Zero-inflated Models & GLMM using R

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Zero-inflated Models & GLMM using R

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What is zero inflation? Suppose you want to study hippos and the effect of habitat variables on their distribution. When sampling, you may count zero hippos at many sites and therefore zero-inflated models should be used. During this course several case studies are presented, in which the statistical theory is integrated with applied analyses in a clear and understandable manner. Zero-inflated models consist of two integrated GLMs and therefore we will start with a revision of GLM. GLMMs and zero-inflated GLMMs are discussed in the second part of the course. This is a non-technical course.

Course Material
  • Chapters 5, 8-11 and 13 in Zuur, Ieno, Walker, Saveliev, Smith (2009). Mixed effects models and extensions in ecology with R. (E-copies of these chapters are provided)
  • Chapter 1 in Zuur, Saveliev, Ieno (2012). Zero-inflated Models and Generalized Linear Mixed Models with R. (E-copy of this chapter is provided)

Most exercises are worked out in detail in:

  • Zuur, Hilbe, Ieno (2013) A Beginner's Guide to GLM and GLMM with R
  • Zuur (2012) A Beginner's Guide to GAM
  • Zuur, Ieno, Saveliev (2014) A Beginner's Guide to GAMM

Note: The course can be followed without buying these three 'Beginner's Guide to ...' books.


Day 1

  • General introduction
  • Revision GLM (Poisson, negative binomial, binomial). Based on Chapters 8 - 10 in Zuur et al. (2009). Or Chapter 1 in Zuur et al. (2013)
  • Revision Bayesian statistics, MCMC and JAGS. Based on Chapter 1 in Zuur et al. (2012), and Chapter 1 in Zuur et al (2013)
  • Two exercises (using glm, JAGS and INLA from R). JAGS is similar to WinBUGS and OpenBUGS

Day 2-3

  • GLM for zero-inflated data. Based on Chapter 11 in Zuur et al. (2009)
  • We discuss mixture models (ZIP and ZINB)
  • Two exercises on the analysis of zero-inflated data using ZIP and ZINB

Day 3-4

  • Revision linear mixed effects models. Based on Chapter 5 in Zuur et al (2009) or Chapter 4 in Zuur et al. (2012)
  • Introduction GLMM (Poisson, negative binomial, binomial) using lme4, glmmADMB, JAGS and INLA
  • Three exercises (count data, proportional data)
  • Zero-inflated GLMM using JAGS and INLA. Based on various chapters in Zuur et al. (2012) and Zuur et al. (2013)

Day 4-5

  • Finalising exercises
  • Time allowing: Generalized additive (mixed) effects models. Based on Zuur (2012) and Zuur et al. (2014)
General information
Target Group The course is aimed at PhD candidates and other academics
Group Size Min. 15, max. 25 participants
Course duration 5 days
Language of instruction English
Frequency of recurrence Once every two years
Number of credits 2.5 ECTS
Lecturers Dr. Alain Zuur and Dr. Elena Ieno (Highland Statistics Ltd, UK)
Prior knowledge The entrance level of this course is reasonably high. Fluent knowledge of data exploration, multiple linear regression and R is required. A short revision (!) of GLM, linear mixed effects models and MCMC (using JAGS) is provided.
Location Wageningen University Campus
Options for accommodation Accommodation is not included in the fee of the course, but there are several possibilities in Wageningen. For information on B&B's and hotels in Wageningen please visit Another option is Short Stay Wageningen. Furthermore Airbnb offers several rooms in the area. Note that besides the restaurants in Wageningen, there are also options to have dinner at Wageningen Campus.
More information

Claudius van de Vijver (PE&RC)
Phone: +31 (0) 317 485116

Lennart Suselbeek (PE&RC)
Phone: +31 (0) 317 485426

Registration of interest

At this moment, this course is not scheduled yet. However, if you register your interest in this activity below, we will inform you as soon as the course is scheduled and registration of participation is opened.