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and an rstanreg model of y, 
this function returns cross validated estimates of the model's posterior classification quality:
sensitivity, specificity, and overall accuracy.
For hierarchical models of class lmerMod, the folds are comprised by collections of groups, not individual observations."><!-- 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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    <h1>Cross-Validated Posterior Classification Summaries</h1>
    <small class="dont-index">Source: <a href="https://github.com/bayes-rules/bayesrules/blob/HEAD/R/classification_summary_cv.R" class="external-link"><code>R/classification_summary_cv.R</code></a></small>
    <div class="hidden name"><code>classification_summary_cv.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Given a set of observed data including a binary response variable y 
and an rstanreg model of y, 
this function returns cross validated estimates of the model's posterior classification quality:
sensitivity, specificity, and overall accuracy.
For hierarchical models of class lmerMod, the folds are comprised by collections of groups, not individual observations.</p>
    </div>

    <div id="ref-usage">
    <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">classification_summary_cv</span><span class="op">(</span><span class="va">model</span>, <span class="va">data</span>, <span class="va">group</span>, <span class="va">k</span>, cutoff <span class="op">=</span> <span class="fl">0.5</span><span class="op">)</span></code></pre></div>
    </div>

    <div id="arguments">
    <h2>Arguments</h2>
    <dl><dt>model</dt>
<dd><p>an rstanreg model object with binary y</p></dd>
<dt>data</dt>
<dd><p>data frame including the variables in the model, both response y (0 or 1) and predictors x</p></dd>
<dt>group</dt>
<dd><p>a character string representing the name of the factor grouping variable, ie. random effect (only used for hierarchical models)</p></dd>
<dt>k</dt>
<dd><p>the number of folds to use for cross validation</p></dd>
<dt>cutoff</dt>
<dd><p>probability cutoff to classify a new case as positive</p></dd>
</dl></div>
    <div id="value">
    <h2>Value</h2>
    <p>a list</p>
    </div>

    <div id="ref-examples">
    <h2>Examples</h2>
    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="va">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="fl">20</span><span class="op">)</span></span>
<span class="r-in"><span class="va">z</span> <span class="op">&lt;-</span> <span class="fl">3</span><span class="op">*</span><span class="va">x</span></span>
<span class="r-in"><span class="va">prob</span> <span class="op">&lt;-</span> <span class="fl">1</span><span class="op">/</span><span class="op">(</span><span class="fl">1</span><span class="op">+</span><span class="fu"><a href="https://rdrr.io/r/base/Log.html" class="external-link">exp</a></span><span class="op">(</span><span class="op">-</span><span class="va">z</span><span class="op">)</span><span class="op">)</span></span>
<span class="r-in"><span class="va">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/Binomial.html" class="external-link">rbinom</a></span><span class="op">(</span><span class="fl">20</span>, <span class="fl">1</span>, <span class="va">prob</span><span class="op">)</span></span>
<span class="r-in"><span class="va">example_data</span> <span class="op">&lt;-</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>x <span class="op">=</span> <span class="va">x</span>, y <span class="op">=</span> <span class="va">y</span><span class="op">)</span></span>
<span class="r-in"><span class="va">example_model</span> <span class="op">&lt;-</span> <span class="fu">rstanarm</span><span class="fu">::</span><span class="fu"><a href="https://mc-stan.org/rstanarm/reference/stan_glm.html" class="external-link">stan_glm</a></span><span class="op">(</span><span class="va">y</span> <span class="op">~</span> <span class="va">x</span>, data <span class="op">=</span> <span class="va">example_data</span>, family <span class="op">=</span> <span class="va">binomial</span><span class="op">)</span></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 'bernoulli' 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 1.5e-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 0.15 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 / 2000 [  0%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration:  200 / 2000 [ 10%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration:  400 / 2000 [ 20%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration:  600 / 2000 [ 30%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration:  800 / 2000 [ 40%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 1200 / 2000 [ 60%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 1600 / 2000 [ 80%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1: Iteration: 2000 / 2000 [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: 0.014529 seconds (Warm-up)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:                0.015542 seconds (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 1:                0.030071 seconds (Total)</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> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: Gradient evaluation took 7e-06 seconds</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: Adjust your expectations accordingly!</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: Iteration:    1 / 2000 [  0%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: Iteration:  200 / 2000 [ 10%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: Iteration:  400 / 2000 [ 20%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: Iteration:  600 / 2000 [ 30%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: Iteration:  800 / 2000 [ 40%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: Iteration: 1000 / 2000 [ 50%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: Iteration: 1001 / 2000 [ 50%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: Iteration: 1200 / 2000 [ 60%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: Iteration: 1400 / 2000 [ 70%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: Iteration: 1600 / 2000 [ 80%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: Iteration: 1800 / 2000 [ 90%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: Iteration: 2000 / 2000 [100%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2:  Elapsed Time: 0.013165 seconds (Warm-up)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2:                0.020015 seconds (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2:                0.03318 seconds (Total)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 2: </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 'bernoulli' NOW (CHAIN 3).</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: Gradient evaluation took 4e-06 seconds</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.04 seconds.</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: Adjust your expectations accordingly!</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: Iteration:    1 / 2000 [  0%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: Iteration:  200 / 2000 [ 10%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: Iteration:  400 / 2000 [ 20%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: Iteration:  600 / 2000 [ 30%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: Iteration:  800 / 2000 [ 40%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: Iteration: 1000 / 2000 [ 50%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: Iteration: 1001 / 2000 [ 50%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: Iteration: 1200 / 2000 [ 60%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: Iteration: 1400 / 2000 [ 70%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: Iteration: 1600 / 2000 [ 80%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: Iteration: 1800 / 2000 [ 90%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: Iteration: 2000 / 2000 [100%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3:  Elapsed Time: 0.013828 seconds (Warm-up)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3:                0.013315 seconds (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3:                0.027143 seconds (Total)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 3: </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 'bernoulli' NOW (CHAIN 4).</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: Gradient evaluation took 6e-06 seconds</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: Adjust your expectations accordingly!</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: Iteration:    1 / 2000 [  0%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: Iteration:  200 / 2000 [ 10%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: Iteration:  400 / 2000 [ 20%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: Iteration:  600 / 2000 [ 30%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: Iteration:  800 / 2000 [ 40%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: Iteration: 1000 / 2000 [ 50%]  (Warmup)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: Iteration: 1001 / 2000 [ 50%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: Iteration: 1200 / 2000 [ 60%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: Iteration: 1400 / 2000 [ 70%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: Iteration: 1600 / 2000 [ 80%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: Iteration: 1800 / 2000 [ 90%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: Iteration: 2000 / 2000 [100%]  (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4:  Elapsed Time: 0.013266 seconds (Warm-up)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4:                0.01449 seconds (Sampling)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4:                0.027756 seconds (Total)</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> Chain 4: </span>
<span class="r-in"><span class="fu">classification_summary_cv</span><span class="op">(</span>model <span class="op">=</span> <span class="va">example_model</span>, data <span class="op">=</span> <span class="va">example_data</span>, k <span class="op">=</span> <span class="fl">2</span>, cutoff <span class="op">=</span> <span class="fl">0.5</span><span class="op">)</span>                   </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> $folds</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>   fold sensitivity specificity overall_accuracy</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1    1   0.8333333        0.75              0.8</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2    2   0.8000000        0.80              0.8</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> $cv</span>
<span class="r-out co"><span class="r-pr">#&gt;</span>   sensitivity specificity overall_accuracy</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1   0.8166667       0.775              0.8</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> </span>
</code></pre></div>
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