Introduction to WinBUGS for Ecologists
Bayesian approach to regression, ANOVA, mixed models and related analysesBy
- Marc Kery
Bayesian statistics has exploded into biology and its sub-disciplines, such as ecology, over the past decade. The free software program WinBUGS and its open-source sister OpenBugs is currently the only flexible and general-purpose program available with which the average ecologist can conduct standard and non-standard Bayesian statistics. Introduction to WINBUGS for Ecologists goes right to the heart of the matter by providing ecologists with a comprehensive, yet concise, guide to applying WinBUGS to the types of models that they use most often: linear (LM), generalized linear (GLM), linear mixed (LMM) and generalized linear mixed models (GLMM).
Introduction to WinBUGS for Ecologists combines the use of simulated data sets "paired" analyses using WinBUGS (in a Bayesian framework for analysis) and in R (in a frequentist mode of inference) and uses a very detailed step-by-step tutorial presentation style that really lets the reader repeat every step of the application of a given mode in their own research.
Ecologists, upper-level graduate and graduate ecology students.
Paperback, 320 Pages
Published: June 2010
Imprint: Academic Press
"I donât believe this book was written with the goal of being treated as the primary text of an intro Bayesian statistics course. That said, it could prove to be a useful supplemental text for an introductory Bayesian course or even a linear models course. Although the book was geared towards ecologists, I believe it would be an excellent library addition for any applied modeler interested in applying Bayesian methodologies in their work."--The American Statistician
Chapter One: Introduction
Chapter Two: Principles of Bayesian Statistics
Chapter Three: WinBUGS (and a little bit on R)
Chapter Four: A First Session in WinBUGS: The "Model of the Mean"
Chapter Five: Running WinBUGS from R via R2WinBUGS
Chapter Six: Key Components of Generalized Linear Models: Statistical Distributions and the Linear Predictor
Chapter Seven: T-Test: Equal and Unequal Variance
Chapter Eight: Normal Linear Regression
Chapter Nine: Normal One-Way ANOVA
Chapter Ten: Interaction
Chapter Eleven: General Linear Model (ANCOVA)
Chapter Twelve: Linear Mixed-Effects Model
Chapter Thirteen: Introduction to the Generalized Linear Model (GLM): Poisson T-Test
Chapter Fourteen: Over dispersion and Offsets in the GLM
Chapter Fifteen: Poisson ANCOVA
Chapter Sixteen: Poisson Mixed-Effects Model (Poisson GLMM)
Chapter Seventeen: Binomial T-Test
Chapter Eighteen: Binomial ANCOVA
Chapter Nineteen: Binomial Mixed-Effects Model (Binomial GLMM)
Chapter Twenty: Non-standard GLMMs 1: Site Occupancy Distribution Model
Chapter Twenty-One: Non-standard GLMMs 2: Binomial Mixture Model for the Modeling of True Abundance
Chapter Twenty-Two: Conclusion and Outlook
Solutions to Exercises