Data analysis and graphics using R
Subtitle: an example-based approach
By: John Maindonald, W. John Braun.
Contributors: Braun, John, 1963-
Edition: 3rd ed.
Series: Cambridge series in statistical and probabilistic mathematics -- 10
Publisher: Cambridge University Press
Published: 2010
Place of publication: Cambridge; New York
Language: eng
Number of pages: 549
ISBN-13: 9780521762939
Subjects: R (Computer program language); Statistics -- Data processing; Statistics -- Graphic methods -- Data processing
Description: "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"--Provided by publisher.
Notes: Includes bibliographical references and indexes.
Table of contents: title: Machine generated contents note: 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.