https://github.com/cran/bayestestR
Revision 40f7c88ddf855896018cf20ec8a7ac5fbd0ea2fb authored by Dominique Makowski on 27 January 2020, 05:30 UTC, committed by cran-robot on 27 January 2020, 05:30 UTC
1 parent d8462ad
Raw File
Tip revision: 40f7c88ddf855896018cf20ec8a7ac5fbd0ea2fb authored by Dominique Makowski on 27 January 2020, 05:30 UTC
version 0.5.1
Tip revision: 40f7c88
bayestestR.html
<!DOCTYPE html>

<html xmlns="http://www.w3.org/1999/xhtml">

<head>

<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />
<meta name="viewport" content="width=device-width, initial-scale=1">

<style type="text/css">
@font-face {
font-family: octicons-link;
src: url(data:font/woff;charset=utf-8;base64,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) format('woff');
}
body {
-webkit-text-size-adjust: 100%;
text-size-adjust: 100%;
color: #333;
font-family: "Helvetica Neue", Helvetica, "Segoe UI", Arial, freesans, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";
font-size: 16px;
line-height: 1.6;
word-wrap: break-word;
}
a {
background-color: transparent;
}
a:active,
a:hover {
outline: 0;
}
strong {
font-weight: bold;
}
h1 {
font-size: 2em;
margin: 0.67em 0;
}
img {
border: 0;
}
hr {
box-sizing: content-box;
height: 0;
}
pre {
overflow: auto;
}
code,
kbd,
pre {
font-family: monospace, monospace;
font-size: 1em;
}
input {
color: inherit;
font: inherit;
margin: 0;
}
html input[disabled] {
cursor: default;
}
input {
line-height: normal;
}
input[type="checkbox"] {
box-sizing: border-box;
padding: 0;
}
table {
border-collapse: collapse;
border-spacing: 0;
}
td,
th {
padding: 0;
}
* {
box-sizing: border-box;
}
input {
font: 13px / 1.4 Helvetica, arial, nimbussansl, liberationsans, freesans, clean, sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol";
}
a {
color: #4078c0;
text-decoration: none;
}
a:hover,
a:active {
text-decoration: underline;
}
hr {
height: 0;
margin: 15px 0;
overflow: hidden;
background: transparent;
border: 0;
border-bottom: 1px solid #ddd;
}
hr:before {
display: table;
content: "";
}
hr:after {
display: table;
clear: both;
content: "";
}
h1,
h2,
h3,
h4,
h5,
h6 {
margin-top: 15px;
margin-bottom: 15px;
line-height: 1.1;
}
h1 {
font-size: 30px;
}
h2 {
font-size: 21px;
}
h3 {
font-size: 16px;
}
h4 {
font-size: 14px;
}
h5 {
font-size: 12px;
}
h6 {
font-size: 11px;
}
blockquote {
margin: 0;
}
ul,
ol {
padding: 0;
margin-top: 0;
margin-bottom: 0;
}
ol ol,
ul ol {
list-style-type: lower-roman;
}
ul ul ol,
ul ol ol,
ol ul ol,
ol ol ol {
list-style-type: lower-alpha;
}
dd {
margin-left: 0;
}
code {
font-family: Consolas, "Liberation Mono", Menlo, Courier, monospace;
font-size: 12px;
}
pre {
margin-top: 0;
margin-bottom: 0;
font: 12px Consolas, "Liberation Mono", Menlo, Courier, monospace;
}
.select::-ms-expand {
opacity: 0;
}
.octicon {
font: normal normal normal 16px/1 octicons-link;
display: inline-block;
text-decoration: none;
text-rendering: auto;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
-webkit-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.octicon-link:before {
content: '\f05c';
}
.markdown-body:before {
display: table;
content: "";
}
.markdown-body:after {
display: table;
clear: both;
content: "";
}
.markdown-body>*:first-child {
margin-top: 0 !important;
}
.markdown-body>*:last-child {
margin-bottom: 0 !important;
}
a:not([href]) {
color: inherit;
text-decoration: none;
}
.anchor {
display: inline-block;
padding-right: 2px;
margin-left: -18px;
}
.anchor:focus {
outline: none;
}
h1,
h2,
h3,
h4,
h5,
h6 {
margin-top: 1em;
margin-bottom: 16px;
font-weight: bold;
line-height: 1.4;
}
h1 .octicon-link,
h2 .octicon-link,
h3 .octicon-link,
h4 .octicon-link,
h5 .octicon-link,
h6 .octicon-link {
color: #000;
vertical-align: middle;
visibility: hidden;
}
h1:hover .anchor,
h2:hover .anchor,
h3:hover .anchor,
h4:hover .anchor,
h5:hover .anchor,
h6:hover .anchor {
text-decoration: none;
}
h1:hover .anchor .octicon-link,
h2:hover .anchor .octicon-link,
h3:hover .anchor .octicon-link,
h4:hover .anchor .octicon-link,
h5:hover .anchor .octicon-link,
h6:hover .anchor .octicon-link {
visibility: visible;
}
h1 {
padding-bottom: 0.3em;
font-size: 2.25em;
line-height: 1.2;
border-bottom: 1px solid #eee;
}
h1 .anchor {
line-height: 1;
}
h2 {
padding-bottom: 0.3em;
font-size: 1.75em;
line-height: 1.225;
border-bottom: 1px solid #eee;
}
h2 .anchor {
line-height: 1;
}
h3 {
font-size: 1.5em;
line-height: 1.43;
}
h3 .anchor {
line-height: 1.2;
}
h4 {
font-size: 1.25em;
}
h4 .anchor {
line-height: 1.2;
}
h5 {
font-size: 1em;
}
h5 .anchor {
line-height: 1.1;
}
h6 {
font-size: 1em;
color: #777;
}
h6 .anchor {
line-height: 1.1;
}
p,
blockquote,
ul,
ol,
dl,
table,
pre {
margin-top: 0;
margin-bottom: 16px;
}
hr {
height: 4px;
padding: 0;
margin: 16px 0;
background-color: #e7e7e7;
border: 0 none;
}
ul,
ol {
padding-left: 2em;
}
ul ul,
ul ol,
ol ol,
ol ul {
margin-top: 0;
margin-bottom: 0;
}
li>p {
margin-top: 16px;
}
dl {
padding: 0;
}
dl dt {
padding: 0;
margin-top: 16px;
font-size: 1em;
font-style: italic;
font-weight: bold;
}
dl dd {
padding: 0 16px;
margin-bottom: 16px;
}
blockquote {
padding: 0 15px;
color: #777;
border-left: 4px solid #ddd;
}
blockquote>:first-child {
margin-top: 0;
}
blockquote>:last-child {
margin-bottom: 0;
}
table {
display: block;
width: 100%;
overflow: auto;
word-break: normal;
word-break: keep-all;
}
table th {
font-weight: bold;
}
table th,
table td {
padding: 6px 13px;
border: 1px solid #ddd;
}
table tr {
background-color: #fff;
border-top: 1px solid #ccc;
}
table tr:nth-child(2n) {
background-color: #f8f8f8;
}
img {
max-width: 100%;
box-sizing: content-box;
background-color: #fff;
}
code {
padding: 0;
padding-top: 0.2em;
padding-bottom: 0.2em;
margin: 0;
font-size: 85%;
background-color: rgba(0,0,0,0.04);
border-radius: 3px;
}
code:before,
code:after {
letter-spacing: -0.2em;
content: "\00a0";
}
pre>code {
padding: 0;
margin: 0;
font-size: 100%;
word-break: normal;
white-space: pre;
background: transparent;
border: 0;
}
.highlight {
margin-bottom: 16px;
}
.highlight pre,
pre {
padding: 16px;
overflow: auto;
font-size: 85%;
line-height: 1.45;
background-color: #f7f7f7;
border-radius: 3px;
}
.highlight pre {
margin-bottom: 0;
word-break: normal;
}
pre {
word-wrap: normal;
}
pre code {
display: inline;
max-width: initial;
padding: 0;
margin: 0;
overflow: initial;
line-height: inherit;
word-wrap: normal;
background-color: transparent;
border: 0;
}
pre code:before,
pre code:after {
content: normal;
}
kbd {
display: inline-block;
padding: 3px 5px;
font-size: 11px;
line-height: 10px;
color: #555;
vertical-align: middle;
background-color: #fcfcfc;
border: solid 1px #ccc;
border-bottom-color: #bbb;
border-radius: 3px;
box-shadow: inset 0 -1px 0 #bbb;
}
.pl-c {
color: #969896;
}
.pl-c1,
.pl-s .pl-v {
color: #0086b3;
}
.pl-e,
.pl-en {
color: #795da3;
}
.pl-s .pl-s1,
.pl-smi {
color: #333;
}
.pl-ent {
color: #63a35c;
}
.pl-k {
color: #a71d5d;
}
.pl-pds,
.pl-s,
.pl-s .pl-pse .pl-s1,
.pl-sr,
.pl-sr .pl-cce,
.pl-sr .pl-sra,
.pl-sr .pl-sre {
color: #183691;
}
.pl-v {
color: #ed6a43;
}
.pl-id {
color: #b52a1d;
}
.pl-ii {
background-color: #b52a1d;
color: #f8f8f8;
}
.pl-sr .pl-cce {
color: #63a35c;
font-weight: bold;
}
.pl-ml {
color: #693a17;
}
.pl-mh,
.pl-mh .pl-en,
.pl-ms {
color: #1d3e81;
font-weight: bold;
}
.pl-mq {
color: #008080;
}
.pl-mi {
color: #333;
font-style: italic;
}
.pl-mb {
color: #333;
font-weight: bold;
}
.pl-md {
background-color: #ffecec;
color: #bd2c00;
}
.pl-mi1 {
background-color: #eaffea;
color: #55a532;
}
.pl-mdr {
color: #795da3;
font-weight: bold;
}
.pl-mo {
color: #1d3e81;
}
kbd {
display: inline-block;
padding: 3px 5px;
font: 11px Consolas, "Liberation Mono", Menlo, Courier, monospace;
line-height: 10px;
color: #555;
vertical-align: middle;
background-color: #fcfcfc;
border: solid 1px #ccc;
border-bottom-color: #bbb;
border-radius: 3px;
box-shadow: inset 0 -1px 0 #bbb;
}
.task-list-item {
list-style-type: none;
}
.task-list-item+.task-list-item {
margin-top: 3px;
}
.task-list-item input {
margin: 0 0.35em 0.25em -1.6em;
vertical-align: middle;
}
:checked+.radio-label {
z-index: 1;
position: relative;
border-color: #4078c0;
}
.sourceLine {
display: inline-block;
}
code .kw { color: #000000; }
code .dt { color: #ed6a43; }
code .dv { color: #009999; }
code .bn { color: #009999; }
code .fl { color: #009999; }
code .ch { color: #009999; }
code .st { color: #183691; }
code .co { color: #969896; }
code .ot { color: #0086b3; }
code .al { color: #a61717; }
code .fu { color: #63a35c; }
code .er { color: #a61717; background-color: #e3d2d2; }
code .wa { color: #000000; }
code .cn { color: #008080; }
code .sc { color: #008080; }
code .vs { color: #183691; }
code .ss { color: #183691; }
code .im { color: #000000; }
code .va {color: #008080; }
code .cf { color: #000000; }
code .op { color: #000000; }
code .bu { color: #000000; }
code .ex { color: #000000; }
code .pp { color: #999999; }
code .at { color: #008080; }
code .do { color: #969896; }
code .an { color: #008080; }
code .cv { color: #008080; }
code .in { color: #008080; }
</style>
<style>
body {
  box-sizing: border-box;
  min-width: 200px;
  max-width: 980px;
  margin: 0 auto;
  padding: 45px;
  padding-top: 0px;
}
</style>

</head>

<body>

<h1 id="get-started-with-bayesian-analysis">Get Started with Bayesian Analysis</h1>
<ul>
<li><a href="#why-use-the-bayesian-framework">Why use the Bayesian Framework?</a></li>
<li><a href="#what-is-the-bayesian-framework">What is the Bayesian Framework?</a></li>
<li><a href="#a-simple-example">A simple example</a>
<ul>
<li><a href="#bayestestr-installation">BayestestR Installation</a></li>
<li><a href="#traditional-linear-regression">Traditional linear regression</a></li>
<li><a href="#bayesian-linear-regression">Bayesian linear regression</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>bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework</em>. Journal of Open Source Software, 4(40), 1541. <a href="https://doi.org/10.21105/joss.01541">https://doi.org/10.21105/joss.01541</a></li>
</ul>
<hr />
<h2 id="why-use-the-bayesian-framework">Why use the Bayesian Framework?</h2>
<p>The Bayesian framework for statistics is quickly gaining in popularity among scientists, associated with the general shift towards <strong>open and honest science</strong>. Reasons to prefer this approach are <strong>reliability</strong>, <strong>accuracy</strong> (in noisy data and small samples), the possibility of introducing <strong>prior knowledge</strong> into the analysis and, critically, <strong>results intuitiveness</strong> and their <strong>straightforward interpretation</strong> (Andrews &amp; Baguley, 2013; Etz &amp; Vandekerckhove, 2016; Kruschke, 2010; Kruschke, Aguinis, &amp; Joo, 2012; Wagenmakers et al., 2018).</p>
<p>In general, 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, Feredoes, Muthukumaraswamy, &amp; Etchells, 2014; Szucs &amp; Ioannidis, 2016). There is a general agreement that the generalization of the Bayesian approach is one way of overcoming these issues (Benjamin et al., 2018; Etz &amp; Vandekerckhove, 2016).</p>
<p>Once we agreed that the Bayesian framework is the right way to go, you might wonder <em>what</em> is the Bayesian framework.</p>
<p><strong>What’s all the fuss about?</strong></p>
<h2 id="what-is-the-bayesian-framework">What is the Bayesian Framework?</h2>
<p>Adopting the Bayesian framework is more of a shift in the paradigm than a change in the methodology. Indeed, all the common statistical procedures (t-tests, correlations, ANOVAs, regressions, …) can be achieved using the Bayesian framework. One of the core difference is that in the <strong>frequentist view</strong> (the “classic” statistics, with <em>p</em> and <em>t</em> values, as well as some weird <em>degrees of freedom</em>), <strong>the effects are fixed</strong> (but unknown) and <strong>data are random</strong>. On the other hand, in the Bayesian inference process, instead of having estimates of the “true effect”, the probability of different effects <em>given the observed data</em> is computed, resulting in a distribution of possible values for the parameters, called the <em><strong>posterior distribution</strong></em>.</p>
<p>The uncertainty in Bayesian inference can be summarized, for instance, by the <strong>median</strong> of the distribution, as well as a range of values of the posterior distribution that includes the 95% most probable values (the 95% <strong><em>credible</em> interval</strong>). <em>Cum grano salis</em>, these are considered the counterparts to the point-estimate and confidence interval in a frequentist framework. To illustrate the difference of interpretation, the Bayesian framework allows to say <em>“given the observed data, the effect has 95% probability of falling within this range”</em>, while the frequentist less straightforward alternative would be <em>“when repeatedly computing confidence intervals from data of this sort, there is a 95% probability that the effect falls within a given range”</em>. In essence, the Bayesian sampling algorithms (such as MCMC sampling) return a probability distribution (<em>the posterior</em>) of an effect that is compatible with the observed data. Thus, an effect can be described by <a href="https://easystats.github.io/bayestestR/articles/guidelines.html">characterizing its posterior distribution</a> in relation to its centrality (point-estimates), uncertainty, as well as existence and significance</p>
<p>In other words, omitting the maths behind it, we can say that:</p>
<ul>
<li>The frequentist bloke tries to estimate “the <strong>real effect</strong>”. For instance, the “real” value of the correlation between <em>x</em> and <em>y</em>. Hence, frequentist models return a “<strong>point-estimate</strong>” (<em>i.e.</em>, a single value) of the “real” correlation (<em>e.g.</em>, r = 0.42) estimated under a number of obscure assumptions (at a minimum, considering that the data is sampled at random from a “parent”, usually normal distribution).</li>
<li><strong>The Bayesian master assumes no such thing</strong>. The data are what they are. Based on this observed data (and a <strong>prior</strong> belief about the result), the Bayesian sampling algorithm (sometimes referred to for example as <strong>MCMC</strong> sampling) returns a probability distribution (called <strong>the posterior</strong>) of the effect that is compatible with the observed data. For the correlation between <em>x</em> and <em>y</em>, it will return a distribution that says, for example, “the most probable effect is 0.42, but this data is also compatible with correlations of 0.12 and 0.74”.</li>
<li>To characterize our effects, <strong>no need of <em>p</em> values</strong> or other cryptic indices. We simply describe the posterior distribution of the effect. For example, we can report the median, the <a href="https://easystats.github.io/bayestestR/articles/credible_interval.html">89% <em>Credible</em> Interval</a> or <a href="https://easystats.github.io/bayestestR/articles/guidelines.html">other indices</a>.</li>
</ul>
<div class="figure" style="text-align: center">

<img src="data:image/jpeg;base64,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" alt="Accurate depiction of a regular Bayesian user estimating a credible interval." width="50%" />

<p class="caption">

<p>Accurate depiction of a regular Bayesian user estimating a credible interval.</p>
</p>

</div>

<p><em>Note: Altough the very purpose of this package is to advocate for the use of Bayesian statistics, please note that there are serious arguments supporting frequentist indices (see for instance <a href="https://discourse.datamethods.org/t/language-for-communicating-frequentist-results-about-treatment-effects/934/16">this thread</a>). As always, the world is not black and white (p &lt; .001).</em></p>
<p><strong>So… how does it work?</strong></p>
<h2 id="a-simple-example">A simple example</h2>
<h3 id="bayestestr-installation">BayestestR Installation</h3>
<p>You can install <code>bayestestR</code> along with the whole <a href="https://github.com/easystats/easystats"><strong>easystats</strong></a> suite by running the following:</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">install.packages</span>(<span class="st">&quot;devtools&quot;</span>)</a>
<a class="sourceLine" id="cb1-2" title="2">devtools<span class="op">::</span><span class="kw">install_github</span>(<span class="st">&quot;easystats/easystats&quot;</span>)</a></code></pre></div>
<!-- Now that it's done, we can *load* the packages we need, in this case [`report`](https://github.com/easystats/report), which combines `bayestestR` and other packages together. -->

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

<!-- library(report)  # Calling library(...) enables you to use the package's functions -->

<!-- ``` -->

<p>Let’s also install and load the <a href="https://mc-stan.org/rstanarm/"><code>rstanarm</code></a>, that allows fitting Bayesian models, as well as <a href="https://github.com/easystats/bayestestR"><code>bayestestR</code></a>, to describe them.</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" title="1"><span class="kw">install.packages</span>(<span class="st">&quot;rstanarm&quot;</span>)</a>
<a class="sourceLine" id="cb2-2" title="2"><span class="kw">library</span>(rstanarm)</a></code></pre></div>
<h3 id="traditional-linear-regression">Traditional linear regression</h3>
<p>Let’s start by fitting a simple frequentist linear regression (the <code>lm()</code> function stands for <em>linear model</em>) between two numeric variables, <code>Sepal.Length</code> and <code>Petal.Length</code> from the famous <a href="https://en.wikipedia.org/wiki/Iris_flower_data_set"><code>iris</code></a> dataset, included by default in R.</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" title="1">model &lt;-<span class="st"> </span><span class="kw">lm</span>(Sepal.Length <span class="op">~</span><span class="st"> </span>Petal.Length, <span class="dt">data=</span>iris)</a>
<a class="sourceLine" id="cb3-2" title="2"><span class="kw">summary</span>(model)</a></code></pre></div>
<pre><code>
Call:
lm(formula = Sepal.Length ~ Petal.Length, data = iris)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.2468 -0.2966 -0.0152  0.2768  1.0027 

Coefficients:
             Estimate Std. Error t value Pr(&gt;|t|)    
(Intercept)    4.3066     0.0784    54.9   &lt;2e-16 ***
Petal.Length   0.4089     0.0189    21.6   &lt;2e-16 ***
---
Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1

Residual standard error: 0.41 on 148 degrees of freedom
Multiple R-squared:  0.76,  Adjusted R-squared:  0.758 
F-statistic:  469 on 1 and 148 DF,  p-value: &lt;2e-16
</code></pre>
<!-- ```{r message=FALSE, warning=FALSE, eval=FALSE} -->

<!-- model <- lm(Sepal.Length ~ Petal.Length, data=iris) -->

<!-- report(model) -->

<!-- ``` -->

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

<!-- library(dplyr) -->

<!-- lm(Sepal.Length ~ Petal.Length, data=iris) %>%  -->

<!--   report() %>%  -->

<!--   to_text(width=100) -->

<!-- ``` -->

<p>This analysis suggests that there is a <strong>significant</strong> (<em>whatever that means</em>) and <strong>positive</strong> (with a coefficient of <code>0.41</code>) linear relationship between the two variables.</p>
<p><em>Fitting and interpreting <strong>frequentist models is so easy</strong> that it is obvious that people use it instead of the Bayesian framework… right?</em></p>
<p><strong>Not anymore.</strong></p>
<h3 id="bayesian-linear-regression">Bayesian linear regression</h3>
<!-- ```{r message=FALSE, warning=FALSE, eval=FALSE} -->

<!-- model <- stan_glm(Sepal.Length ~ Petal.Length, data=iris) -->

<!-- report(model) -->

<!-- ``` -->

<!-- ```{r echo=FALSE, message=FALSE, warning=FALSE, comment=NA, results='hide'} -->

<!-- library(rstanarm) -->

<!-- set.seed(333) -->

<!-- model <- stan_glm(Sepal.Length ~ Petal.Length, data=iris) -->

<!-- ``` -->

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

<!-- model %>%  -->

<!--   report() %>%  -->

<!--   to_text(width=100) -->

<!-- ``` -->

<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1">model &lt;-<span class="st"> </span><span class="kw">stan_glm</span>(Sepal.Length <span class="op">~</span><span class="st"> </span>Petal.Length, <span class="dt">data=</span>iris)</a>
<a class="sourceLine" id="cb5-2" title="2"><span class="kw">describe_posterior</span>(model)</a></code></pre></div>
<table>
<thead>
<tr class="header">
<th align="left">Parameter</th>
<th align="right">Median</th>
<th align="right">CI</th>
<th align="right">CI_low</th>
<th align="right">CI_high</th>
<th align="right">pd</th>
<th align="right">ROPE_CI</th>
<th align="right">ROPE_low</th>
<th align="right">ROPE_high</th>
<th align="right">ROPE_Percentage</th>
<th align="right">Rhat</th>
<th align="right">ESS</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">(Intercept)</td>
<td align="right">4.31</td>
<td align="right">89</td>
<td align="right">4.18</td>
<td align="right">4.43</td>
<td align="right">1</td>
<td align="right">89</td>
<td align="right">-0.08</td>
<td align="right">0.08</td>
<td align="right">0</td>
<td align="right">1</td>
<td align="right">4056</td>
</tr>
<tr class="even">
<td align="left">Petal.Length</td>
<td align="right">0.41</td>
<td align="right">89</td>
<td align="right">0.38</td>
<td align="right">0.44</td>
<td align="right">1</td>
<td align="right">89</td>
<td align="right">-0.08</td>
<td align="right">0.08</td>
<td align="right">0</td>
<td align="right">1</td>
<td align="right">4311</td>
</tr>
</tbody>
</table>
<p><strong>That’s it!</strong> You fitted a Bayesian version of the model by simply using <a href="https://mc-stan.org/rstanarm/reference/stan_glm.html"><code>stan_glm()</code></a> instead of <code>lm()</code> and described the posterior distributions of the parameters. The conclusion that we can drawn, for this example, are very similar. The effect (<em>the median of the effect’s posterior distribution</em>) is about <code>0.41</code>, and it can be also be considered as <em>significant</em> in the Bayesian sense (more on that later).</p>
<p><strong>So, ready to learn more?</strong> Check out the <a href="https://easystats.github.io/bayestestR/articles/example1.html"><strong>next tutorial</strong></a>!</p>
<h2 id="references">References</h2>
<div id="refs" class="references">

<div id="ref-andrews2013prior">

<p>Andrews, M., &amp; Baguley, T. (2013). Prior approval: The growth of bayesian methods in psychology. <em>British Journal of Mathematical and Statistical Psychology</em>, <em>66</em>(1), 1–7.</p>
</div>

<div id="ref-benjamin2018redefine">

<p>Benjamin, D. J., Berger, J. O., Johannesson, M., Nosek, B. A., Wagenmakers, E.-J., Berk, R., … others. (2018). Redefine statistical significance. <em>Nature Human Behaviour</em>, <em>2</em>(1), 6.</p>
</div>

<div id="ref-chambers2014instead">

<p>Chambers, C. D., Feredoes, E., Muthukumaraswamy, S. D., &amp; Etchells, P. (2014). Instead of ’playing the game’ it is time to change the rules: Registered reports at aims neuroscience and beyond. <em>AIMS Neuroscience</em>, <em>1</em>(1), 4–17.</p>
</div>

<div id="ref-etz2016bayesian">

<p>Etz, A., &amp; Vandekerckhove, J. (2016). A bayesian perspective on the reproducibility project: Psychology. <em>PloS One</em>, <em>11</em>(2), e0149794.</p>
</div>

<div id="ref-kruschke2010believe">

<p>Kruschke, J. K. (2010). What to believe: Bayesian methods for data analysis. <em>Trends in Cognitive Sciences</em>, <em>14</em>(7), 293–300.</p>
</div>

<div id="ref-kruschke2012time">

<p>Kruschke, J. K., Aguinis, H., &amp; Joo, H. (2012). The time has come: Bayesian methods for data analysis in the organizational sciences. <em>Organizational Research Methods</em>, <em>15</em>(4), 722–752.</p>
</div>

<div id="ref-szucs2016empirical">

<p>Szucs, D., &amp; Ioannidis, J. P. (2016). Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature. <em>BioRxiv</em>, 071530.</p>
</div>

<div id="ref-wagenmakers2018bayesian">

<p>Wagenmakers, E.-J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Love, J., … others. (2018). Bayesian inference for psychology. Part i: Theoretical advantages and practical ramifications. <em>Psychonomic Bulletin &amp; Review</em>, <em>25</em>(1), 35–57.</p>
</div>

</div>

</body>
</html>
back to top