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雷达卡
Modern Applied Statistics with S
用的是S语言，
但很多都和R语言一样的，
在R中一样能实现。不信可以试试。
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 WhatNext? ............................. 12
2 DataManipulation 13
2.1 Objects ............................... 13
2.2 Connections............................. 20
2.3 DataManipulation ......................... 27
2.4 TablesandCross-Classification................... 37
3The S Language 41
3.1 Language Layout . . ........................ 41
3.2 More on S Objects ......................... 44
3.3 ArithmeticalExpressions...................... 47
3.4 CharacterVectorOperations .................... 51
3.5 Formatting and Printing . . . . . . ................. 54
3.6 Calling Conventions for Functions ................. 55
3.7 ModelFormulae........................... 56
3.8 ControlStructures.......................... 58
3.9 ArrayandMatrixOperations.................... 60
3.10 Introduction to Classes and Methods . . . ............. 66
Graphics 69
4.1 GraphicsDevices .......................... 71
4.2 Basic Plotting Functions . . . . . ................. 72
4.3 EnhancingPlots........................... 77
4.4 FineControlofGraphics ...................... 82
4.5 Trellis Graphics . . . ........................ 89
5 Univariate Statistics 107
5.1 Probability Distributions . . . . . .................107
5.2 Generating Random Data . . . . . .................110
5.3 DataSummaries...........................111
5.4 ClassicalUnivariateStatistics....................115
5.5 RobustSummaries .........................119
5.6 DensityEstimation .........................126
5.7 Bootstrap and Permutation Methods . . . .............133
6 Linear StatisticalModels 139
6.1 AnAnalysisofCovarianceExample................139
6.2 ModelFormulaeandModelMatrices ...............144
6.3 Regression Diagnostics . . . . . . .................151
6.4 SafePrediction ...........................155
6.5 RobustandResistantRegression..................156
6.6 BootstrappingLinearModels....................163
6.7 FactorialDesignsandDesignedExperiments ...........165
6.8 An Unbalanced Four-Way Layout .................169
6.9 PredictingComputerPerformance .................177
6.10 Multiple Comparisons . . . . . . .................178
Generalized Linear Models 183
7.1 Functions for Generalized Linear Modelling . . . .........187
7.2 BinomialData............................190
7.3 PoissonandMultinomialModels..................199
7.4 ANegativeBinomialFamily ....................206
7.5 Over-DispersioninBinomialandPoissonGLMs .........208
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 ConfidenceIntervalsforParameters ................220
8.5 Profiles ...............................226
8.6 ConstrainedNon-LinearRegression ................227
8.7 One-Dimensional Curve-Fitting . .................228
8.8 AdditiveModels ..........................232
8.9 Projection-PursuitRegression ...................238
8.10NeuralNetworks ..........................243
8.11Conclusions.............................249
9 Tree-Based Methods 251
9.1 PartitioningMethods ........................253
9.2 Implementation in rpart ......................258
9.3 Implementation in tree ......................266
10 Random and Mixed Effects 271
10.1LinearModels............................272
10.2ClassicNestedDesigns.......................279
10.3Non-LinearMixedEffectsModels .................286
10.4GeneralizedLinearMixedModels .................292
10.5GEEModels.............................299
11 ExploratoryMultivariate Analysis 301
11.1 Visualization Methods . . . . . . .................302
11.2ClusterAnalysis...........................315
11.3FactorAnalysis ...........................321
11.4DiscreteMultivariateAnalysis ...................325
Classification 331
12.1DiscriminantAnalysis .......................331
12.2ClassificationTheory........................338
12.3Non-ParametricRules........................341
12.4NeuralNetworks ..........................342
12.5 Support Vector Machines . . . . . .................344
12.6ForensicGlassExample ......................346
12.7CalibrationPlots ..........................349
Survival Analysis 353
13.1EstimatorsofSurvivorCurves ...................355
13.2ParametricModels .........................359
13.3 Cox Proportional Hazards Model . .................365
13.4FurtherExamples..........................371
14 Time Series Analysis 387
14.1 Second-Order Summaries . . . . . .................389
14.2ARIMAModels...........................397
14.3 Seasonality . ............................403
14.4 Nottingham Temperature Data . . .................406
14.5RegressionwithAutocorrelatedErrors...............411
14.6ModelsforFinancialSeries.....................414
15 Spatial Statistics 419
15.1SpatialInterpolationandSmoothing ................419
15.2Kriging ...............................425
15.3PointProcessAnalysis .......................430
16 Optimization 435
16.1UnivariateFunctions ........................435
16.2Special-PurposeOptimizationFunctions..............436
16.3GeneralOptimization........................436
Appendices
A Implementation-Specific Details 447
A.1 Using S-PLUS under Unix / Linux .................447
A.2 Using S-PLUS underWindows ...................450
A.3 Using R under Unix / Linux . . . .................453
A.4 Using R underWindows ......................454
A.5 ForEmacsUsers ..........................455
BThe S-PLUS GUI 457
C Datasets, Software and Libraries 461
C.1 OurSoftware ............................461
C.2 UsingLibraries ...........................462
References 465
Index 481
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