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Exploratory multivariate analysis by example using R

By: Husson, Francois.
Contributor(s): Lê, Sébastien [Co-author] | Pagès, Jérôme [Co-author].
Series: Chapman & Hall/ CRC Computer science and data analysis. Publisher: Boca Raton CRC Press 2017Edition: 2nd.Description: xiii, 248 p.ISBN: 9781138196346.Subject(s): Multivariate analysis | R (Computer program language) | Mathematics/ Applied | Mathematics/ Probability & Statistics/ GeneralDDC classification: 519.53502855133 Summary: Features Illustrates each statistical method with several real-world examples Contains datasets from different areas of application, including genomics, marketing, and sensory analysis Presents methods from a geometric point of view that enables new ways to interpret the data Uses clustering techniques in a principal components framework Includes a new chapter on visualization Updated with guidance on handling missing data for each method Provides updated datasets and R code on the book’s Web site Summary 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. The book has been written using minimal mathematics so as to appeal to applied statisticians, as well as researchers from various disciplines, including medical research and the social sciences. Readers can use the theory, examples, and software presented in this book in order to be fully equipped to tackle real-life multivariate data. https://www.crcpress.com/Exploratory-Multivariate-Analysis-by-Example-Using-R-Second-Edition/Husson-Le-Pages/p/book/9781138196346
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Features

Illustrates each statistical method with several real-world examples

Contains datasets from different areas of application, including genomics, marketing, and sensory analysis

Presents methods from a geometric point of view that enables new ways to interpret the data

Uses clustering techniques in a principal components framework

Includes a new chapter on visualization

Updated with guidance on handling missing data for each method

Provides updated datasets and R code on the book’s Web site

Summary

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.

The book has been written using minimal mathematics so as to appeal to applied statisticians, as well as researchers from various disciplines, including medical research and the social sciences. Readers can use the theory, examples, and software presented in this book in order to be fully equipped to tackle real-life multivariate data.

https://www.crcpress.com/Exploratory-Multivariate-Analysis-by-Example-Using-R-Second-Edition/Husson-Le-Pages/p/book/9781138196346

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