## Modern Applied Statistics with SS-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. |

### O que estão dizendo - Escrever uma resenha

Não encontramos nenhuma resenha nos lugares comuns.

### Conteúdo

Introduction | 1 |

11 A Quick Overview of S | 3 |

12 Using S | 5 |

13 An Introductory Session | 6 |

14 What Next? | 12 |

Data Manipulation | 13 |

22 Connections | 20 |

23 Data Manipulation | 27 |

88 Additive Models | 232 |

89 ProjectionPursuit Regression | 238 |

810 Neural Networks | 243 |

811 Conclusions | 249 |

TreeBased Methods | 251 |

91 Partitioning Methods | 253 |

92 Implementation in rpart | 258 |

93 Implementation in tree | 266 |

24 Tables and CrossClassification | 37 |

The S Language | 41 |

32 More on S Objects | 44 |

33 Arithmetical Expressions | 47 |

34 Character Vector Operations | 51 |

35 Formatting and Printing | 54 |

36 Calling Conventions for Functions | 55 |

37 Model Formulae | 56 |

38 Control Structures | 58 |

39 Array and Matrix Operations | 60 |

310 Introduction to Classes and Methods | 66 |

Graphics | 69 |

41 Graphics Devices | 71 |

42 Basic Plotting Functions | 72 |

43 Enhancing Plots | 77 |

44 Fine Control of Graphics | 82 |

45 Trellis Graphics | 89 |

Univariate Statistics | 107 |

52 Generating Random Data | 110 |

53 Data Summaries | 111 |

54 Classical Univariate Statistics | 115 |

55 Robust Summaries | 119 |

56 Density Estimation | 126 |

57 Bootstrap and Permutation Methods | 133 |

Linear Statistical Models | 139 |

62 Model Formulae and Model Matrices | 144 |

63 Regression Diagnostics | 151 |

64 Safe Prediction | 155 |

65 Robust and Resistant Regression | 156 |

66 Bootstrapping Linear Models | 163 |

67 Factorial Designs and Designed Experiments | 165 |

68 An Unbalanced FourWay Layout | 169 |

69 Predicting Computer Performance | 177 |

610 Multiple Comparisons | 178 |

Generalized Linear Models | 183 |

71 Functions for Generalized Linear Modelling | 187 |

72 Binomial Data | 190 |

73 Poisson and Multinomial Models | 199 |

74 A Negative Binomial Family | 206 |

75 OverDispersion in Binomial and Poisson GLMs | 208 |

NonLinear and Smooth Regression | 211 |

82 Fitting NonLinear Regression Models | 212 |

83 NonLinear Fitted Model Objects and Method Functions | 217 |

84 Confidence Intervals for Parameters | 220 |

85 Profiles | 226 |

86 Constrained NonLinear Regression | 227 |

87 OneDimensional CurveFitting | 228 |

Random and Mixed Effects | 271 |

101 Linear Models | 272 |

102 Classic Nested Designs | 279 |

103 NonLinear Mixed Effects Models | 286 |

104 Generalized Linear Mixed Models | 292 |

105 GEE Models | 299 |

Exploratory Multivariate Analysis | 301 |

111 Visualization Methods | 302 |

112 Cluster Analysis | 315 |

113 Factor Analysis | 321 |

114 Discrete Multivariate Analysis | 325 |

Classification | 331 |

122 Classification Theory | 338 |

123 NonParametric Rules | 341 |

124 Neural Networks | 342 |

125 Support Vector Machines | 344 |

126 Forensic Glass Example | 346 |

127 Calibration Plots | 349 |

Survival Analysis | 353 |

131 Estimators of Survivor Curves | 355 |

132 Parametric Models | 359 |

133 Cox Proportional Hazards Model | 365 |

134 Further Examples | 371 |

Time Series Analysis | 387 |

141 SecondOrder Summaries | 389 |

142 ARIMA Models | 397 |

143 Seasonality | 403 |

144 Nottingham Temperature Data | 406 |

145 Regression with Autocorrelated Errors | 411 |

146 Models for Financial Series | 414 |

Spatial Statistics | 419 |

152 Kriging | 425 |

153 Point Process Analysis | 430 |

Optimization | 435 |

162 SpecialPurpose Optimization Functions | 436 |

ImplementationSpecific Details | 447 |

A2 Using SPLUS under Windows | 450 |

A3 Using R under Unix Linux | 453 |

A4 Using R under Windows | 454 |

A5 For Emacs Users | 455 |

The SPLUS GUI | 457 |

Datasets Software and Libraries | 461 |

C2 Using Libraries | 462 |

References | 465 |

481 | |

### Outras edições - Visualizar todos

### Termos e frases comuns

allows analysis apply approximate argument calculate called Chapter character choose Coefficients column commands components compute conditional consider contrasts corresponding covariance data frame dataset default degrees of freedom density deviance device distribution effects error estimate example factors Figure formula frequency function give given graphics groups Intercept intervals known labels length levels likelihood linear matrix mean measure method names negative non-linear normal Note object observations operations parameters plot points possible predict probability problem produce provides random regression residuals response result S-PLUS sample scale selected shown shows smooth specified split squares standard Statistics suggests summary surface tree Trellis units usually values variables variance vector weights window xlab ylab

### Passagens mais conhecidas

Página 471 - Gill, PE, Murray. W.. and Wright. MH (1981). Practical Optimization.