Modern Applied Statistics with S

By W. N. Venables and B. D. Ripley · Springer · 2002

A guide to using the S environments, S-PLUS and R, for statistical analysis: both an introduction to S and a course in modern statistical methods. Readers are assumed to have a basic grounding in statistics, and the emphasis throughout is on practical problems and full analyses of real data sets, across linear, non-linear and smooth regression, tree-based methods, multivariate analysis, pattern recognition, survival analysis, time series and spatial statistics. The fourth edition is written for S-PLUS 6.0 or R 1.5.0 or later and adds coverage of R. It is published in Springer's Statistics and Computing series.

On the syllabus

Exam PCPA — objectives B1–B2; pp. 172–176, which fall within 6.8 An Unbalanced Four-Way Layout (pp. 169–176).

Preface

Typographical Conventions

1 Introduction

1.1 A Quick Overview of S
1.2 Using S
1.3 An Introductory Session
1.4 What Next?

2 Data Manipulation

2.1 Objects
2.2 Connections
2.3 Data Manipulation
2.4 Tables and Cross-Classification

3 The S Language

3.1 Language Layout
3.2 More on S Objects
3.3 Arithmetical Expressions
3.4 Character Vector Operations
3.5 Formatting and Printing
3.6 Calling Conventions for Functions
3.7 Model Formulae
3.8 Control Structures
3.9 Array and Matrix Operations
3.10 Introduction to Classes and Methods

4 Graphics

4.1 Graphics Devices
4.2 Basic Plotting Functions
4.3 Enhancing Plots
4.4 Fine Control of Graphics
4.5 Trellis Graphics

5 Univariate Statistics

5.1 Probability Distributions
5.2 Generating Random Data
5.3 Data Summaries
5.4 Classical Univariate Statistics
5.5 Robust Summaries
5.6 Density Estimation
5.7 Bootstrap and Permutation Methods

6 Linear Statistical Models

6.1 An Analysis of Covariance Example
6.2 Model Formulae and Model Matrices
6.3 Regression Diagnostics
6.4 Safe Prediction
6.5 Robust and Resistant Regression
6.6 Bootstrapping Linear Models
6.7 Factorial Designs and Designed Experiments
6.8 An Unbalanced Four-Way Layout
6.9 Predicting Computer Performance
6.10 Multiple Comparisons

7 Generalized Linear Models

7.1 Functions for Generalized Linear Modelling
7.2 Binomial Data
7.3 Poisson and Multinomial Models
7.4 A Negative Binomial Family
7.5 Over-Dispersion in Binomial and Poisson GLMs

8 Non-Linear and Smooth Regression

8.1 An Introductory Example
8.2 Fitting Non-Linear Regression Models
8.3 Non-Linear Fitted Model Objects and Method Functions
8.4 Confidence Intervals for Parameters
8.5 Profiles
8.6 Constrained Non-Linear Regression
8.7 One-Dimensional Curve-Fitting
8.8 Additive Models
8.9 Projection-Pursuit Regression
8.10 Neural Networks
8.11 Conclusions

9 Tree-Based Methods

9.1 Partitioning Methods
9.2 Implementation in rpart
9.3 Implementation in tree

10 Random and Mixed Effects

10.1 Linear Models
10.2 Classic Nested Designs
10.3 Non-Linear Mixed Effects Models
10.4 Generalized Linear Mixed Models
10.5 GEE Models

11 Exploratory Multivariate Analysis

11.1 Visualization Methods
11.2 Cluster Analysis
11.3 Factor Analysis
11.4 Discrete Multivariate Analysis

12 Classification

12.1 Discriminant Analysis
12.2 Classification Theory
12.3 Non-Parametric Rules
12.4 Neural Networks
12.5 Support Vector Machines
12.6 Forensic Glass Example
12.7 Calibration Plots

13 Survival Analysis

13.1 Estimators of Survivor Curves
13.2 Parametric Models
13.3 Cox Proportional Hazards Model
13.4 Further Examples

14 Time Series Analysis

14.1 Second-Order Summaries
14.2 ARIMA Models
14.3 Seasonality
14.4 Nottingham Temperature Data
14.5 Regression with Autocorrelated Errors
14.6 Models for Financial Series

15 Spatial Statistics

15.1 Spatial Interpolation and Smoothing
15.2 Kriging
15.3 Point Process Analysis

16 Optimization

16.1 Univariate Functions
16.2 Special-Purpose Optimization Functions
16.3 General Optimization

Appendix A Implementation-Specific Details

A.1 Using S-PLUS under Unix / Linux
A.2 Using S-PLUS under Windows
A.3 Using R under Unix / Linux
A.4 Using R under Windows
A.5 For Emacs Users

Appendix B The S-PLUS GUI

Appendix C Datasets, Software and Libraries

C.1 Our Software
C.2 Using Libraries

References

Index

Sources

Table of contents for Modern Applied Statistics with S (Library of Congress, 2002) — the contents with page numbers (LCCN 2002022925), machine-generated from the book: every chapter, section and appendix title, and the pages that place pp. 172–176 in 6.8
Modern Applied Statistics with S, 4th ed. (Springer, 2002) — the publisher's record: edition, year, print ISBN 978-0-387-95457-8, the Statistics and Computing series, and chapter page ranges that agree with the Library of Congress contents
Modern Applied Statistics with S, Fourth Edition (W. N. Venables and B. D. Ripley, 2002) — the authors' description of the book, used for the lead
CAS PCPA Exam & Project Content Outline, v.8 (September 2026) — the citation and the assigned pages