Overview
- Fills a void in the literature for how to do PCA and CA with R, a wildly popular and open source software
- All analyses use R packages, including a package created for this book, and examples provide all data and code
- The "how to" approach used here can be used in courses and self-learning
Part of the book series: SpringerBriefs in Statistics (BRIEFSSTATIST)
Keywords
- Correspondence Analysis with R
- Data Mining with R
- Multiple Correspondence Analysis
- Multivariate Analysis
- Principal Component Analysis with R
- R Software
About this book
With the right R packages, R is uniquely suited to perform Principal Component Analysis (PCA), Correspondence Analysis (CA), Multiple Correspondence Analysis (MCA), and metric multidimensional scaling (MMDS). The analyses depicted in this book use several packages specially developed for theses analyses and include (among others): the ExPosition suite, FactoMiner , ade4, and ca. The authors present each technique with one or several small examples that demonstrate how to enter the data, perform the standard analyses, and obtain professional quality graphics. Through explanations of the major options for how to carry out each method, readers can tailor the content of this book to their particular goals. Explanations include the effects of using particular packages. ExPosition is a great choice for the methods as it was written specifically for this book. However, options abound and are illustrated within unique scenarios. The first chapter includes installation of the packages. At the end of the book, a short appendix presents critical mathematical material for readers who want to go deeper into the theory.
Authors and Affiliations
About the authors
Hervé Abdi is currently a full professor in the School of Behavioral and Brain Sciences at the University of Texas at Dallas and is the author or co-author of more than 250 publications (including 12 books). His recent work is concerned with face and person perception, odor perception, and with computational modeling of these processes. He is also developing statistical techniques to analyze the structure of large data sets as found, for example, in Genomics, brain imaging, and sensory evaluation (e.g., principal component analysis, correspondence analysis, Partial Least Square methods, STATIS, DISTATIS, discriminant correspondence analysis, multiple factor analysis, multi-table analysis, and additive tree representations). He is co-author (with Derek Beaton) of several R packages implementing these techniques. He teaches or has taught classes in cognition, computational modeling, experimental design, multivariate statistics, and the analysis of brain imaging data.
Derek Beaton has a background in computer science and is currently working towards his PhD in Cognition and Neuroscience under his advisor, Dr. Hervé Abdi. Derek's interests are in developing new statistical approaches to better understand the contributions of genetics to brain and behavior. Recently, Derek was awarded a National Institutes of Health Ruth Kirschstein F31 fellowship via National Institute of Drug Abuse. His fellowship (co-sponsored by Drs. Hervé Abdi and Francesca Filbey) aims to reveal the genetic contributions to substance abuse and related traits. He is the main author of several R packages implementing the techniques described in this book.
Bibliographic Information
Book Title: Principal Component and Correspondence Analyses Using R
Authors: Hervé Abdi, Derek Beaton
Series Title: SpringerBriefs in Statistics
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2025
Softcover ISBN: 978-3-319-09255-3Due: 25 May 2025
eBook ISBN: 978-3-319-09256-0Due: 25 May 2025
Series ISSN: 2191-544X
Series E-ISSN: 2191-5458
Edition Number: 1
Number of Pages: X, 110
Number of Illustrations: 30 b/w illustrations, 10 illustrations in colour