Overview
- Guides readers from the first steps of a descriptive analysis to the application of multivariate methods
- Covers the most commonly used statistical methods in scientific applications
- Includes a wealth of code snippets to obtain graphical and numerical representations of all results
- Includes supplementary material: sn.pub/extras
Part of the book series: Use R! (USE R)
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Table of contents (7 chapters)
Keywords
About this book
This book presents the statistical analysis of compositional data sets, i.e., data in percentages, proportions, concentrations, etc. 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. And, though the book primarily serves as a reference guide for the R package “compositions,” it is also a general introductory text on Compositional Data Analysis.
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. 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.
Reviews
From the reviews:
“This book offers not only the theoretical background to analyse and interpret compositional data, but also the R support and guidance for the compositions package. The book is organised in 7 chapters. … The book is built in an accessible manner for undergraduates and postgraduates alike and offers an all in one overview of the analysis of compositional data in R.” (Irina Ioana Mohorianu, zbMATH, Vol. 1276, 2014)Authors and Affiliations
About the authors
Prof. K. Gerald van den Boogaart is the head of the Modeling and Valuation Department of the Helmholtz Institute Freiberg for Resource Technology and holds the chair of Applied Stochastic at TU Bergakademie Freiberg. Previously he was a professor of statistics in Greifswald. Throughout his career he has worked closely with various geoscientists, biologists and engineers.
Raimon Tolosana-Delgado is an Engineering Geologist and holds an MSc in Environmental Sciences. He received his PhD from the University of Girona, recognized as one of the world’s leading centers for Compositional Data Analysis. He has worked, mainly in the fields of sedimentology and oceanography, at several universities in Spain and Germany. He is currently a fellow researcher at the Helmholtz Institute Freiberg for Resource Technology.
Bibliographic Information
Book Title: Analyzing Compositional Data with R
Authors: K. Gerald van den Boogaart, Raimon Tolosana-Delgado
Series Title: Use R!
DOI: https://doi.org/10.1007/978-3-642-36809-7
Publisher: Springer Berlin, Heidelberg
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2013
Softcover ISBN: 978-3-642-36808-0Published: 09 July 2013
eBook ISBN: 978-3-642-36809-7Published: 29 June 2013
Series ISSN: 2197-5736
Series E-ISSN: 2197-5744
Edition Number: 1
Number of Pages: XV, 258
Number of Illustrations: 42 b/w illustrations, 20 illustrations in colour
Topics: Statistical Theory and Methods, Statistics and Computing/Statistics Programs, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Geochemistry