Introduction to WinBUGS for Ecologists Bayesian Approach to Regression, ANOVA, Mixed Models and Related Analyses

Name: Introduction to WinBUGS for Ecologists Bayesian Approach to Regression, ANOVA, Mixed Models and Related Analyses
Price: 22.12 USD
Availability: InStock
ISBN: 9780123786050

ISBN-10: 0123786053

ISBN-13: 9780123786050

Edition: 2010

Authors: Marc Kï¿½ry

List price: $43.99

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Book details

List price: $43.99
Copyright year: 2010
Publisher: Elsevier Science & Technology
Publication date: 7/19/2010
Binding: Paperback
Pages: 320
Size: 5.94" wide x 9.00" long x 0.75" tall
Weight: 1.430
Language: English

Dr KÃ©ry is a Population Ecologist with the Swiss Ornithological Institute and a courtesy professor ("Privatdozent") at the University of ZÃ¼rich/Switzerland, from where he received his PhD in Ecology in 2000. He is an expert in the estimation and modeling of abundance, distribution and species richness in "metapopulation designs" (i.e., collections of replicate sites). For most of his work, he uses the Bayesian model fitting software BUGS and JAGS, about which he has published three books with Academic Press (2010, 2012 and 2016). He has authored/coauthored over 100 peer-reviewed articles and book chapters and is teaching statistical modeling workshops about the methods in this book at…



Foreword


Preface



Introduction



Advantages of the Bayesian Approach to Statistics



So Why Then Isn't Everyone a Bayesian?



WinBUGS



Why This Book?



What This Book Is Not About: Theory of Bayesian Statistics and Computation



Further Reading



Summary



Introduction to the Bayesian Analysis of a Statistical Model



Probability Theory and Statistics



Two Views of Statistics: Classical and Bayesian



The Importance of Modem Algorithms and Computers for Bayesian Statistics



Markov chain Monte Carlo (MCMC) and Gibbs Sampling



What Comes after MCMC?



Some Shared Challenges in the Bayesian and the Classical Analysis of a Statistical Model



Pointer to Special Topics in This Book



Summary



WinBUGS



What Is WinBUGS?



Running WinBUGS from R



WinBUGS Frees the Modeler in You



Some Technicalities and Conventions



A First Session in WinBUGS: The ï¿½Model of the Meanï¿½



Introduction



Setting Up the Analysis



Starting the MCMC blackbox



Summarizing the Results



Summary



Running WinBUGS from R via R2WinBUGS



Introduction



Data Generation



Analysis Using R



Analysis Using WinBUGS



Summary



Key Components of (Generalized) Linear Models: Statistical Distributions and the Linear Predictor



Introduction



Stochastic Part of Linear Models: Statistical Distributions



Deterministic Part of Linear Models: Linear Predictor and Design Matrices



Summary



t-Test: Equal and Unequal Variances



t-Test with Equal Variances



t-Test with Unequal Variances



Summary and a Comment on the Modeling of Variances



Normal Linear Regression



Introduction



Data Generation



Analysis Using R



Analysis Using WinBUGS



Summary



Normal One-Way ANOVA



Introduction: Fixed and Random Effects



Fixed-Effects ANOVA



Random-Effects ANOVA



Summary



Normal Two-Way ANOVA



Introduction: Main and Interaction Effects



Data Generation



Aside: Using Simulation to Assess Bias and Precision of an Estimator



Analysis Using R



Analysis Using WinBUGS



Summary



General Linear Model (ANCOVA)



Introduction



Data Generation



Analysis Using R



Analysis Using WinBUGS (and a Cautionary Tale About the Importance of Covaviate Standardization)



Summary



Linear Mixed-Effects Model



Introduction



Data Generation



Analysis Under a Random-Intercepts Model



Analysis Under a Random-Coefficients Model without Correlation between Intercept and Slope



The Random-Coefficients Model with Correlation between Intercept and Slope



Summary



Introduction to the Generalized Linear Model: Poisson ï¿½t-testï¿½



Introduction



An Important but Often Forgotten Issue with Count Data



Data Generation



Analysis Using R



Analysis Using WinBUGS



Summary



Overdispersion, Zero-Inflation, and Offsets in the GLM



Overdispersion



Zero-Inflation



Offsets



Summary



Poisson ANCOVA



Introduction



Data Generation



Analysis Using R



Analysis Using WinBUGS



Summary



Poisson Mixed-Effects Model (Poisson GLMM)



Introduction



Data Generation



Analysis Under a Random-Coefficients Model



Summary



Binomial ï¿½t-Testï¿½



Introduction



Data Generation



Analysis Using R



Analysis Using WinBUGS



Summary



Binomial Analysis of Covariance



Introduction



Data Generation



Analysis Using R



Analysis Using WinBUGS



Summary



Binomial Mixed-Effects Model (Binomial GLMM)



Introduction



Data Generation



Analysis Under a Random-Coefficients Model



Summary



Nonstandard GLMMs 1: Site-Occupancy Species Distribution Model



Introduction



Data Generation



Analysis Using WinBUGS



Summary



Nonstandard GLMMs 2: Binomial Mixture Model to Model Abundance



Introduction



Data Generation



Analysis Using WinBUGS



Summary



Conclusions


Appendix


References


Index