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parse_dist() into specs for the marginal distribution of
a single cell in an LKJ-distributed correlation matrix (i.e., lkjcorr_marginal()).
Useful for visualizing prior correlations from LKJ distributions."><title>Turn spec for LKJ distribution into spec for marginal LKJ distribution — marginalize_lkjcorr • ggdist</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Source_Sans_Pro-0.4.8/font.css" rel="stylesheet"><link href="../deps/Source_Code_Pro-0.4.8/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Turn spec for LKJ distribution into spec for marginal LKJ distribution — marginalize_lkjcorr"><meta property="og:description" content="Turns specs for an LKJ correlation matrix distribution as returned by
parse_dist() into specs for the marginal distribution of
a single cell in an LKJ-distributed correlation matrix (i.e., lkjcorr_marginal()).
Useful for visualizing prior correlations from LKJ distributions."><meta property="og:image" content="http://mjskay.github.io/ggdist/logo.svg"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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  <main id="main" class="col-md-9"><div class="page-header">
      <img src="../logo.svg" class="logo" alt=""><h1>Turn spec for LKJ distribution into spec for marginal LKJ distribution</h1>
      <small class="dont-index">Source: <a href="https://github.com/mjskay/ggdist/blob/HEAD/R/lkjcorr_marginal.R" class="external-link"><code>R/lkjcorr_marginal.R</code></a></small>
      <div class="d-none name"><code>marginalize_lkjcorr.Rd</code></div>
    </div>

    <div class="ref-description section level2">
    <p>Turns specs for an LKJ correlation matrix distribution as returned by
<code><a href="parse_dist.html">parse_dist()</a></code> into specs for the marginal distribution of
a single cell in an LKJ-distributed correlation matrix (i.e., <code><a href="lkjcorr_marginal.html">lkjcorr_marginal()</a></code>).
Useful for visualizing prior correlations from LKJ distributions.</p>
    </div>

    <div class="section level2">
    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">marginalize_lkjcorr</span><span class="op">(</span></span>
<span>  <span class="va">data</span>,</span>
<span>  <span class="va">K</span>,</span>
<span>  predicate <span class="op">=</span> <span class="cn">NULL</span>,</span>
<span>  dist <span class="op">=</span> <span class="st">".dist"</span>,</span>
<span>  args <span class="op">=</span> <span class="st">".args"</span>,</span>
<span>  dist_obj <span class="op">=</span> <span class="st">".dist_obj"</span></span>
<span><span class="op">)</span></span></code></pre></div>
    </div>

    <div class="section level2">
    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
    <dl><dt>data</dt>
<dd><p>A data frame containing a column with distribution names (<code>".dist"</code> by default)
and a list column of distribution arguments (<code>".args"</code> by default), such as output by
<code><a href="parse_dist.html">parse_dist()</a></code>.</p></dd>


<dt>K</dt>
<dd><p>Dimension of the correlation matrix. Must be greater than or equal to 2.</p></dd>


<dt>predicate</dt>
<dd><p>a bare expression for selecting the rows of <code>data</code> to modify. This is useful
if <code>data</code> contains more than one row with an LKJ prior in it and you only want to modify some
of the distributions; if this is the case, give row a predicate expression that evaluates to
<code>TRUE</code> on the rows you want to modify.
If <code>NULL</code> (the default), all <code>lkjcorr</code> distributions in <code>data</code> are modified.</p></dd>


<dt>dist</dt>
<dd><p>The name of the column containing distribution names. See <code><a href="parse_dist.html">parse_dist()</a></code>.</p></dd>


<dt>args</dt>
<dd><p>The name of the column containing distribution arguments. See <code><a href="parse_dist.html">parse_dist()</a></code>.</p></dd>


<dt>dist_obj</dt>
<dd><p>The name of the column to contain a <span class="pkg">distributional</span> object representing the
distribution. See <code><a href="parse_dist.html">parse_dist()</a></code>.</p></dd>

</dl></div>
    <div class="section level2">
    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
    

<p>A data frame of the same size and column names as the input, with the <code>dist</code>, and <code>args</code>,
and <code>dist_obj</code> columns modified on rows where <code>dist == "lkjcorr"</code> such that they represent a
marginal LKJ correlation distribution with name <code>lkjcorr_marginal</code> and <code>args</code> having
<code>K</code> equal to the input value of <code>K</code>.</p>
    </div>
    <div class="section level2">
    <h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>
    <p>The LKJ(eta) prior on a correlation matrix induces a marginal prior on each correlation
in the matrix that depends on both the value of <code>eta</code> <em>and</em> <code>K</code>, the dimension
of the \(K \times K\) correlation matrix. Thus to visualize the marginal prior
on the correlations, it is necessary to specify the value of <code>K</code>, which depends
on what your model specification looks like.</p>
<p>Given a data frame representing parsed distribution specifications (such
as returned by <code><a href="parse_dist.html">parse_dist()</a></code>), this function updates any rows with <code>.dist == "lkjcorr"</code>
so that the first argument to the distribution (stored in <code>.args</code>) is equal to the specified dimension
of the correlation matrix (<code>K</code>), changes the distribution name in <code>.dist</code> to <code>"lkjcorr_marginal"</code>,
and assigns a <span class="pkg">distributional</span> object representing this distribution to <code>.dist_obj</code>.
This allows the distribution to be easily visualized using the <code><a href="stat_slabinterval.html">stat_slabinterval()</a></code>
family of ggplot2 stats.</p>
    </div>
    <div class="section level2">
    <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
    <div class="dont-index"><p><code><a href="parse_dist.html">parse_dist()</a></code>, <code><a href="lkjcorr_marginal.html">lkjcorr_marginal()</a></code></p></div>
    </div>

    <div class="section level2">
    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span></span></span>
<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Say we have an LKJ(3) prior on a 2x2 correlation matrix. We can visualize</span></span></span>
<span class="r-in"><span><span class="co"># its marginal distribution as follows...</span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>prior <span class="op">=</span> <span class="st">"lkjcorr(3)"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
<span class="r-in"><span>  <span class="fu"><a href="parse_dist.html">parse_dist</a></span><span class="op">(</span><span class="va">prior</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
<span class="r-in"><span>  <span class="fu">marginalize_lkjcorr</span><span class="op">(</span>K <span class="op">=</span> <span class="fl">2</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
<span class="r-in"><span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>y <span class="op">=</span> <span class="va">prior</span>, xdist <span class="op">=</span> <span class="va">.dist_obj</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span></span>
<span class="r-in"><span>  <span class="fu"><a href="stat_halfeye.html">stat_halfeye</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span></span>
<span class="r-in"><span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/lims.html" class="external-link">xlim</a></span><span class="op">(</span><span class="op">-</span><span class="fl">1</span>, <span class="fl">1</span><span class="op">)</span> <span class="op">+</span></span></span>
<span class="r-in"><span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">xlab</a></span><span class="op">(</span><span class="st">"Marginal correlation for LKJ(3) prior on 2x2 correlation matrix"</span><span class="op">)</span></span></span>
<span class="r-plt img"><img src="marginalize_lkjcorr-1.png" alt="" width="700" height="433"></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Say our prior list has multiple LKJ priors on correlation matrices</span></span></span>
<span class="r-in"><span><span class="co"># of different sizes, we can supply a predicate expression to select</span></span></span>
<span class="r-in"><span><span class="co"># only those rows we want to modify</span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>coef <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"a"</span>, <span class="st">"b"</span><span class="op">)</span>, prior <span class="op">=</span> <span class="st">"lkjcorr(3)"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
<span class="r-in"><span>  <span class="fu"><a href="parse_dist.html">parse_dist</a></span><span class="op">(</span><span class="va">prior</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
<span class="r-in"><span>  <span class="fu">marginalize_lkjcorr</span><span class="op">(</span>K <span class="op">=</span> <span class="fl">2</span>, <span class="va">coef</span> <span class="op">==</span> <span class="st">"a"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span></span>
<span class="r-in"><span>  <span class="fu">marginalize_lkjcorr</span><span class="op">(</span>K <span class="op">=</span> <span class="fl">4</span>, <span class="va">coef</span> <span class="op">==</span> <span class="st">"b"</span><span class="op">)</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span>   coef      prior            .dist .args              .dist_obj</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1    a lkjcorr(3) lkjcorr_marginal  2, 3 lkjcorr_marginal(2, 3)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2    b lkjcorr(3) lkjcorr_marginal  4, 3 lkjcorr_marginal(4, 3)</span>
<span class="r-in"><span></span></span>
</code></pre></div>
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