Introduction to WinBUGS for ecologists : A Bayesian approach to regression, ANOVA, mixed models and related analyses
著者
書誌事項
Introduction to WinBUGS for ecologists : A Bayesian approach to regression, ANOVA, mixed models and related analyses
Academic Press, an imprint of Elsevier, 2010
1st ed
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内容説明・目次
内容説明
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.
目次
1. Introduction
2. Principles of Bayesian Statistics
3. WinBUGS
4. A First Session in WinBUGS
5. Running WinBUGS from R via R2WinBUGS
6. Key Components of Generalized Linear Models
7. T-Test, Normal Linear Regression
8. Normal One-Way ANOVA
9. Interaction, General Linear Model
10. Linear Mixed-Effects Model
11. Introduction to the Generalized Linear Model (GLM)
12. Overdispersion and Offsets in the GLM
13. Poisson ANCOVA
14. Poisson Mixed-Effects Model
15. Binomial T-Test
16. Binomial ANCOVA
17. Binomial Mixed-Effects Model
18. Non-Standard GLMMs 1
19. Non-Standard GLMMs 2
20. Conclusion and Outlook
Acknowledgements
References
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