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Exploratory multivariate analysis by example using R / François Husson, Sébastien Le and Jérôme Pagès.

By: Husson, François [author].
Contributor(s): Lê, Sébastien [author] | Pagès, Jérôme [author].
Material type: TextTextSeries: Computer Science and Data Analysis Series.Edition: 2nd ed.Description: xiii, 248 pages, 24 cm.ISBN: 9781138196346 (hardback); 9781138196346.Subject(s): Multivariate analysis | R (Computer program language)DDC classification: 000SA.07
Contents:
Preface -- Principal Component Analysis (PCA) -- Correspondence Analysis (CA) -- Multiple Correspondence Analysis (MCA) --Clustering -- Visualisation -- Appendix
Summary: This 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.
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Item type Current location Call number Status Date due Barcode Item holds
Books Books ISI Library, Kolkata
 
000SA.07 H972 (Browse shelf) Available 138477
Total holds: 0

Includes bibliographical references and index.

Preface -- Principal Component Analysis (PCA) -- Correspondence Analysis (CA) -- Multiple Correspondence Analysis (MCA) --Clustering -- Visualisation -- Appendix

This 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.

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