| |
| |
| Preface | |
| |
| |
| Typographical Conventions | |
| |
| |
| |
| Introduction | |
| |
| |
| |
| A Quick Overview of S | |
| |
| |
| |
| Using S | |
| |
| |
| |
| An Introductory Session | |
| |
| |
| |
| What Next? | |
| |
| |
| |
| Data Manipulation | |
| |
| |
| |
| Objects | |
| |
| |
| |
| Connections | |
| |
| |
| |
| Data Manipulation | |
| |
| |
| |
| Tables and Cross-Classification | |
| |
| |
| |
| The S Language | |
| |
| |
| |
| Language Layout | |
| |
| |
| |
| More on S Objects | |
| |
| |
| |
| Arithmetical Expressions | |
| |
| |
| |
| Character Vector Operations | |
| |
| |
| |
| Formatting and Printing | |
| |
| |
| |
| Calling Conventions for Functions | |
| |
| |
| |
| Model Formulae | |
| |
| |
| |
| Control Structures | |
| |
| |
| |
| Array and Matrix Operations | |
| |
| |
| |
| Introduction to Classes and Methods | |
| |
| |
| |
| Graphics | |
| |
| |
| |
| Graphics Devices | |
| |
| |
| |
| Basic Plotting Functions | |
| |
| |
| |
| Enhancing Plots | |
| |
| |
| |
| Fine Control of Graphics | |
| |
| |
| |
| Trellis Graphics | |
| |
| |
| |
| Univariate Statistics | |
| |
| |
| |
| Probability Distributions | |
| |
| |
| |
| Generating Random Data | |
| |
| |
| |
| Data Summaries | |
| |
| |
| |
| Classical Univariate Statistics | |
| |
| |
| |
| Robust Summaries | |
| |
| |
| |
| Density Estimation | |
| |
| |
| |
| Bootstrap and Permutation Methods | |
| |
| |
| |
| Linear Statistical Models | |
| |
| |
| |
| An Analysis of Covariance Example | |
| |
| |
| |
| Model Formulae and Model Matrices | |
| |
| |
| |
| Regression Diagnostics | |
| |
| |
| |
| Safe Prediction | |
| |
| |
| |
| Robust and Resistant Regression | |
| |
| |
| |
| Bootstrapping Linear Models | |
| |
| |
| |
| Factorial Designs and Designed Experiments | |
| |
| |
| |
| An Unbalanced Four-Way Layout | |
| |
| |
| |
| Predicting Computer Performance | |
| |
| |
| |
| Multiple Comparisons | |
| |
| |
| |
| Generalized Linear Models | |
| |
| |
| |
| Functions for Generalized Linear Modelling | |
| |
| |
| |
| Binomial Data | |
| |
| |
| |
| Poisson and Multinomial Models | |
| |
| |
| |
| A Negative Binomial Family | |
| |
| |
| |
| Over-Dispersion in Binomial and Poisson GLMs | |
| |
| |
| |
| Non-Linear and Smooth Regression | |
| |
| |
| |
| An Introductory Example | |
| |
| |
| |
| Fitting Non-Linear Regression Models | |
| |
| |
| |
| Non-Linear Fitted Model Objects and Method Functions | |
| |
| |
| |
| Confidence Intervals for Parameters | |
| |
| |
| |
| Profiles | |
| |
| |
| |
| Constrained Non-Linear Regression | |
| |
| |
| |
| One-Dimensional Curve-Fitting | |
| |
| |
| |
| Additive Models | |
| |
| |
| |
| Projection-Pursuit Regression | |
| |
| |
| |
| Neural Networks | |
| |
| |
| |
| Conclusions | |
| |
| |
| |
| Tree-Based Methods | |
| |
| |
| |
| Partitioning Methods | |
| |
| |
| |
| Implementation in rpart | |
| |
| |
| |
| Implementation in tree | |
| |
| |
| |
| Random and Mixed Effects | |
| |
| |
| |
| Linear Models | |
| |
| |
| |
| Classic Nested Designs | |
| |
| |
| |
| Non-Linear Mixed Effects Models | |
| |
| |
| |
| Generalized Linear Mixed Models | |
| |
| |
| |
| GEE Models | |
| |
| |
| |
| Exploratory Multivariate Analysis | |
| |
| |
| |
| Visualization Methods | |
| |
| |
| |
| Cluster Analysis | |
| |
| |
| |
| Factor Analysis | |
| |
| |
| |
| Discrete Multivariate Analysis | |
| |
| |
| |
| Classification | |
| |
| |
| |
| Discriminant Analysis | |
| |
| |
| |
| Classification Theory | |
| |
| |
| |
| Non-Parametric Rules | |
| |
| |
| |
| Neural Networks | |
| |
| |
| |
| Support Vector Machines | |
| |
| |
| |
| Forensic Glass Example | |
| |
| |
| |
| Calibration Plots | |
| |
| |
| |
| Survival Analysis | |
| |
| |
| |
| Estimators of Survivor Curves | |
| |
| |
| |
| Parametric Models | |
| |
| |
| |
| Cox Proportional Hazards Model | |
| |
| |
| |
| Further Examples | |
| |
| |
| |
| Time Series Analysis | |
| |
| |
| |
| Second-Order Summaries | |
| |
| |
| |
| ARIMA Models | |
| |
| |
| |
| Seasonality | |
| |
| |
| |
| Nottingham Temperature Data | |
| |
| |
| |
| Regression with Autocorrelated Errors | |
| |
| |
| |
| Models for Financial Series | |
| |
| |
| |
| Spatial Statistics | |
| |
| |
| |
| Spatial Interpolation and Smoothing | |
| |
| |
| |
| Kriging | |
| |
| |
| |
| Point Process Analysis | |
| |
| |
| |
| Optimization | |
| |
| |
| |
| Univariate Functions | |
| |
| |
| |
| Special-Purpose Optimization Functions | |
| |
| |
| |
| General Optimization | |
| |
| |
| Appendices | |
| |
| |
| |
| Implementation-Specific Details | |
| |
| |
| |
| Using S-PLUS under Unix/Linux | |
| |
| |
| |
| Using S-PLUS under Windows | |
| |
| |
| |
| Using R under Unix/Linux | |
| |
| |
| |
| Using R under Windows | |
| |
| |
| |
| For Emacs Users | |
| |
| |
| |
| The S-PLUS GUI | |
| |
| |
| |
| Datasets, Software and Libraries | |
| |
| |
| |
| Our Software | |
| |
| |
| |
| Using Libraries | |
| |
| |
| References | |
| |
| |
| Index | |