Bayesian data analysis in ecology using linear models with R, Bugs, and Stan / Franzi Korner-Nievergelt...[et al.].

Includes bibliographical references and index.

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.

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.