Compositional Data Analysis in Practice
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€173.60
A01=Michael Greenacre
Additive Logratio Transformation
Additive Logratios
advanced compositional data analysis techniques
Aitchison Geometry
Author_Michael Greenacre
biological data interpretation
Box Cox Power Transformation
Category=JMB
Category=PBT
Category=UFM
CLR
CLRs
clustering
Coda
Compositional Data
Compositional Data Analysis
Compositional Data Set
Compositional Parts
Constant Sum Constraint
correspondence analysis
Data Set
dimension reduction
environmental data statistics
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
ILR
Im En
John Aitchison
Logratio Transformations
Marine Biochemistry
Morphometric Measurements
multivariate analysis
Multivariate Normal
PCA Dimension
Positional Data Set
Principal Coordinates
quantitative research methods
R programming for scientists
Ratio PUFA
RDA
relative frequency
statistical modeling
visualization
Product details
- ISBN 9781138316614
- Weight: 453g
- Dimensions: 156 x 234mm
- Publication Date: 27 Jun 2018
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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Compositional data are quantitative descriptions of the parts of some whole, conveying exclusively relative information. Examples are found in various fields, including geology, medicine, chemistry, agriculture, economics, social science, etc. This concise book presents a very applied introduction to compositional data analysis, focussing on the use of R for analysis. It includes lots of real examples, code snippets, and colour figures, to illustrate the methods.
Michael Greenacre is Professor of Statistics at the Universitat Pompeu Fabra, Barcelona, Spain, where he teaches a course, amongst others, on Data Visualization. He has authored and co-edited nine books and 80 journal articles and book chapters, mostly on correspondence analysis, the latest being Correspondence Analysis in Practice (Third Edition) in 2016. He has given short courses in fifteen countries to environmental scientists, sociologists, data scientists and marketing professionals, and has specialized in statistics in ecology and social science.
