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   <media:description>What are the important questions that are necessary to answer before performing a principal component method such as Principal Component Analysis, Correspondence Analysis, Multiple Correspondence Analysis, Multiple Factor Analysis ?
How to interpret the results of the principal component method ?


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   <name>François Husson</name>
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And how can we improve the graphs obtained by the method?</media:description>
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   <name>François Husson</name>
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   <media:description>Multiple Factor Analysis takes into account a group structure on the variables and gives graphical representations that are specific to study the links between groups: a group represntation that gives globally the link between groups, a partial representation that shows how each individual is seen from one group to another, and a group with the partial axes.</media:description>
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  <author>
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   <media:description>Multiple Factor Analysis is a principal Component Methods that deal with datasets that contain variables that are structured by groups.
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  <author>
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Comparaison de la description de méthodes d'analyse de données</media:description>
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  <author>
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  <author>
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   <media:description>L'inertie joue un rôle particulier en analyse des correspondances. Cela permet de comprendre l'intensité de la liaison mise en évidence en AFC.</media:description>
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  <author>
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  <author>
   <name>François Husson</name>
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  <author>
   <name>François Husson</name>
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  <published>2016-12-15T15:45:40+00:00</published>
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How to consolidate the clustering?
And how can we describe the clusters?</media:description>
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  <author>
   <name>François Husson</name>
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  <title>Handling missing values in PCA</title>
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  <author>
   <name>François Husson</name>
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Presentation of the missMDA package.
How to perform multiple imputation in PCA.</media:description>
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