GLMMadaptive/docs/reference/scoring_rules.html at master · drizopoulos/GLMMadaptive · GitHub

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    <h1>Proper Scoring Rules for Categorical Data</h1>

    <div class="hidden name"><code>scoring_rules.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Calculates the logarithmic, quadratic/Brier and spherical based on a fitted mixed model
for categorical data.</p>
    </div>

    <div id="ref-usage">
    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">scoring_rules</span><span class="op">(</span><span class="va">object</span>, <span class="va">newdata</span>, newdata2 <span class="op">=</span> <span class="cn">NULL</span>, max_count <span class="op">=</span> <span class="fl">2000</span>, </span>
<span>    return_newdata <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
    </div>

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


<dl><dt id="arg-object">object<a class="anchor" aria-label="anchor" href="#arg-object"></a></dt>
<dd><p>an object inheriting from class <code>"MixMod"</code>.</p></dd>

<dt id="arg-newdata">newdata<a class="anchor" aria-label="anchor" href="#arg-newdata"></a></dt>
<dd><p>a data.frame based on which to estimate the random effect and calculate
    predictions. It should contain the response variable.</p></dd>

<dt id="arg-newdata-">newdata2<a class="anchor" aria-label="anchor" href="#arg-newdata-"></a></dt>
<dd><p>a data.frame based on which to estimate the random effect and calculate
    predictions. It should contain the response variable.</p></dd>

<dt id="arg-max-count">max_count<a class="anchor" aria-label="anchor" href="#arg-max-count"></a></dt>
<dd><p>numeric scalar denoting the maximum count up to which to calculate
    probabilities; this is relevant for count response data.</p></dd>

<dt id="arg-return-newdata">return_newdata<a class="anchor" aria-label="anchor" href="#arg-return-newdata"></a></dt>
<dd><p>logical; if <code>TRUE</code> the values of the scoring rules are
    ruturned as extra columns of the <code>newdata</code> or <code>newdata2</code> data.frame.</p></dd>

</dl></div>
    <div id="value">
    <h2>Value</h2>
    <p>A data.frame with (extra) columns the values of the logarithmic, quadratic and spherical
scoring rules calculated based on the fitted model and the observed responses in
<code>newdata</code> or <code>newdata2</code>.</p>
    </div>
    <div id="references">
    <h2>References</h2>
    <p>Carvalho, A. (2016). An overview of applications of proper scoring rules.
<em>Decision Analysis</em> <b>13</b>, 223–242. doi:10.1287/deca.2016.0337</p>
    </div>
    <div id="author">
    <h2>Author</h2>
    <p>Dimitris Rizopoulos <a href="mailto:d.rizopoulos@erasmusmc.nl">d.rizopoulos@erasmusmc.nl</a></p>
    </div>
    <div id="see-also">
    <h2>See also</h2>
    <div class="dont-index"><p><code><a href="mixed_model.html">mixed_model</a></code>,
<code><a href="methods.html">predict.MixMod</a></code></p></div>
    </div>

    <div id="ref-examples">
    <h2>Examples</h2>
    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># \donttest{</span></span></span>
<span class="r-in"><span><span class="cn">NA</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> [1] NA</span>
<span class="r-in"><span><span class="cn">NA</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> [1] NA</span>
<span class="r-in"><span><span class="cn">NA</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> [1] NA</span>
<span class="r-in"><span><span class="co"># }</span></span></span>
</code></pre></div>
    </div>
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