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  1. Analyzing Compositional Data with R
  2. Analyzing Compositional Data with R | K. Gerald van den Boogaart | Springer
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Analyzing Compositional Data with R

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For Instructors Request Inspection Copy. 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.

Analyzing Compositional Data with R | K. Gerald van den Boogaart | Springer

The applicability of these methods extends to other fields such as linguistics, geochemistry, marketing, economics and finance. The latest version of the package will always be available on R-Forge and can be installed from R with this instruction: install. 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.

Exploratory Analysis of Biological Data using R Session 1

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. 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.

  1. Analyzing Compositional Data With R | Regression Analysis | Statistical Analysis.
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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.

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. We provide complimentary e-inspection copies of primary textbooks to instructors considering our books for course adoption.


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Preview this Book. Add to Wish List. Authors: van den Boogaart , K. Gerald, Tolosana-Delgado , Raimon. This book presents the statistical analysis of compositional data sets, i. The subject is covered from its grounding principles to the practical use in descriptive exploratory analysis, robust linear models and advanced multivariate statistical methods, including zeros and missing values, and paying special attention to data visualization and model display issues.

Many illustrated examples and code chunks guide the reader into their modeling and interpretation. Awareness of their special characteristics spread in the Geosciences in the early sixties, but a strategy for properly dealing with them was not available until the works of Aitchison in the eighties.

Phyloseq import qiime2

Since then, research has expanded our understanding of their theoretical principles and the potentials and limitations of their interpretation. This is the first comprehensive textbook addressing these issues, as well as their practical implications with regard to software. The book is intended for scientists interested in statistically analyzing their compositional data. The subject enjoys relatively broad awareness in the geosciences and environmental sciences, but the spectrum of recent applications also covers areas like medicine, official statistics, and economics.

Readers should be familiar with basic univariate and multivariate statistics. Knowledge of R is recommended but not required, as the book is self-contained.