Commit 0b4b3015 authored by Laurence Viry's avatar Laurence Viry
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modification Multidim

parent d47e043c
...@@ -1037,28 +1037,38 @@ ...@@ -1037,28 +1037,38 @@
## Factoshiny: interactive graphs in exploratory multivariate data analysis ## Factoshiny: interactive graphs in exploratory multivariate data analysis
The [Factoshiny](http://factominer.free.fr/graphs/factoshiny.html) package allows you to use the [FactoMineR](http://factominer.free.fr) package using a graphical interface, and also allows you to modify the graphics interactively. This package is very useful to optimize these graphics before distributing them. The [Factoshiny](http://factominer.free.fr/graphs/factoshiny.html) package allows you to use the [FactoMineR](http://factominer.free.fr) package using a graphical interface, and also allows you to modify the graphics interactively. This package is very useful to optimize these graphics before distributing them.
   
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## Management of missing data in ACP ## Handling of missing data in ACP
[PCA with missing data usinf MissMDA R Package](https://www.youtube.com/watch?v=OOM8_FH6_8o) <br\>
[Methodology on the treatment of missing data](https://www.youtube.com/watch?v=hQ6tDtgotx0)
   
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# Autres méthodes d'analyse multidimentionnelles # Other methods of multidimensional analysis
## Analyse factorielle des correspondances [Approach in multidimensional data analysis](http://math.agrocampus-ouest.fr/infoglueDeliverLive/membres/Francois.Husson/Rcorner) <br\>
### Données et objectifs <br\>
<img src="../../figures/demarcheAD.jpg",width="80%",height="80%">
## Analyse des correspondances multiples
### Données et objectifs [Approach in multidimensional data analysis](http://math.agrocampus-ouest.fr/infoglueDeliverLive/membres/Francois.Husson/Rcorner)
## Correspondence Analysis
## Classification ### Data et objectifs
### Données et objectifs The main point of **correspondence analysis** is studying the **links between pairs of qualitative variables**. This really means looking at the difference between the given data, and what it would be like if the variables were independent. We're therefore going to see how the analysis captures deviation from independence. Our reasoning will mainly be geometrical, creating point clouds for the rows and point clouds for the columns. Projecting these clouds onto planes will give some useful representations.
%% Cell type:markdown id: tags: ## Multiple Correspondence Analysis
### Data et objectifs
## Analyse Factorielle Multiple In the MCA context, we have a point cloud of individuals, and a point clouds of categories. We see how to visualize the point cloud of individuals, and how to interpret it using the categories and how to directly visualize the point cloud of categories. The point cloud of
### Données et objectifs individuals, and that of the categories, can be shown simultaneously on the same graph. This is called the simultaneous representation of the point clouds.
## Multiple Factor Analysis
### Data et objectifs <br\>
Method to study more complex data tables, where a group of individuals is
characterized by variables structured as groups, and possibly coming from different information sources. The interest in the method is due to it being able to analyze a data table as a whole, but also its ability to compare information provided by the various information sources.<br\>
<br\>
[MOOC AgroCampus Ouest Exploratory Multivariate Data Analysis](https://www.fun-mooc.fr/courses/course-v1:agrocampusouest+40001S04EN+session04/info)<br\>
[Courses AgroCampus Ouest F. Husson](http://math.agrocampus-ouest.fr/infoglueDeliverLive/membres/Francois.Husson/teaching)
   
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# Quelques références # Quelques références
   
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