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a generic version of add_predicted_draws() that can be used with model types
that have their own prediction functions that are not yet supported by tidybayes."><meta property="og:image" content="https://mjskay.github.io/tidybayes/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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    <div class="page-header">
    <h1>Add draws to a data frame in tidy format</h1>
    <small class="dont-index">Source: <a href="https://github.com/mjskay/tidybayes/blob/HEAD/R/add_draws.R" class="external-link"><code>R/add_draws.R</code></a></small>
    <div class="hidden name"><code>add_draws.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Add draws from a matrix of draws (usually draws from a predictive distribution) to a data frame in tidy format. This is
a generic version of <code><a href="add_predicted_draws.html">add_predicted_draws()</a></code> that can be used with model types
that have their own prediction functions that are not yet supported by tidybayes.</p>
    </div>

    <div id="ref-usage">
    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">add_draws</span><span class="op">(</span><span class="va">data</span>, <span class="va">draws</span>, value <span class="op">=</span> <span class="st">".value"</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 add draws to, with M rows.</p></dd>


<dt id="arg-draws">draws<a class="anchor" aria-label="anchor" href="#arg-draws"></a></dt>
<dd><p>N by M matrix of draws, with M columns corresponding to the M rows in <code>data</code>, and N draws in each column.</p></dd>


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

</dl></div>
    <div id="value">
    <h2>Value</h2>
    <p>A data frame (actually, a <a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble</a>) with a <code>.row</code> column (a
factor grouping rows from the input <code>data</code>), a <code>.draw</code> column (a unique index corresponding to each draw
from the distribution), and a column with its name specified by the <code>value</code> argument (default is <code>.value</code>)
containing the values of draws from <code>draws</code>. The data frame is grouped by all rows in <code>data</code> plus the <code>.row</code> column.</p>
    </div>
    <div id="details">
    <h2>Details</h2>
    <p>Given a data frame with M rows and an N by M matrix of N draws, adds a <code>.row</code>, <code>.draw</code>, and <code>.value</code>
column (or another name if <code>value</code> is set) to <code>data</code>, and expands <code>data</code> into a long-format dataframe of draws.</p>
<p><code>add_epred_draws(df, m)</code> is roughly equivalent to <code>add_draws(df, posterior_epred(m, newdata = df))</code>, except
that <code>add_epred_draws</code> standardizes argument names and values across packages and has additional features for some
model types (like handling ordinal responses and distributional parameters in brms).</p>
<p><code>add_predicted_draws(df, m)</code> is roughly equivalent to <code>add_draws(df, posterior_predict(m, newdata = df))</code>, except
that <code>add_predicted_draws</code> standardizes argument names and values across packages.</p>
    </div>
    <div id="see-also">
    <h2>See also</h2>
    <div class="dont-index"><p><code><a href="add_predicted_draws.html">add_predicted_draws()</a></code>, <code>add_draws()</code></p></div>
    </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-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Attaching package: 'dplyr'</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> The following objects are masked from 'package:stats':</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span>     filter, lag</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> The following objects are masked from 'package:base':</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span>     intersect, setdiff, setequal, union</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-msg co"><span class="r-pr">#&gt;</span> Loading required package: Rcpp</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Loading 'brms' package (version 2.21.0). Useful instructions</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> can be found by typing help('brms'). A more detailed introduction</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> to the package is available through vignette('brms_overview').</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> Attaching package: 'brms'</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> The following objects are masked from 'package:tidybayes':</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span>     dstudent_t, pstudent_t, qstudent_t, rstudent_t</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> The following object is masked from 'package:stats':</span>
<span class="r-msg co"><span class="r-pr">#&gt;</span> </span>
<span class="r-msg co"><span class="r-pr">#&gt;</span>     ar</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 3.7e-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 0.37 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: 0.187 seconds (Warm-up)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:                0.044 seconds (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:                0.231 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>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"># plot posterior predictive intervals</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="co"># the line below is roughly equivalent to add_epred_draws(m_mpg), except</span></span></span>
<span class="r-in"><span>  <span class="co"># that it does not standardize arguments across model types.</span></span></span>
<span class="r-in"><span>  <span class="fu">add_draws</span><span class="op">(</span><span class="fu"><a href="https://mc-stan.org/rstantools/reference/posterior_epred.html" class="external-link">posterior_epred</a></span><span class="op">(</span><span class="va">m_mpg</span>, newdata <span class="op">=</span> <span class="va">.</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="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="http://mjskay.github.io/ggdist/reference/stat_lineribbon.html" class="external-link">stat_lineribbon</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">.value</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 class="op">+</span></span></span>
<span class="r-in"><span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_brewer.html" class="external-link">scale_fill_brewer</a></span><span class="op">(</span>palette <span class="op">=</span> <span class="st">"Greys"</span><span class="op">)</span></span></span>
<span class="r-plt img"><img src="add_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>
    </div>
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