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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.
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