Ecological Models and Data in R
目录
Chapter 1. Introduction 1
1.1 Introduction 2
1.2 What this book is not about 4
1.3 Frameworks for modeling 6
1.4 Frameworks for statistical inference 12
1.5 Frameworks for computing 23
1.6 Outline of the modeling process 26
1.7 R supplement 31
Chapter 2. Exploratory data analysis and graphics 41
2.1 Summary 42
2.2 Introduction 42
2.3 Getting data into R 43
2.4 Data types 48
2.5 Exploratory data analysis and graphics 56
2.6 Conclusion 82
2.7 R supplement 84
Chapter 3. Deterministic functions for ecological modeling 99
3.1 Summary 100
3.2 Introduction 100
3.3 Finding out about functions numerically 102
3.4 Finding out about functions analytically 106
3.5 Bestiary of functions 119
3.6 Conclusion 134
3.7 R supplement 135
Chapter 4. Probability and stochastic distributions for ecological
modeling 139
4.1 Introduction: why does variability matter? 140
4.2 Basic probability theory 141
4.3 Bayes’ Rule 145
4.4 Analyzing probability distributions 155
book August 29, 2007
vi CONTENTS
4.5 Bestiary of distributions 160
4.6 Extending simple distributions; compounding and generalizing 182
4.7 R supplement 188
Chapter 5. Stochastic simulation and power analysis 197
5.1 Introduction 198
5.2 Stochastic simulation 199
5.3 Power analysis 209
Chapter 6. Likelihood and all that 227
6.1 Introduction 228
6.2 Parameter estimation: single distributions 228
6.3 Estimation for more complex functions 243
6.4 Likelihood surfaces, profiles, and confidence intervals 249
6.5 Confidence intervals for complex models: quadratic approximation
260
6.6 Comparing models 267
Chapter 7. Optimization and all that 293
7.1 Introduction 294
7.2 Fitting methods 294
7.3 Markov chain Monte Carlo 310
7.4 Fitting challenges 319
7.5 Estimating confidence limits of functions of parameters 330
Chapter 8. Likelihood examples 347
8.1 Tadpole predation experiments 348
8.2 Goby survival analysis 363
8.3 Seed removal 374
Chapter 9. Standard statistics revisited 393
9.1 Introduction 394
9.2 General linear models 397
9.3 Nonlinearity: nonlinear least squares 404
9.4 Non-normal errors: generalized linear models 406
Chapter 10. Modeling variance 417
10.1 Introduction 418
10.2 Variable variance 420
10.3 Correlations: time-series and spatial data 423
10.4 Multi-level models: special cases 428
10.5 General multi-level models 431
10.6 Challenges 438
10.7 Conclusion 440
book August 29, 2007
CONTENTS vii
Chapter 11. Dynamic models 445
11.1 Introduction 446
11.2 Simulating dynamic models 447
11.3 Observation and process error 452
11.4 Process and observation error 455
11.5 SIMEX 458
11.6 State space models 459
11.7 Conclusions 470
Chapter 12. Afterword 477
Appendix A. Algebra and calculus basics 479
A.1 Exponentials and logarithms 480
A.2 Differential calculus 481
A.3 Partial differentiation 482
A.4 Integral calculus 482
A.5 Factorials and the gamma function 482
A.6 Probability 483
A.7 The delta method: formula and derivation 483
A.8 Linear algebra basics 484
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发表于 2013-2-15 09:13:25