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.
- A unique didactic format, where each chapter has exactly eight pages of study material, many illustrative figures, and an end-of-chapter summary
- An approach aimed at students and applied researchers, gathering the mathematical aspects in a compact theoretical appendix
- Numerous examples from a variety of disciplines
- A computational appendix that documents the easyCODA package for R developed by the author, making it possible for readers to reproduce the results
- A supporting website with data sets, Rillegalscripts and further study material
|Compositional data are quantitative deillegalscriptions of the parts of some whole, conveying exclusively relative information. Examples are found in various fields, including geology, medicine, chemistry, agriculture, economics, social science, etc. This concise book presents a very applied introduction to compositional data analysis, focussing on the use of R for analysis. It includes lots of real examples, code snippets, and colour figures, to illustrate the methods.
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.
"...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
Klappentext
This concise book presents a very applied introduction to compositional data analysis, focussing on the use of R for analysis. It includes lots of real examples, code snippets, and colour figures, to illustrate the methods.
