Exploratory Multivariate Analysis by Example Using R

Francois Husson, Sebastien Le, Jerome Pages

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November 15, 2010 by CRC Press
Professional - 240 Pages - 87 B/W Illustrations
ISBN 9781439835807 - CAT# K11614
Series: Chapman & Hall/CRC Computer Science & Data Analysis

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Features

  • Illustrates each statistical method with several real-world examples
  • Contains data sets 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
  • Provides data sets and code on the book’s website

Summary

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R 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 visualizing 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 and the ways they can be exploited using examples from various fields.

Throughout the text, each result correlates with an R command accessible in the FactoMineR package developed by the authors. All of the data sets and code are available at http://factominer.free.fr/book

By using the theory, examples, and software presented in this book, readers will be fully equipped to tackle real-life multivariate data.