Produktbild: Applied Compositional Data Analysis
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Applied Compositional Data Analysis With Worked Examples in R

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

13.11.2018

Abbildungen

XVII, 74 illus., 57 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen

Verlag

Springer

Seitenzahl

280

Maße (L/B/H)

24,1/16,4/2,3 cm

Gewicht

607 g

Auflage

1st ed. 2018

Sprache

Englisch

ISBN

978-3-319-96420-1

Beschreibung

Rezension

“Its great advantage is that it is very well written, easy to follow, very didactical, and self-contained. Its great advantage is that it is very well written, easy to follow, very didactical, and self-contained. … I would definitely recommend researchers to use this book, but they should be aware that compositional data analysis is not just based on simple transformations.” (Vera Pawlowsky-Glahn, Statistical Papers, Vol. 61, 2020)

“Its easy-to-read format and didactic layout are designed for researchers from different fields. … Applied Compositional Data Analysis is a nice book for scholars because it offers a wide spectrum of different types of statistical analysis.” (Jan Graffelman and Josep Antoni Martín-Fernández, Biometrical Journal, Vol. 62, 2020)

“The book is appropriate for graduate students with a basic statistical background as an introductory book to compositional data analysis using R as non-beginners. It can also be successfully used by PhD students, researchers and teachers requiring a consistent and through reference.” (Márta Ladányi, ISCB News, Vol. 68, December, 2019)

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

13.11.2018

Abbildungen

XVII, 74 illus., 57 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen

Verlag

Springer

Seitenzahl

280

Maße (L/B/H)

24,1/16,4/2,3 cm

Gewicht

607 g

Auflage

1st ed. 2018

Sprache

Englisch

ISBN

978-3-319-96420-1

Herstelleradresse

Springer Nature Customer Service Center GmbH
Europaplatz 3
69115 Heidelberg
DE
ProductSafety@springernature.com

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  • Produktbild: Applied Compositional Data Analysis
  • 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.-