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    <h1>Calculate sample entropy.</h1>
    <small class="dont-index">Source: <a href="https://github.com/pascalkieslich/mousetrap/blob/HEAD/R/sample_entropy.R" class="external-link"><code>R/sample_entropy.R</code></a></small>
    <div class="hidden name"><code>mt_sample_entropy.Rd</code></div>
    </div>

    <div class="ref-description">
    <p>Calculate sample entropy for each trajectory as a measure of the complexity
of movements along one specific dimension.</p>
    </div>

    <div id="ref-usage">
    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">mt_sample_entropy</span><span class="op">(</span></span>
<span>  <span class="va">data</span>,</span>
<span>  use <span class="op">=</span> <span class="st">"tn_trajectories"</span>,</span>
<span>  save_as <span class="op">=</span> <span class="st">"measures"</span>,</span>
<span>  dimension <span class="op">=</span> <span class="st">"xpos"</span>,</span>
<span>  m <span class="op">=</span> <span class="fl">3</span>,</span>
<span>  r <span class="op">=</span> <span class="cn">NULL</span>,</span>
<span>  use_diff <span class="op">=</span> <span class="cn">TRUE</span>,</span>
<span>  verbose <span class="op">=</span> <span class="cn">FALSE</span></span>
<span><span class="op">)</span></span></code></pre></div>
    </div>

    <div id="arguments">
    <h2>Arguments</h2>
    <dl><dt>data</dt>
<dd><p>a mousetrap data object created using one of the mt_import
functions (see <a href="mt_example.html">mt_example</a> for details). Alternatively, a trajectory
array can be provided directly (in this case <code>use</code> will be ignored).</p></dd>


<dt>use</dt>
<dd><p>a character string specifying which trajectory data should be
used.</p></dd>


<dt>save_as</dt>
<dd><p>a character string specifying where the calculated measures
should be stored.</p></dd>


<dt>dimension</dt>
<dd><p>a character string specifying the dimension based on which
sample entropy should be calculated. By default (xpos), the x-positions are
used.</p></dd>


<dt>m</dt>
<dd><p>an integer passed on to the sample entropy function (see Details).</p></dd>


<dt>r</dt>
<dd><p>a numeric value passed on to the sample entropy function (see
Details).</p></dd>


<dt>use_diff</dt>
<dd><p>logical indicating if the differences of the dimension values
should be computed before calculating sample entropy (which is done by
default, see Details).</p></dd>


<dt>verbose</dt>
<dd><p>logical indicating whether function should report its
progress.</p></dd>

</dl></div>
    <div id="value">
    <h2>Value</h2>
    

<p>A mousetrap data object (see <a href="mt_example.html">mt_example</a>).</p>


<p>If a data.frame with label specified in <code>save_as</code> (by default
"measures") already exists, the sample entropy values are added as
additional column.</p>


<p>If not, an additional <a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a> will be added.</p>


<p>If a trajectory array was provided directly as <code>data</code>, only the
data.frame will be returned.</p>
    </div>
    <div id="details">
    <h2>Details</h2>
    <p><code>mt_sample_entropy</code> calculates the sample entropy for each trajectory as
a measure of its complexity. Hehman et al (2015) provide details on how
sample entropy can be calculated and applied in mouse-tracking (following
Dale et al., 2007). They apply the sample entropy measure to the
differences between adjacent x-positions
(which is also the default here, as in a standard mouse-tracking task with
buttons located in the top-left and right corners mostly the movements in the
horizontal direction are of interest). Besides, they recommend using the
time-normalized trajectories so all trajectories have the same length.</p>
<p>Sample entropy is computed by comparing windows of a fixed size (specified
using <code>m</code>) across all recorded positions. Sample entropy is the
negative natural logarithm of the conditional probability that this window
remains similar across the trial (Hehman et al., 2015). A window is
considered to be similar to another if their distance is smaller than a
specified tolerance value (which can be specified using <code>r</code>). Hehman et
al. (2015) use a tolerance value of 0.2 * standard deviation of all
differences between adjacent x-positions in the dataset (which is the default
implemented here).</p>
    </div>
    <div id="references">
    <h2>References</h2>
    <p>Dale, R., Kehoe, C., &amp; Spivey, M. J. (2007). Graded motor
responses in the time course of categorizing atypical exemplars.
<em>Memory &amp; Cognition, 35</em>(1), 15-28.</p>
<p>Hehman, E., Stolier, R. M., &amp; Freeman, J. B. (2015). Advanced mouse-tracking
analytic techniques for enhancing psychological science. <em>Group
Processes &amp; Intergroup Relations, 18</em>(3), 384-401.</p>
    </div>
    <div id="see-also">
    <h2>See also</h2>
    <div class="dont-index"><p><a href="mt_measures.html">mt_measures</a> for calculating other mouse-tracking
measures.</p></div>
    </div>
    <div id="author">
    <h2>Author</h2>
    <p>Pascal J. Kieslich</p>
<p>Dirk Wulff</p>
<p>Felix Henninger</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"># Calculate sample entropy based on time-normalized</span></span></span>
<span class="r-in"><span><span class="co"># trajectories and merge results with other meausres</span></span></span>
<span class="r-in"><span><span class="co"># derived from raw trajectories</span></span></span>
<span class="r-in"><span><span class="va">mt_example</span> <span class="op">&lt;-</span> <span class="fu"><a href="mt_measures.html">mt_measures</a></span><span class="op">(</span><span class="va">mt_example</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="va">mt_example</span> <span class="op">&lt;-</span> <span class="fu"><a href="mt_time_normalize.html">mt_time_normalize</a></span><span class="op">(</span><span class="va">mt_example</span>,</span></span>
<span class="r-in"><span>  save_as<span class="op">=</span><span class="st">"tn_trajectories"</span>, nsteps<span class="op">=</span><span class="fl">101</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="va">mt_example</span> <span class="op">&lt;-</span> <span class="fu">mt_sample_entropy</span><span class="op">(</span><span class="va">mt_example</span>,</span></span>
<span class="r-in"><span>  use<span class="op">=</span><span class="st">"tn_trajectories"</span>, save_as<span class="op">=</span><span class="st">"measures"</span>,</span></span>
<span class="r-in"><span>  dimension<span class="op">=</span><span class="st">"xpos"</span>, m<span class="op">=</span><span class="fl">3</span><span class="op">)</span></span></span>
<span class="r-in"><span></span></span>
</code></pre></div>
    </div>
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