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Tables of Contents for Modern Applied Statistics With S-Plus
Chapter/Section Title
Page #
Page Count
Preface
vii
10
Typographical Conventions
xvii
1 Introduction
1
18
1.1 A quick overview of S
2
2
1.2 Using S-PLUS under Unix
4
4
1.3 Using S-PLUS under Windows
8
2
1.4 An introductory session
10
8
1.5 What next?
18
1
2 The S Language
19
50
2.1 A concise description of S objects
19
10
2.2 Calling conventions for functions
29
1
2.3 Arithmetical expressions
30
6
2.4 Reading data
36
5
2.5 Model formulae
41
1
2.6 Character vector operations
42
2
2.7 Finding S objects
44
3
2.8 Indexing vectors, matrices and arrays
47
7
2.9 Matrix operations
54
7
2.10 Input/Output facilities
61
2
2.11 Customizing your S environment
63
3
2.12 History and audit trails
66
1
2.13 BATCH operation
67
1
2.14 Exercises
68
1
3 Graphical Output
69
44
3.1 Graphics devices
69
4
3.2 Basic plotting functions
73
5
3.3 Enhancing plots
78
4
3.4 Fine control of graphics
82
6
3.5 Trellis graphics
88
20
3.6 Object-oriented editable graphics
108
4
3.7 Exercises
112
1
4 Programming in S
113
50
4.1 Control structures
113
4
4.2 Vectorized calculations and loop avoidance functions
117
8
4.3 Writing your own functions
125
7
4.4 Introduction to object orientation
132
7
4.5 Editing, correcting and documenting functions
139
8
4.6 Calling the operating system
147
2
4.7 Recursion and handling vectorization
149
4
4.8 Frames
153
4
4.9 Using C and FORTRAN routines
157
1
4.10 Working within limited memory
157
5
4.11 Exercises
162
1
5 Distributions and Data Summaries
163
28
5.1 Probability distributions
163
3
5.2 Generating random data
166
2
5.3 Data summaries
168
4
5.4 Classical univariate statistics
172
5
5.5 Density estimation
177
9
5.6 Bootstrap and permutation methods
186
4
5.7 Exercises
190
1
6 Linear Statistical Models
191
32
6.1 A linear regression example
191
5
6.2 Model formulae and model matrices
196
8
6.3 Regression diagnostics
204
4
6.4 Safe prediction
208
1
6.5 Factorial designs and designed experiments
209
5
6.6 An unbalanced four-way layout
214
9
7 Generalized Linear Models
223
24
7.1 Functions for generalized linear modelling
227
3
7.2 Binomial data
230
8
7.3 Poisson models
238
4
7.4 A negative binomial family
242
5
8 Robust Statistics
247
20
8.1 Univariate samples
248
6
8.2 Median polish
254
2
8.3 Robust regression
256
5
8.4 Resistant regression
261
5
8.5 Multivariate location and scale
266
1
9 Non-linear Models
267
30
9.1 Fitting non-linear regression models
268
6
9.2 Non-linear fitted model objects and method functions
274
1
9.3 Taking advantage of linear parameters
275
1
9.4 Confidence intervals for parameters
276
6
9.5 Assessing the linear approximation
282
2
9.6 Constrained non-linear regression
284
2
9.7 General optimization and maximum likelihood estimation
286
9
9.8 Exercises
295
2
10 Random and Mixed Effects
297
26
10.1 Random effects and variance components
297
2
10.2 Multistratum models
299
5
10.3 Linear mixed effects models
304
10
10.4 Non-linear mixed effects models
314
7
10.5 Exercises
321
2
11 Modern Regression
323
20
11.1 Additive models and scatterplot smoothers
323
8
11.2 Projection-pursuit regression
331
3
11.3 Response transformation models
334
3
11.4 Neural networks
337
4
11.5 Conclusions
341
2
12 Survival Analysis
343
38
12.1 Estimators of survivor curves
345
5
12.2 Parametric models
350
6
12.3 Cox proportional hazards model
356
7
12.4 Further examples
363
16
12.5 Expected survival rates
379
2
13 Multivariate Analysis
381
32
13.1 Graphical methods
382
7
13.2 Cluster analysis
389
6
13.3 Discriminant analysis
395
6
13.4 An example: Leptograpsus variegatus crabs
401
4
13.5 Factor analysis
405
8
14 Tree-based Methods
413
18
14.1 Partitioning methods
414
11
14.2 Cutting trees down to size
425
3
14.3 Low birth weights revisited
428
3
15 Time Series
431
38
15.1 Second-order summaries
434
9
15.2 ARIMA models
443
6
15.3 Seasonality
449
6
15.4 Multiple time series
455
3
15.5 Nottingham temperature data
458
5
15.6 Regression with autocorrelated errors
463
4
15.7 Other time-series functions
467
2
16 Spatial Statistics
469
16
16.1 Spatial interpolation and smoothing
469
6
16.2 Kriging
475
5
16.3 Point process analysis
480
5
17 Classification
485
12
17.1 Theory
485
2
17.2 A simple example
487
6
17.3 Forensic glass
493
4
Appendices
497
24
A Datasets and Software
497
2
A.1 Libraries
497
1
A.2 Caveat
498
1
B Common S-PLUS Functions
499
18
C Using S-PLUS Libraries
517
4
C.1 Sources of libraries
519
1
C.2 Creating a library section
520
1
References
521
12
Index
533