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Exploratory Multivariate Analysis by Example Using R
Taschenbuch von Francois Husson (u. a.)
Sprache: Englisch

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Beschreibung
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.
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.
Über den Autor
Francois Husson, Sebastien Le, Jérôme Pagès
Inhaltsverzeichnis
Preface Principal Component Analysis (PCA) Correspondence Analysis (CA) Multiple Correspondence Analysis (MCA) Clustering Visualisation Appendix
Details
Erscheinungsjahr: 2020
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
ISBN-13: 9780367658021
ISBN-10: 036765802X
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Husson, Francois
Le, Sebastien
Pagès, Jérôme
Auflage: 2. Auflage
Hersteller: Chapman and Hall/CRC
Maße: 234 x 156 x 14 mm
Von/Mit: Francois Husson (u. a.)
Erscheinungsdatum: 30.09.2020
Gewicht: 0,406 kg
Artikel-ID: 128400198
Über den Autor
Francois Husson, Sebastien Le, Jérôme Pagès
Inhaltsverzeichnis
Preface Principal Component Analysis (PCA) Correspondence Analysis (CA) Multiple Correspondence Analysis (MCA) Clustering Visualisation Appendix
Details
Erscheinungsjahr: 2020
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
ISBN-13: 9780367658021
ISBN-10: 036765802X
Sprache: Englisch
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Husson, Francois
Le, Sebastien
Pagès, Jérôme
Auflage: 2. Auflage
Hersteller: Chapman and Hall/CRC
Maße: 234 x 156 x 14 mm
Von/Mit: Francois Husson (u. a.)
Erscheinungsdatum: 30.09.2020
Gewicht: 0,406 kg
Artikel-ID: 128400198
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