Data Analysis and Graphics Using R
Data Analysis and Graphics Using R
Join the revolution ignited by the ground-breaking R system! Starting with an introduction to R, covering standard regression methods, then presenting more advanced topics, this book guides users through the practical and powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display and interpretation of data. The many worked examples, taken from real-world research, are accompanied by commentary on what is done and why. A website provides computer code and data sets, allowing readers to reproduce all analyses. Updates and solutions to selected exercises are also available. Assuming only basic statistical knowledge, 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 revised edition reflects changes in R since 2003 and has new material on survival analysis, random coefficient models, and the handling of high-dimensional data.
- Practical, hands-on, example-based approach deals with real-world issues
- Extensive use of graphs for exploration of data and interpretation of analyses
- R code, data sets, updates and exercise solutions, all provided on companion website
Reviews & endorsements
From reviews of previous edition: 'I would strongly recommend the book to scientists who have already had a regression or a linear models course and who wish to learn to use R … an easy-to-read and an understandable reference on the use of R for practical data analysis.' R News
From reviews of previous edition: 'This book does an excellent job of describing the basics of a variety of statistical tools, both classical and modern, through examples from a wide variety of disciplines … the book's writing style is very readable, with clear explanations and precise introductions of all topics and terminology … the book also provides a wealth of examples from various physical and social sciences, engineering, and medicine that have been effectively chosen to illustrate not only the basics of the statistical methods, but also some of the interesting subtleties of the analyses that may require careful interpretation and discussion … I believe that they have … created a readable book that is rich with clear explanations and illustrative examples of the capability of a diverse set of tools. The packaging of the material with the R language is natural, and the extensive web pages of resources complement the book's usefulness for a road audience of statisticians and practitioners.' Biometrics
From reviews of previous edition: 'This book does an excellent job of describing the basics of a variety of statistical tools, both classical and modern, through examples from a wide variety of disciplines … With its focus on ideas and concepts, rather than an extensive formula-based presentation, the book finds a nice balance between discussing statistical concepts and teaching the basics of the freely-available statistical package R … a readable book that is rich with clear explanations and illustrative examples of the capability of a diverse set of tools. The packaging of the material with the R language is natural, and the extensive web pages of resources complement the book's usefulness for a broad audience of statisticians and practitioners.' Journal of the American Statistical Association
'…a very useful book that can be recommended for applies statisticians and other scientists that want to use R for data analysis, and as a textbook for an applied statistics course using R.' Journal of Applied Statistics
'… an excellent intermediate-level text … Though a bit more terse than Dalgaard's Introductory Statistics with R, Maindonald and Braun's exposition of the R language is nonetheless first rate.' Steve Miller, DM Review Online
Product details
April 2007Adobe eBook Reader
9780511247941
0 pages
0kg
12 colour illus. 50 tables 150 exercises
This ISBN is for an eBook version which is distributed on our behalf by a third party.
Table of Contents
- Preface
- 1. A brief introduction to R
- 2. Styles of data analysis
- 3. Statistical models
- 4. An introduction to formal inference
- 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
- Epilogue - models
- References
- Index of R symbols and functions
- Index of terms
- Index of names.
