Applied Compositional Data Analysis: With Worked Examples in R

Applied Compositional Data Analysis: With Worked Examples in R

Hardcover(1st ed. 2018)

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Product Details

ISBN-13: 9783319964201
Publisher: Springer International Publishing
Publication date: 11/04/2018
Series: Springer Series in Statistics
Edition description: 1st ed. 2018
Pages: 280
Product dimensions: 6.10(w) x 9.25(h) x (d)

About the Author

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 a lecturer 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).

Table of Contents

Preface.- Acknowledgements.- Compositional data as a methodological concept.- Analyzing compositional data using R.- Geometrical properties of compositional data.- Exploratory data analysis and visualization.- First steps for a statistical analysis.- Cluster analysis.- Principal component analysis.- Correlation analysis.- Discriminant analysis.- Regression analysis.- Methods for high-dimensional compositional data.- Compositional tables.- Preprocessing issues.- Index.-

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