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Generalized Additive Models:an introduction with R
author： Simon N. Wood

1 Linear Models 1
1.1 A simple linear model 2
Simple least squares estimation 3
1.1.1 Sampling properties ofˆβ 3
1.1.2 So how old is the universe? 5
1.1.3 Adding a distributional assumption 7
       Testing hypotheses about β 7
       Confidence intervals 9
1.2 Linear models in general 10
1.3 The theory of linear models 12
1.3.1 Least squares estimation of β 12
1.3.2 The distribution ofˆβ 13
1.3.3 ( ˆ β i − β i )/ˆ σ ˆβ i∼ t n−p 14
1.3.4 F-ratio results 15
1.3.5 The influence matrix 16
1.3.6 The residuals, ˆ ?, and fitted values, ˆ µ 16
1.3.7 Results in terms of X 17
1.3.8 The Gauss Markov Theorem: what’s special about least squares? 17
1.4 The geometry of linear modelling 18
1.4.1 Least squares 19
1.4.2 Fitting by orthogonal decompositions 20
1.4.3 Comparison of nested models 21
1.5 Practical linear models 22
1.5.1 Model fitting and model checking 23
1.5.2 Model summary 28
1.5.3 Model selection 30
1.5.4 Another model selection example 31
     A follow up 34
1.5.5 Confidence intervals 35
1.5.6 Prediction 36
1.6 Practical modelling with factors 36
1.6.1 Identifiability 37
1.6.2 Multiple factors 39
1.6.3 ‘Interactions’ of factors 40
1.6.4 Using factor variables in R 41
1.7 General linear model specification in R 44
1.8 Further linear modelling theory 45
1.8.1 Constraints I: general linear constraints 45
1.8.2 Constraints II: ‘contrasts’ and factor variables 46
1.8.3 Likelihood 48
1.8.4 Non-independent data with variable variance 49
1.8.5 AIC and Mallow’s statistic, 50
1.8.6 Non-linear least squares 52
1.8.7 Further reading 54
1.9 Exercises 55
2 Generalized Linear Models 59
2.1 The theory of GLMs 60
2.1.1 The exponential family of distributions 62
2.1.2 Fitting Generalized Linear Models 63
2.1.3 The IRLS objective is a quadratic approximation to the
        log-likelihood 66
2.1.4 AIC for GLMs 67
2.1.5 Large sample distribution of ˆβ 68
2.1.6 Comparing models by hypothesis testing 68
        Deviance 69
        Model comparison with unknown φ 70
2.1.7ˆφ and Pearson’s statistic 70
2.1.8 Canonical link functions 71
2.1.9 Residuals 72
       Pearson Residuals 72
       Deviance Residuals 73
2.1.10 Quasi-likelihood 73
2.2 Geometry of GLMs 75
2.2.1 The geometry of IRLS 76
2.2.2 Geometry and IRLS convergence 79
2.3 GLMs with R 80
2.3.1 Binomial models and heart disease 80
2.3.2 A Poisson regression epidemic model 87
2.3.3 Log-linear models for categorical data 92
2.3.4 Sole eggs in the Bristol channel 96
2.4 Likelihood 101
2.4.1 Invariance 102
2.4.2 Properties of the expected log-likelihood 102
2.4.3 Consistency 105
2.4.4 Large sample distribution of ˆθ 107
2.4.5 The generalized likelihood ratio test (GLRT) 107
2.4.6 Derivation of 2λ ∼ χ 2r under H0 108
2.4.7 AIC in general 110
2.4.8 Quasi-likelihood results 112
2.5 Exercises 114
3 Introducing GAMs 119
3.1 Introduction 119
3.2 Univariate smooth functions 120
3.2.1 Representing a smooth function: regression splines 120
        A very simple example: a polynomial basis 120
        Another example: a cubic spline basis 122
        Using the cubic spline basis 124
3.2.2 Controlling the degree of smoothing with penalized regres-sion splines 126
3.2.3 Choosing the smoothing parameter, λ: cross validation 128
3.3 Additive Models 131
3.3.1 Penalized regression spline representation of an additive model 132
3.3.2 Fitting additive models by penalized least squares 132
3.4 Generalized Additive Models 135
3.5 Summary 137
3.6 Exercises 138
4 Some GAM theory 141
4.1 Smoothing bases 142
4.1.1 Why splines? 142
        Natural cubic splines are smoothest interpolators 142
        Cubic smoothing splines 144
4.1.2 Cubic regression splines 145
4.1.3 A cyclic cubic regression spline 147
4.1.4 P-splines 148
4.1.5 Thin plate regression splines 150
         Thin plate splines 150
         Thin plate regression splines 153
         Properties of thin plate regression splines 154
         Knot based approximation 156
4.1.6 Shrinkage smoothers 156
4.1.7 Choosing the basis dimension 157
4.1.8 Tensor product smooths 158
        Tensor product bases 158
        Tensor product penalties 161
4.2 Setting up GAMs as penalized GLMs 163
4.2.1 Variable coefficient models 164
4.3 Justifying P-IRLS 165
4.4 Degrees of freedom and residual variance estimation 166
4.4.1 Residual variance or scale parameter estimation 167
4.5 Smoothing Parameter Estimation Criteria 168
4.5.1 Known scale parameter: UBRE 168
4.5.2 Unknown scale parameter: Cross Validation 169
         Problems with Ordinary Cross Validation 170
4.5.3 Generalized Cross Validation 171
4.5.4 GCV/UBRE/AIC in the Generalized case 173
        Approaches to GAM GCV/UBRE minimization 175
4.6 Numerical GCV/UBRE: performance iteration 177
4.6.1 Minimizing the GCV or UBRE score 177
        Stable and efficient evaluation of the scores and derivatives 178
        The weighted constrained case 181
4.7 Numerical GCV/UBRE optimization by outer iteration 182
4.7.1 Differentiating the GCV/UBRE function 182
4.8 Distributional results 185
4.8.1 Bayesian model, and posterior distribution of the parameters,for an additive model 185
4.8.2 Structure of the prior 187
4.8.3 Posterior distribution for a GAM 187
4.8.4 Bayesian confidence intervals for non-linear functions of parameters 190
4.8.5 P-values 190
4.9 Confidence interval performance 192
4.9.1 Single smooths 192
4.9.2 GAMs and their components 195
4.9.3 Unconditional Bayesian confidence intervals 198
4.10 Further GAM theory 200
4.10.1 Comparing GAMs by hypothesis testing 200
4.10.2 ANOVA decompositions and Nesting 202
4.10.3 The geometry of penalized regression 204
4.10.4 The “natural” parameterization of a penalized smoother 205
4.11 Other approaches to GAMs 208
4.11.1 Backfitting GAMs 209
4.11.2 Generalized smoothing splines 211
4.12 Exercises 213
5 GAMs in practice: mgcv 217
5.1 Cherry trees again 217
5.1.1 Finer control of gam 219
5.1.2 Smooths of several variables 221
5.1.3 Parametric model terms 224
5.2 Brain Imaging Example 226
5.2.1 Preliminary Modelling 228
5.2.2 Would an additive structure be better? 232
5.2.3 Isotropic or tensor product smooths? 233
5.2.4 Detecting symmetry (with by variables) 235
5.2.5 Comparing two surfaces 237
5.2.6 Prediction with predict.gam 239
         Prediction with lpmatrix 241
5.2.7 Variances of non-linear functions of the fitted model 242
5.3 Air Pollution in Chicago Example 243
5.4 Mackerel egg survey example 249
5.4.1 Model development 250
5.4.2 Model predictions 255
5.5 Portuguese larks 257
5.6 Other packages 261
5.6.1 Package gam 261
5.6.2 Package gss 263
5.7 Exercises 265
6 Mixed models: GAMMs 273
6.1 Mixed models for balanced data 273
6.1.1 A motivating example 273
        The wrong approach: a fixed effects linear model 274
        The right approach: a mixed effects model 276
6.1.2 General principles 277
6.1.3 A single random factor 278
6.1.4 A model with two factors 281
6.1.5 Discussion 286
6.2 Linear mixed models in general 287
6.2.1 Estimation of linear mixed models 288
6.2.2 Directly maximizing a mixed model likelihood in R 289
6.2.3 Inference with linear mixed models 290
         Fixed effects 290
         Inference about the random effects 291
6.2.4 Predicting the random effects 292
6.2.5 REML 293
        The explicit form of the REML criterion 295
6.2.6 A link with penalized regression 296
6.3 Linear mixed models in R 297
6.3.1 Tree Growth: an example using lme 298
6.3.2 Several levels of nesting 303
6.4 Generalized linear mixed models 303
6.5 GLMMs with R 305
6.6 Generalized Additive Mixed Models 309
6.6.1 Smooths as mixed model components 309
6.6.2 Inference with GAMMs 311
6.7 GAMMs with R 312
6.7.1 A GAMM for sole eggs 312
6.7.2 The Temperature in Cairo 314
6.8 Exercises 318
A Some Matrix Algebra 325
A.1 Basic computational efficiency 325
A.2 Covariance matrices 326
A.3 Differentiating a matrix inverse 326
A.4 Kronecker product 327
A.5 Orthogonal matrices and Householder matrices 327
A.6 QR decomposition 328
A.7 Choleski decomposition 328
A.8 Eigen-decomposition 329
A.9 Singular value decomposition 330
A.10 Pivoting 331
A.11 Lanczos iteration 331
B Solutions to exercises 335
B.1 Chapter 1 335
B.2 Chapter 2 340
B.3 Chapter 3 345
B.4 Chapter 4 347
B.5 Chapter 5 354
B.6 Chapter 6 363
Bibliography 373
Index 378

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The R Book
author：Michael J. Crawley

Preface xxiii
1 Getting Started 1
2 Essentials of the R Language 12
3 Data Input 137
4 Dataframes 159
5 Graphics 189
6 Tables 244
7 Mathematics 258
8 Classical Tests 344
9 Statistical Modelling 388
10 Regression 449
11 Analysis of Variance 498
12 Analysis of Covariance 537
13 Generalized Linear Models 557
14 Count Data 579
15 Count Data in Tables 599
16 Proportion Data 628
17 Binary Response Variables 650
18 Generalized Additive Models 666
19 Mixed-Effects Models 681
20 Non-Linear Regression 715
21 Meta-Analysis 740
22 Bayesian Statistics 752
23 Tree Models 768
24 Time Series Analysis 785
25 Multivariate Statistics 809
26 Spatial Statistics 825
27 Survival Analysis 869
28 Simulation Models 893
29 Changing the Look of Graphics 907
References and Further Reading 971
Index 977

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