Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan

A valuable reference that provides insight into applied Bayesian data analyses for non-mathematicians

Franzi Korner-Nievergelt (Author), Tobias Roth (Author), Stefanie von Felten (Author), Jérôme Guélat (Author), Bettina Almasi (Author), Pius Korner-Nievergelt (Author)

9780128013700

Paperback / softback, published 13 April 2015

328 pages, 60 illustrations
22.9 x 15.1 x 2.1 cm, 0.5 kg

"...an excellent statistical toolbox book that provides examples of ecological analyses that increase in complexity using frequentist and Bayesian methods...it will have a permanent place on many bookshelves, including mine..." --The Journal of Wildlife Management

Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN examines the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy-to-understand approach, encouraging readers to examine the processes that generated their data. Including discussions of model selection, model checking, and multi-model inference, the book also uses effect plots that allow a natural interpretation of data.Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces Bayesian software, using R for the simple modes, and flexible Bayesian software (BUGS and Stan) for the more complicated ones. Guiding the ready from easy toward more complex (real) data analyses ina step-by-step manner, the book presents problems and solutions—including all R codes—that are most often applicable to other data and questions, making it an invaluable resource for analyzing a variety of data types.

1. Why Do We Need Statistical Models?2. Prerequisites and Vocabulary3. The Bayesian and Frequentist Ways of Analyzing Data4. Normal Linear Models5. Likelihood6. Assessing Model Assumptions: Residual Analysis7. Linear Mixed Effects Model LMM8. Generalized Linear Model GLM9. Generalized Linear Mixed Model GLMM10. Posterior Predictive Model Checking and Proportion of Explained Variance11. Model Selection and Multi-Model Inference12. Markov Chain Monte Carlo Simulation (MCMC)13. Modeling Spatial Data Using GLMM14. Advanced Ecological Models15. Prior Influence and Parameter Estimability16. Checklist17. What Should I Report in a Paper?

Subject Areas: Applied ecology [RNC], The environment [RN], Ecological science, the Biosphere [PSAF], Probability & statistics [PBT]