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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 (and the text below) how to access the pdfs of these chapters.

- 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 data

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The paperback is priced at 49 GBP. Click to order the book.

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Video file with general comments Alain Zuur

All data sets used in the book are provided as *.txt or *.xls files. Right-mouse
click on a data file and click on Save-As.
R code for each chapter is password protected. The password is given on page vi in the preface of the book. See the paragraph "Data sets and R code used in this book" |
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Support routines that we source in
various chapters:
HighstatLibV6.R and
MCMCSupportHighstat.R. Just copy these two files in the working directory (use Save As) and type: source(file = "HighstatLibV6.R") source(file = "MCMCSupportHighstat.R") |
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pdf file with some simple explanations on matrix notation | ||||

Chapter |
Title |
Data sets |
R code* |
Video files |

1 | Introduction to generalized linear models |
Baileyetal2008.xls WorkerBees.xls DrugMites.xls |
Chapter1.R.zip | 5 July |

2 | Generalized linear modelling applied to red squirrel data | RedSquirrels.txt | Chapter2.R.zip | 5 July |

3 | GLM applied to presence-absence polychaeta data | PolychaetaV3.txt | Chapter3.R.zip | 5 July |

4 | Introduction to mixed effects models | Spiders.txt | Chapter4.R.zip | 5 July |

5 | GLMM applied to honeybee pollination data | pollen.txt | Chapter5.R.zip | 5 July |

6 | GLMM for strictly positive data: biomass of rainforest trees | seedling.txt | Chapter6.R.zip | 5 July |

7 | Binomial, Beta-binomial, and Beta GLMM applied to Cheetah Data | Cheetah.txt | Chapter7.R.zip | 5 July |

Discussion boardJoin the Discussion board (for free) to ask questions on the book chapters. Access Discussion board In case you have problems accessing the Discussion board: Instructions for accessing the Discussion board. |
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Rather than reproducing the material on MCMC, we give the reader of this book electronic access to Chapter 1 of Zuur et al. (2012a), which contains an introduction to Bayesian statistics and MCMC. Chapter 1 of Zuur (2012b) provides an introduction to multiple linear regression, which is also prerequisite knowledge for this book. These two chapters are downloadable from:

- Introduction to Bayesian
statistics, Markov Chain Monte Carlo techniques, and WinBUGS.
Chapter 1 in:
*Zero Inflated Models and Generalized Linear Mixed Models with R*(2012). Zuur AF, Saveliev AA, Ieno EN. We are in the process of changing the WinBUGS code in this chapter to JAGS code. The modified chapter will be available on 21 June 2013 - Review of multiple linear
regression. Chapter 1 in:
*A Beginner’s Guide to Generalized Additive Models with R*(2012). Zuur AF.

Both chapters are password protected. The password is given on page vi in the preface. See the paragraph labelled "Chapter 1 of Zuur et al. (2012a) and Zuur (2012b)".