
Compositional Data Analysis in Practice
Michael Greenacre
Résumé
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.
Features
- 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, R scripts and further study material
The R package easyCODA, which accompanies this book, can be downloaded from R-Forge as follows: install.packages("easyCODA", repos="http://R-Forge.R-project.org") and will be available on CRAN soon. Notice that the R packages ca and vegan also have to be installed (from CRAN in the usual way).
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.
Caractéristiques techniques
PAPIER | |
Éditeur(s) | Taylor&francis |
Auteur(s) | Michael Greenacre |
Parution | 15/07/2018 |
Nb. de pages | 122 |
EAN13 | 9781138316430 |
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