News bulletin

The new package FactoInvestigate describes and interprets automatically the results of your principal component analysis (PCA, CA or MCA), choosing the best graphs to show. Just just have to write Investigate(res.pca) to obtain the following report on the PCA results of the decathlon example


A MOOC (Massive Open Online Courses) will start in March 2018. You will find many videos on exploratory multivariate data analysis, you will find the slides, some quizz and exercises. See here the MOOC played in 2017.

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

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.

About FactoMineR

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...

  1. Download the R software at the following adress:
  2. Download the FactoMineR package from the CRAN.
  3. Load FactoMineR in your R session by writting the following line code:
  4. library(FactoMineR)

  5. Download the RcmdrPlugin.FactoMineR package from the CRAN.
  6. FactoMineR is then included in the R commander environment. You now beneficiate from all the functionality proposed in the Rcmdr package and consequently have now a very pleasant working environment.
    Every time you want to realize multivariate analysis with FactoMineR and its GUI:

    Load Rcmdr and load the FactoMineR plug-in


    In the menu of Rcmdr, Tools ==> Load the Rcmdr plug-in(s) ... and choose the plug-in RcmdrPlugin.FactoMineR. You then have to restart Rcmdr.

    If you want to have the Plug-in of FactoMineR available in Rcmdr all the time, you can do: Tools ==> Save Rcmdr options