It is difficult to imagine that the statistical analysis of
compositional data has been a major issue of concern for more than
100 years. It is even more difficult to realize that so many
statisticians and users of statistics are unaware of the particular
problems affecting compositional data, as well as their solutions.
The issue of “spurious correlation”, as the situation was phrased
by Karl Pearson back in 1897, affects all data that measures parts
of some whole, such as percentages, proportions, ppm and ppb. Such
measurements are present in all fields of science, ranging from
geology, biology, environmental sciences, forensic sciences,
medicine and hydrology.
This book presents the history and development of compositional
data analysis along with Aitchison’s log-ratio approach.
Compositional Data Analysis describes the state of the art
both in theoretical fields as well as applications in the different
fields of science.
Key Features:
* Reflects the state-of-the-art in compositional data
analysis.
* Gives an overview of the historical development of
compositional data analysis, as well as basic concepts and
procedures.
* Looks at advances in algebra and calculus on the simplex.
* Presents applications in different fields of science,
including, genomics, ecology, biology, geochemistry, planetology,
chemistry and economics.
* Explores connections to correspondence analysis and the
Dirichlet distribution.
* Presents a summary of three available software packages for
compositional data analysis.
* Supported by an accompanying website featuring R code.
Applied scientists working on compositional data analysis in any
field of science, both in academia and professionals will benefit
from this book, along with graduate students in any field of
science working with compositional data.
关于作者
Vera Pawlowsky-Glahn, Department of Computer Science and Applied Mathematics, University of Girona, Spain.
Antonella Buccianti, Department of Earth Sciences, University of Florence, Italy.
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