Advertisement

Applied Compositional Data Analysis

With Worked Examples in R

  • Peter Filzmoser
  • Karel Hron
  • Matthias Templ

Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Peter Filzmoser, Karel Hron, Matthias Templ
    Pages 1-16
  3. Peter Filzmoser, Karel Hron, Matthias Templ
    Pages 17-34
  4. Peter Filzmoser, Karel Hron, Matthias Templ
    Pages 35-68
  5. Peter Filzmoser, Karel Hron, Matthias Templ
    Pages 69-83
  6. Peter Filzmoser, Karel Hron, Matthias Templ
    Pages 85-106
  7. Peter Filzmoser, Karel Hron, Matthias Templ
    Pages 107-130
  8. Peter Filzmoser, Karel Hron, Matthias Templ
    Pages 131-148
  9. Peter Filzmoser, Karel Hron, Matthias Templ
    Pages 149-162
  10. Peter Filzmoser, Karel Hron, Matthias Templ
    Pages 163-179
  11. Peter Filzmoser, Karel Hron, Matthias Templ
    Pages 181-205
  12. Peter Filzmoser, Karel Hron, Matthias Templ
    Pages 207-225
  13. Peter Filzmoser, Karel Hron, Matthias Templ
    Pages 227-243
  14. Peter Filzmoser, Karel Hron, Matthias Templ
    Pages 245-272
  15. Back Matter
    Pages 273-280

About this book

Introduction

This book presents the statistical analysis of compositional data using the log-ratio approach. It includes a wide range of classical and robust statistical methods adapted for compositional data analysis, such as supervised and unsupervised methods like PCA, correlation analysis, classification and regression. In addition, it considers special data structures like high-dimensional compositions and compositional tables. The methodology introduced is also frequently compared to methods which ignore the specific nature of compositional data. It focuses on practical aspects of compositional data analysis rather than on detailed theoretical derivations, thus issues like graphical visualization and preprocessing (treatment of missing values, zeros, outliers and similar artifacts) form an important part of the book. Since it is primarily intended for researchers and students from applied fields like geochemistry, chemometrics, biology and natural sciences, economics, and social sciences, all the proposed methods are accompanied by worked-out examples in R using the package robCompositions.

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

Authors and affiliations

  • Peter Filzmoser
    • 1
  • Karel Hron
    • 2
  • Matthias Templ
    • 3
  1. 1.Institute of Statistics and Mathematical Methods in EconomicsTU WienViennaAustria
  2. 2.Department of Mathematical Analysis and Applications of MathematicsPalacký University OlomoucOlomoucCzech Republic
  3. 3.Institute of Data Analysis and Process DesignZHAW Zurich University of Applied SciencesWinterthurSwitzerland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-96422-5
  • Copyright Information Springer Nature Switzerland AG 2018
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-96420-1
  • Online ISBN 978-3-319-96422-5
  • Series Print ISSN 0172-7397
  • Series Online ISSN 2197-568X
  • Buy this book on publisher's site
Industry Sectors
Pharma
Materials & Steel
Biotechnology
Aerospace