Introduction to WinBUGS for Ecologists
Bayesian Approach to Regression, ANOVA, Mixed Models and Related Analyses
- 1st Edition - June 17, 2010
- Latest edition
- Author: Marc Kéry
- Language: English
Introduction to WinBUGS for Ecologists introduces applied Bayesian modeling to ecologists using the highly acclaimed, free WinBUGS software. It offers an understanding of statis… Read more
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Description
Description
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.
Key features
Key features
- 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 WinBUGS
- Provides every detail of R and WinBUGS code required to conduct all analyses
- Companion Web Appendix that contains all code contained in the book and additional material (including more code and solutions to exercises)
Readership
Readership
Ecologists, upper-level graduate and graduate ecology students
Table of contents
Table of contents
Foreword
Preface
1. Introduction
1.1 Advantages of the Bayesian Approach to Statistics
1.2 So Why Then Isn’t Everyone a Bayesian?
1.3 WinBUGS
1.4 Why This Book?
1.5 What This Book Is Not About: Theory of Bayesian Statistics and Computation
1.6 Further Reading
1.7 Summary
2. Introduction to the Bayesian Analysis of a Statistical Model
2.1 Probability Theory and Statistics
2.2 Two Views of Statistics: Classical and Bayesian
2.3 The Importance of Modern Algorithms and Computers for Bayesian Statistics
2.4 Markov chain Monte Carlo (MCMC) and Gibbs Sampling
2.5 What Comes after MCMC?
2.6 Some Shared Challenges in the Bayesian and the Classical Analysis of a Statistical Model
2.7 Pointer to Special Topics in This Book
2.8 Summary
3. WinBUGS
3.1 What Is WinBUGS?
3.2 Running WinBUGS from R
3.3 WinBUGS Frees the Modeler in You
3.4 Some Technicalities and Conventions
4. A First Session in WinBUGS: The “Model of the Mean”
4.1 Introduction
4.2 Setting Up the Analysis
4.3 Starting the MCMC blackbox
4.4 Summarizing the Results
4.5 Summary
5. Running WinBUGS from R via R2WinBUGS
5.1 Introduction
5.2 Data Generation
5.3 Analysis Using R
5.4 Analysis Using WinBUGS
5.5 Summary
6. Key Components of (Generalized) Linear Models: Statistical Distributions and the Linear Predictor
6.1 Introduction
6.2 Stochastic Part of Linear Models: Statistical Distributions
6.3 Deterministic Part of Linear Models: Linear Predictor and Design Matrices
6.4 Summary
7. t-Test: Equal and Unequal Variances
7.1 t-Test with Equal Variances
7.2 t-Test with Unequal Variances
7.3 Summary and a Comment on the Modeling of Variances
8. Normal Linear Regression
8.1 Introduction
8.2 Data Generation
8.3 Analysis Using R
8.4 Analysis Using WinBUGS
8.5 Summary
9. Normal One-Way ANOVA
9.1 Introduction: Fixed and Random Effects
9.2 Fixed-Effects ANOVA
9.3 Random-Effects ANOVA
9.4 Summary
10. Normal Two-Way ANOVA
10.1 Introduction: Main and Interaction Effects
10.2 Data Generation
10.3 Aside: Using Simulation to Assess Bias and Precision of an Estimator
10.4 Analysis Using R
10.5 Analysis Using WinBUGS
10.6 Summary
11. General Linear Model (ANCOVA)
11.1 Introduction
11.2 Data Generation
11.3 Analysis Using R
11.4 Analysis Using WinBUGS (and a Cautionary Tale About the Importance of Covariate Standardization)
11.5 Summary
12. Linear Mixed-Effects Model
12.1 Introduction
12.2 Data Generation
12.3 Analysis Under a Random-Intercepts Model
12.4 Analysis Under a Random-Coefficients Model without Correlation between Intercept and Slope
12.5 The Random-Coefficients Model with Correlation between Intercept and Slope
12.6 Summary
13. Introduction to the Generalized Linear Model: Poisson “t-test”
13.1 Introduction
13.2 An Important but Often Forgotten Issue with Count Data
13.3 Data Generation
13.4 Analysis Using R
13.5 Analysis Using WinBUGS
13.6 Summary
14. Overdispersion, Zero-Inflation, and Offsets in the GLM
14.1 Overdispersion
14.2 Zero-Inflation
14.3 Offsets
14.4 Summary
15. Poisson ANCOVA
15.1 Introduction
15.2 Data Generation
15.3 Analysis Using R
15.4 Analysis Using WinBUGS
15.5 Summary
16. Poisson Mixed-Effects Model (Poisson GLMM)
16.1 Introduction
16.2 Data Generation
16.3 Analysis Under a Random-Coefficients Model
16.4 Summary
17. Binomial “t-Test”
17.1 Introduction
17.2 Data Generation
17.3 Analysis Using R
17.4 Analysis Using WinBUGS
17.5 Summary
18. Binomial Analysis of Covariance
18.1 Introduction
18.2 Data Generation
18.3 Analysis Using R
18.4 Analysis Using WinBUGS
18.5 Summary
19. Binomial Mixed-Effects Model (Binomial GLMM)
19.1 Introduction
19.2 Data Generation
19.3 Analysis Under a Random-Coefficients Model
19.4 Summary
20. Nonstandard GLMMs 1: Site-Occupancy Species Distribution Model
20.1 Introduction
20.2 Data Generation
20.3 Analysis Using WinBUGS
20.4 Summary
21. Nonstandard GLMMs 2: Binomial Mixture Model to Model Abundance
21.1 Introduction
21.2 Data Generation
21.3 Analysis Using WinBUGS
21.4 Summary
22. Conclusions
Appendix
References
Index
Review quotes
Review quotes
"I don’t believe this book was written with the goal of being treated as the primary text of an intro Bayesian statistics course. That said, it could prove to be a useful supplemental text for an introductory Bayesian course or even a linear models course. Although the book was geared towards ecologists, I believe it would be an excellent library addition for any applied modeler interested in applying Bayesian methodologies in their work."—The American Statistician
Product details
Product details
- Edition: 1
- Latest edition
- Published: June 17, 2010
- Language: English
About the author
About the author
MK
Marc Kéry
Dr. Marc works as a senior scientist at the Swiss Ornithological Institute, Seerose 1, 6204 Sempach, Switzerland. This is a non-profit NGO with about 160 employees dedicated primarily to bird research, monitoring, and conservation. Marc was trained as a plant population ecologist at the Swiss Universities of Basel and Zuerich. After a 2-year postdoc at the (then) USGS Patuxent Wildlife Center in Laurel, MD. During the last 20 years he has worked at the interface between population ecology, biodiversity monitoring, wildlife management, and statistics. He has published more than 100 peer-reviewed journal articles and five textbooks on applied statistical modeling. He has also been very active in teaching fellow biologists and wildlife managers the concepts and tools of modern statistical analysis in their fields in workshops all over the world, something which goes together with his books, which target the same audiences.
Affiliations and expertise
Senior Scientist, Swiss Ornithological Institute, Basel, SwitzerlandView book on ScienceDirect
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