| Description |
1 online resource (xv, 258 pages) : illustrations (some color) |
| Series |
Use R! |
|
Use R!
|
| Contents |
Introduction -- What are compositional data? -- Getting started with R -- References -- Fundamental concepts of compositional data analysis -- A practical view to compositional concepts -- Principles of compositional analysis -- Elementary compositional graphics -- Multivariate scales -- The Aitchison simplex -- References -- Distributions for random compositions -- Continuous distribution models -- Models for count compositions -- Relations between distributions -- References -- Descriptive analysis of compositional data -- Descriptive statistics -- Exploring marginals -- Exploring projections -- References -- Linear models for compositions -- Introduction -- Compositions as independent variables -- Compositions as dependent variables -- Compositions as both dependent and independent variables -- Advanced considerations -- References -- Multivariate statistics -- Principal component analysis: exploring codependence -- Cluster analysis: detecting natural groups -- Discriminant analysis -- Other multivariate techniques -- References -- Zeros, missings, and outliers -- Descriptive analysis with and of missings -- Working with missing values -- Outliers -- Descriptive analysis of outliers -- Working with outliers -- References |
| Summary |
This book presents the statistical analysis of compositional data sets, i.e., data in percentages, proportions, concentrations, etc. The subject is covered from its grounding principles to the practical use in descriptive exploratory analysis, robust linear models and advanced multivariate statistical methods, including zeros and missing values, and paying special attention to data visualization and model display issues. Many illustrated examples and code chunks guide the reader into their modeling and interpretation. And, though the book primarily serves as a reference guide for the R package "compositions," it is also a general introductory text on Compositional Data Analysis. Awareness of their special characteristics spread in the Geosciences in the early sixties, but a strategy for properly dealing with them was not available until the works of Aitchison in the eighties. Since then, research has expanded our understanding of their theoretical principles and the potentials and limitations of their interpretation. This is the first comprehensive textbook addressing these issues, as well as their practical implications with regard to software. The book is intended for scientists interested in statistically analyzing their compositional data. The subject enjoys relatively broad awareness in the geosciences and environmental sciences, but the spectrum of recent applications also covers areas like medicine, official statistics, and economics. Readers should be familiar with basic univariate and multivariate statistics. Knowledge of R is recommended but not required, as the book is self-contained |
| Analysis |
Statistics |
|
Mathematical statistics |
|
Statistical Theory and Methods |
|
Statistics and Computing/Statistics Programs |
|
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences |
|
toegepaste statistiek |
|
applied statistics |
|
statistiek |
|
geochemie |
|
geochemistry |
|
statistische analyse |
|
statistical analysis |
|
Statistics (General) |
|
Statistiek (algemeen) |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (SpringerLink, viewed July 17, 2013) |
| Subject |
Mathematical statistics -- Data processing.
|
|
R (Computer program language)
|
|
MATHEMATICS -- Probability & Statistics -- General.
|
|
Estadística matemática -- Datos-Tratamiento
|
|
R (Lenguaje de programación)
|
|
Mathematical statistics -- Data processing
|
|
R (Computer program language)
|
| Form |
Electronic book
|
| Author |
Tolosana-Delgado, Raimon.
|
| ISBN |
9783642368097 |
|
3642368093 |
|
