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, 280 p. 74 illus., 57 illus. in color.

Verlag

Springer

Seitenzahl

280

Maße (B/H)

15,5/23,5 cm

Gewicht

596 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, 280 p. 74 illus., 57 illus. in color.

Verlag

Springer

Seitenzahl

280

Maße (B/H)

15,5/23,5 cm

Gewicht

596 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.-