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    <div class="page-header">
    <h1>Sample draws from a tidy-format data frame of draws</h1>
    <small class="dont-index">Source: <a href="https://github.com/mjskay/tidybayes/blob/HEAD/R/sample_draws.R" class="external-link"><code>R/sample_draws.R</code></a></small>
    <div class="hidden name"><code>sample_draws.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Given a tidy-format data frame of draws with a column indexing each draw, subsample the data frame to a given size
based on a column indexing draws, ensuring that rows in sub-groups of a grouped data frame are sampled from the
same draws.</p>
    </div>

    <div id="ref-usage">
    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">sample_draws</span><span class="op">(</span><span class="va">data</span>, <span class="va">ndraws</span>, draw <span class="op">=</span> <span class="st">".draw"</span>, seed <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span></span></code></pre></div>
    </div>

    <div id="arguments">
    <h2>Arguments</h2>


<dl><dt id="arg-data">data<a class="anchor" aria-label="anchor" href="#arg-data"></a></dt>
<dd><p>Data frame to sample from</p></dd>


<dt id="arg-ndraws">ndraws<a class="anchor" aria-label="anchor" href="#arg-ndraws"></a></dt>
<dd><p>The number of draws to return, or <code>NULL</code> to return all draws.</p></dd>


<dt id="arg-draw">draw<a class="anchor" aria-label="anchor" href="#arg-draw"></a></dt>
<dd><p>The name of the column indexing the draws; default <code>".draw"</code>.</p></dd>


<dt id="arg-seed">seed<a class="anchor" aria-label="anchor" href="#arg-seed"></a></dt>
<dd><p>A seed to use when subsampling draws (i.e. when <code>ndraws</code> is not <code>NULL</code>).</p></dd>

</dl></div>
    <div id="details">
    <h2>Details</h2>
    <p><code>sample_draws()</code> makes it easier to sub-sample a grouped, tidy-format data frame of draws. On a grouped data frame,
the naive approach of using <code>filter</code> with the <code>.draw</code> column will give incorrect results as it will
select a different sample within each group. <code>sample_draws()</code> ensures the same sample is selected within
each group.</p>
    </div>
    <div id="author">
    <h2>Author</h2>
    <p>Matthew Kay</p>
    </div>

    <div id="ref-examples">
    <h2>Examples</h2>
    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \dontrun{</span></span></span>
<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://ggplot2.tidyverse.org" class="external-link">ggplot2</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://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://github.com/paul-buerkner/brms" class="external-link">brms</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://modelr.tidyverse.org" class="external-link">modelr</a></span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/theme_get.html" class="external-link">theme_set</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_light</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="va">m_mpg</span> <span class="op">=</span> <span class="fu"><a href="https://paul-buerkner.github.io/brms/reference/brm.html" class="external-link">brm</a></span><span class="op">(</span><span class="va">mpg</span> <span class="op">~</span> <span class="va">hp</span> <span class="op">*</span> <span class="va">cyl</span>, data <span class="op">=</span> <span class="va">mtcars</span>,</span></span>
<span class="r-in"><span>  <span class="co"># 1 chain / few iterations just so example runs quickly</span></span></span>
<span class="r-in"><span>  <span class="co"># do not use in practice</span></span></span>
<span class="r-in"><span>  chains <span class="op">=</span> <span class="fl">1</span>, iter <span class="op">=</span> <span class="fl">500</span><span class="op">)</span></span></span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Compiling Stan program...</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Start sampling</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Gradient evaluation took 4e-05 seconds</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.4 seconds.</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Adjust your expectations accordingly!</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration:   1 / 500 [  0%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration:  50 / 500 [ 10%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 100 / 500 [ 20%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 150 / 500 [ 30%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 200 / 500 [ 40%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 250 / 500 [ 50%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 251 / 500 [ 50%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 300 / 500 [ 60%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 350 / 500 [ 70%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 400 / 500 [ 80%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 450 / 500 [ 90%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 500 / 500 [100%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:  Elapsed Time: 0.128 seconds (Warm-up)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:                0.045 seconds (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:                0.173 seconds (Total)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: </span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>The largest R-hat is 1.07, indicating chains have not mixed.</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> Running the chains for more iterations may help. See</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> https://mc-stan.org/misc/warnings.html#r-hat</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> Running the chains for more iterations may help. See</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> https://mc-stan.org/misc/warnings.html#bulk-ess</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> <span class="warning">Warning: </span>Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> Running the chains for more iterations may help. See</span>
<span class="r-wrn co"><span class="r-pr">#&gt;</span> https://mc-stan.org/misc/warnings.html#tail-ess</span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># draw 100 fit lines from the posterior and overplot them</span></span></span>
<span class="r-in"><span><span class="va">mtcars</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://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">cyl</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://modelr.tidyverse.org/reference/data_grid.html" class="external-link">data_grid</a></span><span class="op">(</span>hp <span class="op">=</span> <span class="fu"><a href="https://modelr.tidyverse.org/reference/seq_range.html" class="external-link">seq_range</a></span><span class="op">(</span><span class="va">hp</span>, n <span class="op">=</span> <span class="fl">101</span><span class="op">)</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="add_predicted_draws.html">add_epred_draws</a></span><span class="op">(</span><span class="va">m_mpg</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="co"># NOTE: only use sample_draws here when making spaghetti plots; for</span></span></span>
<span class="r-in"><span>  <span class="co"># plotting intervals it is always best to use all draws</span></span></span>
<span class="r-in"><span>  <span class="fu">sample_draws</span><span class="op">(</span><span class="fl">100</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>x <span class="op">=</span> <span class="va">hp</span>, y <span class="op">=</span> <span class="va">mpg</span>, color <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">ordered</a></span><span class="op">(</span><span class="va">cyl</span><span class="op">)</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/geom_path.html" class="external-link">geom_line</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">.epred</span>, group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste</a></span><span class="op">(</span><span class="va">cyl</span>, <span class="va">.draw</span><span class="op">)</span><span class="op">)</span>, alpha <span class="op">=</span> <span class="fl">0.25</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/geom_point.html" class="external-link">geom_point</a></span><span class="op">(</span>data <span class="op">=</span> <span class="va">mtcars</span><span class="op">)</span></span></span>
<span class="r-plt img"><img src="sample_draws-1.png" alt="" width="700" height="433"></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># }</span></span></span>
</code></pre></div>
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