Data Analysis and Graphics Using R

Starting with an introduction to R, covering standard regression methods, then presenting more advanced topics, Data Analysis and Graphics Using R 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, Data Analysis and Graphics Using R 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.

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