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.
