Applied Compositional Data Analysis With Worked Examples in R /
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...
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|---|---|
| Médium: | Elektronický zdroj E-kniha |
| Jazyk: | angličtina |
| Vydáno: |
Cham :
Springer International Publishing,
2018.
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| Vydání: | 1st ed. 2018. |
| Edice: | Springer Series in Statistics,
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| Témata: | |
| ISBN: | 9783319964225 |
| ISSN: | 0172-7397 |
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| 007 | cr nn 008mamaa | ||
| 008 | 181103s2018 gw | s |||| 0|eng d | ||
| 020 | |a 9783319964225 | ||
| 024 | 7 | |a 10.1007/978-3-319-96422-5 |2 doi | |
| 035 | |a CVTIDW06890 | ||
| 040 | |a Springer-Nature |b eng |c CVTISR |e AACR2 | ||
| 041 | |a eng | ||
| 100 | 1 | |a Filzmoser, Peter. |4 aut | |
| 245 | 1 | 0 | |a Applied Compositional Data Analysis |h [electronic resource] : |b With Worked Examples in R / |c by Peter Filzmoser, Karel Hron, Matthias Templ. |
| 250 | |a 1st ed. 2018. | ||
| 260 | 1 | |a Cham : |b Springer International Publishing, |c 2018. | |
| 300 | |a XVII, 280 p. 74 illus., 57 illus. in color. |b online resource. | ||
| 490 | 1 | |a Springer Series in Statistics, |x 0172-7397 | |
| 500 | |a Mathematics and Statistics | ||
| 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.-. | |
| 516 | |a text file PDF | ||
| 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. | ||
| 650 | 0 | |a Statistics . | |
| 650 | 0 | |a Geochemistry. | |
| 856 | 4 | 0 | |u http://hanproxy.cvtisr.sk/han/cvti-ebook-springer-eisbn-978-3-319-96422-5 |y Vzdialený prístup pre registrovaných používateľov |
| 910 | |b ZE04170 | ||
| 919 | |a 978-3-319-96422-5 | ||
| 974 | |a andrea.lebedova |f Elektronické zdroje | ||
| 992 | |a SUD | ||
| 999 | |c 272813 |d 272813 | ||