6IgTuwdPB4GtoRqRlMAk/Y34PDAE9gwENiKQwEn9jTojBwT2jLHA/d2tq5NtDv23E9IR2MDfaALHnBOr2ePwx/ATIPA0wu5WNZ9hA9WIxAVW+RtPYMWslF49C8YwDQSeRNzd6jq9gGpEY4HVOxmuGSn6q3yNVzjpQiaEwJOIu1u7u/3fUY1oKvDEToZ/gf+brsABiBAQAk8i7m6d/qr/+hiqEQ0FntrJcMxI0l/lLnQIILBvDO/jENbWVyd3vsyHemMw1YiGAk/tZAQQOLm/cV9lTVtg8YJ4PraLAe6h3HCY3og13N3KeqtuohEzU4EndjLcMko8pSBwostIgT7ApfaDC+K5z/QFNjwJPdzdypoVeEY2YmYo8NRORjSBXT7GmKgTuxMWeLgmuzpZoMCD3a2sd5qHaMTMReD6R352L2lugLO4E7uHTuhQ++Hg3t354+IEFlZSVeBl70JXP2p2MpwykvV3wHoFFns+/5bBMbDxjVj93a1S5GWfxKp+9K6PSywSgRX+Lllgwg/0i/texbZqgQI3u1uvn5TybrfbZuNENWLmchlpsJPhktFgAwyB7fEm8C6Xd4kCqyEaMXO6kUPcyXDIyMbf9Qos7EKX30BgGhjeSqneyfArsMpfCOyAr5NYu22Fj7MfAYHAZlhn5LMBXq/Aw8tIDLbA56sQ2N+cdtYZJZpS3QCvV+DRjRwQmAYkBSa7AV6xwOLBU8ZBYI/+Eo2Y0RRY7e86BA5W/LoeZoDAhlhm5OQvBI5arhsQ2BC7jBJ/IXAgILA1NCNm6QXm5S8EjlquGxDYEF8CK/2FwI5AYGtoRsySCzyWNLXAL9/7vPinfXw94sTuENgnXq8i0YxYkFZgmb9p96CvTsqbjdqrnlGfB4bAHoHApvgQeMrfGALn29tC4Pa+o6gzckBgn0BgU8wzEvT3fnm/fnvnb9Q5sSCwTyCwKcYZzfyNdAqrErh59ibmrJRZ3SLhiofA1pCMWJBQ4LGiBPytZG2ffo04L3QBBPbHagT2VpRhRkN/VyFwBoF9ce7x7d4ZzYglyQQm6m/aXWgI7A0IbIyjwNMHwKEF3jVTXCc9iQWBvQGBjTHKKNGXxAa4EjjVZSQI7A2/h8AkI5akEXgs6Iy/kQVOdSMHBPYGBDZGP6PM30l9owvczecec2J3COwPCGyMdkbjzS+FxxgqIHDMch04h8DG6GaU+QuBKyCwJ+qtgrfyCEasiC7wWM55fcn4C4GjlutAfVTmrTyCESsiCyyRU8NfCOwHCDyD8Oyo8A3BiCXncQWW6svIXwgctVwHrA6BhUsO4vUHghFLogos13feXwjsCQg8iXDRf3AHAMGIJfEEVtiroS8hfyFw1HIdsDmHJdx2N7gHj2DEklgCO+hLyV8IHLVcB2wOgccvcJO84Pu/qWEYUn2Yr+5Gubt69kJgj6xH4PrUqNkfCY+eDZ5DW4rAE4f58m5UbHn/W1tfCOyPVQls3pRaAhPDMOTUYf4wo0pcE3ep+Ru+HyGwD86tBNbahSaGYcipw/w24/loK//f1vZCYJ841d7m4CkR7c0FZn/G8iSWocAT66g2oydxU/j7+kn7WHCSeaEJC2x88JSO8/bhGLO/43gZKTMLOXWU0BPY0lUC/uZhdtu76R4nJCuwycFTYs5tBeZ4I0cIgbuimfnb7TYle6CfrMBaB0+O51O9YjV6hHcg7zjcSmkosNYutOQzePhbk6+Skk2pQ1dgh4OnFPgePmQFzvyfxFJ8Dhd/s9PeXHaxJ7UjK7DRvldSwowfUhEFgl1GknwUC3/32/vpppWFwO5A4Ancz0VSF3jfnMOCwH3sDp4SAYHVqA/zHabUIeVvsVuBXeghDgdPKYDAFjhNK5va33pe6F11FRgnsYYwuoxUAoHNcZzYncL2d7d9WP6Ly0gjGN3IUQCBzTHPSEzffMN7v/4KN3KM8HHwFA8IbI5VRkr+lvvR22poJpoXmrDACcp1AQIbY5uRir46RBA4YOFrEthzS5KMWJJe4Brq7paEFzhk4asS2C8riIiM5IHA1qwgIjKSBwJbs4KIyEgeCGzNCiIiI3kgsDUriIiM5AkmMEtWEHENGY0iMs/Ie/UDwMqBwAAwBgIDwBgIDABjIDAAjIHAADAGAgPAGAgMAGOCCiy+g0ZB++6aq5Piyeu7ygW738+Uu6+e4d4+nCny5XufC7XUqq7kw2b/iHnEPoqH4mVL5Jze+VK9gLLmbQkv78mT1Qt0jeCZ2ZCEMoYUeDB1iZzu3TXVbIFq2t9rlXt1Mlfk1Uk5r5lqphU9dP6IecQ+p/kfl5PCFXPCSUtqlyg+dysZ3O0Cqpq3C+zzvy4zTnyEdAFHZkNSyhhQ4OHkYXK6OQK7iT7lNL/XK7ecdXCqyHzdVvxWOdeZFlp/xDtin6uTooDdnS/LL6qplBRLlF9LBne3gKLm7QJVbXejpfofIfu9K7MhSWUMKPBw+s4pivXcbm683jUot5rLbKLI/fZ+2brK2Ua1MPgjrhGFEoU50WUC95fY3fnjeHB3Cyhq3i6g2noJlejmrPPGbEhSGUMKPJhAe4rTfKnTXzUvclUsU/9eq9zT8tfTRVajWzXftxYGf8Q1olBcNa7qI7KdZO+yt0T+aZLjw24BRc3bBfZvf3YyuUDxzan3DfB8SFIZAwo8fIXF1KJ5hquToiVOlWOx/b1OudUuyEyR5UhWvnFDC/0/YhuxT70JL8fVXrre6JYo9g4lg7tdQFXzdoFdsf8v2QD2K1E1gl9mQ5LKSELgfXceTuMoUafc/m+VRcYUmG9EgercSjtVuvL8Tb5EcQAnO0MrFCGrebNAte2bWMAxi5LZkJQyUtiF3vfWcu2ZN1Wh9x7qlNtvVWWREXehGUccfOp2+/Znv+udK1ctUX6W9BKLUISs5vUC1bH6xAKZ4gOcmQ1JKCOBk1i7/l6KxoUWjXKFvQ5lkfFOYnGOKKnge3UByvVGvsRu8gJmV4Si5vkC1YpmYoEwe9D98ssvFCGJZEx+Gal9d03VUOrNQ/t7jXLrRp8pch/pMhLviGPyHceZj22ufyg3HrNFlFdxZhaY35lxQKeG5b+JMya/kaM7R14OLPUZnu738+U2v50uch/pRg7mEXuUdxSUBRTDtlofqJYoKye7RtoWIa95t8BO/hm9jwhyCDwbklTGoLdSKm+3E5dp3l1zOnNbXPv72XLbvcTJIusV3/zdgZMfNf9H3CP2KG4NrApQfWy3RCbfOs0W0S2wl18h6xbwfxeHYQ2z5BnxMAMAjIHAADAGAgPAGAgMAGMgMACMgcAAMAYCA8AYCAwAYyAwAIyBwAAwBgIDwBgIDABjIDAAjIHAADAGAgPAGAgMAGMgMACMgcAAMAYCA8AYFgI/vfHsYvNA9pv/+pdM8RtmrCAiMobIyEVg+S9eHd+OW5NgrCAiMoaAoMAXm7cON0ftt2ebm/+7XK29Ov7e4Y1PL9/f3HycZfk/Bx9cP9psbv1nvlqrfnh5+Nb7Bx+kq7g+K4iIjHEykhT41ouzdmfj8vDo1XHdKvnaLf/2Ol/L5euzF/9Z/Oh2Vv7m9ouzg8eXh7f+9ki1BiTFCiIiY5yMJAU+KsLX3+Vp6wOLciek+Db/uj6aqFul+GH+F8UfKXdhSLGCiMgYJyNJgR/0jhiKxGe9VtkUHJ2NWiX/kk/PryAiMsbJSFLgmdValmXMV90riIiMcTKSFPjWizp8Jh5Y3C6/LVqmOJQof1O2yuVhfWDBpedXEBEZ42QkKbBwhq53aq/YV7k43Bw8KE/tbf5X8csb/9Y7tcel51cQERnjZCQp8NH8QrxZQURkjANZgS8Py3MA7f7JolhBRGSMA0GBAQC6QGAAGAOBAWAMBAaAMRAYAMZAYAAYA4EBYAwEBoAxEBgAxkBgABgDgQFgDAQGgDEQGADG0Bf44nBz89Osfo4yq56Tzspnojeb4jcLmFB4BRGbjBfNYzvI6AfyApdz+9168er4dvFP1m+Vo+z6kXIebUasIGKTsXuKHRn9QF7gIvTFweP6n0xsld6sf4xZQcQm4/Wjcg2VIaMveAhcZa/yC62S/4d/z68gYpPx1XG+d/nb4gfI6AfyAl8e3s6ebh6c9UZ3Nf1B2Sr5bif/nl9BxCbj5fFRPVsjMvqBvMDFmYGfHC5887T8iHXG4qtqHlZk9AN9gXPUB4iXh0s4eMpWEbEOJx/cyGgJeYEvq3OzilO06rc5MmIFEZuM9VzJGTL6grzA2V8PNz99seyLpCuI2GTM/0FGn9AXGACgBAIDwBgIDABjIDAAjIHAADAGAgPAGAgMAGMgMACMgcAAMAYCA8AYCAwAYyAwAIyBwAAwBgIDwBgIDABjIDAAjCEv8MXmqPqH//PeapYxreoUq+jGFP3IReBls/Bxna2kG1P0IwOB3zrMOz9vmsvDt94/+KCcsST/59Xx9w4PDouZpBYwMqo1dxOwnlqny3nj09QVdGYV3ZiiHxkIfOvFWd0ut/726Mazyx89Ll5e8er4xrPsbBGToXUdXwUs3tAh5uTPKroxRT8yEPionlK3mCKseO3M9V9+vTl4/Or4djlV59kSxnfT8WXAYkbDYjz3cvJnFd2Yoh8ZCPygntS+bpfLw5sfPa0b5PrR7adLGN9ixxfT+W+O+jn5s4puTNGPDAQerLqLFVu5S1I0yNnBj5d07NQGzMrpSLuc7FlFN6boRwYC5wdP5WsL2hXbg38cN2u0i+pFrNwROr6eGFzIyZ5VdGOKfmQgcHf6smyX60ebg98U74ArGuTVcfMqR9YIHV+8YucgP3Tq52TPKroxRT+SF3iahQzvtYNutIa3wGebm0s4ebl20I328BYYgJUDgQFgDAQGgDEQGADGhBJ4BSuGFURERvJAYGtWEBEZyWNX+5fvfd58ud9uf/6x8IVDuaxYQURkJI9V7a9O3v68/nKfS1v8v/vCoVxerCAiMpLHpvb5trYR+PWT+/l/T+92XziUy4wVRERG8ljUfr+9v28EfnnvYf7f3duft1/Yl8uNFURcQMalH+3Z1b4T+N2Pq2/bL5zKZYUq4rkGUSvqQMxuDNI0CznaU7eLo8BVO+T/bb8YlMtquJrRNZ2OsnokjCMl2uAO1A7LONqbapcoAlMcmu60TefPX3JGRxrcobIv42hvslFC70KTGo5+iSLwkFQZgxIyLv+jvekWcRV4brWWegQGJInAkVszxuAOG1J/Z5Eo/Zb4/ve/P/y1o8CzBxZpxl0UKAg8IlTGcAQOtiCBv18y+LWjwLOn9uKNteiQFFiCl4zBCB2G+y50m7y01+su9OsnxfZ211xT20kvroUbW8nhInCFY8ZghI7E/CRWl7bU15fA8+gJ7DC00gOBPWAe5PtqZB+gf7RHkl5seS8SENhhfIVk9hYeCOyOdvUntNUUmOWNHL38il6kIrDDIAvD/C08ENgZrZoPNTWLpH+0R5FeGygSkxLYYaT5RuMWHgjsynytJdpaZ1FBWOBeO6hy0xM4RB8Zo3MLDwR2ZK7KMncDjAwGAqv9JStwoM4yYPb6AwR2ZLrCUeQtoC/whL/EBQ7Xa/PM3gEAgR2ZrG8kezMGAk/5y0HgoL2nBgJnYQf3VG3j6QuBp8slMQytwC50lkrgWDvPFXQFbptjogWYCRy+O1uWdBJLcY10noCDe7K28fSFwNPlBhqR4bt1/hYe4gJr3OQwT7jBPVlv8SfB6lBBXeCqPVTLcBY4bP/O3sJDUWDVPUr0dqGnAog/ClaFGrICt00y1QoLEDhQP8/ewkNGYKW0fewaIdjgnsjio94mEBd4egO8IIFj9fcoYjKB563t4ZjRM+pIPmptBnmBJxtiaQKn6Pjo6bSt7eGY0TPKXF5qbQZVgdtGmWyHhQrcEiheP2LMjObiNjhm9IwynI9KG0Jb4Jk96MUL3BAwYpSM9ubWTAQRHpLMD/O3D8cZPaOIqF1ln1AXeLop1iJwQ4CIoTO6qluhziGcW98V33QGBxoeipy6Ndant27Kvyx5mF2dFP+Sf6C/1yoTS9nVXv99FdYDLjRWuQWCC+zH3BplDOHqdnXWPfhsFfK0mhU2YPDofvGU993m1roWogK3Y2C6Laxqb/C+CtdxFwGbFhCaLkCdvLpboowh3F8WR2B5Yt0K6zOcPKeO2V3hLyEu8ExT2NTe5H0VXgZfcCwaIaDAfs2tUcYQ7/COsQutyKxZXwOG09flPyhG6e6usNTqBDZ6X4XPMRgO80bIwggcxN0SdXeKz1hFeHOfPLhmdU0YTiCbnZZfnv4qPwS+3y5FU+B2PMw0huO80IavVqGKVSN4Ftj3PnOfyb4UBC6GeLWdEjN6RB5ev8L6DKdwvzq5X/73zpd50KAZ3em3y9RyjgLrv9yMNFaN4FPgNO6WCKveGHMmS+MbVlqP4aDsn8+iPrF7v2GmloPABVaN4EvgdO6WCM5GeO2ItAlsKj7PcLfwtNj0Nr+71xzoExb4+8EFxi60Y8ZA9hrEEE4/VsM66NZJ2gjWtZ9ksD9R7UE3v3s34ErKA/2GmVrOVWCcxLLOGGy32bQz+xcAgx8DS1vCLYCSwaWReqMbYSXlgXqIzDaHo8C4jGSXMdgJK5sg9UOS9TXgwGdopY3hnkGOeHNCe6KuzMnhJFZ4gZdwI8d5fIHJuKud0RfSxggXRFg3tfuHp1Hu93ajHiaz7eEgsIe3E1LBqhEsBSax22yc0ReyBokTRQllgecbZG0PM8hximiSMciOs4/+msvoC1mTREsjh6TAvaaZXhACFzhF1M4Y5KjXR2dpZPSFpFFi5pEBgafK9TtaQ+EUUTMjP3sz/8ND0iqRE42BwFPl+h2woXCKqJOR146zJKMnJA2TIJUIBJ4q1+uYDYZTxPmMEZ8t8ozn4SFrmjTBehAXeGZBCFwwrv9wmpnyG3EqB02BuW58xYx+kLVOqmgtFAXuNc7MkhC4YFR98e6k/Ivy9h1xKgctgflufMWMXpA1T8JwNWQF1tmDhsAlw9oL95VVt9Du7nw5mMphXmDm9mbBBU4yjewQCDxVrs/RG45h7RVP6IhTOcwJzF9fz8ND0kTJA2YQeLpcn+M3HMPaC89W1QJvHw6mcpgWeAH2ZqEFprADTVLgXvPMLQqBC4a1Fx6LrTfH24eDqRwmBF7CxlfM6AFJK5EISVXg+el0CtYh8PdnGNZ+MM1MeRKruf19/gXf3vUN1EcahBSYiL+EBdZplEUJPOeptsGD6QlOt9u3P/tdfQa6ncpBJfBCNr5iRg+Me4tGTgg8Va7PkSx0vhkzxQ1rP5qPtDeXfXstSS7wYja+JdwF7l/O767iR5h5042ufWYXZSCwP0+VDGs/nhG8uIw0mMpBIrBbLebrFR2Pw2MULoLAwuX8ds2reHSdDr32mV2WnsBhVZUzqn6/j8vXcZTfiFM5jAT2XLNAPWNEQIEjHAKL6+FmzauaPIYOzWjSaRQCAkeWVca4/v2pHIp9r0pmYSqHgcBL2/iW+Bseo4ARTmGJR0LNVfwYU+e6UY8nrVZJI3BiX0c4RWxXmIvTN6TAMc5Bi+cim6v4qglU6dBroNll7Wpv8XZCWsYOsGqEvsCeY1jVJwTeBvcoYoxrSMLVwPYqfoS5r92oG0hrD9qu9gZvJyTrrIBNI/QE9hrJqi6hCCZwlIvAQ1WzcsPLQ2DNQ2Cr2pu8nZCsswIWjdBrusWduurwNbiHKePcxDF6uVl5AEx+F7rXQvMLW9QebyccNN1i9Q0m8LjFPH2OiOxy/rsfkz+J1Wui+YUd54XGq1V8ZrSqRlA8De5h0Eh3UcrfGkP+MlKvieYX/v/bO5smuW0jDI8sXaWUfoAUSeWUK2dFTqKbk02OG28qvo0vWyVV2SWX9f9PIUgOSWAAEmx0A92c9zlIM7sg2d2Lh98kCgXG4GaMOZKikKWSwGb85MsAAAvZSURBVDxLucZ/L8PlKr72GznmEmU0hsAOUhGYBSbFII1xga9Hjfmw+OmISoFzz2FhF7qHVARWgUkRMOFfAPRG4eTp3GGyWp5DGlAqcGZdSgXGSSyGHEmLZyPYo+x8FhdYVQFuWmCMTlieI2nhfIR/uS/vuQUOE9a1AVYn8KJGGa0LBcbohKU5khbNSbjvdH7zD2mBddXgZgXG6IQMOZKWzEpw9qL7ynwMHKasbAN8kwLnz7ekc9ejKMWCHDkqXYp//cDtUMsKXOkmjnwg8Np86Z27JkUpknPkqHM5vsDu9de8AodZa/NXm8BjlSDwHopSpObIUWYGvF3o/gurwGHW6jbAKgXOGdp7AAKTe1CZwOUlZsI7iXV+NXB5awG7wJH7xosXUQgEXpsvqXPXgyNFUo5FxWXl+v1fgltghf5C4NX5Ujp3FfhSpORYvnQ+whs5WAUO8obAWyzrlNP+FgVmTpGQI1METHg3DH+tLHDpAspRKPBzCJxAIsXdOXIGIUxp9whTh8BbLMuU0/5WBN4bf3SAb/+nRIELilqfwu4R5q5xDxoCr86X4Bo3lPDjA3z7R4s0gUuKWh9egWOvLuGJM8Vyjfv7D+PYkvMQDT2qBPYKlTPBsQWmRh8f4Ds4X0sRmBpQK8q6R5h9fX8Xa9zff3AjPDtvpyEaBvQJ/BwCF/aM+ADfwW3/+wUuiqkJRd0jTL/6BjjySh331/PecqdT4PzaHFLg4ujjA3wHt/1D4A2C7OvvQEdeauf+mOe3XitNAnuVypriaALzRB8f4Dt4bdBugXliq0pJ9wjTj727ky3QKJHXyt53Xy5DNIyoE3jPHvSRBGaMPj7Ad5nAjOHVg1Hg+hvgyIvdP3XiTkM0jCgUeEd1jiEwd/TxAb5LdqG5I6xEQfcIChB9eTZfoFGuBP40n3vW+WJ3r1RZUxxAYIHo4wN8F5zEEoixCvTuERQgOngFY6BRwl3oT4sd5+GP6dAqcN4UtgUWCj4xwDf9MpJUnOKQu0dYgRYb4HA9fF74u7iWpE3gXYfAxOgJoxPyQ4o8k/gA39QbOSQjlYXaucMKNPE3WA+fx4ckpyEaxlaKBPZqlTcJKfodoxMWKdpQifgA32fKrZTisQrCJHB89CjWSKMsO+Uvf7psf6chGgZuT+A9oxOWearah0yBW4ZYDLFzhzWIj/7GG2qUxXp4fFeB+3q/eGXBDQrccnTC/dHKkSdwywjLoXXusAaJ4RuZY6WiR2C/WnnTFL4XuurQKqRQ5cgSuGWADJC6R1CC1OjJ3LFSUSbwPn9LBa42uBkpTFkyBG4ZHguU7hHUIDn8OXuwRNQIfKnXrvKYEJgUozjbAreMjgdC9whqkNJXT3F0Cfy8rsAVdqFJAdZgU+CWwTGxv3uERVDvrxqBg3plTlUqsOxJLFJwtdgSuGVsXOzuHrn+KqrOTQsseRmJFFlF1gVuGRkfe7vHlb76/dUi8FSyfQUqFFjsRg5SWHWBwCHX+kLgbHx/awgsNjohKaT6rArcMjBGdnWPiL8W/sSaBJ4rljuZuocZhOIRYEXglmGxsqN77NBXV4F0CBz4a1NgoViESAvcMipesrtHRN8Vf1VVSIXAl7IZFlgoEDmSArcMipm87nH1t9zQV1eFNAg81c2swEJhSJISuGVM3OR0j4i86/oqK5ECgefK2RRYKAZhEgK3DImG9yR3/LHuJFF7TfmrQOCIv4YEFgpAnrjALSOi4V0ATFwNjBN1d0PeuiWKr5t2rqSkmepHqVFbgYUWXoeYwC3jIeLdgpO6H+eKmLlZ9lYtUnzdtGslJc5cRVKNmgostOxKHERg7ybY1B2xHc+3yHG3bo3i66bslVQNFmtAWpGaCSy03IpEBG4ZDhXvMZTUMyks5lavUXzdlFpJba6hZCFWqZXAQoutybXALaMh4z0Imnoq1PUw9/8+V1v/4ePrJtJKqqK7+6rUQmChRdbmSuCWwdDJEjiGen8Tqe3JUZryMlUXWGh5DQgEbhlKCVm70CtA4DJsCSy0NH68Kw3n8S2G/sjQRxE48yTWOhr93bkL3Y6SMtUUWGhREnhXGs7uizPYHxnaF7hygHzQLiPF0ObvzpNYLbEgsNCCRAg69dvxiz8ytCdw1fB4od/IEUORvxYuI01Q61RJYKGlSOGtpGeB/ZGhlwJXjY6b5SgUice6d6FE3682buS4oFlgoWXI4R8mTbvQ/sjQs8DV46sGtXvo8De1bmJYSUlAqhQl+vAEz9YbOQiLaExwonJMOBgZGgKv017fPNQITDodSoje2wMZRq//sPhAnq8ifIHvuy3xPDTW9QDfBwY5amd/9N45gC/v3Zfzm5+nD+T5asK//uCftZzWUrZTzAM5amd/9F5/1np1vJB4juPvLteSbKeYB3LUDkHg5dZp7NyvPkwfyPPVhLeXMQ0IHYwMbTvFPJCjdvZH722Qxk3Vqw/TB/J8VeEf51+Ogf2RoY2nmAVy1E6hwOO5q87b6QN1vrrwrj/cX64eeSNDW08xB+SoncJd6L5P//E/f/nn4sMwX5PsK51NbiDHXSkaz7HwJNb4o+9+DD4AAGpQeBlpYLp6NF9GAgBUoPBGji/v3w4/mD4AAOpBOYJfnuBxj8j22k4fAADVsH0KDoAbBwIDYBgIDIBhIDAAhpEReLwenBiMJqcBwywYgpDNUUODLZaPe883oYk1uA6OoadsUSHH0iTTDUQE/vK+v80j9Q6TjAYMs2AIQjZHDQ22mJ7ynt5KItcgeKI8NwMLOZYmudJAQuBu9eCWl3yL2HYDhlkwBCGbo4YGW0xPec/vBRNrED5RnpmBhRxLk1xrICDwp1d/7u+UTr7Hc7MBwywYgpDNUUOD7TRfjw+upLomY4PwifK8DEzkWJjkagOZY+Bheak3aec0YJgFQxCyOWposJHj68tT3omdQ8YG4RPl2Rnoz7E8yXQDQYHXVjubDRhmwRCEbI4aGqyzeMo7fmqIsUH4RHl2BvpzLE8y3QACQ+AV5se9Ly81EGzw2nuiPDsDAzkWJ9lEYOxCG2iwxfiUd/rIkq1B+ER5dgYWcixNssku9Np5hc0GDLNgCEI2Rw0Ncvjlux/Xt2ssDYIP+RlYybEkyXQDQYHXzuxvNmCYBUMQsjlqaJDD+c3Pwfv8JBoEH/IzsJJjSZLpBoIC96uifnU0fchvwDALhiBkc9TQYJ35Ke/E0R1jg8QT5Qw9pXmO5UmmG0gKvHL/2GYDhlkwBCGbo4YGG8xPed8vh4USaRB/opyhp2xQIcfiJNMN8DADAIaBwAAYBgIDYBgIDIBhIDAAhoHAABgGAgNgGAgMgGEgMACGgcAAGAYCA2AYCAyAYSAwAIaBwAAYBgIDYBgIDIBhIDAAhoHAABgGAgNgGAgMgGEgMACGgcAAGAYCA2AYCAyAYSAwAIaBwAAYBgIDYBgIDIBhIDAAhoHAABgGAh+ax9O34Y/unv4Ua/nb99/8e/zwbPrh5xdXkwNdQOBDky/w1HQpLQRWDwQ+NDsE/vXdsOW9G7fEDgisHgh8aJzAv33/8u506rV1///VfXo4nU4vu/+efOzEfTm0fejNHb52Dd0UncDDHvXdYiKgCQh8aAaBu3/67avbuD44MZ24n1+87I93py3ysLl97H7V/8z9zhP4MhHQBAQ+NIPATtFOwcHQ/oPT0G1xH5/8/Q8fx7azqkPD7vdLgeeJgCIg8KEZBB4VdJvQTsGnP/Vb2UHTu9N8ztnJ+eu74ai322yffIEXEwE9QOBD4wvcbz07gd3BrONb/yyXk3doc+fsDbbAi4mAHiDwoVnbAju6De1in/ju6f/6to+ncW/5egsMlAGBD40ncHAM7Ix8+OZf7+bTUpcj4t5Vd2fHRWB3GD1PBBQBgQ+NJ/DXu86+x9P4wZ2XdjvNj/MmuNse96ekP7945i4a9QL3p669iYAmIPCh8QXuj23/drmk+2y+XHThYbzO++guA//3yUcnsDud9exhMRHQBAQGwDAQGADDQGAADAOBATAMBAbAMBAYAMNAYAAMA4EBMAwEBsAwEBgAw/wf41J4eGwFayUAAAAASUVORK5CYII=" /><!-- --></p>
<h4 id="relationship-between-rope-full-and-p-direction">Relationship between ROPE (full) and p (direction)</h4>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1">df <span class="op">%&gt;%</span></a>
<a class="sourceLine" id="cb7-2" title="2"><span class="st">  </span><span class="kw">mutate</span>(<span class="dt">true_effect =</span> <span class="kw">as.factor</span>(true_effect)) <span class="op">%&gt;%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb7-3" title="3"><span class="st">  </span><span class="kw">ggplot</span>(<span class="kw">aes</span>(<span class="dt">x=</span>p_direction, <span class="dt">y=</span>ROPE_full, <span class="dt">color=</span>true_effect)) <span class="op">+</span></a>
<a class="sourceLine" id="cb7-4" title="4"><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">alpha=</span><span class="fl">0.025</span>, <span class="dt">stroke =</span> <span class="dv">0</span>, <span class="dt">shape=</span><span class="dv">16</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb7-5" title="5"><span class="st">  </span><span class="kw">facet_wrap</span>(<span class="op">~</span><span class="st"> </span>outcome_type, <span class="dt">scales =</span> <span class="st">&quot;free&quot;</span>, <span class="dt">ncol=</span><span class="dv">2</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb7-6" title="6"><span class="st">  </span><span class="kw">theme_modern</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb7-7" title="7"><span class="st">  </span><span class="kw">scale_color_manual</span>(<span class="dt">values =</span> <span class="kw">c</span>(<span class="st">`</span><span class="dt">0</span><span class="st">`</span> =<span class="st"> &quot;#f44336&quot;</span>, <span class="st">`</span><span class="dt">1</span><span class="st">`</span> =<span class="st"> &quot;#8BC34A&quot;</span>), <span class="dt">name=</span><span class="st">&quot;Effect&quot;</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb7-8" title="8"><span class="st">  </span><span class="kw">ylab</span>(<span class="st">&quot;ROPE (full)&quot;</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb7-9" title="9"><span class="st">  </span><span class="kw">xlab</span>(<span class="st">&quot;Probability of Direction (pd)&quot;</span>)</a></code></pre></div>
<p><img 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cw9bg4+ue8hiWXjhMBVk7e6OrG+YuBxORL7rKsTy8YJLPZ+bJBWVzGar86PO3md5/KUTcy1t0ZKI6sjLVGIY1RXHy8Wi9vND6PZ0vOagFZEPaWR1YHiiBKcrpPwBTzuGAqPGQCPBYieoxBiOVwgk1JPUdTV9InDY1wtI42Gx4A+UXhM7f7XlFGvq+MhmhCHwGPJT8i/xtC9raTEU4iItsYDovBYu1+piRlFuWkjJaZCMF1sKah67GWNK4qZgMdyqz2EEDY+MV6NqXrsp6eoihmBxtMpya727FWenYh5LLVB0h5HIaYiVjz27xU8noaoxzGXQGGxUVfvAniFDjEJVY+BI2ylZNurYJ7GsYGIzuM4YnEHjZQGBKubIynYuXo8tvIjicUd8JjIB9uFqcejaz+SWNxB1GPU1WbE5nEksbiDqsfACKYei3WFw/PA4yjh6rEI1NQSBE+JEvH0F3icGHZTEp1IYz0EZ2frRNRhYvI4os2rO6ymJDqx1boc7k5rh8eOmwLOsO3xqm8CI48j2vDD48SwXFevhtbSrKvjUVYIZ48jj8YNllPiUppyWU5d2Hns71lBcWI7JSYZxN5Z2HhcxdDKI/Zo3BDX3o98D4i8r3DxuJa2+/A+l58YKeHP1pFEZq8Ym/Iabh4rdw2nQTPsRTTOnp1HapYaHtdw8VhXGadJc+xGFD0WvUruaBO/9e8IUh47SAUe9yBYV4v3lRK855IB4T12PAGNuroLwXkuzF2aE9xj3iGyW2CKHuNYojmUPJawgljKDLsdRY9bsFufRAjusVJyFrwx3e3qLECyHrP7s2OnE+vjg5Ib5k25wdwb05PuLy56Ipstjn8sPd+J3x8eN/tYn1w/uFR+9+zOwae/ZdKUOywMx0Yeby8ykQ0XISzwOEqaWJ/8SVvd7v8UmyKN2XC8zUS2tSRh4FNXY1OhQPj9Y3W8BSz4IPadSyelIIdyMearwNBjbwFPfhDXTqaRUojnTG63rmKO8/wSeh7PrmcSHrMdLcw89vVHu9Q4SpHJeSyxnkPW1ftfcfD4+PX3sq+bxeK1e/WPjOpqEptQM2L3+Mn1gwad2WqPHvtnZEe5taQkrd4sco83mcSbRuTZlKZWv7+tl7vPIdi9LEBuPJ5cz2oZWOsLI923U28SFPnprdzj53cPs++Pr1U/nEtpejtK8K8EOWE81twmqo3V9txi6vHy9b/NPD6/eTv//ur98odmHgOiBPFYt69Y81j140caqprJf0tQ4/M37uX7x9k/2X82sh5Xf5TVv4fgyhFBYDGPqj3bF94pvn+l/DLgZPCzIPvH2tt8S3W13TGH5Fi8K+rp3ONy17jZQZZLqf6bLN1yluQK6kNhMY/26n388o3qy4An18c9Nsd9Xa1PN6EkPF5mDpt6bGdFEV1BfSgsZsvjx5/6SvVlABmPfdOPyO52JHz8AopyWqeuLqmHYys31zFaQf62+QRy3HucV9gv/FVZYp8cVNV19k0m9scvD0vmdqxNaR3yuJMTKGxqPbNclNxWnOfqEv6mlfolAcfIhePxSeHuK+U3J9kr5Mbjx3oaEz4DP0GNS47VjzupQ87j4R2S2VDPc91oeVwonPv75PqlsqaWrKuPLmktA9kr4lgmaoVjjfNApPB4EbaGxsUCCVOn3hFE43G5j/zxyzeaSS9Jj08C1tWReEzkGGx5XubS9LzMAaS3jPXCCQ4Hkl7uHJHHJ/UYTcZjmZXopq72/EbCJ1PE7/HEbftoL/duajzetQ5CyXlclOEazPYQ8muxv3jaCxy7x+IN3MhJb1Y+UI9BgKp5+u6wIo/L/ePsu0LMZ3cuzXlcz1fnJ5NowN5jQer6A7KF5XGCu6MKwrUVeItm2OG891jhfPVR9qVw+KQanUMfP6atsSA14gusQ1oeG+Lf42pfuC6ny5o630MuauTy+HH+7+jx48zxJ38sOnlEGns9JJQ+dj+XZgd2eJRftq5mtHVks6gUPQ5Uf49+qN7SEB2Jwp+tQ333iiX7WOvp7fDnc4UJevRTJxZnajnh8QhWr8EwJZZNSmeei8h4HGblang8vcHp9lMqHSa8x0rnbjsmmtogjeskpFAfdRV6AZkOQyYlOx4bttE+Z4Q1RD1msl7lFxMeD7CjsbHI5Vci6WhD0+OA69XVB1PpKHQ8toGl4jwij0k9FybceuWf6AxxeWxrsox96ESf0xZwOGYf6TSReWyPKvf1mmUH8PbcVC5rh8ty6gKPxTR3MlqtOHYBX/vH0Y9zMxA4xlISpccWTuGBx1JNReMx95O7YvRY5xQewWuLf7p1NZXUZvE2X+1YY6vNT6SnuT2Cxy6x4rEIOrHNQfO4kzJWh/up9HQ/iEx/iNFj26fGN8Bjz3jzmP08GHuPvd4KgIvGsXismNZMPGzS04C7x6pbbPnXs049Fo+V8F4uEeoifFIS48xjPjW0iPb1x/W3Ie+X6QXfkVHqInxSGsFVXU0pJBGPq3uBiOl6XMocvce+x0dKXYRRSp6hk5GIzp0zh0TtMZk5KUJdhF5K45DJzz/9P/3ZndzM8SdExOwxnXNPqCzHjmBK49DJzzuDP728d/W4mfDYA2QWZEcwpXEorTbPDD3+fF5Sjz96LWaPyQyDlDqkekrhlp3OWvNO/08vd43Hd5Cj9pgMhDqkckqUNkLpAo9BB3jMEoW6msx9b4FDONXVoEF6nsucpD1m09n1Upr58wgdV4sU6eNO5qTscVF8snBZK6WZ2prSeS6xInseSPlACb07+gyaSo5tRejlmAceq0Ao0RO58zKLhzKWj1rVJD2PWykXwzGh1EdJp662kAaPSHOaWMsC/ETz2cedpiSg9SA27fa6LbLIPJmtrQ0HGXpcHnIan9hWaGoeC+tHvQHrofBJuQV/jyUHfyvpsAm47/FECS7d1DzT63j4u2F0GinZ106vvbB9g73H0jvjbBy0gZ7H51+8n/+zWSxeuzdsSoJpjfu/FUSnIyWNYAOP4ul4bBMaXWcCLY+f3rqae7zJJN7sRbbVQ6Q8DrVm2e9zefXYyV8aRGPqIut4nA3DucfP7x5m/zm+NmhKhMrKl6mrA2Fn8sTGkuji0+P92iIToB6sPJY9L3OzONzkHp/fvJ39b1kMzZ2mBAiLIYbh0g90jiAesz/ATD51vVhLj9+4V38725QoSJbhmgRKojMEqatZRs0KA4/LXeP9DrJqXV2FS6J3zzO2mPIdlMZYHmaeCxo7phPrUV5Uj56K3ULdYxGVxhR69yyiybfyq3QXpfGXsp+vBiJasT4uFT6SOMlaua4eh0bvnmO96i9mJbBKxUjiD6XkMUZpJT7+nfF5q32s5RWOO6k5a+V5rglI9O4ZMllXI8fCtPpiwL+ZkMfYa1biyfWJ+ed9rEf1qdXP7syW1hvl406SEA1WPE2n3VzIGgQe69DJy8dSDz/j8eRxJOHzJGYvltg4Og+EbLJSiyUtJzwuIRr2kE5gPjrp8DMeH7wydfFDy+Nmt3j+Yolqp3ipfV7mCGQ9lkHBTtTVvCDg8W7aS5/Pd5LovYw1lvI4/N8Hj3UgUFdLeqy0fzzTlBgeM9MGyGgcXOR0PB6JQ7ITEuyrch6rzFfPNCVivY7e4/ktNTz2x0h3m+uF1W8pdlY5j/M96fwfmePHc00NyXswvTWjx6iL85oG1xgeTwta/5qxxwrnc8031cfPSORl3Y//KQSG21mS8Vivrm78paexvMdmzNXV9j5pDD8b0Qlb818Q7ABtOHgcchUSjk/Z479n+jwJT8XQ5CaJYkHWJnxKs1hahSONTA8olMOT9PikOmFk8vyvCQj0EAIpwGNj7KzCkVam932opzfKPtZ8mjo/9pTprHcTay89hPxqHpyITQwGHtsJOVGPi6PGT67fOJm6a71cUw7RW88esyE/1UXPY1fpRFdXT9E9n+vZnd91NF9tCbHHM+r43MjCY1XYDoGk6J1ffaRZU3eacolY47ltrNcB2dtHaUHeYzJWk1kQGfoee3oujFVmx8BgiRDsCuQ83vU1JrLSyCyIFH2PtavqkD2E6hgo3xX8dRl6Hndx8PQevd/DY5pQvn25xz5DPCXrm7T586nHRba6IG7h67Hiag71OBGpxYTHzpD3mGpVJ4XGfehnm/KCatd35rHu5l6hEZuw8dhwlTRvF7ez7wxm99imIr+386ttozyEOdNY6voZMnDx2HDFzbxdIO38bOnFsEUyhxnZeqyzvXaxziWvZyUDPM7RuHHi9uJiKDI8lsXienKz0plNk3Dx2FpdLUa9Jwg9Rl0tiU33XF3mr3XfiVCw8ZgcorqaDCl57Oi2O2Jjqdbb8DhKqHtsu3BR3muSaXJkQNZ4k3s4ekyzsiEFeY/tMjv9odBW82K9S7DgsSyT6wqOF8BjXdbrszO991VLYmk5FOGQUo8yNPE2luo0hG8S89iiPpnGZ4pVeN4VuzcO9d8JWaTUo9RYKDI8LknNY4tkGqt5XHTF4tGNdd/Le6HnIxdcUxqbu7CpsegTmGwm4LE+qgaWXbF9Q/7sm5XnMwnYphTmoUpj4z01veGxR+pu0q6rfZ8RFFVKJqtuaKKCx+TKeXg8h8U9amGvQ12tjeY2sEhUZKJ8XQ2PuSGRmGSkrV7Xf4fHThFTSibH+1u5Er91oxTweIZ5j7uvGH/1vtcNb0mF6xa10ByOqwG5+387jYcCHs8xo9h22+kHU0q2hmN4HJDeyta60TU14LEZPY0nlGz3CtTVlBgbjuFxOmy3Fxe9H4hfSKVbJJmSFjTykgQeG3JxITeYwmP20AhQDDw2RHrflkgvSDMlG1DZEguBx5NIOErtCMQMMaY0xEUo8Jgt5A73mxNhSkPc5EZYY3g8iVJ/GL6U4kYgwpSGRLj9nQEeT6Kk8fBoUsDjS2PEmNIQAivaL/DYFhIeUxgmEk8pVuCxNebranjsl/Br2xvwWBYbN/Eg0LEiT6kNhc2mL+DxJG152zfxCLU85sSY0gi8g1IDHk/RubgNHnNj/PZ80QGPp2grG/LmeBaJMCUx21VuMOmTNywCjydRUHa93mlu/b32tBhTErFdrVZrKx6z2GzDY0vUN8Oc7zW9l3i+014qKVUem28kJ3ajCBkOjy0h7XHvNdusw8FjB5R1dfmtmyewUpoogce2kK2rOx4XdyFYoa52ivGjlOfaJbALDo9tIhVoX2O/2/QEU3K2jmuNlR8sYh94bBHlSRU8T8ILjjeVZ2d6T/qyCTzWYuxW1KpS4j70hCmCkdgGYDzmyph8GkrCY6oUyUjV5Ng/ZopN+VBXE0XeYwLAYy0IbIE1SSklQ2TragrA48RIOiUOZ3ToAY8TI+WURotkLtXzOPCYED46U8opwWPPTaWJl96UdEqoq/02lSbwODhs5y/hMSGsavz87mKxOMy/2ywWr92rf4yUJjA6nhh0TIfHkfL8bubucnEt0zj7ZtOIjJQmmPG4fP756C9DigyP6WD1NjTnN29nX5dX7z+/mw/Kx9eqnyOlKWY0nrquBR6nxETW5UUTdvfQsnG4Ebr8CVLSZtpj1NUJIewGlbouPD6+ev/8jbyk3sDjLjrWjdXVBGbH4LFXRB437tq/veNmcVjtGjc7yEipwGYVTOFefvDYFeJ+IhyOXXWDTT3NBY/7wGMfTUWAbEfJXuVO43yGC3W1EAsaN7mF1xgeu0LSY4eznMvy6DHmuRwxMgyHkRoeu0J2OK5eZ13n5eJ28S+OOzmi5XHL3UBFNjwOTKOxZZHPbx5W3+E8EAd05iTb7sLjpLHu8XJRkPu7xHmZtunEVT1JIOhNcOExEXydRYCUbNB98Nf20dl6jfO5gAhH23WkZIX2cLxdrc7O1qvS40Ayw2MraEjXD7z3f1f7WSmn5Iisrj4rBuRduLOs4bENNKTrB179Xzh3YpOEU3LHPix4zBlrHouPZdgk4ZQsIw4IdTVnbNXV7o9apJySPuLTaVd07gYEj2nh/KgFUtKgXzytiyekr1bNT4Pf3wseByLUOblISYOex2XVVM9QD38fAHgchmDXyCAlHfrDcRleaziuv41g84weokD3VL7u75xu25GSBQa27jXmv3lGD1Fh/Ixct0UaUnIJPOaPhn3COWp4zBjU1dzR0K9/7kfzY1vLJCDxlOQhcHMABUxifXorv6KmvrA19R6i77FXEk9JGgo361HAJNbyjjFWmooB3bq6wFunST0lWRLyuLnnk3lTqeOv1yAlSSQDCX3guMIk1uW1zn/RQ/SBx0wJfwZIiUmsx1+qnwRm3FTyoK7mSQQeP731+nuZzI3I6CEcQEp2aTQOuzttHOt+Jxk9hANIyQ2B58WMYy1vj2ylKeABpOQG9h43B5/QQ8xxv7OFlBzBtq4uR2LU1RbxMGuClBQo0+BwINlovjo/7oR5LovAY1IM7rVEF6NYjxeLxe3mf+gh5oxpbK8nISV5UvHYWVMgZ/9YZItdCSkpkEZd7a6pNOn1l0refFCAx2AaeEyGvqwtj1FXcwHXHyfPYNDd19UWQUouWa+rFH37DI/p4CV7pOSSTOKzXGTvc2PwODGQklOqARkegxEslddISYv5td99oA/qalDT6QO4fb0AAAzQSURBVAu2ThFBSjrMr/3uCOx9ugsek6XbM+BxSNQ83q68nzoCj8nSe/Yi6uqQKNTVxYPN4TGocfIsZKRkmWE02di9Ql2dOKINPzymiyibALf6gce0GDzZr/2PDZCSPfIHIBO5igIeB0GUfSFw12MHnQQpWWO9Wq1Ep8yGEBseh0BcjFUii15nr1JDSvr0UlivfvlLx3tB0sDjEEx43Hvhajv6Oz2QkjaDFFa/vOjFUjzfHB4ngyDpbb9PFD/cCqptI5CSNoMUBj8oFUZdnTB1n+j2DPFPTUBK+gxS6P8g3KQXPCbCyMhr/RAGUnJFeQ+gQB8Oj6mwtb0nLAYpOSLsE2LgMTGs3vxDAFJyBDwGHdzuZCElR1SHFvb/91piw2NydDzGeSBMGLm7mi/gMT06GtvuDUjJDSKPPZoMj0kDj9nQD8qvyPCYNqir+QKPgTOQkhd836QLHicGUvJBddNMfx8Ij0nhPnqk5IP1GvNcCTMWPe4jwIcyK9/3sIbHVJi45A339SHJXFaoqxOkzH+16vyo8zs7ICVbiFMJdMkTPCZCkX/nHF03W3akZIvR4sn7kuzgMR3Ke1SLPe68yAykZA0SN9irgMekaF8yI7gJcvE4P7NPQErOCXFPEHhMFdF1cOv16alhD0FKrvF+zCkHHlNFeD3r2emZ4YCMlFzT8hjnZQLhLX3OMsxaRUrOaepqj6MyPOZEtntsqDFS8gg8BkIsdAyk5ITR8/A8mQyPWYHjTjQZ3cD6GpLhMXXy3eRyV9lKn0BKLthH04sIHoOCbUW5c2zeLZCSE/YaD0T28vnwmDg9j89MRUZKTgn1RAl4TJ1uXW3cT5CSWwKdrAmPmWF6YiZSckfniL/fx8TAY2aYDshIyRmdM/Cy7y9WOH4MRoDHZOl4fJGxWsFjMAL2j8nSHo5Xv/y/reCKNVfA48RASl5YZfh8bhs8Tgyk5IX1yt9YnAOPqdPZquO8TC54vtsePCbO7K1+FEFKfvF0Ygg8Jg485sy6OAnPwwfBY8qU997r/MB08gQpeaTQGB6njmD4Fd7tRwWk5JLh1U7GJ8TLAY8JA4+ZUQfWxHZ2hnkuIOoDqKsJ09xerwrOwn2KJYHHiYGUXFIPx43HxaWmHj4YHnPEYFBGSh7YHzzOND71ITI85kV+yu46P+dPd7cLKfllfWp8q2IZ4DErtpnCZ+tHj1aPdKdBkZIfqptYb1d+DjzBY1ZkHj86XZ89WmkfzkBKXihu3HJ2tnqUR+Xh8+AxF8p94u2j0+Jue9qHM5CSF0qNzz569OgRjjuBPfWB4/zJMPnTzuExbfJNbR7VIw87xzt4zIba4/Xp6aNVfqMJ1NXk8fc4CXjMhupY0/rs9CN4TJ99PDgvEwzJi7VHqKups9/O5tMZ7j8PHnPDsF8gJS+UHlc7yfAYDMH9MjlQaJxvc/3MdMHjxEBK/jg7Oz3drlY+brcHj3miPSgjJX+sT0/PVtv8KKFz4DFL8pMM9N6JlDxS1NVebkYPj1mSb+n1jjwhJY8UV7V4uSEIPGZJ7rHeRChS8kd57o6Py53gMU/yuvr0I52ZUKTkj8JjPzfMhMc8yUT+6Be/0JhBQUoeKYbjhw9x/TEYI/f4I3hMn9MPPzx1/ynwmCnr9ekvdC6JQ0qeefjgwUP3nwKPubJ6hHkuDpw9fIDxGIyx3a4+OoXH5NluTx96mOiCx0zZbh/94sFD9Q6ClLyyzXI6PXU/0QWPubL96OGHH6pv6ZGSV4qy6aH7whoes2X94IMH6pU1UvLLanX2QCMmVeAxXx4+0NjQIyW/rNdnH37wwHlhDY/Zsj578P4DjMfUWZ9++P4Hzgtro1g3i8Vr9+w0BZRZn37w05/LzHR1YkJKvjn78Kc/I+3xJusdG/SQYJz+/Cc/eX/e425MSMk36/d/+rMPXX+IQazP7x5mX4+vWWgKaPE///0v/zn7ol5MSMk77//rjz5w/RkGsZ7fvJ19XV69b94U0OLBj/7um7M9pBcTUvLOz7/2tZ+7/gwTj9/Ia7UNPA7G2Te/+e3vzr2oFxNS8s43vv71b7j+DINYy30u7HkF5J+//d0fzL2mFxNS8s73v/GN77v+DHjMmh/MagyPw/N95xqjro4e1NUpgHmu2ME8VwrguFPs4LhTCuA8kOjBeSAJYBTrEudlcmCJ8zKjB9dJJAZSihJ4nBhIKUrgcWIgpSiBx4mBlKIEHicGUooSeJwYSClK4HFiIKUogceJgZSiBB4nBlKKEnicGEgpSuBxYiClKIHHiYGUogQeJwZSihJ4nBhIKUpsegwogJQ4YE+7KlbbDQIAvAOPAeAPPAaAP/AYAP7AYwD4A48B4A88BoA/8BgA/tj3+OmtRUb++IJN5/bWypzfLJsxamezKLltvDjLshXTdpqbSZs0c/7F+50m1NqilRDBjEikpIZ9j8vngu0Gj5tQZfP6e1mPu2bczm5npZll/vaiqxm1c5y9s3jikkkzT28VT2tqmlBsi2BCpDKikZIa9j2un+zXf/yTIuXbl1fvG7azK1sxX5xr5dvN2nl6q/irXn/PpJlsu56v46YJ1bYIJkQpIyIpqWHf42W1pP3HMSrSDBqG7RQtHBo30/QRs3aahxEbNLNZHBYmNk2otkUvIVIZEUlJDfseH38p20k5HD4eWZHN1R/estFOvkTF+jRspq7ZzNqpesjitmEznb9ItS16CZHKiEhKalj3+OmtbK9pd3y436jptbNcFIXWNdN26irJuJlqlsKsnWqjvLht1kzRG5omFNuilxCtjGikpIij407Zn2DaS167Z6WdbqoGE1RZKHnxZ9hOOYNCoIdQSohYRoRSksaRx9kWzbCOKHckzNvJQsmHH9Naa7+bY7g4x4vF1R/+5b3gFRulhKhlRCclaVx5/IbJHEFO+Qebt1OVbKZzHzamPhrOv2g462FhBoVQQiQzopGSNNY9Lhd3Y3w04uktO+1UC2Q67W/rzyowPKJR9RDdIxrkEiKZUeiUFHEwX11M/h+aH9TPSq3ibzc9W6F6q2Ez9b6XWTvN2Q5mzWyMzjCglhCxjIikpIaDuvq4PjluaXYe2qY8OmLaTlPJGC7OsZXFyc+JLN9t0ky1l7XUbItYQtQyIpKSErhOAgD+wGMA+AOPAeAPPAaAP/AYAP7AYwD4A48B4A88BoA/8BgA/sBjAPgDjwHgDzwGgD/wGAD+wGMA+AOPAeAPPAaAP/AYAP7AYwD4A48B4A88BoA/8BgA/sBjAPgDjwHgDzwGgD/wGAD+wGMA+AOPAeAPPAaAP/AYAP7AYwD4A48DcnRQ8MI7/V88uf7K6E+OXnjn45dv5P/sTvqv6rb9SvtDXqneO/qGvLGp32eLcKP/GS+88+zOxDIAb8DjgBx9+lu73I+BPVMeZ+QeC181eEnzIR+/fGl6WaYaKxAYm2s/tBsEAB4HpFRsd/Kpr/R+Ye7x46bN6kMeH0z7Nuvx4+FYXQzfJ1NjOPAEPA5IS7Gj37pzkKl3UlbAT67//vW6FC4L7/1Pmrr6318+OPj0Xxe+Prlej7ZVA0f7cr36kGd3sv/n4nU/qXhH9t+P88a+VYpZ/iYbf49aNX/h+ZPrX2gt1wt/lf92dgMAPACPA9Iaj49KRwujXsncyNTOvynMe3bnUvcn1f5xblDxo/3oWzcwHI+LfwuPi086qV+Yf3OS/arQsfh99Ztnd7JP3G8gigZbS5G95aS0fHKnGvgBHgektetafFtKmZlV6pPpVVbQ2TfNT3oelxbVKjUNTHnc+qSq3byhurGmiWIA30t6VI69neUqfzncLQDegccBqaaSczcKJUr5MkfKyaNSlmxcPMh9q3/S8/hxMWpW+75NA1Medz6pmQ+rPW5+k1cBe49Lq8sPyl5SulvuGs/teQMPwOOA1Irtxj0+yiU+mfA41+2kbkfa45NyC3Kg4PGlXePxwY3yE0uP91PjIBjwOCBijzNNmnHv8UFT/1Y/6Xuc/fIfmiNCTQNDj4uSuDce71oSdj3OmsB4zAl4HJC+x63940rfwqpnd3KPq58MPP745T/9fO3sxP5xYVvjcTM9VuhdTqQN9o/bHpfNNEuB/WNiwOOA9D2u5opzqapJ42IO7KTYP64nmPseZ6X3fsK4bmDg8eNmL3z/SYXDtfS9+epLu57H1Xx1/uriwFbxDearqQCPAzLweH/8uD5O+zg/EPxv+bj5hf7x43cKwXt1bX1YuOVxtSdcf0rrky5V35RF8uD4ccfj6vjx771cN5UfvN7X2iAs8Jg9j/dbA9cfIzrnA+dzUQAec8fXlQr55wg8xnBMAnjMmyfXD2YugLD4UTeGHuN6JxrAYwD4A48B4A88BoA/8BgA/sBjAPgDjwHgDzwGgD//D4ij1psm+QMsAAAAAElFTkSuQmCC" /><!-- --></p>
<h2 id="study-2-relationship-with-sampling-characteristics">Study 2: Relationship with Sampling Characteristics</h2>
<h2 id="study-3-relationship-with-priors-specification">Study 3: Relationship with Priors Specification</h2>
<h2 id="discussion">Discussion</h2>
<p>Based on the simulation of multiple linear regressions, the present work aimed at comparing several indices of effect existence solely derived from the posterior distribution, provide visual representations of the “behaviour” of such indices in relationship with sample size, noise, priors and the frequentist <em>p</em>-value.</p>
<p>While this comparison with a frequentist index may seem counterintuitive or wrong (as the Bayesian thinking is intrinsically different from the frequentist framework), we believe that this comparison is interesting for didactic reasons. The frequentist <em>p</em>-value “speaks” to many and can thus be seen as a reference and a way to facilitate the shift toward the Bayesian framework. This does not preclude, however, that a change in the general paradigm of effect existence seeking in necessary, and that Bayesian indices are fundamentally different from the frequentist <em>p</em>, rather than mere approximations or equivalents. Critically, we strongly agree on the distinction and possible dissociation between an effect’s existence and meaningfulness (Lakens, Scheel, and Isager 2018). Nevertheless, we believe that assessing whether an effect is “meaningful” is highly dependent on the literature, priors, novelty, context or field, and that it cannot be assessed based solely on a statistical index (even though some of the indices, such as the ROPE-related ones, attempt at bridging existence with meaningfulness). Thus, researchers should rely on statistics to assess effect existence (as well as size and direction estimation), and systematically, but contextually, discuss its meaning and importance within a larger perspective.</p>
<p>Conclusions can be found in the <a href="https://easystats.github.io/bayestestR/articles/guidelines.html">guidelines section</a>.</p>
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