Applied Compositional Data Analysis - With Worked Examples in R (Hardcover, 1st ed. 2018)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Springer Series in Statistics
Release date:	November 2018
First published:	2018
Authors:	Peter Filzmoser • Karel Hron • Matthias Templ
Dimensions:	235 x 155 x 18mm (L x W x T)
Format:	Hardcover
Pages:	280
Edition:	1st ed. 2018
ISBN-13:	978-3-319-96420-1
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Social sciences > Sociology, social studies > Social research & statistics > General Books > Medicine > General issues > Public health & preventive medicine > Epidemiology & medical statistics Books > Earth & environment > Earth sciences > Geology & the lithosphere > Geochemistry Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software
LSN:	3-319-96420-8
Barcode:	9783319964201

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