Titlebook: Applied Compositional Data Analysis; With Worked Examples Peter Filzmoser,Karel Hron,Matthias Templ Book 2018 Springer Nature Switzerland A

特征

https://doi.org/10.1007/978-3-319-96422-5Compositional data; Applications of compositional data analysis; Multivariate statistical methods; Robu

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織布機(jī)

arbovirus

Springer Series in Statisticshttp://image.papertrans.cn/a/image/159681.jpg

異常

Chivalrous

https://doi.org/10.1007/978-3-540-92723-5iscriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors, (robust) Anderson-Darling normality tests for compositional data, as well as functions to express compositional data in coordinates.

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Analyzing Compositional Data Using R,iscriminant analysis for compositional data (Fisher rule), robust regression with compositional predictors, (robust) Anderson-Darling normality tests for compositional data, as well as functions to express compositional data in coordinates.

殖民地

Book 2018stical 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

ethnology

Compositional Data as a Methodological Concept,spectively). Nevertheless, from a practical perspective it is much more intuitive to consider them as observations carrying relative information, where proportions stand just for one possible representation. Equivalently, all relevant information in compositional data is contained in ratios between

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Analyzing Compositional Data Using R,a brief introduction to some functionalities of . is given. This introduction does not replace a general introduction to ., but it provides the background that is necessary to understand the examples and the . code in the book..The methods explained in this book are exclusively available in the . pa