Titlebook: Analyzing Compositional Data with R; K. Gerald van den Boogaart,Raimon Tolosana-Delgado Book 2013 Springer-Verlag Berlin Heidelberg 2013 C

通情達理

Ergots

Fundamental Concepts of Compositional Data Analysis, data analysis and this book cannot be understood without a basic knowledge of these issues and how they are represented in .. Therefore, this chapter introduces the basic geometric concepts of compositional data and of the .-package “.”: the relative nature of compositional data; how to load, repre

agonist

Distributions for Random Compositions,irichlet and the additive logistic normal (or normal on the simplex). For count compositions, the multinomial distribution and the double stochastic distribution are the reference models. The first two sections of this chapter summarize the definitions of these models, together with some other compl

cogent

Descriptive Analysis of Compositional Data,n inappropriate approach for compositional data, because the parts of a composition are intrinsically linked to each other: the dataset is multivariate in nature, not by decision of the analyst. Following the principle of working in coordinates and the golden rule of working with log ratios, we can

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采納

Multivariate Statistics, have their compositional counterparts. These are mostly applications of the principle of working in coordinates, though not always in a straightforward fashion. The definition, the corresponding tools, and the compositional interpretation of these exploratory techniques are discussed here.

Nuance

Zeroes, Missings, and Outliers, and statistical tests. Since all of these methods depend on every value of the dataset, we cannot ignore them in any task. Some ad hoc procedures are needed, with the same aims as the classical methods, but which can still be computed despite the existence of irregular data. It is necessary to . th

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預知

mitral-valve

https://doi.org/10.1007/978-3-642-36809-7Compositional data analysis; Logratio analysis; Multivariate linear regression; Proportions and percent