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The R Book 2nd Edition
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The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research.
- Features full colour text and extensive graphics throughout.
- Introduces a clear structure with numbered section headings to help readers locate information more efficiently.
- Looks at the evolution of R over the past five years.
- Features a new chapter on Bayesian Analysis and Meta-Analysis.
- Presents a fully revised and updated bibliography and reference section.
- Is supported by an accompanying website allowing examples from the text to be run by the user.
Praise for the first edition:
??if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.? (The American Statistician, August 2008)
?The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book?? (Professional Pensions, July 2007)
Fler böcker av Michael J Crawley
Recensioner i media
"It is a classic that does not just sell to students during term time but has a much wider appeal ... This edition will sell really well on publication." (The Bookseller, 16 December 2011)
Bloggat om The R Book 2nd Edition
Michael Crawley is Professor at Imperial College at Silwood Park. He is a fellow of the Royal Society and author of the bestselling titles "Statistics: An Introduction using R" and Statistical "Computing: An Introduction to Data Analysis Using S-Plus."
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