Multivariate analysis of mixed data: The PCAmixdata R package. Mixed data type arise when observations are described by a mixture of numerical and categorical variables. The R package PCAmixdata extends standard multivariate analysis methods to incorporate this type of data. The key techniques included in the package are PCAmix (PCA of a mixture of numerical and categorical variables), PCArot (rotation in PCAmix) and MFAmix (multiple factor analysis with mixed type data within a dataset). This paper gives a synthetic presentation of the three algorithms with details and elements of proof to help the user to well understand graphical and numerical outputs of the package. The three main procedures are illustrated on real data composed of four datasets caracterizing conditions of life of cities of Gironde, a south-west region of France.
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Urbano Lorenzo-Seva; Michel van de Velden: MultipleCar: A Graphical User Interface MATLAB Toolbox to Compute Multiple Correspondence Analysis (2019) not zbMATH
- Marie Chavent, Vanessa Kuentz-Simonet, Amaury Labenne, J. Saracco: Multivariate analysis of mixed data: The PCAmixdata R package (2014) arXiv
- Chavent, Marie; Kuentz-Simonet, Vanessa; Saracco, Jérôme: Orthogonal rotation in PCAMIX (2012)