Multivariate


factoextra : Extract and Visualize the Results of Multivariate Data Analyses

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    • Principal Component Analysis (PCA), which is used to summarize the information contained in a continuous (i.e, quantitative) multivariate data by reducing the dimensionality of the data without loosing important information.

    • Correspondence Analysis (CA), which is an extension of the principal component analysis suited to analyse a large contingency table formed by two qualitative variables (or categorical data).

    • Multiple Correspondence Analysis (MCA), which is an adaptation of CA to a data table containing more than two categorical variables.

    • Multiple Factor Analysis (MFA) dedicated to datasets where variables are organized into groups (qualitative and/or quantitative variables).

    • Hierarchical Multiple Factor Analysis (HMFA): An extension of MFA in a situation where the data are organized into a hierarchical structure.

    • Factor Analysis of Mixed Data (FAMD), a particular case of the MFA, dedicated to analyze a data set containing both quantitative and qualitative variables.

factoextra
factoextra


Dimension reduction
Dimension reduction


MCA
MCA


License

To the extent possible under law, factoextra has waived all copyright and related or neighboring rights to this work.