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

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.

Autorentext
Fränzi Korner-Nievergelt has been working as a statistical consultant since 2003. Dr. Korner-Nievergelt conducts research in ecology and ecological statistics at the Swiss Ornithological Institute and oikostat GmbH. Additionally, she provides data analyses for scientific projects in the public and private sector. A large part of her work involves teaching courses for scientists at scientific institutions and private organizations.

Klappentext
Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN introduces the Bayesian and frequentist methods of conducting data analyses. The book provides the theoretical background in an easy to understand way and it encourages the reader to think about the processes that generated their data. Model selection and multi-model inference are discussed and effort is made to create effect plots that allow a much more natural interpretation of the data rather than simple parameter estimates. Model checking by graphical analysis and by posterior predictive checking is also discussed. . Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and STAN encourages readers to think about the processes that generated their data and to build models that reflect these processes as close as possible. Therefore, the Bayesian software, BUGS, JAGS, STAN and LaplacesDemon are introduced. This book guides the reader from easy towards more complex (real) data analyses, step by step. The problems and solutions, including all R codes, presented in the book are most often replicable to other data and questions. Thus, this book can be used continually as a resource for all sorts of questions and field data collected.

Zusammenfassung
"...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

Inhalt
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?

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