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…mehr
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,
Brian D. Ripley PhD, is Professor of Applied Statistics at Oxford University. He is a Fellow of the Institute of Mathematical Statistics and the Royal Society of Edinburgh and is also a member of the International Statistical Institute.
Inhaltsangabe
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.
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.
Rezensionen
"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
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