Transforming and analyzing categorical data : Factorization techniques

  • Multiple Correspondence Analysis : MCA is similar to the Principle component analysis. The only difference is that PCA is used to analyse the quantitative i.e. continuous or numeric data whereas MCA is used for qualitative data i.e. categorical variables. There are several packages in R in order to perform multiple correspondence analysis on categorical data.
    • MCA() (FactoMineR)
    • mca() (MASS)
    • dudi.acm()(ade4)
    • mjca() (ca)
    • homals() (homals)


  • Factor analysis of mixed data : FAMD is, a particular case of MFA, used to analyze a data set containing both quantitative and qualitative variables. FAMD can be seen as a hybrid of principal component analysis (PCA) and multiple correspondence analysis (MCA). It acts as PCA for quantitative variables and as MCA for qualitative variables. There are several packages which helps us to analyze the mixed data with FAMD.
    • FAMD(FactoMineR)
    • MFAmix(PCAmixdata)
    • PCAmix

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