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Modern Applied Statistics with S

Statistics and Computing

Hardcover

Published: 12th August 2002
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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.

"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

Prefacep. v
Typographical Conventionsp. xi
Introductionp. 1
A Quick Overview of Sp. 3
Using Sp. 5
An Introductory Sessionp. 6
What Next?p. 12
Data Manipulationp. 13
Objectsp. 13
Connectionsp. 20
Data Manipulationp. 27
Tables and Cross-Classificationp. 37
The S Languagep. 41
Language Layoutp. 41
More on S Objectsp. 44
Arithmetical Expressionsp. 47
Character Vector Operationsp. 51
Formatting and Printingp. 54
Calling Conventions for Functionsp. 55
Model Formulaep. 56
Control Structuresp. 58
Array and Matrix Operationsp. 60
Introduction to Classes and Methodsp. 66
Graphicsp. 69
Graphics Devicesp. 71
Basic Plotting Functionsp. 72
Enhancing Plotsp. 77
Fine Control of Graphicsp. 82
Trellis Graphicsp. 89
Univariate Statisticsp. 107
Probability Distributionsp. 107
Generating Random Datap. 110
Data Summariesp. 111
Classical Univariate Statisticsp. 115
Robust Summariesp. 119
Density Estimationp. 126
Bootstrap and Permutation Methodsp. 133
Linear Statistical Modelsp. 139
An Analysis of Covariance Examplep. 139
Model Formulae and Model Matricesp. 144
Regression Diagnosticsp. 151
Safe Predictionp. 155
Robust and Resistant Regressionp. 156
Bootstrapping Linear Modelsp. 163
Factorial Designs and Designed Experimentsp. 165
An Unbalanced Four-Way Layoutp. 169
Predicting Computer Performancep. 177
Multiple Comparisonsp. 178
Generalized Linear Modelsp. 183
Functions for Generalized Linear Modellingp. 187
Binomial Datap. 190
Poisson and Multinomial Modelsp. 199
A Negative Binomial Familyp. 206
Over-Dispersion in Binomial and Poisson GLMsp. 208
Non-Linear and Smooth Regressionp. 211
An Introductory Examplep. 211
Fitting Non-Linear Regression Modelsp. 212
Non-Linear Fitted Model Objects and Method Functionsp. 217
Confidence Intervals for Parametersp. 220
Profilesp. 226
Constrained Non-Linear Regressionp. 227
One-Dimensional Curve-Fittingp. 228
Additive Modelsp. 232
Projection-Pursuit Regressionp. 238
Neural Networksp. 243
Conclusionsp. 249
Tree-Based Methodsp. 251
Partitioning Methodsp. 253
Implementation in rpartp. 258
Implementation in treep. 266
Random and Mixed Effectsp. 271
Linear Modelsp. 272
Classic Nested Designsp. 279
Non-Linear Mixed Effects Modelsp. 286
Generalized Linear Mixed Modelsp. 292
GEE Modelsp. 299
Exploratory Multivariate Analysisp. 301
Visualization Methodsp. 302
Cluster Analysisp. 315
Factor Analysisp. 321
Discrete Multivariate Analysisp. 325
Classificationp. 331
Discriminant Analysisp. 331
Classification Theoryp. 338
Non-Parametric Rulesp. 341
Neural Networksp. 342
Support Vector Machinesp. 344
Forensic Glass Examplep. 346
Calibration Plotsp. 349
Survival Analysisp. 353
Estimators of Survivor Curvesp. 355
Parametric Modelsp. 359
Cox Proportional Hazards Modelp. 365
Further Examplesp. 371
Time Series Analysisp. 387
Second-Order Summariesp. 389
ARIMA Modelsp. 397
Seasonalityp. 403
Nottingham Temperature Datap. 406
Regression with Autocorrelated Errorsp. 411
Models for Financial Seriesp. 414
Spatial Statisticsp. 419
Spatial Interpolation and Smoothingp. 419
Krigingp. 425
Point Process Analysisp. 430
Optimizationp. 435
Univariate Functionsp. 435
Special-Purpose Optimization Functionsp. 436
General Optimizationp. 436
Appendices
Implementation-Specific Detailsp. 447
Using S-PLUS under Unix/Linuxp. 447
Using S-PLUS under Windowsp. 450
Using R under Unix/Linuxp. 453
Using R under Windowsp. 454
For Emacs Usersp. 455
The S-PLUS GUIp. 457
Datasets, Software and Librariesp. 461
Our Softwarep. 461
Using Librariesp. 462
Referencesp. 465
Indexp. 481
Table of Contents provided by Syndetics. All Rights Reserved.

ISBN: 9780387954578
ISBN-10: 0387954570
Series: Statistics and Computing
Audience: Professional
Format: Hardcover
Language: English
Number Of Pages: 508
Published: 12th August 2002
Publisher: SPRINGER VERLAG GMBH
Dimensions (cm): 23.6 x 15.7  x 2.8
Weight (kg): 0.862