Exploratory Multivariate Analysis by Example Using R, Second Edition

by François Husson (contributor)

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.

262 pages, ebook

Published May 8, 2017

Reviews

[Terran M · Rating 2 out of 5]

The explanations in this book are terse and somewhat opaque. It comes across like the authors are trying their best to explain things without rigorous mathematics, but are the kind of people who think internally in formal math and don't really know any other way to explain things, so their attempt falls short. It provides neither the math which would explain the concepts to those who are mathematically inclined nor the words which would explain it to those who are not. The book presents itself as if principal components analysis is a basic technique to be introduced in the first chapter, but if you don't already understand what PCA does, I think you are unlikely to learn it from this explanation, and it's a required building block for understanding the other material.

Surprisingly, the online videos by the same author are much better than the book! I would recommend those instead for learning about correspondence analysis. If you want a general explanation of techniques such as PCA, I recommend the Hastie and Tibshirani books instead (or first), which are better written. If you want a book on correspondence analysis, I preferred Greenacre.