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

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

• Publisher: Academic Press
• Year: 2015
• Print ISBN: 9780128013700
• eBook ISBN: 9780128016787
• Topic: Biological Sciences
• Subtopic: Ecology
• Index: Biological Sciences

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. - Introduces Bayesian data analysis, allowing users to obtain uncertainty measurements easily for any derived parameter of interest - Written in a step-by-step approach that allows for eased understanding by non-statisticians - Includes a companion website containing R-code to help users conduct Bayesian data analyses on their own data - All example data as well as additional functions are provided in the R-package blmeco

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Digital Assets
• Acknowledgments
• Chapter 1. Why do we Need Statistical Models and What is this Book About?
• Chapter 2. Prerequisites and Vocabulary
• Chapter 3. The Bayesian and the Frequentist Ways of Analyzing Data
• Chapter 4. Normal Linear Models
• Chapter 5. Likelihood
• Chapter 6. Assessing Model Assumptions: Residual Analysis
• Chapter 7. Linear Mixed Effects Models
• Chapter 8. Generalized Linear Models
• Chapter 9. Generalized Linear Mixed Models
• Chapter 10. Posterior Predictive Model Checking and Proportion of Explained Variance
• Chapter 11. Model Selection and Multimodel Inference
• Chapter 12. Markov Chain Monte Carlo Simulation
• Chapter 13. Modeling Spatial Data Using GLMM
• Chapter 14. Advanced Ecological Models
• Chapter 15. Prior Influence and Parameter Estimability
• Chapter 16. Checklist
• Chapter 17. What Should I Report in a Paper
• References
• Index