The R Book

by Crawley, Michael J.

• ISBN13: 9780470973929
• ISBN10: 0470973927
• Edition: 2nd
• Format: Hardcover
• Copyright: 2012-12-26
• Publisher: Wiley

Summary

The R language is recognized as one of the most powerful and flexible statistical software packages, and it enables the user to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R is becoming essential both to carry out research and to understand it, as more and more people present their results in the context of R. This edition introduces the advantages of the R environment, in a user-friendly format, to beginners and intermediate users in a range of disciplines, from science and engineering to medicine and economics. The format enables it to be either read as a text, or dipped-into as a reference manual. The early chapters assume no background in statistics or computing, and introduce the reader to the basic concepts involved. In this way the reader is introduced to the assumptions that lie behind the tests, fostering a critical approach to statistical modeling. These early chapters have been thoroughly updated to take account of the way language has evolved since the publication of the first edition. Subsequent chapters examine more advanced topics, cementing what is learnt in the opening chapters, as well as benefiting more intermediate readers. Throughout the book, the reader's experience is furthered by practical guidance and the inclusion of numerous worked examples.

Author Biography

Michael J. Crawley FRS, Department of Biological Sciences, Imperial College of Science, Technology and Medicine
Author of three bestselling Wiley statistics titles and five life science books.

Table of Contents

Preface vii

1 Getting Started 1

2 Essentials of the R Language 9

3 Data Input 97

4 Dataframes 107

5 Graphics 135

6 Tables 183

7 Mathematics 195

8 Classical Tests 279

9 Statistical Modelling 323

10 Regression 387

11 Analysis of Variance 449

12 Analysis of Covariance 489

13 Generalized Linear Models 511

14 Count Data 527

15 Count Data in Tables 549

16 Proportion Data 569

17 Binary Response Variables 593

18 Generalized Additive Models 611

19 Mixed-Effects Models 627

20 Non-linear Regression 661

21 Meta-analysis xxx

22 Bayesian statistics xxx

23 Tree Models 685

24 Time Series Analysis 701

25 Multivariate Statistics 731

26 Spatial Statistics 749

27 Survival Analysis 787

28 Simulation Models 811

29 Changing the Look of Graphics 827

References and Further Reading 873

Index 877s