News bulletin

A new package Factoshiny is available on CRAN. It allows us to perform principal component method with a menu and to draw interactive graphs.

A MOOC (Massive Open Online Courses) will start the 2nd of March 2015. You will find many videos on exploratory multivariate data analysis, you will find the slides, some quizz and exercises. Click here to subscribe or to have more information.

New Graphical User Interface in the package RcmdrPlugin.FactoMineR

Videos on the use of FactoMineR (for PCA, multiple factor analysis, clustering, etc.)

The version 1.24 of FactoMineR has a new graphical module that place the labels in an "optimal" way, that allows to select some elements to draw, etc. (slides, movie in French)

Four reviews on the book Exploratory Multivariate Analysis by Example using R are available in this site. To see the complete review done by Gary Evans (for Journal of Statistical Software)

A new useR group to ask questions on FactoMineR and on Exploratory Multivariate Data Analysis has been created. Join this group to have news about FactoMineR and to ask questions

missMDA: a new package to handle missing values in PCA, MCA or MFA with FactoMineR

The MFA function (version 1.16 and more) allows you to take into account quantitative groups of variables and/or qualitative groups of variables and/or contingency tables

A description of FactoMineR is available in "FactoMineR: an R package for multivariate analysis", Journal of Statistical Software.

FactoMineR is an R package dedicated to multivariate Exploratory Data Analysis. It is developped and maintained by F. Husson*, J. Josse*, S. Lê*, from Agrocampus Rennes, and J. Mazet.

Why Use FactoMineR?

1. It performs classical methods such as Principal Components Analysis (PCA), Correspondence analysis (CA), Multiple Correspondence Analysis (MCA) as well as more advanced methods.
2. It allows to add supplementary informations such as supplementary individuals and/or variables.
3. It provides a geometrical point of view and a lot of graphical outputs.
4. It provides a lot of helps to interpret (automatic description of the dimensions, various indicators, ...).
5. It can take into account a structure on the data (structure on the variables, hierarchy on the variables, structure on the individuals).
6. A GUI is available.

Installing FactoMineR and its Graphical User Interface

You have the possibility to install FactoMineR just as an usual R package or to install FactoMineR and its GUI, in order to perform multivariate analysis in a more user-friendly way.

Installing FactoMineR...

3. Load FactoMineR in your R session by writting the following line code:
4. `library(FactoMineR)`

`library(Rcmdr)`