.

0 (hodnocen0 x )

(0.3) Půjčeno:1x 

BK

New York : Springer, 1994

xii, 462 s. + 1 disketa

ISBN 0-387-94350-1

S-Plus is a powerful environment for 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. /The aim of the book is to show how to use S-Plus as a powerful and graphical system. Readers are assumed to have a basic grounding in statistics, and so the book is 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 and non-linear regression models, robust and smooth regression methods, survival analysis, multivariate analysis, tree-based methods, time series, and spatial statistics. All the data sets and S-Plus functions used are supplied with the book on a diskette..

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Preface v // Typographical Conventions xiii // 1 Introduction 1 // 1.1 A quick overview of S 3 // 1.2 Getting started 4 // 1.3 Bailing out 6 // 1.4 Getting help with functions and features 7 // 1.5 An introductory session 8 // 1.6 What next? 16 // 2 The S Language 17 // 2.1 A concise description of S objects 17 // 2.2 Calling conventions for functions 25 // 2.3 Arithmetical expressions 26 // 2.4 Reading data 32 // 2.5 Finding S objects 36 // 2.6 Character vector operations 38 // 2.7 Indexing vectors, matrices and arrays 40 // 2.8 Matrix operations 45 // 2.9 Functions operating on factors and lists 51 // 2.10 Input/Output facilities 54 // 2.11 Customizing your S environment 57 // 2.12 History and audit trails 59 // 2.13 Exercises 59 // 3 Graphical Output 61 // 3.1 Graphics devices 61 // 3.2 Basic plotting functions 65 // 3.3 Enhancing plots 70 // ix // x Contents // 3.4 Conditioning plots 74 // 3.5 Fine control of graphics 76 // 3.6 Exercises 83 // 4 Programming in S 85 // 4.1 Control structures 85 // 4.2 Writing your own functions 90 // 4.3 Finding errors 97 // 4.4 Calling the operating system 103 // 4.5 Some more advanced features. Recursion and frames 105 // 4.6 Generic functions and object-oriented programming 110 // 4.7 Using С and FORTRAN routines 113 // 4.8 Exercises 119 // 5 Distributions and Data Summaries 121 // 5.1 Probability distributions 121 // 5.2 Generating random data 123 // 5.3 Data summaries 125 // 5.4 Classical univariate statistics 129 // 5.5 Density estimation 134 // 5.6 Bootstrap and permutation methods 141 // 5.7 Exercises 146 // 6 Linear Statistical Models 147 // 6.1 A linear regression example 147 //

6.2 Model formulae 153 // 6.3 Regression diagnostics 157 // 6.4 Safe prediction 161 // 6.5 Factorial designs and designed experiments 162 // 6.6 An unbalanced four-way layout 169 // 6.7 Multistratum models 177 // 7 Generalized Linear Models 183 // 7.1 Functions for generalized linear modelling 187 // 7.2 Binomial data 189 // 7.3 Poisson models 196 // 7.4 A negative binomial family 200 // Contents xi // 8 Robust Statistics 203 // 8.1 Univariate samples 204 // 8.2 Median polish 210 // 8.3 Robust regression 212 // 8.4 Resistant regression 217 // 8.5 Multivariate location and scale 222 // 9 Non-linear Regression Models 223 // 9.1 Fitting non-linear regression models 224 // 9.2 Parametrized data frames 226 // 9.3 Using function derivative information 226 // 9.4 Non-linear fitted model objects and method functions 229 // 9.5 Taking advantage of linear parameters 230 // 9.6 Examples 231 // 9.7 Assessing the linear approximation: profiles 237 // 9.8 General minimization and maximum likelihood estimation 239 // 10 Modern Regression 247 // 10.1 Additive models and scatterplot smoothers 247 // 10.2 Projection-pursuit regression 255 // 10.3 Response transformation models 258 // 10.4 Neural networks 261 // 10.5 Conclusions 265 // 11 Survival Analysis 267 // 11.1 Estimators of survivor curves 269 // 11.2 Parametric models 273 // 11.3 Cox proportional hazards model 279 // 11.4 Further examples 285 //

11.5 Expected survival rates 298 // 11.6 Superseded functions 299 // 12 Multivariate Analysis 301 // 12.1 Graphical methods 301 // 12.2 Cluster analysis 311 // 12.3 Discriminant analysis 315 // 12.4 An example: Leptograpsus variegatus crabs 322 // xii Contents // 13 Tree-based Methods 329 // 13.1 Partitioning methods 330 // 13.2 Cutting trees down to size 342 // 13.3 Low birth weights revisited 345 // 14 Time Series 349 // 14.1 Second-order summaries 352 // 14.2 ARIMA models 361 // 14.3 Seasonality 367 // 14.4 Multiple time series 373 // 14.5 Nottingham temperature data 376 // 14.6 Other time-series functions 380 // 14.7 Backwards compatibility 382 // 15 Spatial Statistics 383 // 15.1 Interpolation and kriging 383 // 15.2 Point process analysis 392 // References 397 // Appendices // A Datasets and Software 407 // A.l Directories 408 // A.2 Sources of machine-readable versions 411 // A.3 Caveat 412 // В Common S-PLUS Functions 413 // C S versus S-PLUS 429 // D Using S Libraries 431 // D.l Creating a library 433 // D.2 Sources of libraries 434 // E Command Line Editing 437 // F Answers to Selected Exercises 439 // Index // 447