Keywords: Zero inflated count data. Zero inflated continuous data.
Dependency. ZIP and ZAP models. Zero inflated GLMMs with random effects.
Bayesian statistics, MCMC and JAGS. lme4, glmmADMB, JAGS. Overdispersion
and solutions. Bayesian model selection.
Description: 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, potentially resulting in overdispersed Poisson
GLMs. In such cases zero inflated models can be applied. During the
course several case studies are presented, in which the statistical
theory for zero inflated models 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. Zero
inflated GLMMs for nested data (repeated measurements, short time
series, clustered data, etc.) are discussed in the second part of the
course. We will focus on zero inflated count data, and zero inflated
Dr. Alain F. Zuur
First author of:
1. Beginner's Guide to GAMM with R (2014).
2. Beginner's Guide to GLM and GLMM with R (2013).
3. Beginner's Guide to GAM with R (2012).
4. Zero Inflated Models and GLMM with R (2012).
5. A Beginner's Guide to R (2009).
6. Mixed effects models and extensions in ecology with R (2009).
7. Analysing Ecological Data (2007).