.

0 (hodnocen0 x )

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BK

4th ed.

[New York] : Springer, 2002

xi, 495 s. ; 24 cm

ISBN 0-387-95457-0 (váz.) ISBN !978-0-387-95457-8 (chyb.)

Statistics and computing

Obsahuje bibliografii na s. 465-480

000060800

Preface v // Typographical Conventions xi // 1 Introduction 1 // 1.1 A Quick Overview of S 3 // 1.2 Using S 5 // 1.3 An Introductory Session 6 // 1.4 What Next? 12 // 2 Data Manipulation 13 // 2.1 Objects 13 // 2.2 Connections 20 // 2.3 Data Manipulation 27 // 2.4 Tables and Cross-Classification 37 // 3 The S Language 41 // 3.1 Language Layout 41 // 3.2 More on S Objects 44 // 3.3 Arithmetical Expressions 47 // 3.4 Character Vector Operations 51 // 3.5 Formatting and Printing 54 // 3.6 Calling Conventions for Functions 55 // 3.7 Model Formulae 56 // 3.8 Control Structures 58 // 3.9 Array and Matrix Operations 60 // 3.10 Introduction to Classes and Methods 66 // 4 Graphics 69 // 4.1 Graphics Devices 71 // 4.2 Basic Plotting Functions 72 // 4.3 Enhancing Plots 77 // 4.4 Fine Control of Graphics 82 // 4.5 Trellis Graphics 89 // 5 Univariate Statistics 107 // 5.1 Probability Distributions 107 // 5.2 Generating Random Data 110 // 5.3 Data Summaries Ill // 5.4 Classical Univariate Statistics 115 // 5.5 Robust Summaries 119 // 5.6 Density Estimation 126 // 5.7 Bootstrap and Permutation Methods 133 // 6 Linear Statistical Models 139 // 6.1 An Analysis of Covariance Example 139 // 6.2 Model Formulae and Model Matrices 144 // 6.3 Regression Diagnostics 151 // 6.4 Safe Prediction 155 // 6.5 Robust and Resistant Regression 156 // 6.6 Bootstrapping Linear Models 163 //

6.7 Factorial Designs and Designed Experiments 165 // 6.8 An Unbalanced Four-Way Layout 169 // 6.9 Predicting Computer Performance 177 // 6.10 Multiple Comparisons 178 // 7 Generalized Linear Models 183 // 7.1 Functions for Generalized Linear Modelling 187 // 7.2 Binomial Data 190 // 7.3 Poisson and Multinomial Models 199 // 7.4 A Negative Binomial Family 206 // 7.5 Over-Dispersion in Binomial and Poisson GLMs 208 // 8 Non-Linear and Smooth Regression 211 // 8.1 An Introductory Example 211 // 8.2 Fitting Non-Linear Regression Models 212 // 8.3 Non-Linear Fitted Model Objects and Method Functions 217 // 8.4 Confidence Intervals for Parameters 220 // 8.5 Profiles 226 // Contents ix // 8.6 Constrained Non-Linear Regression 227 // 8.7 One-Dimensional Curve-Fitting 228 // 8.8 Additive Models 232 // 8.9 Projection-Pursuit Regression 238 // 8.10 Neural Networks 243 // 8.11 Conclusions 249 // 9 Tree-Based Methods 251 // 9.1 Partitioning Methods 253 // 9.2 Implementation in rpart 258 // 9.3 Implementation in tree 266 // 10 Random and Mixed Effects 271 // 10.1 Linear Models 272 // 10.2 Classic Nested Designs 279 // 10.3 Non-Linear Mixed Effects Models 286 // 10.4 Generalized Linear Mixed Models 292 // 10.5 GEE Models 299 // 11 Exploratory Multivariate Analysis 301 // 11.1 Visualization Methods 302 // 11.2 Cluster Analysis 315 // 11.3 Factor Analysis 321 // 11.4 Discrete Multivariate Analysis 325 //

12 Classification 331 // 12.1 Discriminant Analysis 331 // 12.2 Classification Theory 338 // 12.3 Non-Parametric Rules 341 // 12.4 Neural Networks 342 // 12.5 Support Vector Machines 344 // 12.6 Forensic Glass Example 346 // 12.7 Calibration Plots 349 // 13 Survival Analysis 353 // 13.1 Estimators of Survivor Curves 355 // 13.2 Parametric Models 359 // 13.3 Cox Proportional Hazards Model 365 // 13.4 Further Examples 371 // 14 Time Series Analysis 387 // 14.1 Second-Order Summaries 389 // 14.2 ARIMA Models 397 // 14.3 Seasonality 403 // 14.4 Nottingham Temperature Data 406 // 14.5 Regression with Autocorrelated Errors 411 // 14.6 Models for Financial Series 414 // 15 Spatial Statistics 419 // 15.1 Spatial Interpolation and Smoothing 419 // 15.2 Kriging 425 // 15.3 Point Process Analysis 430 // 16 Optimization 435 // 16.1 Univariate Functions 435 // 16.2 Special-Purpose Optimization Functions 436 // 16.3 General Optimization 436 // Appendices // A Implementation-Specific Details 447 // A.1 Using S-PLUS under Unix/Linux 447 // A.2 Using S-PLUS under Windows 450 // A.3 Using R under Unix / Linux 453 // A.4 Using R under Windows 454 // A.5 For Emacs Users 455 // Đ’ The S-PLUS GUI 457 // C Datasets, Software and Libraries 461 // C.I Our Software 461 // C.2 Using Libraries 462 // References 465 // Index // 481