Exploratory Multivariate Analysis by Example Using R

Francois Husson

ISBN 10: 1138196347 / ISBN 13: 9781138196346

Editore: CRC Press Apr 2017, 2017

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Neuware - Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis. The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the principles, indicators, and ways of representing and visualising objects that are common to the exploratory methods. The authors show how to use categorical variables in a PCA context in which variables are quantitative, how to handle more than two categorical variables in a CA context in which there are originally two variables, and how to add quantitative variables in an MCA context in which variables are categorical. They also illustrate the methods using examples from various fields, with related R code accessible in the FactoMineR package developed by the authors.

Codice articolo 9781138196346

Dati bibliografici

• Titolo: Exploratory Multivariate Analysis by Example Using R
• Editore: CRC Press Apr 2017
• Data di pubblicazione: 2017
• Lingua: Inglese
• ISBN 10: 1138196347
• ISBN 13: 9781138196346
• Rilegatura: Buch
• Edizione: seconda edizione
• Condizione: Neu
• Dimensioni: 241 L x 159 A x 20 P (millimetri) 241 millimetri di larghezza per 159 millimetri di altezza per 20 millimetri di profondità
• Peso: 521 grammi

Descrizione articolo

Riassunto

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, and hierarchical cluster analysis.

The authors take a geometric point of view that provides a unified vision for exploring multivariate data tables. Within this framework, they present the principles, indicators, and ways of representing and visualising objects that are common to the exploratory methods. The authors show how to use categorical variables in a PCA context in which variables are quantitative, how to handle more than two categorical variables in a CA context in which there are originally two variables, and how to add quantitative variables in an MCA context in which variables are categorical. They also illustrate the methods using examples from various fields, with related R code accessible in the FactoMineR package developed by the authors.

Informazioni sull?autore

Francois Husson, Sebastien Le, Jérôme Pagès