Modern Applied Statistics with S

ISBN-10: 0387954570

ISBN-13: 9780387954578

Edition: 4th 2002 (Revised)

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Description: S-PLUS is a powerful environment for the statistical and graphical analysis of data. It provides the tools to implement many statistical ideas which have been made possible by the widespread availability of workstations having good graphics and computational capabilities. This book is a guide to using S-PLUS to perform statistical analyses and provides both an introduction to the use of S-PLUS and a course in modern statistical methods. S-PLUS is available for both Windows and UNIX workstations, and both versions are covered in depth.The aim of the book is to show how to use S-PLUS as a powerful and graphical data analysis system. Readers are assumed to have a basic grounding in statistics, and so the book in intended for would-be users of S-PLUS 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 and 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 third edition is intended for users of S-PLUS 4.5, 5.0, 2000 or later, although S-PLUS 3.3/4 are also considered. The major change from the second edition is coverage of the current versions of S-PLUS. The material has been extensively rewritten using new examples and the latest computationally intensive methods. The companion volume on S Programming will provide an in-depth guide for those writing software in the S language.The authors have written several software libraries that enhance S-PLUS; these and all the datasets used are available on the Internet in versions for Windows and UNIX. There are extensive on-line complements covering advanced material, user-contributed extensions, further exercises, and new features of S-PLUS as they are introduced.Dr. Venables is now Statistician with CSRIO in Queensland, having been at the Department of Statistics, University of Adelaide, for many years previously. He has given many short courses on S-PLUS in Australia, Europe, and the USA. Professor Ripley holds the Chair of Applied Statistics at the University of Oxford, and is the author of four other books on spatial statistics, simulation, pattern recognition, and neural networks.

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Book details

List price: $179.00
Edition: 4th
Copyright year: 2002
Publisher: Springer
Publication date: 9/2/2003
Binding: Hardcover
Pages: 498
Size: 6.25" wide x 9.50" long x 1.00" tall
Weight: 2.090

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
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