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Applied compositional data analysisw...

Filzmoser, Peter.

Applied compositional data analysiswith worked examples in R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applied compositional data analysisby Peter Filzmoser, Karel Hron, Matthias Templ.
Reminder of title:	with worked examples in R /
Author:	Filzmoser, Peter.
other author:	Hron, Karel.
Published:	Cham :Springer International Publishing :2018.
Description:	xvii, 280 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Mathematical statistics.
Online resource:	https://doi.org/10.1007/978-3-319-96422-5
ISBN:	9783319964225$q(electronic bk.)

Applied compositional data analysiswith worked examples in R /
Filzmoser, Peter.

Applied compositional data analysiswith worked examples in R /[electronic resource] :by Peter Filzmoser, Karel Hron, Matthias Templ. - Cham :Springer International Publishing :2018. - xvii, 280 p. :ill. (some col.), digital ;24 cm. - Springer series in statistics,0172-7397. - Springer series in statistics..

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.

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.

ISBN: 9783319964225$q(electronic bk.)

Standard No.: 10.1007/978-3-319-96422-5doiSubjects--Topical Terms:

181877
Mathematical statistics.

LC Class. No.: QA276 / .F559 2018

Dewey Class. No.: 519.5

Applied compositional data analysiswith worked examples in R /
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505 0 $a 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.
520 $a 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.
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