Exploratory Multivariate Analysis by Example Using R

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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.

Table of Contents

Principal Component Analysis (PCA)
Data — Notation — Examples
Objectives
Studying Individuals
Studying Variables
Relationships between the Two Representations NI and NK
Interpreting the Data
Implementation with FactoMineR
Additional Results
Example: The Decathlon Dataset
Example: The Temperature Dataset
Example of Genomic Data: The Chicken Dataset

Correspondence Analysis (CA)
Data — Notation — Examples
Objectives and the Independence Model
Fitting the Clouds
Interpreting the Data
Supplementary Elements (= Illustrative)
Implementation with FactoMineR
CA and Textual Data Processing
Example: The Olympic Games Dataset
Example: The White Wines Dataset
Example: The Causes of Mortality Dataset

Multiple Correspondence Analysis (MCA)
Data — Notation — Examples
Objectives
Defining Distances between Individuals and Distances between Categories
CA on the Indicator Matrix
Interpreting the Data
Implementation with FactoMineR
Addendum
Example: The Survey on the Perception of Genetically Modified Organisms
Example: The Sorting Task Dataset

Clustering
Data — Issues
Formalising the Notion of Similarity
Constructing an Indexed Hierarchy
Ward’s Method
Direct Search for Partitions: K-means Algorithm
Partitioning and Hierarchical Clustering
Clustering and Principal Component Methods
Example: The Temperature Dataset
Example: The Tea Dataset
Dividing Quantitative Variables into Classes

Appendix
Percentage of Inertia Explained by the First Component or by the First Plane
R Software

Bibliography of Software Packages

Bibliography

Index

Author Bio(s)

Editorial Reviews

Exploratory Multivariate Analysis by Example Using R provides a very good overview of the application of three multivariate analysis techniques … There is a clear exposition of the use of [R] code throughout … this book does not express the mathematical concepts in matrix form. This is clearly advantageous for those who are considering the book from an applied perspective. This, I think, is refreshing and is done well. … I therefore recommend the book to those who are interested in an introduction to these multivariate techniques. … the book does provide a solid starting point for those who are just starting out. … definitely a book to have in one’s … library.
—Eric J. Beh, Journal of Applied Statistics, June 2012

Its strength is its detailed advice on interpretation, in the context of varied examples. It is written in a pleasant and engaging style … This text is a great source of worked examples and accompanying commentary.
—John H. Maindonald, International Statistical Review (2011), 79

It is an excellent book which I would strongly recommend as a secondary text, supporting or accompanying the main text for any advanced undergraduate or graduate course in multivariate analysis. … this is a compact book with a plethora of visualizations teaching all subtleties of major data exploratory methods. It would supplement well any primary textbook in an advanced undergraduate or graduate course in multivariate analysis.
MAA Reviews, July 2011

… a truly excellent [chapter] on clustering … is an example of what upper-division undergraduate writing should aspire to. … this enjoyable book and the FactoMineR package are highly recommended for an upper-division undergraduate or beginning graduate-level course in MVA. The acid test for such a work must be whether it is likely to spark an interest in students and prepare them adequately for more detailed, serious study of the subject and this book easily passes that test.
Journal of Statistical Software, April 2011, Vol. 40

Downloads / Updates

Resource OS Platform Updated Description Instructions
Cross Platform May 20, 2013 Author web site click on http://factominer.free.fr/book/