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- Product code: 20972
- ISBN: 0387954570,
ISBN13: 9780387954578,
506 pages, hardback
Published by Springer-Verlag New York Inc. on 2002
, 4th Revised edition Rate this book...
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Description of Modern Applied Statistics with S |
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.
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Contents of Modern Applied Statistics with S |
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 PoissonGLMs
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 Profile
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
8.11Conclusions
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
1.1 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 GeneralOptimization
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
Index
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