test render

Author: TommasoGhilardi

CONTENT/EyeTracking/PupilDataAnalysis.qmd

@@ -357,7 +357,7 @@ The fixed effects (*Parametric coefficients*) show a strong negative effect for
 
 The smooth terms indicate whether the non-linear relationships modeled by s() explain significant variance in the data. A significant smooth term confirms that the function captures meaningful, non-linear patterns beyond random noise or simpler terms. While fixed effects are typically more important for hypothesis testing, it’s crucial to ensure the model specification captures the data's fluctuations accurately.
 
-You did it!!! You started from a simpler model and little by little you built a very complex Generalized Additive Model!! Amazing work!!!
+You did it!! You started from a simpler model and little by little you built a very complex Generalized Additive Model!! Amazing work!!!
 
 ::: callout-warning
 **This is just a very basic tutorial!**

docs/CONTENT/ContentList.html

@@ -526,7 +526,7 @@ <h5 class="no-anchor card-title listing-title">
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-<div class="g-col-1" data-index="8" data-categories="Pupillometry,R,Pre-processing" data-listing-date-sort="1734480000000" data-listing-file-modified-sort="1756293266459" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="40" data-listing-word-count-sort="7877">
+<div class="g-col-1" data-index="8" data-categories="Pupillometry,R,Pre-processing" data-listing-date-sort="1734480000000" data-listing-file-modified-sort="1756306737218" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="40" data-listing-word-count-sort="7877">
 <a href="../CONTENT/EyeTracking/PupilPreprocessing.html" class="quarto-grid-link">
 <div class="quarto-grid-item card h-100 card-left">
 <p class="card-img-top"><img src="EyeTracking\PupilPreprocessing_files\figure-html\PlotRaw-1.png" style="height: 150px;"  class="thumbnail-image card-img"/></p>
@@ -560,7 +560,7 @@ <h5 class="no-anchor card-title listing-title">
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-<div class="g-col-1" data-index="9" data-categories="Stats,R,Modelling,Additive models" data-listing-date-sort="1735776000000" data-listing-file-modified-sort="1756305831355" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="15" data-listing-word-count-sort="2934">
+<div class="g-col-1" data-index="9" data-categories="Stats,R,Modelling,Additive models" data-listing-date-sort="1735776000000" data-listing-file-modified-sort="1756306752983" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="15" data-listing-word-count-sort="2934">
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docs/CONTENT/ContentList.xml

@@ -5990,7 +5990,7 @@ Note
 <p>Before starting any type of analysis, let’s import the data and take a quick look at it.</p>
 <section id="import-data" class="level2"><h2 class="anchored" data-anchor-id="import-data">Import data</h2>
 <p>The data used in this tutorial comes from the pre-processing tutorial of pupil dilation. If you haven’t run that tutorial yet, it’s a good idea to check it out first to ensure your data is prepared and ready for analysis: <a href="../../CONTENT/EyeTracking/PupilPreprocessing.html">Pre-processing pupil data</a>. In case you did not save the result of the pre-processing you can download them from here :</p>
-<p><button class="btn btn-secondary downloadthis  " id="dnldts42022"><i class="bi bi-download"></i> Preprocessed_PupilData.csv </button></p>
+<p><button class="btn btn-secondary downloadthis  " id="dnldts23982"><i class="bi bi-download"></i> Preprocessed_PupilData.csv </button></p>
 <p>Now that you have the data, let’s import it along with the necessary libraries. We’ll also ensure that the <code>Event</code> and <code>Subjects</code> columns are properly set as factors (categorical for easier analysis. Here’s how:</p>
 <div class="cell">
 <div class="sourceCode" id="cb1" style="background: #f1f3f5;"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="kw" style="color: #003B4F;
@@ -7961,7 +7961,7 @@ fREML = 594.43  Scale est. = 0.097128  n = 2200</code></pre>
 </div>
 <p>The fixed effects (<em>Parametric coefficients</em>) show a strong negative effect for <code>EventSquare</code>, indicating that pupil size for Square is significantly lower than for Circle. This suggests that pupil size is greater when expecting a complex stimulus compared to a simple one.</p>
 <p>The smooth terms indicate whether the non-linear relationships modeled by s() explain significant variance in the data. A significant smooth term confirms that the function captures meaningful, non-linear patterns beyond random noise or simpler terms. While fixed effects are typically more important for hypothesis testing, it’s crucial to ensure the model specification captures the data’s fluctuations accurately.</p>
-<p>You did it!!! You started from a simpler model and little by little you built a very complex Generalized Additive Model!! Amazing work!!!</p>
+<p>You did it!! You started from a simpler model and little by little you built a very complex Generalized Additive Model!! Amazing work!!!</p>
 <div class="callout callout-style-default callout-warning callout-titled">
 <div class="callout-header d-flex align-content-center">
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docs/CONTENT/EyeTracking/PupilDataAnalysis.html

@@ -50,7 +50,7 @@
   </url>
   <url>
     <loc>https://tommasoghilardi.github.io/DevStart/CONTENT/EyeTracking/PupilDataAnalysis.html</loc>
-    <lastmod>2025-08-27T14:43:51.355Z</lastmod>
+    <lastmod>2025-08-27T14:59:12.983Z</lastmod>
   </url>
   <url>
     <loc>https://tommasoghilardi.github.io/DevStart/CONTENT/GettingStarted/CreateYourFirstParadigm.html</loc>