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Article Dans Une Revue Electronic Journal of Applied Statistical Analysis Année : 2022

Multivariate Analysis of Mixed Data: The R Package PCAmixdata

Résumé

Mixed data arise when observations are described by a mixture of numerical and categorical variables. The R package PCAmixdata extends to this type of data standard multivariate analysis methods which allow description, exploration and visualization of the data. The key techniques/methods included in the package are principal component analysis for mixed data (PCAmix), varimax-like orthogonal rotation for PCAmix, and multiple factor analysis for mixed multi-table data. This paper proposes a unified mathematical presentation of the different methods with common notations, as well as providing a summarised presentation of the three algorithms, with details to help the user understand graphical and numerical outputs of the corresponding R functions. This then allows the user to easily provide relevant interpretations of the results obtained. The three main methods are illustrated on a real dataset composed of four data tables characterizing living conditions in different municipalities in the Gironde region of southwest France.

Dates et versions

hal-01662595 , version 1 (13-12-2017)

Identifiants

Citer

Marie Chavent, Vanessa Kuentz-Simonet, Amaury Labenne, Jérôme Saracco. Multivariate Analysis of Mixed Data: The R Package PCAmixdata. Electronic Journal of Applied Statistical Analysis, 2022, 15 (3), ⟨10.1285/i20705948v15n3p606⟩. ⟨hal-01662595⟩
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