Kéry / Kery Introduction to WinBUGS for Ecologists

Bayesian Approach to Regression, ANOVA, Mixed Models and Related Analyses
1. Auflage 2010
ISBN: 978-0-12-378606-7
Verlag: Elsevier Science & Techn.
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, 320 Seiten

Introduction to WinBUGS for Ecologists introduces applied Bayesian modeling to ecologists using the highly acclaimed, free WinBUGS software. It offers an understanding of statistical models as abstract representations of the various processes that give rise to a data set. Such an understanding is basic to the development of inference models tailored to specific sampling and ecological scenarios. The book begins by presenting the advantages of a Bayesian approach to statistics and introducing the WinBUGS software. It reviews the four most common statistical distributions: the normal, the uniform, the binomial, and the Poisson. It describes the two different kinds of analysis of variance (ANOVA): one-way and two- or multiway. It looks at the general linear model, or ANCOVA, in R and WinBUGS. It introduces generalized linear model (GLM), i.e., the extension of the normal linear model to allow error distributions other than the normal. The GLM is then extended contain additional sources of random variation to become a generalized linear mixed model (GLMM) for a Poisson example and for a binomial example. The final two chapters showcase two fairly novel and nonstandard versions of a GLMM. The first is the site-occupancy model for species distributions; the second is the binomial (or N-) mixture model for estimation and modeling of abundance.
Introduction to the essential theories of key models used by ecologists Complete juxtaposition of classical analyses in R and Bayesian analysis of the same models in WinBUGSProvides every detail of R and WinBUGS code required to conduct all analysesCompanion Web Appendix that contains all code contained in the book and additional material (including more code and solutions to exercises)

Dr Kery is a Population Ecologist with the Swiss Ornithological Institute and a courtesy professor ('Privatdozent') at the University of Zürich/Switzerland, from where he received his PhD in Ecology in 2000. He is an expert in the estimation and modeling of abundance, distribution and species richness in 'metapopulation designs' (i.e., collections of replicate sites). For most of his work, he uses the Bayesian model fitting software BUGS and JAGS, about which he has published two books with Academic Press (2010 and 2012). He has authored/coauthored 70 peer-reviewed articles and four book chapters. Since 2007, and for a total of 103 days, he has taught 23 statistical modeling workshops about the methods in the proposed book at research institutes and universities all over the world.

Autoren/Hrsg.

Kéry, Marc

Kery, Marc

Inhaltsverzeichnis

1;Front Cover;1
2;Introduction to WinBUGS for Ecologists;4
3;Copyright;5
4;A Creed for Modeling;6
5;Table of Contents;8
6;Foreword;12
7;Preface;16
7.1; Acknowledgments;19
8;Chapter 1. Introduction;20
8.1;1.1 Advantages of the Bayesian Approach to Statistics;21
8.2;1.2 So Why Then Isn’t Everyone a Bayesian?;23
8.3;1.3 WinBUGS;23
8.4;1.4 Why This Book?;24
8.5;1.5 What This Book Is Not About: Theory of Bayesian Statistics and Computation;27
8.6;1.6 Further Reading;28
8.7;1.7 Summary;30
9;Chapter 2. Introduction to the Bayesian Analysis of a Statistical Model;32
9.1;2.1 Probability Theory and Statistics;33
9.2;2.2 Two Views of Statistics: Classical and Bayesian;34
9.3;2.3 The Importance of Modern Algorithms and Computers for Bayesian Statistics;38
9.4;2.4 Markov chain Monte Carlo (MCMC) and Gibbs Sampling;38
9.5;2.5 What Comes after MCMC?;40
9.6;2.6 Some Shared Challenges in the Bayesian and the Classical Analysis of a Statistical Model;43
9.7;2.7 Pointer to Special Topics in This Book;46
9.8;2.8 Summary;46
10;Chapter 3. WinBUGS;48
10.1;3.1 What Is WinBUGS?;48
10.2;3.2 Running WinBUGS from R;49
10.3;3.3 WinBUGS Frees the Modeler in You;49
10.4;3.4 Some Technicalities and Conventions;50
11;Chapter 4. A First Session in WinBUGS: The “Model of the Mean”;52
11.1;4.1 Introduction;52
11.2;4.2 Setting Up the Analysis;53
11.3;4.3 Starting the MCMC Blackbox;59
11.4;4.4 Summarizing the Results;60
11.5;4.5 Summary;63
12;Chapter 5. Running WinBUGS from R via R2WinBUGS;66
12.1;5.1 Introduction;66
12.2;5.2 Data Generation;67
12.3;5.3 Analysis Using R;68
12.4;5.4 Analysis Using WinBUGS;68
12.5;5.5 Summary;74
13;Chapter 6. Key Components of (Generalized) Linear Models: Statistical Distributions and the Linear Predictor;76
13.1;6.1 Introduction;77
13.2;6.2 Stochastic Part of Linear Models: Statistical Distributions;78
13.3;6.3 Deterministic Part of Linear Models: Linear Predictor and Design Matrices;85
13.4;6.4 Summary;108
14;Chapter 7. t-Test: Equal and Unequal Variances;110
14.1;7.1 t-Test with Equal Variances;111
14.2;7.2 t-Test with Unequal Variances;116
14.3;7.3 Summary and a Comment on the Modeling of Variances;119
15;Chapter 8. Normal Linear Regression;122
15.1;8.1 Introduction;122
15.2;8.2 Data Generation;123
15.3;8.3 Analysis Using R;124
15.4;8.4 Analysis Using WinBUGS;124
15.5;8.5 Summary;132
16;Chapter 9. Normal One-Way ANOVA;134
16.1;9.1 Introduction: Fixed and Random Effects;134
16.2;9.2 Fixed-Effects ANOVA;138
16.3;9.3 Random-Effects ANOVA;141
16.4;9.4 Summary;146
17;Chapter 10. Normal Two-Way ANOVA;148
17.1;10.1 Introduction: Main and Interaction Effects;148
17.2;10.2 Data Generation;150
17.3;10.3 Aside: Using Simulation to Assess Bias and Precision of an Estimator;152
17.4;10.4 Analysis Using R;153
17.5;10.5 Analysis Using WinBUGS;154
17.6;10.6 Summary;158
18;Chapter 11. General Linear Model (ANCOVA);160
18.1;11.1 Introduction;160
18.2;11.2 Data Generation;162
18.3;11.3 Analysis Using R;164
18.4;11.4 Analysis Using WinBUGS (And A Cautionary Tale About the Importance of Covariate Standardization);164
18.5;11.5 Summary;168
19;Chapter 12. Linear Mixed-Effects Model;170
19.1;12.1 Introduction;170
19.2;12.2 Data Generation;173
19.3;12.3 Analysis under a Random-Intercepts Model;175
19.4;12.4 Analysis under a Random-Coefficients Model without Correlation between Intercept and Slope;177
19.5;12.5 The Random-Coefficients Model with Correlation between Intercept and Slope;180
19.6;12.6 Summary;184
20;Chapter 13. Introduction to the Generalized Linear Model: Poisson “t-test”;186
20.1;13.1 Introduction;186
20.2;13.2 An Important but Often Forgotten Issue with Count Data;189
20.3;13.3 Data Generation;189
20.4;13.4 Analysis Using R;190
20.5;13.5 Analysis Using WinBUGS;190
20.6;13.6 Summary;196
21;Chapter 14. Overdispersion, Zero-Inflation, and Offsets in the GLM;198
21.1;14.1 Overdispersion;199
21.2;14.2 Zero-Inflation;203
21.3;14.3 Offsets;207
21.4;14.4 Summary;209
22;Chapter 15. Poisson ANCOVA;212
22.1;15.1 Introduction;212
22.2;15.2 Data Generation;213
22.3;15.3 Analysis Using R;215
22.4;15.4 Analysis Using WinBUGS;216
22.5;15.5 Summary;220
23;Chapter 16. Poisson Mixed-Effects Model (Poisson GLMM);222
23.1;16.1 Introduction;222
23.2;16.2 Data Generation;224
23.3;16.3 Analysis Under a Random-Coefficients Model;225
23.4;16.4 Summary;228
24;Chapter 17. Binomial “t-Test”;230
24.1;17.1 Introduction;230
24.2;17.2 Data Generation;232
24.3;17.3 Analysis Using R;232
24.4;17.4 Analysis Using WinBUGS;233
24.5;17.5 Summary;235
25;Chapter 18. Binomial Analysis of Covariance;238
25.1;18.1 Introduction;238
25.2;18.2 Data Generation;240
25.3;18.3 Analysis Using R;242
25.4;18.4 Analysis Using WinBUGS;243
25.5;18.5 Summary;247
26;Chapter 19. Binomial Mixed-Effects Model (Binomial GLMM);248
26.1;19.1 Introduction;248
26.2;19.2 Data Generation;249
26.3;19.3 Analysis Under a Random-Coefficients Model;250
26.4;19.4 Summary;255
27;Chapter 20. Nonstandard GLMMs 1: Site-Occupancy Species Distribution Model;256
27.1;20.1 Introduction;256
27.2;20.2 Data Generation;261
27.3;20.3 Analysis Using WinBUGS;265
27.4;20.4 Summary;270
28;Chapter 21. Nonstandard GLMMs 2: Binomial Mixture Model to Model Abundance ;272
28.1;21.1 Introduction;272
28.2;21.2 Data Generation;276
28.3;21.3 Analysis Using WinBUGS;281
28.4;21.4 Summary;292
29;Chapter 22. Conclusions;294
30;Appendix: A List of WinBUGS Tricks;298
31;References;304
32;Index;310