Data Analysis and Graphics Using R

3. Auflage 2010
ISBN: 978-0-521-76293-9
Verlag: Cambridge University Press

Buch, Englisch, Band 10, 549 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 1242 g

Reihe: Cambridge Series in Statistical and Probabilistic Mathematics

Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), the book is ideal for research scientists, final-year undergraduate or graduate-level students of applied statistics, and practising statisticians. It is both for learning and for reference. This third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests.

Autoren/Hrsg.

Maindonald, John

Braun, W. John

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung

Inhaltsverzeichnis

Preface; Content - how the chapters fit together; 1. A brief introduction to R; 2. Styles of data analysis; 3. Statistical models; 4. A review of inference concepts; 5. Regression with a single predictor; 6. Multiple linear regression; 7. Exploiting the linear model framework; 8. Generalized linear models and survival analysis; 9. Time series models; 10. Multi-level models, and repeated measures; 11. Tree-based classification and regression; 12. Multivariate data exploration and discrimination; 13. Regression on principal component or discriminant scores; 14. The R system - additional topics; 15. Graphs in R; Epilogue; Index of R symbols and functions; Index of authors.

Über Autor(innen)

Braun, W. John
W. John Braun is Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario. He has collaborated with biostatisticians, biologists, psychologists, and most recently has become involved with a network of forestry researchers.

Maindonald, John
John Maindonald is Visiting Fellow at the Mathematical Sciences Institute at the Australian National University. He has collaborated extensively with scientists in a wide range of application areas, from medicine and public health to population genetics, machine learning, economic history, and forensic linguistics.