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Compositional Data Analysis in Practice de Michael Greenacre

Compositional Data Analysis in Practice: Chapman & Hall/CRC Interdisciplinary Statistics

Autor Michael Greenacre
en Limba Engleză Hardback – 27 iun 2018

Ne-a atras atenția Compositional Data Analysis in Practice prin abordarea sa extrem de aplicată asupra unui subiect tehnic adesea intimidant: datele care reprezintă părți dintr-un întreg (proporții, procente, concentrații). Această ediție se distinge printr-o orientare clară către execuție, punând la dispoziția cercetătorului nu doar teoria transformărilor logratio, ci și instrumentele computaționale necesare, grupate în jurul pachetului R easyCODA. Descoperim aici o structură logică ce pornește de la geometria datelor compoziționale și avansează spre modele de regresie și tehnici de clustering, abordând frontal probleme spinoase precum prezența valorilor de zero în seturile de date.

Subliniem modul în care Michael Greenacre își valorifică experiența vastă din lucrări precum Multiple Correspondence Analysis and Related Methods sau Correspondence Analysis in the Social Sciences. Autorul reușește să creeze o punte între analiza corespondențelor și datele compoziționale, oferind o perspectivă vizuală asupra biplot-urilor și reducerii dimensiunilor care lipsește adesea din manualele pur teoretice. Volumul reprezintă o alternativă viabilă la Analyzing Compositional Data with R pentru cursurile de statistică aplicată, având avantajul unei concizii remarcabile (136 de pagini) și al unui conținut vizual bogat, cu 30 de ilustrații color care facilitează interpretarea modelelor complexe. Progresia materialului este susținută de un studiu de caz final despre acizii grași, care demonstrează aplicabilitatea metodelor în biologie și ecologie marină, transformând conceptele abstracte în soluții de analiză concrete.

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Specificații

ISBN-13: 9781138316614
ISBN-10: 113831661X
Pagini: 136
Ilustrații: 30 Illustrations, color
Dimensiuni: 156 x 234 x 14 mm
Greutate: 0.45 kg
Ediția:1
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Interdisciplinary Statistics

De ce să citești această carte

Recomandăm această carte cercetătorilor din geologie, medicină sau economie care lucrează cu date relative și doresc o implementare rapidă în R. Cititorul câștigă o înțelegere practică a transformărilor logratio, esențiale pentru a evita capcanele statistice ale datelor sub formă de procente. Este un ghid concis care elimină ambiguitatea prin cod R gata de utilizare și exemple vizuale clare, fiind ideal pentru auto-studiu sau cursuri intensive.

Despre autor

Michael Greenacre este un statistician de renume, profesor la Universitatea Pompeu Fabra din Barcelona, cunoscut la nivel internațional pentru contribuțiile sale fundamentale în vizualizarea datelor multivariante. Opera sa este marcată de o preocupare constantă pentru analiza corespondențelor, temă explorată pe larg în lucrările sale anterioare publicate la CRC Press. În Compositional Data Analysis in Practice, Greenacre își adaptează expertiza în biplot-uri și metode geometrice pentru a oferi soluții inovatoare în domeniul analizei datelor compoziționale, consolidându-și reputația de autor care îmbină rigoarea matematică cu utilitatea practică.

Cuprins

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.

Recenzii

"(…This book) avoids cumbersome theoretical digressions and only presents to the reader the essential basic concepts for the application of CODA, using ratios and logratios that retain most of the original data structure and, subsequently, may lead to proper conclusions. … The simplification of the analysis and the straightforward interpretability of results is, clearly, one of the primary values of the publication. In addition, the emphasis on the general application of weights in the calculus of most of the operations and methodologies used throughout the book deserves a special mention.. … Altogether, the book and the easyCODA R package may represent a promising instrument for introducing CODA in the fat and oils field, where fatty acid compositions have been treated until now exclusively by classical multivariate techniques without considering their compositional structure. Predicting the future is risky, but the book may represent an essential instrument for CODA spreading since it represents just what many practitioners were expecting to initiate their experience in this promising new statistical field of compositional data analysis."
—A. Garrido Fernández in Gracas y Aceites – International Journal of Fats and Oils, July-September 2019
"…an interesting book, certainly controversial in some respects for scholars in the field. It has a strong data analytic focus and requires some background in multivariate analysis and biplot theory for a good understanding. It overemphasizes links to correspondence analysis at times, but is very well written and didactically nicely sliced into modules numbering exactly eight pages each. Most examples in the book are reproducible in the R environment. Finally, it will help the analyst to reflect on the use of weights, to the benefit of the analysis of compositional data."
—Jan Graffelman in the Biometrical Journal, March 2019
"This book provides a essential reference as a practical way to evaluate and interpret compositional data across a broad spectrum of disciplines in the life and natural sciences for both academia and industry. The book takes a prescribed approach starting with the definition of compositional data, the use of logratios for dimension reduction, clustering and variable selection issues along with several practical examples and a case study. The theory of compositional data analysis and computational aspects are included as Appendices.
This book can be used at the undergraduate level as part of a course in data analysis. At the graduate level, for research studies, this book is essential in understanding how to collect and interpret compositional data. Using the methods described in this book will help to avoid costly mistakes made from misinterpreting compositional data."
—Professor Eric Grunsky, Department of Earth and Environmental Sciences, University of Waterloo
Waterloo, Ontario, Canada
"Clearly the best introduction to compositional data analysis"
—Professor John Bacon-Shone

"Compositional Data Analysis in Practice is a short book by Michael Greenacre that introduces the statistician to the analysis of data partitions adding to a constant total. These data appear frequently in biology, chemistry, sociology, and other areas. ...The book is organised in to 10 chapters, each of eight pages, with a final summary, which makes it easy to read and very didactic. Easy to follow examples are used throughout the book, analyzed with R packages. This book is short, which I find appealing for a fast introduction to the topic. It covers the important practical analytical problems and provides easy solutions with example code. I recommend it for those who need to use compositional data analysis, or require a study guide for courses on the topic."
- Victor Moreno in ISCB, June 2019

"…an interesting book, certainly controversial in some respects for scholars in the field. It has a strong data analytic focus and requires some background in multivariate analysis and biplot theory for a good understanding. It overemphasizes links to correspondence analysis at times, but is very well written and didactically nicely sliced into modules numbering exactly eight pages each. Most examples in the book are reproducible in the R environment. Finally, it will help the analyst to reflect on the use of weights, to the benefit of the analysis of compositional data."
—Jan Graffelman in the Biometrical Journal, March 2019
"This book provides a essential reference as a practical way to evaluate and interpret compositional data across a broad spectrum of disciplines in the life and natural sciences for both academia and industry. The book takes a prescribed approach starting with the definition of compositional data, the use of logratios for dimension reduction, clustering and variable selection issues along with several practical examples and a case study. The theory of compositional data analysis and computational aspects are included as Appendices.
This book can be used at the undergraduate level as part of a course in data analysis. At the graduate level, for research studies, this book is essential in understanding how to collect and interpret compositional data. Using the methods described in this book will help to avoid costly mistakes made from misinterpreting compositional data."
—Professor Eric Grunsky, University of Waterloo, Ontario, Canada
"Clearly the best introduction to compositional data analysis"
—Professor John Bacon-Shone
"Compositional Data Analysis in Practice is a short book by Michael Greenacre that introduces the statistician to the analysis of data partitions adding to a constant total. These data appear frequently in biology, chemistry, sociology, and other areas. ...The book is organised in to 10 chapters, each of eight pages, with a final summary, which makes it easy to read and very didactic. Easy to follow examples are used throughout the book, analyzed with R packages. This book is short, which I find appealing for a fast introduction to the topic. It covers the important practical analytical problems and provides easy solutions with example code. I recommend it for those who need to use compositional data analysis, or require a study guide for courses on the topic."
- Victor Moreno in ISCB, June 2019

Notă biografică

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.

Descriere scurtă

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.