Beginner's Guide to GLM and GLMM with R.  Zuur AF, Hilbe JM and Ieno EN

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Data sets and R code used

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Outline

 This book presents Generalized Linear Models (GLM) and Generalized Linear Mixed Models (GLMM) based on both frequency-based and Bayesian concepts. Using ecological data from real-world studies, the text introduces the reader to the basics of GLM and mixed effects models, with demonstrations of binomial, gamma, Poisson, negative binomial regression, and beta and beta-binomial GLMs and GLMMs. 

The book uses the functions glm, lmer, glmer, glmmADMB, and also JAGS from within R. JAGS results are compared with frequentist results.

R code to construct, fit, interpret, and comparatively evaluate models is provided at every stage. Otherwise challenging procedures are presented in a clear and comprehensible manner with each step of the modelling process explained in detail, and all code is provided so that it can be reproduced by the reader.  

Readers of this book have free access to:

•  Chapter 1 of Zero Inflated Models and Generalized Linear Mixed Models with R. (2012a) Zuur, Saveliev, Ieno.
• Chapter 1 of Beginner's Guide to Generalized Additive Models with R. (2012b) Zuur, AF.

See the Preface how to access the pdfs of these chapters.

Keywords

• Introduction to GLM
  • Poisson GLM and Negative binomial GLM for count data
  • Binomial GLM for binary data
  • Binomial GLM for proportional data
  • Other distributions
• GLM applied to red squirrel data
  • Bayesian approach – running the Poisson GLM
  • Running JAGS via R
  • Applying a negative binomial GLM in JAGS
• GLM applied to presence-absence Polychaeta data
  • Model selection using AIC, DIC and BIC in jags
• Introduction to mixed effects models
• GLMM applied on honeybee pollination data
  • Poisson GLMM using glmer and JAGS
  • Negative binomial GLMM using glmmADMD and JAGS
  •  GLMM with auto-regressive correlation
• GLMM for strictly positive data: biomass of rainforest trees
  • gamma GLM using a frequentist approach
  • Fitting a gamma GLM using JAGS
  • Truncated Gaussian linear regression
  • Tobit model in JAGS
  • Tobit model with random effects in JAGS
• Binomial, beta-binomial, and beta GLMM applied to cheetah 

Table of contents

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Data sets and R code used in the book

All data sets used in the book are provided as *.txt files. Right-mouse click on a data file and click on Save-As.

• Data sets and R code will be available from 1 June 2013

Support chapters

Support chapters will be available from 1 June 2013

Discussion board

Available from 1 June 2013