Compositional Data Analysis in Practice

Omtale

“This book would make for a wonderful one semester course in applied multivariate statistics, suitable for upper-level undergraduates who are masters students in data science.”~Trevor Hastie, The John A. Overdeck Professor, Professor of Statistics and Biomedical Data Science (emeritus), Stanford University, USA“This book is a small gem that will find its place among those who are interested in learning about this important topic from a master of clarity and exposition…Graduate students and applied researchers, across the quantitative sciences who encounter proportional data, will find this an authoritative yet accessible guide.”~Michael Friendly, York University, Canada“This book is an inspiring text for applied scientists and graduate students with backgrounds in multivariate analysis and are interested in mastering the analysis of compositional data.”~Jan Graffelman, Professor, Technical University of Catalonia, SpainCompositional data – proportions, percentages, and any data that sum to a constant – appear across virtually every scientific discipline, from geochemistry and ecology to political science and genomics. This thoroughly revised and extended second edition continues to provide a simple and accessible treatment of compositional data analysis (CoDA) in a pedagogical format. Key Features of the Second Edition:60% more material than the first editionAdditional chapters on weighting of compositional parts, the analysis of complete compositions, the analysis of shape, high-dimensional genomic compositions, as well as two more case studiesPractical, example-driven approach, using 13 different data sets across different disciplinesUnique eight-pages-per-chapter format to facilitate both self-study and teaching of the subjectRevision of the easyCODA R package to include new features such as amalgamation clustering and variable selection in unsupervised and supervised learningThe book is a valuable resource for practitioners in all applied areas where compositional data are encountered, including bio- and geochemistry, food research, genomics, ecology, linguistics, marketing, financial budgets in economics, and time-use in behavioural research and epidemiology.