<p><div><div><div><div data-show-faces="false" data-height="21" data-width="100" data-layout="button_count" data-send="true" data-href="https://www.r-bloggers.com/multiple-correspondence-analysis-with-factominer/"></div></div><div></div></div><div></div></div>
<div>
(This article was first published on <strong><a href="https://francoishusson.wordpress.com/2017/07/18/multiple-correspondence-analysis-with-factominer/"> François Husson</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Here is a course with videos that present <strong>Multiple Correspondence Analysis</strong> in a <em>French way</em>. The most well-known use of Multiple Correspondence Analysis is: surveys.</p>
<p>Four videos present a course on MCA, highlighting the way to interpret the data. Then  you will find videos presenting the way to implement MCA in <strong><em>FactoMineR</em></strong>, to deal with missing values in MCA thanks to the package <strong><em>missMDA</em> </strong>and lastly a video to draw interactive graphs with <strong><em>Factoshiny</em></strong>. And finally you will see that the new package <strong><em>FactoInvestigate</em> </strong>allows you to obtain automatically an interpretation of your MCA results.</p>
<p>With this course, you will be stand-alone to perform and interpret results obtain with MCA.</p>
<p><img data-orig-file="https://francoishusson.files.wordpress.com/2017/07/mca4.png?w=450&amp;h=386&amp;fit=542%2C386" data-orig-size="977,695" data-medium-file="https://francoishusson.files.wordpress.com/2017/07/mca4.png?w=450&amp;h=386&amp;fit=542%2C386?w=300" data-attachment-id="330" data-image-meta="{" data-image-description="" src="https://francoishusson.files.wordpress.com/2017/07/mca4.png?w=450&h=386&fit=542%2C386" data-permalink="https://francoishusson.wordpress.com/?attachment_id=330" data-large-file="https://francoishusson.files.wordpress.com/2017/07/mca4.png?w=450&amp;h=386&amp;fit=542%2C386?w=634" alt="MCA4" data-image-title="MCA4" sizes="(max-width: 542px) 100vw, 542px" data-comments-opened="1" class="wp-image-330" data-recalc-dims="1"></p>
<p> </p>
<ul>
<li>Course
<ol>
<li><a rel="nofollow" href="https://www.youtube.com/watch?v=gZ_7WWEVlTg" target="_blank">Data – issues</a></li>
<li><a rel="nofollow" href="https://www.youtube.com/watch?v=b4kRAt4mkB8" target="_blank">Visualizing the point cloud of individuals</a></li>
<li><a rel="nofollow" href="https://www.youtube.com/watch?v=OZW1KrKO5Ac" target="_blank">Visualizing the cloud of categories</a></li>
<li><a rel="nofollow" href="https://www.youtube.com/watch?v=wTCuThGiy4w" target="_blank">Interpretation aids</a></li>
<li>Material on the course videos: the <a rel="nofollow" href="https://francoishusson.files.wordpress.com/2017/07/pca_course_slides.pdf" title="PCA_course_slides" target="_blank">slides</a>, the <a rel="nofollow" href="https://francoishusson.files.wordpress.com/2017/07/pca_course_transcription.pdf" title="PCA_course_transcription" target="_blank">PCA_transcription</a></li>
</ol>
</li>
<li>Tutorial in R
<ul>
<li><a rel="nofollow" href="https://www.youtube.com/watch?v=Sl1-UYD6iac" target="_blank">MCA in practice with FactoMineR</a></li>
<li><a rel="nofollow" href="https://www.youtube.com/watch?v=uyIH1CtrfsU" target="_blank">Handling missing values in MCA with missMDA and FactoMineR</a></li>
<li><a rel="nofollow" href="https://www.youtube.com/watch?v=QO0bwMbp50I" target="_blank">Interactive graphs with Factoshiny</a></li>
</ul>
</li>
<li>Automatic interpretation
<ul>
<li>The package <a rel="nofollow" href="https://cran.r-project.org/web/packages/FactoInvestigate/index.html" target="_blank">FactoInvestigate</a> allows you to obtain a first automatic description of your MCA results.</li>
</ul>
</li>
</ul>
<p>For more information, you can see the book blow. Here are <a rel="nofollow" href="https://www.crcpress.com/Exploratory-Multivariate-Analysis-by-Example-Using-R-Second-Edition/Husson-Le-Pages/p/book/9781138196346?tab=rev" target="_blank">some reviews on the book and a link to order the book</a>.</p>
<p><img data-orig-file="https://francoishusson.files.wordpress.com/2017/07/bookr.jpg?w=177&amp;h=278&amp;fit=177%2C278&amp;resize=177%2C278" data-orig-size="413,648" data-medium-file="https://francoishusson.files.wordpress.com/2017/07/bookr.jpg?w=177&amp;h=278&amp;fit=177%2C278&amp;resize=177%2C278?w=191" data-attachment-id="271" data-image-meta="{" data-image-description="" src="https://francoishusson.files.wordpress.com/2017/07/bookr.jpg?w=177&h=278&fit=177%2C278&resize=177%2C278" data-permalink="https://francoishusson.wordpress.com/2017/07/13/course-on-pca-with-factominer/bookr/" data-large-file="https://francoishusson.files.wordpress.com/2017/07/bookr.jpg?w=177&amp;h=278&amp;fit=177%2C278&amp;resize=177%2C278?w=413" alt="bookR" data-image-title="bookR" sizes="(max-width: 177px) 100vw, 177px" data-comments-opened="1" class="wp-image-271" data-recalc-dims="1"></p>
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<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="https://francoishusson.wordpress.com/2017/07/18/multiple-correspondence-analysis-with-factominer/"> François Husson</a></strong>.</div>
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