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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. & 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 (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” 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 (90%)</strong>: this index refers to the percentage of the 90% HDI that lies within the ROPE.</li>
<li><strong>ROPE (95%)</strong>: this index refers to the percentage of the 95% HDI that lies within the ROPE.</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 (scale = 1)</strong>: TODO.</li>
<li><strong>Bayes factor (scale = 0.707)</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"></a>
<a class="sourceLine" id="cb1-5" title="5">df <-<span class="st"> </span><span class="kw">read.csv</span>(<span class="st">"https://raw.github.com/easystats/circus/master/data/bayesSim_study1.csv"</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb1-6" title="6"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">bayesfactor_0707 =</span> <span class="kw">log</span>(bayesfactor_<span class="dv">0707</span>),</a>
<a class="sourceLine" id="cb1-7" title="7"> <span class="dt">bayesfactor_1 =</span> <span class="kw">log</span>(bayesfactor_<span class="dv">1</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">%>%</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">90</span>, ROPE_<span class="dv">95</span>, ROPE_full, bayesfactor_<span class="dv">0707</span>, bayesfactor_<span class="dv">1</span>) <span class="op">%>%</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">%>%</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">"p_value"</span>, <span class="st">"p_direction"</span>, <span class="st">"p_MAP"</span>, <span class="st">"p_ROPE"</span>, <span class="st">"ROPE_90"</span>, <span class="st">"ROPE_95"</span>, <span class="st">"ROPE_full"</span>, <span class="st">"bayesfactor_0707"</span>, <span class="st">"bayesfactor_1"</span>))) <span class="op">%>%</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">%>%</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">%>%</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">%>%</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">%>%</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">"free"</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_classic</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb2-15" title="15"><span class="st"> </span><span class="kw">theme</span>(<span class="dt">strip.background =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb2-16" title="16"> <span class="dt">strip.text =</span> <span class="kw">element_text</span>(<span class="dt">face=</span><span class="st">"bold"</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb2-17" title="17"><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"> "#f44336"</span>, <span class="st">`</span><span class="dt">1</span><span class="st">`</span> =<span class="st"> "#8BC34A"</span>, <span class="dt">name=</span><span class="st">"Effect"</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb2-18" title="18"><span class="st"> </span><span class="kw">scale_fill_manual</span>(<span class="dt">values =</span> <span class="kw">c</span>(<span class="st">"p_value"</span>=<span class="st">"#607D8B"</span>, <span class="st">"p_MAP"</span> =<span class="st"> "#4CAF50"</span>, <span class="st">"p_direction"</span> =<span class="st"> "#2196F3"</span>,</a>
<a class="sourceLine" id="cb2-19" title="19"> <span class="st">"ROPE_90"</span> =<span class="st"> "#FFC107"</span>, <span class="st">"ROPE_95"</span> =<span class="st"> "#FF9800"</span>, <span class="st">"ROPE_full"</span> =<span class="st"> "#FF5722"</span>,</a>
<a class="sourceLine" id="cb2-20" title="20"> <span class="st">"p_ROPE"</span>=<span class="st">"#E91E63"</span>, <span class="st">"bayesfactor_0707"</span>=<span class="st">"#9C27B0"</span>, <span class="st">"bayesfactor_1"</span>=<span class="st">"#673AB7"</span>), <span class="dt">guide=</span><span class="ot">FALSE</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb2-21" title="21"><span class="st"> </span><span class="kw">ylab</span>(<span class="st">"Index Value</span><span class="ch">\n</span><span class="st">"</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb2-22" title="22"><span class="st"> </span><span class="kw">xlab</span>(<span class="st">"</span><span class="ch">\n</span><span class="st">Noise"</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">%>%</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">90</span>, ROPE_<span class="dv">95</span>, ROPE_full, bayesfactor_<span class="dv">0707</span>, bayesfactor_<span class="dv">1</span>) <span class="op">%>%</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">%>%</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">"p_value"</span>, <span class="st">"p_direction"</span>, <span class="st">"p_MAP"</span>, <span class="st">"p_ROPE"</span>, <span class="st">"ROPE_90"</span>, <span class="st">"ROPE_95"</span>, <span class="st">"ROPE_full"</span>, <span class="st">"bayesfactor_0707"</span>, <span class="st">"bayesfactor_1"</span>))) <span class="op">%>%</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">%>%</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">%>%</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">%>%</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">%>%</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">"free"</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_classic</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb3-15" title="15"><span class="st"> </span><span class="kw">theme</span>(<span class="dt">strip.background =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb3-16" title="16"> <span class="dt">strip.text =</span> <span class="kw">element_text</span>(<span class="dt">face=</span><span class="st">"bold"</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb3-17" title="17"><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"> "#f44336"</span>, <span class="st">`</span><span class="dt">1</span><span class="st">`</span> =<span class="st"> "#8BC34A"</span>, <span class="dt">name=</span><span class="st">"Effect"</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb3-18" title="18"><span class="st"> </span><span class="kw">scale_fill_manual</span>(<span class="dt">values =</span> <span class="kw">c</span>(<span class="st">"p_value"</span>=<span class="st">"#607D8B"</span>, <span class="st">"p_MAP"</span> =<span class="st"> "#4CAF50"</span>, <span class="st">"p_direction"</span> =<span class="st"> "#2196F3"</span>,</a>
<a class="sourceLine" id="cb3-19" title="19"> <span class="st">"ROPE_90"</span> =<span class="st"> "#FFC107"</span>, <span class="st">"ROPE_95"</span> =<span class="st"> "#FF9800"</span>, <span class="st">"ROPE_full"</span> =<span class="st"> "#FF5722"</span>,</a>
<a class="sourceLine" id="cb3-20" title="20"> <span class="st">"p_ROPE"</span>=<span class="st">"#E91E63"</span>, <span class="st">"bayesfactor_0707"</span>=<span class="st">"#9C27B0"</span>, <span class="st">"bayesfactor_1"</span>=<span class="st">"#673AB7"</span>), <span class="dt">guide=</span><span class="ot">FALSE</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb3-21" title="21"><span class="st"> </span><span class="kw">ylab</span>(<span class="st">"Index Value</span><span class="ch">\n</span><span class="st">"</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb3-22" title="22"><span class="st"> </span><span class="kw">xlab</span>(<span class="st">"</span><span class="ch">\n</span><span class="st">Sample Size"</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">%>%</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">90</span>, ROPE_<span class="dv">95</span>, ROPE_full, bayesfactor_<span class="dv">0707</span>, bayesfactor_<span class="dv">1</span>) <span class="op">%>%</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">%>%</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">"p_direction"</span>, <span class="st">"p_MAP"</span>, <span class="st">"p_ROPE"</span>, <span class="st">"ROPE_90"</span>, <span class="st">"ROPE_95"</span>, <span class="st">"ROPE_full"</span>, <span class="st">"bayesfactor_0707"</span>, <span class="st">"bayesfactor_1"</span>))) <span class="op">%>%</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">%>%</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">%>%</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.factor</span>(<span class="kw">round</span>(<span class="kw">mean</span>(sample_size)))) <span class="op">%>%</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">%>%</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">"free"</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_classic</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb4-14" title="14"><span class="st"> </span><span class="kw">theme</span>(<span class="dt">strip.background =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb4-15" title="15"> <span class="dt">strip.text =</span> <span class="kw">element_text</span>(<span class="dt">face=</span><span class="st">"bold"</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb4-16" title="16"><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"> "#f44336"</span>, <span class="st">`</span><span class="dt">1</span><span class="st">`</span> =<span class="st"> "#8BC34A"</span>), <span class="dt">name=</span><span class="st">"Effect"</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb4-17" title="17"><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="cb4-18" title="18"> <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">"Sample Size"</span>))</a></code></pre></div>
<p><img 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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">$</span>sig_<span class="dv">1</span> <-<span class="st"> </span><span class="kw">factor</span>(<span class="kw">ifelse</span>(df<span class="op">$</span>p_value <span class="op">>=</span><span class="st"> </span><span class="fl">.1</span>, <span class="st">"n.s."</span>, <span class="st">"-"</span>), <span class="dt">levels=</span><span class="kw">c</span>(<span class="st">"n.s."</span>, <span class="st">"-"</span>))</a>
<a class="sourceLine" id="cb5-2" title="2">df<span class="op">$</span>sig_<span class="dv">05</span> <-<span class="st"> </span><span class="kw">factor</span>(<span class="kw">ifelse</span>(df<span class="op">$</span>p_value <span class="op">>=</span><span class="st"> </span><span class="fl">.05</span>, <span class="st">"n.s."</span>, <span class="st">"*"</span>), <span class="dt">levels=</span><span class="kw">c</span>(<span class="st">"n.s."</span>, <span class="st">"*"</span>))</a>
<a class="sourceLine" id="cb5-3" title="3">df<span class="op">$</span>sig_<span class="dv">01</span> <-<span class="st"> </span><span class="kw">factor</span>(<span class="kw">ifelse</span>(df<span class="op">$</span>p_value <span class="op">>=</span><span class="st"> </span><span class="fl">.01</span>, <span class="st">"n.s."</span>, <span class="st">"**"</span>), <span class="dt">levels=</span><span class="kw">c</span>(<span class="st">"n.s."</span>, <span class="st">"**"</span>))</a>
<a class="sourceLine" id="cb5-4" title="4">df<span class="op">$</span>sig_<span class="dv">001</span> <-<span class="st"> </span><span class="kw">factor</span>(<span class="kw">ifelse</span>(df<span class="op">$</span>p_value <span class="op">>=</span><span class="st"> </span><span class="fl">.001</span>, <span class="st">"n.s."</span>, <span class="st">"***"</span>), <span class="dt">levels=</span><span class="kw">c</span>(<span class="st">"n.s."</span>, <span class="st">"***"</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 <-<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 <-<span class="st"> </span><span class="kw">glm</span>(<span class="kw">paste</span>(outcome, <span class="st">"~ outcome_type * "</span>, predictor), <span class="dt">data=</span>df, <span class="dt">family =</span> <span class="st">"binomial"</span>)</a>
<a class="sourceLine" id="cb5-9" title="9"> <span class="co"># data <- data.frame(x=rep(1:100, 2))</span></a>
<a class="sourceLine" id="cb5-10" title="10"> data <-<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">"linear"</span>, <span class="st">"binary"</span>), <span class="dt">each=</span><span class="dv">100</span>))</a>
<a class="sourceLine" id="cb5-11" title="11"> data[predictor] <-<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">$</span>index <-<span class="st"> </span>predictor</a>
<a class="sourceLine" id="cb5-13" title="13"> predict_fit <-<span class="st"> </span><span class="kw">predict</span>(fit, <span class="dt">newdata=</span>data, <span class="dt">type=</span><span class="st">"response"</span>, <span class="dt">se.fit =</span> <span class="ot">TRUE</span>)</a>
<a class="sourceLine" id="cb5-14" title="14"> data[outcome] <-<span class="st"> </span>predict_fit<span class="op">$</span>fit </a>
<a class="sourceLine" id="cb5-15" title="15"> data<span class="op">$</span>CI_lower <-<span class="st"> </span>predict_fit<span class="op">$</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 <-<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 <-<span class="st"> </span><span class="kw">select_</span>(data, <span class="st">"value"</span>=predictor, outcome, <span class="st">"outcome_type"</span>, <span class="st">"index"</span>, <span class="st">"CI_lower"</span>, <span class="st">"CI_upper"</span>)</a>
<a class="sourceLine" id="cb5-18" title="18"> <span class="kw">return</span>(data)</a>
<a class="sourceLine" id="cb5-19" title="19">}</a>
<a class="sourceLine" id="cb5-20" title="20"></a>
<a class="sourceLine" id="cb5-21" title="21"></a>
<a class="sourceLine" id="cb5-22" title="22"></a>
<a class="sourceLine" id="cb5-23" title="23"><span class="kw">rbind</span>(</a>
<a class="sourceLine" id="cb5-24" title="24"> <span class="kw">rbind</span>(</a>
<a class="sourceLine" id="cb5-25" title="25"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"p_direction"</span>, <span class="dt">outcome=</span><span class="st">"sig_001"</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="cb5-26" title="26"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"p_MAP"</span>, <span class="dt">outcome=</span><span class="st">"sig_001"</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="cb5-27" title="27"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"p_ROPE"</span>, <span class="dt">outcome=</span><span class="st">"sig_001"</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="cb5-28" title="28"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"ROPE_90"</span>, <span class="dt">outcome=</span><span class="st">"sig_001"</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="cb5-29" title="29"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"ROPE_95"</span>, <span class="dt">outcome=</span><span class="st">"sig_001"</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="cb5-30" title="30"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"ROPE_full"</span>, <span class="dt">outcome=</span><span class="st">"sig_001"</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="cb5-31" title="31"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"bayesfactor_0707"</span>, <span class="dt">outcome=</span><span class="st">"sig_001"</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="cb5-32" title="32"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"bayesfactor_1"</span>, <span class="dt">outcome=</span><span class="st">"sig_001"</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="cb5-33" title="33"> ) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-34" title="34"><span class="st"> </span><span class="kw">rename</span>(<span class="st">"sig"</span>=sig_<span class="dv">001</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-35" title="35"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">threshold=</span><span class="st">"p < .001"</span>),</a>
<a class="sourceLine" id="cb5-36" title="36"> <span class="kw">rbind</span>(</a>
<a class="sourceLine" id="cb5-37" title="37"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"p_direction"</span>, <span class="dt">outcome=</span><span class="st">"sig_01"</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="cb5-38" title="38"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"p_MAP"</span>, <span class="dt">outcome=</span><span class="st">"sig_01"</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="cb5-39" title="39"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"p_ROPE"</span>, <span class="dt">outcome=</span><span class="st">"sig_01"</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="cb5-40" title="40"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"ROPE_90"</span>, <span class="dt">outcome=</span><span class="st">"sig_01"</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="cb5-41" title="41"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"ROPE_95"</span>, <span class="dt">outcome=</span><span class="st">"sig_01"</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="cb5-42" title="42"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"ROPE_full"</span>, <span class="dt">outcome=</span><span class="st">"sig_01"</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="cb5-43" title="43"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"bayesfactor_0707"</span>, <span class="dt">outcome=</span><span class="st">"sig_01"</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="cb5-44" title="44"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"bayesfactor_1"</span>, <span class="dt">outcome=</span><span class="st">"sig_01"</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="cb5-45" title="45"> ) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-46" title="46"><span class="st"> </span><span class="kw">rename</span>(<span class="st">"sig"</span>=sig_<span class="dv">01</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-47" title="47"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">threshold=</span><span class="st">"p < .01"</span>),</a>
<a class="sourceLine" id="cb5-48" title="48"> <span class="kw">rbind</span>(</a>
<a class="sourceLine" id="cb5-49" title="49"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"p_direction"</span>, <span class="dt">outcome=</span><span class="st">"sig_05"</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="cb5-50" title="50"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"p_MAP"</span>, <span class="dt">outcome=</span><span class="st">"sig_05"</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="cb5-51" title="51"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"p_ROPE"</span>, <span class="dt">outcome=</span><span class="st">"sig_05"</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="cb5-52" title="52"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"ROPE_90"</span>, <span class="dt">outcome=</span><span class="st">"sig_05"</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="cb5-53" title="53"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"ROPE_95"</span>, <span class="dt">outcome=</span><span class="st">"sig_05"</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="cb5-54" title="54"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"ROPE_full"</span>, <span class="dt">outcome=</span><span class="st">"sig_05"</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="cb5-55" title="55"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"bayesfactor_0707"</span>, <span class="dt">outcome=</span><span class="st">"sig_05"</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="cb5-56" title="56"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"bayesfactor_1"</span>, <span class="dt">outcome=</span><span class="st">"sig_05"</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="cb5-57" title="57"> ) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-58" title="58"><span class="st"> </span><span class="kw">rename</span>(<span class="st">"sig"</span>=sig_<span class="dv">05</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-59" title="59"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">threshold=</span><span class="st">"p < .05"</span>),</a>
<a class="sourceLine" id="cb5-60" title="60"> <span class="kw">rbind</span>(</a>
<a class="sourceLine" id="cb5-61" title="61"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"p_direction"</span>, <span class="dt">outcome=</span><span class="st">"sig_1"</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="cb5-62" title="62"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"p_MAP"</span>, <span class="dt">outcome=</span><span class="st">"sig_1"</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="cb5-63" title="63"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"p_ROPE"</span>, <span class="dt">outcome=</span><span class="st">"sig_1"</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="cb5-64" title="64"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"ROPE_90"</span>, <span class="dt">outcome=</span><span class="st">"sig_1"</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="cb5-65" title="65"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"ROPE_95"</span>, <span class="dt">outcome=</span><span class="st">"sig_1"</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="cb5-66" title="66"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"ROPE_full"</span>, <span class="dt">outcome=</span><span class="st">"sig_1"</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="cb5-67" title="67"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"bayesfactor_0707"</span>, <span class="dt">outcome=</span><span class="st">"sig_1"</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="cb5-68" title="68"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"bayesfactor_1"</span>, <span class="dt">outcome=</span><span class="st">"sig_1"</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="cb5-69" title="69"> ) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-70" title="70"><span class="st"> </span><span class="kw">rename</span>(<span class="st">"sig"</span>=sig_<span class="dv">1</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-71" title="71"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">threshold=</span><span class="st">"p < .1"</span>)</a>
<a class="sourceLine" id="cb5-72" title="72">) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb5-73" title="73"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">index =</span> <span class="kw">as.factor</span>(index)) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb5-74" title="74"><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="cb5-75" title="75"><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="cb5-76" title="76"><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="cb5-77" title="77"><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">"free"</span>, <span class="dt">ncol=</span><span class="dv">8</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-78" title="78"><span class="st"> </span><span class="kw">theme_classic</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb5-79" title="79"><span class="st"> </span><span class="kw">theme</span>(<span class="dt">strip.background =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb5-80" title="80"> <span class="dt">strip.text =</span> <span class="kw">element_text</span>(<span class="dt">face=</span><span class="st">"bold"</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-81" title="81"><span class="st"> </span><span class="kw">scale_color_manual</span>(<span class="dt">values =</span> <span class="kw">c</span>(<span class="st">"p_value"</span>=<span class="st">"#607D8B"</span>, <span class="st">"p_MAP"</span> =<span class="st"> "#4CAF50"</span>, <span class="st">"p_direction"</span> =<span class="st"> "#2196F3"</span>,</a>
<a class="sourceLine" id="cb5-82" title="82"> <span class="st">"ROPE_90"</span> =<span class="st"> "#FFC107"</span>, <span class="st">"ROPE_95"</span> =<span class="st"> "#FF9800"</span>, <span class="st">"ROPE_full"</span> =<span class="st"> "#FF5722"</span>,</a>
<a class="sourceLine" id="cb5-83" title="83"> <span class="st">"p_ROPE"</span>=<span class="st">"#E91E63"</span>, <span class="st">"bayesfactor_0707"</span>=<span class="st">"#9C27B0"</span>, <span class="st">"bayesfactor_1"</span>=<span class="st">"#673AB7"</span>), <span class="dt">guide=</span><span class="ot">FALSE</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-84" title="84"><span class="st"> </span><span class="kw">ylab</span>(<span class="st">"Probability of being significant</span><span class="ch">\n</span><span class="st">"</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb5-85" title="85"><span class="st"> </span><span class="kw">xlab</span>(<span class="st">"</span><span class="ch">\n</span><span class="st">Index Value"</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">$</span>equivalence_<span class="dv">95</span> <-<span class="st"> </span><span class="kw">factor</span>(<span class="kw">ifelse</span>(df<span class="op">$</span>ROPE_<span class="dv">95</span> <span class="op">==</span><span class="st"> </span><span class="dv">0</span>, <span class="st">"significant"</span>, <span class="st">"n.s."</span>), <span class="dt">levels=</span><span class="kw">c</span>(<span class="st">"n.s."</span>, <span class="st">"significant"</span>))</a>
<a class="sourceLine" id="cb6-2" title="2">df<span class="op">$</span>equivalence_<span class="dv">90</span> <-<span class="st"> </span><span class="kw">factor</span>(<span class="kw">ifelse</span>(df<span class="op">$</span>ROPE_<span class="dv">90</span> <span class="op">==</span><span class="st"> </span><span class="dv">0</span>, <span class="st">"significant"</span>, <span class="st">"n.s."</span>), <span class="dt">levels=</span><span class="kw">c</span>(<span class="st">"n.s."</span>, <span class="st">"significant"</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">"p_direction"</span>, <span class="dt">outcome=</span><span class="st">"equivalence_95"</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="cb6-8" title="8"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"p_MAP"</span>, <span class="dt">outcome=</span><span class="st">"equivalence_95"</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="cb6-9" title="9"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"p_ROPE"</span>, <span class="dt">outcome=</span><span class="st">"equivalence_95"</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="cb6-10" title="10"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"ROPE_90"</span>, <span class="dt">outcome=</span><span class="st">"equivalence_95"</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="cb6-11" title="11"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"ROPE_full"</span>, <span class="dt">outcome=</span><span class="st">"equivalence_95"</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="cb6-12" title="12"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"bayesfactor_0707"</span>, <span class="dt">outcome=</span><span class="st">"equivalence_95"</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="cb6-13" title="13"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"bayesfactor_1"</span>, <span class="dt">outcome=</span><span class="st">"equivalence_95"</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="cb6-14" title="14"> ) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb6-15" title="15"><span class="st"> </span><span class="kw">rename</span>(<span class="st">"equivalence"</span>=equivalence_<span class="dv">95</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb6-16" title="16"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">level=</span><span class="st">"95 HDI"</span>),</a>
<a class="sourceLine" id="cb6-17" title="17"> <span class="kw">rbind</span>(</a>
<a class="sourceLine" id="cb6-18" title="18"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"p_direction"</span>, <span class="dt">outcome=</span><span class="st">"equivalence_90"</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="cb6-19" title="19"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"p_MAP"</span>, <span class="dt">outcome=</span><span class="st">"equivalence_90"</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="cb6-20" title="20"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"p_ROPE"</span>, <span class="dt">outcome=</span><span class="st">"equivalence_90"</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="cb6-21" title="21"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"ROPE_95"</span>, <span class="dt">outcome=</span><span class="st">"equivalence_90"</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">"ROPE_full"</span>, <span class="dt">outcome=</span><span class="st">"equivalence_90"</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="cb6-23" title="23"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"bayesfactor_0707"</span>, <span class="dt">outcome=</span><span class="st">"equivalence_90"</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="cb6-24" title="24"> <span class="kw">get_data</span>(<span class="dt">predictor=</span><span class="st">"bayesfactor_1"</span>, <span class="dt">outcome=</span><span class="st">"equivalence_90"</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="cb6-25" title="25"> ) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb6-26" title="26"><span class="st"> </span><span class="kw">rename</span>(<span class="st">"equivalence"</span>=equivalence_<span class="dv">90</span>) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb6-27" title="27"><span class="st"> </span><span class="kw">mutate</span>(<span class="dt">level=</span><span class="st">"90 HDI"</span>)</a>
<a class="sourceLine" id="cb6-28" title="28">) <span class="op">%>%</span><span class="st"> </span></a>
<a class="sourceLine" id="cb6-29" title="29"><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="cb6-30" title="30"><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="cb6-31" title="31"><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="cb6-32" title="32"><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">"free"</span>, <span class="dt">nrow=</span><span class="dv">7</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb6-33" title="33"><span class="st"> </span><span class="kw">theme_classic</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb6-34" title="34"><span class="st"> </span><span class="kw">theme</span>(<span class="dt">strip.background =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb6-35" title="35"> <span class="dt">strip.text =</span> <span class="kw">element_text</span>(<span class="dt">face=</span><span class="st">"bold"</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb6-36" title="36"><span class="st"> </span><span class="kw">scale_color_manual</span>(<span class="dt">values =</span> <span class="kw">c</span>(<span class="st">"p_value"</span>=<span class="st">"#607D8B"</span>, <span class="st">"p_MAP"</span> =<span class="st"> "#4CAF50"</span>, <span class="st">"p_direction"</span> =<span class="st"> "#2196F3"</span>,</a>
<a class="sourceLine" id="cb6-37" title="37"> <span class="st">"ROPE_90"</span> =<span class="st"> "#FFC107"</span>, <span class="st">"ROPE_95"</span> =<span class="st"> "#FF9800"</span>, <span class="st">"ROPE_full"</span> =<span class="st"> "#FF5722"</span>,</a>
<a class="sourceLine" id="cb6-38" title="38"> <span class="st">"p_ROPE"</span>=<span class="st">"#E91E63"</span>, <span class="st">"bayesfactor_0707"</span>=<span class="st">"#9C27B0"</span>, <span class="st">"bayesfactor_1"</span>=<span class="st">"#673AB7"</span>), <span class="dt">guide=</span><span class="ot">FALSE</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb6-39" title="39"><span class="st"> </span><span class="kw">ylab</span>(<span class="st">"Probability of rejecting H0 with the equivalence test</span><span class="ch">\n</span><span class="st">"</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb6-40" title="40"><span class="st"> </span><span class="kw">xlab</span>(<span class="st">"</span><span class="ch">\n</span><span class="st">Index Value"</span>)</a></code></pre></div>
<p><img 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" /><!-- --></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">%>%</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">%>%</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">"free"</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_classic</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb7-7" title="7"><span class="st"> </span><span class="kw">theme</span>(<span class="dt">strip.background =</span> <span class="kw">element_blank</span>(),</a>
<a class="sourceLine" id="cb7-8" title="8"> <span class="dt">strip.text =</span> <span class="kw">element_text</span>(<span class="dt">face=</span><span class="st">"bold"</span>)) <span class="op">+</span></a>
<a class="sourceLine" id="cb7-9" title="9"><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"> "#f44336"</span>, <span class="st">`</span><span class="dt">1</span><span class="st">`</span> =<span class="st"> "#8BC34A"</span>), <span class="dt">name=</span><span class="st">"Effect"</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb7-10" title="10"><span class="st"> </span><span class="kw">ylab</span>(<span class="st">"ROPE (full)</span><span class="ch">\n</span><span class="st">"</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb7-11" title="11"><span class="st"> </span><span class="kw">xlab</span>(<span class="st">"</span><span class="ch">\n</span><span class="st">Probability of Direction"</span>)</a></code></pre></div>
<p><img 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" /><!-- --></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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