Michael Greenacre (Universitat Pompeu Fabra, Barcelona, Spain)
Compositional Data Analysis in Practice
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Michael Greenacre (Universitat Pompeu Fabra, Barcelona, Spain)
Compositional Data Analysis in Practice
- Gebundenes Buch
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.
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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.
Produktdetails
- Produktdetails
- Chapman & Hall/CRC Interdisciplinary Statistics
- Verlag: Taylor & Francis Ltd
- Seitenzahl: 136
- Erscheinungstermin: 27. Juni 2018
- Englisch
- Abmessung: 243mm x 164mm x 14mm
- Gewicht: 414g
- ISBN-13: 9781138316614
- ISBN-10: 113831661X
- Artikelnr.: 53691902
- Chapman & Hall/CRC Interdisciplinary Statistics
- Verlag: Taylor & Francis Ltd
- Seitenzahl: 136
- Erscheinungstermin: 27. Juni 2018
- Englisch
- Abmessung: 243mm x 164mm x 14mm
- Gewicht: 414g
- ISBN-13: 9781138316614
- ISBN-10: 113831661X
- Artikelnr.: 53691902
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.
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.
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.
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.