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]
< Reduce
Institut de Mathématiques de Bordeaux [IMB]
Quality control and dynamic reliability [CQFD]
Ecole Nationale Supérieure de Cognitique [ENSC]
Language
en
Communication dans un congrès
This item was published in
The useR! Conference 2015, 2015-06-30, Aalborg.
English Abstract
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 ...Read more >
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.Read less <
English Keywords
mixture of numerical and categorical variables
Multivariate data analysis
rotation
multi-group data
principal component analysis
Origin
Hal imported