Hardback : $238.00
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.
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.
Preface Principal Component Analysis (PCA) Correspondence Analysis (CA) Multiple Correspondence Analysis (MCA) Clustering Visualisation Appendix
Francois Husson, Sebastien Le, Jérôme Pagès
"While the book has some of the clearest geometric explanations
written on the topic, in terms of inertia possessed by clouds of
individuals and variables, its primary function is to operate as a
step-by-step walk through on how to visualize, analyze and portray
the results of analyses in R. This is accomplished via
thought-provoking examples, ranging from wine ratings, decathlons
to high-dimensional text-mining and genomic breeding. Data and code
are available online, enabling fast cut-and-paste
implementation…the book makes an excellent self-tutorial or
teaching aid for the whole gamut of students and researchers
working in applied fields. The authors are to be congratulated for
their contribution to making the implementation of complex analyses
ideas simple and implementable in practice."
—Donna Ankherst, in Biometrics, September 2018"In the days of "big
data" every researcher should be able to summarize and explain
multivariate data sets. The purpose of "Exploratory Multivariate
Analysis by Example using R" is to provide the practitioner with a
sound understanding of, and the tools to apply, an array of
multivariate technique (including Principal Components,
Correspondence Analysis, and Clustering). The focus is on
descriptive techniques, whose purpose is to explore the data from
different perspectives, trying to find patterns, but without going
into the realm of inferential statistics, with its formal tests of
hypotheses, confidence intervals and other more advanced topics.
This seems to be the right choice for the audience of
non-statisticians to whom the book is directed. The second edition
of the book includes a more extensive treatment of missing data and
a new chapter on multivariate data visualization - both of which I
consider very welcome additions.
In summary, I consider "Exploratory Multivariate Analysis by
Example using R" to be a good introduction, with an applied slant,
to the fundamental multivariate techni
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