Overview
- Fills the gap in the existing literature by providing a practical approach to compositional data analysis
- Presents a concise and easy-to-interpret methodology which guarantees a scale invariant analysis of data carrying relative information
- Uses the log-ratio approach, including various aspects of data processing
- Includes numerous real-world examples with implementations in R from a wide range of applications
Part of the book series: Springer Series in Statistics (SSS)
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Table of contents (13 chapters)
Keywords
- Compositional data
- Applications of compositional data analysis
- Multivariate statistical methods
- Robust statistics
- Statistical environment R
- Statistical methodology for compositional data
- R package robCompositions
- Analyzing compositional data using R
- Methods for high-dimensional compositional data
- Compositional tables
- CoDa
About this book
Reviews
“Its great advantage is that it is very well written, easy to follow, very didactical, and self-contained. Its great advantage is that it is very well written, easy to follow, very didactical, and self-contained. … I would definitely recommend researchers to use this book, but they should be aware that compositional data analysis is not just based on simple transformations.” (Vera Pawlowsky-Glahn, Statistical Papers, Vol. 61, 2020)
“Its easy-to-read format and didactic layout are designed for researchers from different fields. … Applied Compositional Data Analysis is a nice book for scholars because it offers a wide spectrum of different types of statistical analysis.” (Jan Graffelman and Josep Antoni Martín-Fernández, Biometrical Journal, Vol. 62, 2020)
“The book is appropriate for graduate students with a basic statistical background as an introductory book to compositional data analysis using R as non-beginners. It can also be successfully used by PhD students, researchers and teachers requiring a consistent and through reference.” (Márta Ladányi, ISCB News, Vol. 68, December, 2019)
Authors and Affiliations
About the authors
Peter Filzmoser is a Professor of Statistics at the Vienna University of Technology, Austria. He received his Ph.D. and postdoctoral lecture qualification from the same university. He was a Visiting Professor at Toulouse, France and Belarus. Furthermore, he has authored more than 200 research articles and several R packages and is a co-author of a book on multivariate methods in chemometrics (CRC Press, 2009) and on analyzing environmental data (Wiley, 2008).
Karel Hron is an Associate Professor at Palacký University in Olomouc, Czech Republic. He holds a Ph.D. in applied mathematics and is active in promoting his discipline. His research activities focus on statistical analysis of compositional data and multivariate statistical analysis in general. His methods and algorithms are implemented in the statistical software R. He primarily collaborates with researchers from chemometrics and environmental sciences.
Matthias Templ is alecturer at the Zurich University of Applied Sciences, Switzerland. His main research interests include computational statistics, statistical modeling and official statistics. He is author of several R packages, such as the R package sdcMicro for statistical disclosure control, the simPop package for simulation of synthetic data, the VIM package for visualization and imputation of missing values and the package robCompositions for robust analysis of compositional data. He is author of the books Statistical Simulation in Data Science with R (Packt, 2016) and Statistical Disclosure Control (Springer, 2017).
Bibliographic Information
Book Title: Applied Compositional Data Analysis
Book Subtitle: With Worked Examples in R
Authors: Peter Filzmoser, Karel Hron, Matthias Templ
Series Title: Springer Series in Statistics
DOI: https://doi.org/10.1007/978-3-319-96422-5
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2018
Hardcover ISBN: 978-3-319-96420-1Published: 13 November 2018
eBook ISBN: 978-3-319-96422-5Published: 03 November 2018
Series ISSN: 0172-7397
Series E-ISSN: 2197-568X
Edition Number: 1
Number of Pages: XVII, 280
Number of Illustrations: 17 b/w illustrations, 57 illustrations in colour
Topics: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Statistics and Computing/Statistics Programs, Statistical Theory and Methods, Geochemistry, Statistics for Life Sciences, Medicine, Health Sciences, Statistics for Social Sciences, Humanities, Law