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

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

Publisher
Academic Press
Year
2015
Tongue
English
Leaves
316
Edition
1
Category
Library
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✦ Synopsis

Bayesian Data Analysis in Ecology Using Linear Modelswith 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 Modelswith 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

    Content:
    Front Matter, Page iii
    Copyright, Page iv
    Digital Assets, Page x
    Acknowledgments, Pages xi-xii
    Chapter 1 - Why do we Need Statistical Models and What is this Book About?, Pages 1-4
    Chapter 2 - Prerequisites and Vocabulary, Pages 5-18
    Chapter 3 - The Bayesian and the Frequentist Ways of Analyzing Data, Pages 19-31
    Chapter 4 - Normal Linear Models, Pages 33-68
    Chapter 5 - Likelihood, Pages 69-74
    Chapter 6 - Assessing Model Assumptions: Residual Analysis, Pages 75-94
    Chapter 7 - Linear Mixed Effects Models, Pages 95-114
    Chapter 8 - Generalized Linear Models, Pages 115-139
    Chapter 9 - Generalized Linear Mixed Models, Pages 141-159
    Chapter 10 - Posterior Predictive Model Checking and Proportion of Explained Variance, Pages 161-174
    Chapter 11 - Model Selection and Multimodel Inference, Pages 175-196
    Chapter 12 - Markov Chain Monte Carlo Simulation, Pages 197-212
    Chapter 13 - Modeling Spatial Data Using GLMM, Pages 213-224
    Chapter 14 - Advanced Ecological Models, Pages 225-264
    Chapter 15 - Prior Influence and Parameter Estimability, Pages 265-278
    Chapter 16 - Checklist, Pages 279-287
    Chapter 17 - What Should I Report in a Paper, Pages 289-296
    References, Pages 297-307
    Index, Pages 309-316

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