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<title>Adding fixed effects programmatically in Julia</title>
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<h1 class="title toc-ignore">Adding fixed effects programmatically in Julia</h1>
<h4 class="author"><em>Laurel Brehm</em></h4>
<h4 class="date"><em>3/4/2020</em></h4>
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<ul>
<li>Get the repository: <a href="https://github.com/RePsychLing/addFixef" class="uri">https://github.com/RePsychLing/addFixef</a> or <code>git clone https://github.com/RePsychLing/addFixef.git</code></li>
</ul>
<p>For data sets that contain many possible predictors that could predict overlapping variance, it can be useful to build a model in a data-driven way.</p>
<p>This example script walks through how to build a series of mixed models in Julia where one predictor is added at a time, and then evaluated for contribution to model fit. In doing so, it explores 2 properties of Julia: its lightning speed and the ease with which one can modify the model formula to fit new models.</p>
<p>The test case explored here is what cues speakers use in order to synchronise their speech production with an interlocutor: for timely responding in dialogue, do properties like the last turn’s onset or offset matter, and do they account for more variance than speaker-internal properties such as recent reaction time? See Brehm & Meyer, Psychonomics 2019 talk.</p>
<p>This repo contains:</p>
<ul>
<li><p><code>bm2020partialData.csv</code> (some sample data for the example)</p></li>
<li><p><code>addFixef.jmd</code> (code in Julia markdown)</p></li>
<li><p><code>addFixef.ipynb</code> (exactly the same code in a Julia notebook if that’s more your speed)</p></li>
</ul>
<p>Dependencies are in general, common to the rest of the RePsychLing tutorials, but I’ve also used Distributions (and include code for downloading it).</p>
<p>For more specific instructions on running Julia code: <a href="https://repsychling.github.io/intro.html" class="uri">https://repsychling.github.io/intro.html</a></p>
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