Modern Applied Statistics with S

1. Front Matter

   Pages i-xi

   PDF

2. Introduction

   • W. N. Venables, B. D. Ripley

   Pages 1-12

3. Data Manipulation

   • W. N. Venables, B. D. Ripley

   Pages 13-39

4. The S Language

   • W. N. Venables, B. D. Ripley

   Pages 41-68

5. Graphics

   • W. N. Venables, B. D. Ripley

   Pages 69-105

6. Univariate Statistics

   • W. N. Venables, B. D. Ripley

   Pages 107-138

7. Linear Statistical Models

   • W. N. Venables, B. D. Ripley

   Pages 139-181

8. Generalized Linear Models

   • W. N. Venables, B. D. Ripley

   Pages 183-210

9. Non-Linear and Smooth Regression

   • W. N. Venables, B. D. Ripley

   Pages 211-250

10. Tree-Based Methods

    • W. N. Venables, B. D. Ripley

    Pages 251-269

11. Random and Mixed Effects

    • W. N. Venables, B. D. Ripley

    Pages 271-300

12. Exploratory Multivariate Analysis

    • W. N. Venables, B. D. Ripley

    Pages 301-330

13. Classification

    • W. N. Venables, B. D. Ripley

    Pages 331-351

14. Survival Analysis

    • W. N. Venables, B. D. Ripley

    Pages 353-385

15. Time Series Analysis

    • W. N. Venables, B. D. Ripley

    Pages 387-418

16. Spatial Statistics

    • W. N. Venables, B. D. Ripley

    Pages 419-434

17. Optimization

    • W. N. Venables, B. D. Ripley

    Pages 435-446

18. Back Matter

    Pages 447-497

    PDF

S is a powerful environment for the statistical and graphical analysis of data. It provides the tools to implement many statistical ideas that have been made possible by the widespread availability of workstations having good graphics and computational capabilities. This book is a guide to using S environments to perform statistical analyses and provides both an introduction to the use of S and a course in modern statistical methods. Implementations of S are available commercially in S-PLUS(R) workstations and as the Open Source R for a wide range of computer systems. The aim of this book is to show how to use S as a powerful and graphical data analysis system. Readers are assumed to have a basic grounding in statistics, and so the book is intended for would-be users of S-PLUS or R and both students and researchers using statistics. Throughout, the emphasis is on presenting practical problems and full analyses of real data sets. Many of the methods discussed are state of the art approaches to topics such as linear, nonlinear and smooth regression models, tree-based methods, multivariate analysis, pattern recognition, survival analysis, time series and spatial statistics. Throughout modern techniques such as robust methods, non-parametric smoothing and bootstrapping are used where appropriate. This fourth edition is intended for users of S-PLUS 6.0 or R 1.5.0 or later. A substantial change from the third edition is updating for the current versions of S-PLUS and adding coverage of R. The introductory material has been rewritten to emphasis the import, export and manipulation of data. Increased computational power allows even more computer-intensive methods to be used, and methods such as GLMMs,

• Bootstrapping
• Cluster analysis
• Estimator
• Factor analysis
• Fitting
• Generalized linear model
• STATISTICA
• Survival analysis
• Time series
• best fit
• classification
• data analysis
• linear regression

"Modern Applied Statistics With S meets its goal of serving as an introduction to S for new users, as well as a reference and resource for those with more S experience." Journal of the American Statistical Association, December 2005

• CSIRO Mathematics and Information Science, Cleveland, Australia

  W. N. Venables

• Department of Statistics, University of Oxford, Oxford, England

  B. D. Ripley

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