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<title>Classification Accuracy in the Rasch Model — class.accuracy.rasch • sirt</title>
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<h1>Classification Accuracy in the Rasch Model</h1>
<div class="hidden name"><code>class.accuracy.rasch.Rd</code></div>
</div>
<div class="ref-description">
<p>This function computes the classification accuracy in the Rasch model
for the maximum likelihood (person parameter) estimate according
to the method of Rudner (2001).</p>
</div>
<pre class="usage"><span class='fu'>class.accuracy.rasch</span>(<span class='no'>cutscores</span>, <span class='no'>b</span>, <span class='no'>meantheta</span>, <span class='no'>sdtheta</span>, <span class='no'>theta.l</span>, <span class='kw'>n.sims</span><span class='kw'>=</span><span class='fl'>0</span>)</pre>
<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
<table class="ref-arguments">
<colgroup><col class="name" /><col class="desc" /></colgroup>
<tr>
<th>cutscores</th>
<td><p>Vector of cut scores</p></td>
</tr>
<tr>
<th>b</th>
<td><p>Vector of item difficulties</p></td>
</tr>
<tr>
<th>meantheta</th>
<td><p>Mean of the trait distribution</p></td>
</tr>
<tr>
<th>sdtheta</th>
<td><p>Standard deviation of the trait distribution</p></td>
</tr>
<tr>
<th>theta.l</th>
<td><p>Discretized theta distribution</p></td>
</tr>
<tr>
<th>n.sims</th>
<td><p>Number of simulated persons in a data set. The default is 0
which means that no simulation is performed.</p></td>
</tr>
</table>
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p>A list with following entries:</p>
<dt>class.stats</dt><dd><p>Data frame containing classification accuracy statistics. The
column <code>agree0</code> refers to absolute agreement, <code>agree1</code> to
the agreement of at most a difference of one level.</p></dd>
<dt>class.prob</dt><dd><p>Probability table of classification</p></dd>
%% ~Describe the value returned
%% If it is a LIST, use
%% \item{comp1 }{Description of 'comp1'}
%% \item{comp2 }{Description of 'comp2'}
%% ...
<h2 class="hasAnchor" id="references"><a class="anchor" href="#references"></a>References</h2>
<p>Rudner, L.M. (2001). Computing the expected proportions of misclassified examinees.
<em>Practical Assessment, Research & Evaluation, 7</em>(14).</p>
<h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
<div class='dont-index'><p>Classification accuracy of other IRT models
can be obtained with the <span style="R">R</span> package <span class="pkg">cacIRT</span>.</p></div>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><span class='co'>#############################################################################</span>
<span class='co'># EXAMPLE 1: Reading dataset</span>
<span class='co'>#############################################################################</span>
<span class='fu'><a href='https://rdrr.io/r/utils/data.html'>data</a></span>( <span class='no'>data.read</span>, <span class='kw'>package</span><span class='kw'>=</span><span class='st'>"sirt"</span>)
<span class='no'>dat</span> <span class='kw'><-</span> <span class='no'>data.read</span>
<span class='co'># estimate the Rasch model</span>
<span class='no'>mod</span> <span class='kw'><-</span> <span class='kw pkg'>sirt</span><span class='kw ns'>::</span><span class='fu'><a href='https://rdrr.io/pkg/sirt/man/rasch.mml.html'>rasch.mml2</a></span>( <span class='no'>dat</span> )
<span class='co'># estimate classification accuracy (3 levels)</span>
<span class='no'>cutscores</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>( -<span class='fl'>1</span>, <span class='fl'>.3</span> ) <span class='co'># cut scores at theta=-1 and theta=.3</span>
<span class='fu'>class.accuracy.rasch</span>( <span class='kw'>cutscores</span><span class='kw'>=</span><span class='no'>cutscores</span>, <span class='kw'>b</span><span class='kw'>=</span><span class='no'>mod</span>$<span class='no'>item</span>$<span class='no'>b</span>,
<span class='kw'>meantheta</span><span class='kw'>=</span><span class='fl'>0</span>, <span class='kw'>sdtheta</span><span class='kw'>=</span><span class='no'>mod</span>$<span class='no'>sd.trait</span>,
<span class='kw'>theta.l</span><span class='kw'>=</span><span class='fu'><a href='https://rdrr.io/r/base/seq.html'>seq</a></span>(-<span class='fl'>4</span>,<span class='fl'>4</span>,<span class='kw'>len</span><span class='kw'>=</span><span class='fl'>200</span> ), <span class='kw'>n.sims</span><span class='kw'>=</span><span class='fl'>3000</span>)
<span class='co'>## Cut Scores</span>
<span class='co'>## [1] -1.0 0.3</span>
<span class='co'>##</span>
<span class='co'>## WLE reliability (by simulation)=0.671</span>
<span class='co'>## WLE consistency (correlation between two parallel forms)=0.649</span>
<span class='co'>##</span>
<span class='co'>## Classification accuracy and consistency</span>
<span class='co'>## agree0 agree1 kappa consistency</span>
<span class='co'>## analytical 0.68 0.990 0.492 NA</span>
<span class='co'>## simulated 0.70 0.997 0.489 0.599</span>
<span class='co'>##</span>
<span class='co'>## Probability classification table</span>
<span class='co'>## Est_Class1 Est_Class2 Est_Class3</span>
<span class='co'>## True_Class1 0.136 0.041 0.001</span>
<span class='co'>## True_Class2 0.081 0.249 0.093</span>
<span class='co'>## True_Class3 0.009 0.095 0.294</span></pre>
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<h2>Contents</h2>
<ul class="nav nav-pills nav-stacked">
<li><a href="#arguments">Arguments</a></li>
<li><a href="#value">Value</a></li>
<li><a href="#references">References</a></li>
<li><a href="#see-also">See also</a></li>
<li><a href="#examples">Examples</a></li>
</ul>
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