Multivariate analysis of mixed data: The PCAmixdata R package
CHAVENT, Marie
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
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Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
CHAVENT, Marie
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
SARACCO, Jerome
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Ecole Nationale Supérieure de Cognitique [ENSC]
< Réduire
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Ecole Nationale Supérieure de Cognitique [ENSC]
Langue
en
Communication dans un congrès
Ce document a été publié dans
The useR! Conference 2015, 2015-06-30, Aalborg.
Résumé en anglais
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 ...Lire la suite >
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 data within a dataset). A synthetic presentation of the three algorithms will be provided and the three main procedures will be illustrated on real data composed of four datasets caracterizing conditions of life of cities of Gironde, a south-west region of France.< Réduire
Mots clés en anglais
mixture of numerical and categorical variables
Multivariate data analysis
rotation
multi-group data
principal component analysis
Origine
Importé de halUnités de recherche