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.
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.
Inhaltsangabe
What are compositional data, and why are they special? Geometry and visualization of compositional data. Logratio transformations. Properties and distributions of logratios. Regression models involving compositional data. Dimension reduction using logratio analysis. Clustering of compositional data. The problem of zeros, with some solutions. Simplifying the task: variable selection. Case study: Fatty acids of marine amphipods. Appendix A: Theory of compositional data analysis. Appendix B: Commented Bibliography. Appendix C: Computational examples using the R package easyCODA. Appendix D: Epilogue.
What are compositional data, and why are they special? Geometry and visualization of compositional data. Logratio transformations. Properties and distributions of logratios. Regression models involving compositional data. Dimension reduction using logratio analysis. Clustering of compositional data. The problem of zeros, with some solutions. Simplifying the task: variable selection. Case study: Fatty acids of marine amphipods. Appendix A: Theory of compositional data analysis. Appendix B: Commented Bibliography. Appendix C: Computational examples using the R package easyCODA. Appendix D: Epilogue.
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