What are compositional data, and why are they special?
Geometry and visualization of compositional data.
Logratio transformations.
Properties and distributions of logratios.
Regression models involving compositional data.
Dimension reduction using logratio analysis.
Clustering of compositional data.
The problem of zeros, with some solutions.
Simplifying the task: variable selection.
Case study: Fatty acids of marine amphipods.
Appendix A: Theory of compositional data analysis.
Appendix B Bibliography of compositional data analysis
Appendix C Computation of compositional data analysis
Appendix D Glossary of terms
Appendix E Epilogue
Michael Greenacre is Professor of Statistics at the Universitat Pompeu Fabra, Barcelona, Spain, where he teaches a course, amongst others, on Data Visualization. He has authored and co-edited nine books and 80 journal articles and book chapters, mostly on correspondence analysis, the latest being Correspondence Analysis in Practice (Third Edition) in 2016. He has given short courses in fifteen countries to environmental scientists, sociologists, data scientists and marketing professionals, and has specialized in statistics in ecology and social science.
"(...This book) avoids cumbersome theoretical digressions and
only presents to the reader the essential basic concepts for the
application of CODA, using ratios and logratios that retain most of
the original data structure and, subsequently, may lead to proper
conclusions. ... The simplification of the analysis and the
straightforward interpretability of results is, clearly, one of the
primary values of the publication. In addition, the emphasis on the
general application of weights in the calculus of most of the
operations and methodologies used throughout the book deserves a
special mention.. ... Altogether, the book and the easyCODA R
package may represent a promising instrument for introducing CODA
in the fat and oils field, where fatty acid compositions have been
treated until now exclusively by classical multivariate techniques
without considering their compositional structure. Predicting the
future is risky, but the book may represent an essential instrument
for CODA spreading since it represents just what many practitioners
were expecting to initiate their experience in this promising new
statistical field of compositional data analysis."
-A. Garrido Fernandez in Gracas y Aceites - International
Journal of Fats and Oils, July-September 2019
"...an interesting book, certainly controversial in some
respects for scholars in the field. It has a strong data analytic
focus and requires some background in multivariate analysis and
biplot theory for a good understanding. It overemphasizes links to
correspondence analysis at times, but is very well written and
didactically nicely sliced into modules numbering exactly eight
pages each. Most examples in the book are reproducible in the R
environment. Finally, it will help the analyst to reflect on the
use of weights, to the benefit of the analysis of compositional
data."
-Jan Graffelman in the Biometrical Journal, March 2019
"This book provides a essential reference as a practical way to evaluate and interpret compositional data across a broad spectrum of disciplines in the life and natural sciences for both academia and industry. The book takes a prescribed approach starting with the definition of compositional data, the use of logratios for dimension reduction, clustering and variable selection issues along with several practical examples and a case study. The theory of compositional data analysis and computational aspects are included as Appendices.
This book can be used at the undergraduate level as part of a
course in data analysis. At the graduate level, for research
studies, this book is essential in understanding how to collect and
interpret compositional data. Using the methods described in this
book will help to avoid costly mistakes made from misinterpreting
compositional data."
-Professor Eric Grunsky, Department of Earth and
Environmental Sciences, University of Waterloo
Waterloo, Ontario, Canada
"Clearly the best introduction to compositional data
analysis"
-Professor John Bacon-Shone
"Compositional Data Analysis in Practice is a short book by
Michael Greenacre that introduces the statistician to the analysis
of data partitions adding to a constant total. These data appear
frequently in biology, chemistry, sociology, and other areas.
...The book is organised in to 10 chapters, each of eight pages,
with a final summary, which makes it easy to read and very
didactic. Easy to follow examples are used throughout the book,
analyzed with R packages. This book is short, which I find
appealing for a fast introduction to the topic. It covers the
important practical analytical problems and provides easy solutions
with example code. I recommend it for those who need to use
compositional data analysis, or require a study guide for courses
on the topic."
- Victor Moreno in ISCB, June 2019
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