The official online documentation is available at. The figure below shows methods, which outputs can be visualized using the factoextra package. We’ll use i) the FactoMineR package (Sebastien Le, et al., 2008) to compute PCA, (M)CA, FAMD, MFA and HCPC ii) and the factoextra package for extracting and visualizing the results. It contains also many functions facilitating clustering analysis and visualization.It produces a ggplot2-based elegant data visualization with less typing.The R package factoextra has flexible and easy-to-use methods to extract quickly, in a human readable standard data format, the analysis results from the different packages mentioned above.To help in the interpretation and in the visualization of multivariate analysis - such as cluster analysis and dimensionality reduction analysis - we developed an easy-to-use R package named factoextra. However, the result is presented differently according to the used packages. These packages include: FactoMineR, ade4, stats, ca, MASS and ExPosition. There are a number of R packages implementing principal component methods. Hierarchical Multiple Factor Analysis (HMFA): An extension of MFA in a situation where the data are organized into a hierarchical structure.įactor Analysis of Mixed Data (FAMD), a particular case of the MFA, dedicated to analyze a data set containing both quantitative and qualitative variables.
Multiple Factor Analysis (MFA) dedicated to datasets where variables are organized into groups (qualitative and/or quantitative variables). Multiple Correspondence Analysis (MCA), which is an adaptation of CA to a data table containing more than two categorical variables. 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.Ĭorrespondence 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). Factoextra : Extract and Visualize the Results of Multivariate Data Analysesįactoextra is an R package making easy to extract and visualize the output of exploratory multivariate data analyses, including:
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