• 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.- Appendices.- 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.- B The S-PLUS GUI.- C Datasets, Software and Libraries.- C.1 Our Software.- C.2 Using Libraries.- References.

"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

• 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.- Appendices.- 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.- B The S-PLUS GUI.- C Datasets, Software and Libraries.- C.1 Our Software.- C.2 Using Libraries.- References.

"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

• 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.- Appendices.- 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.- B The S-PLUS GUI.- C Datasets, Software and Libraries.- C.1 Our Software.- C.2 Using Libraries.- References.

"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