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: Commented Bibliography. Appendix C: Computational examples using the R package easyCODA. Appendix D: 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 Fernández 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
"…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, University of 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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