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## Details

Genre/Form: | Electronic books |
---|---|

Additional Physical Format: | Print version: Greenacre, Michael J. Compositional data analysis in practice. Boca Raton, Florida : CRC Press, [2018] (DLC) 2018017447 (OCoLC)1033546829 |

Material Type: | Document, Internet resource |

Document Type: | Internet Resource, Computer File |

All Authors / Contributors: |
Michael J Greenacre |

ISBN: | 9780429849015 042984901X 9780429849022 0429849028 |

OCLC Number: | 1046068064 |

Description: | 1 online resource. |

Contents: | Cover; Half Title; Title Page; Copyright Page; Dedication; Table of Contents; Preface; 1 What are compositional data, and why are they special?; 1.1 Examples of compositional data; 1.2 Why are compositional data different from other types of data?; 1.3 Basic terminology and notation in compositional data analysis; 1.4 Basic principles of compositional data analysis; 1.5 Ratios and logratios; 2 Geometry and visualization of compositional data; 2.1 Simple graphics; 2.2 Geometry in a simplex; 2.3 Moving out of the simplex; 2.4 Distances between points in logratio space. 3 Logratio transformations3.1 Additive logratio transformations; 3.2 Centred logratio transformations; 3.3 Logratios incorporating amalgamations; 3.4 Isometric logratio transformations; 3.5 Comparison of logratios in practice; 3.6 Practical interpretation of logratios; 4 Properties and distributions of logratios; 4.1 Lognormal distribution; 4.2 Logit function; 4.3 Additive logistic normal distribution; 4.4 Logratio variances and covariances; 4.5 Testing for multivariate normality; 4.6 When logratios are not normal; 5 Regression models involving compositional data. 5.1 Visualizing ratios as a graph5.2 Using simple logratios as predictors; 5.3 Compositions as responses -- total logratio variance; 5.4 Redundancy analysis; 6 Dimension reduction using logratio analysis; 6.1 Weighted principal component analysis; 6.2 Logratio analysis; 6.3 Different biplot scaling options; 6.4 Constrained compositional biplots; 7 Clustering of compositional data; 7.1 Logratio distances between rows and between columns; 7.2 Clustering based on logratio distances; 7.3 Weighted Ward clustering; 7.4 Isometric logratio versus amalgamation balances. 8 Problem of zeros, with some solutions8.1 Zero replacement; 8.2 Sensitivity to zero replacement; 8.3 Subcompositional incoherence; 8.4 Correspondence analysis alternative; 9 Simplifying the task: variable selection; 9.1 Explaining total logratio variance; 9.2 Stepwise selection of logratios; 9.3 Parsimonious variable selection; 9.4 Amalgamation logratios as variables for selection; 9.5 Signal and noise in compositional data; 10 Case study: Fatty acids of marine amphipods; 10.1 Introduction; 10.2 Material and methods; 10.3 Results; 10.4 Discussion and conclusion. A Appendix: Theory of compositional data analysisA. 1 Basic notation; A.2 Ratios and logratios; A.3 Logratio distance; A.4 Logratio variance; A.5 Logratio analysis (LRA); A.6 Principal component analysis (PCA); A.7 Procrustes analysis; A.8 Constrained logratio analysis and redundancy analysis; A.9 Permutation tests; A.10 Weighted Ward clustering; B Appendix: Bibliography of compositional data analysis; B.1 Books; B.2 Articles; B.3 Web resources; C Appendix: Computation of compositional data analysis; C.1 Simple graphics for compositional data; C.2 Logratio transformations. |

Series Title: | Chapman & Hall/CRC interdisciplinary statistics |

Responsibility: | Michael Greenacre. |

### Abstract:

## Reviews

*Editorial reviews*

Publisher Synopsis

"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 WaterlooWaterloo, Ontario, Canada"Clearly the best introduction to compositional data analysis"-Professor John Bacon-Shone Read more...

*User-contributed reviews*