Compositional Data Analysis in Practice: 1st Edition (Paperback) book cover

Compositional Data Analysis in Practice

1st Edition

By Michael Greenacre

Chapman and Hall/CRC

122 pages | 46 Color Illus.

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pub: 2018-08-01
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Description

Compositional Data Analysis in Practice is a user-oriented practical guide to the analysis of data with the property of a constant sum, for example percentages adding up to 100%. Compositional data can give misleading results if regular statistical methods are applied, and are best analysed by first transforming them to logarithms of ratios. This book explains how this transformation affects the analysis, results and interpretation of this very special type of data. All aspects of compositional data analysis are considered: visualization, modelling, dimension-reduction, clustering and variable selection, with many examples in the fields of food science, archaeology, sociology and biochemistry, and a final chapter containing a complete case study using fatty acid compositions in ecology. The applicability of these methods extends to other fields such as linguistics, geochemistry, marketing, economics and finance.

R Software

The following repository contains data files and R scripts from the book https://github.com/michaelgreenacre/CODAinPractice . The R package easyCODA, which accompanies this book, is available on CRAN -- note that you should have version 0.25 or higher. The latest version of the package will always be available on R-Forge and can be installed from R with this instruction: install.packages("easyCODA", repos="http://R-Forge.R-project.org").

Reviews

"…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

Table of Contents

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

 

About the Author

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.

About the Series

Chapman & Hall/CRC Interdisciplinary Statistics

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Subject Categories

BISAC Subject Codes/Headings:
BUS061000
BUSINESS & ECONOMICS / Statistics
MAT029000
MATHEMATICS / Probability & Statistics / General