23554a
Lecture
SoSe 26: L Introduction to Generalized Linear Mixed Models in R
Oksana Buzhdygan, Felix May, Felix Nößler
Information for students
Additional module information: Introduction to Generalized Linear Mixed Models in R
Additional information / Pre-requisites
Prior knowledge in R and linear models including regression, ANOVA and ANCOVA is required. Please bring your own laptop with R and RStudio installed.
Comments
This course builds on the course Introduction to Advanced Biostatistics and covers the following topics:
- Recapitulation of linear and generalized linear models
- Introduction to Linear Mixed Models for grouped data in R
- Examples for data sets with spatially nested structure or longitudinal data with repeated measures
- Introduction to Generalized Linear Models for count, proportion and binary data
- Fundamentals of Bayesian Statistics
- Maximum Likelihood Estimation and Bayesian Inference using Markov-Chain-Monte Methods
- Zero-inflation and overdispersion in GLMMs
- Outlook on more complex hierarchical models
Suggested reading
Statistical rethinking : a Bayesian course with examples in R and Stan / Richard McElreath, 2020. Boca Raton ; London ; New York : CRC Press
A beginner's guide to GLM and GLMM with R : a frequentist and Bayesian perspective for ecologists / Alain F. Zuur ; Joseph M. Hilbe ; Elena N. Ieno. 2013. Highland statistics
Mixed effects models and extensions in ecology with R / Alain F. Zuur 2009. Springer
The R book / Michael J. Crawley. Wiley, 2013.
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10 Class schedule
Regular appointments
Mon, 2026-09-21 10:00 - 11:00
Tue, 2026-09-22 10:00 - 11:00
Wed, 2026-09-23 10:00 - 11:00
Thu, 2026-09-24 10:00 - 11:00
Fri, 2026-09-25 10:00 - 11:00
Mon, 2026-09-28 10:00 - 11:00
Tue, 2026-09-29 10:00 - 11:00
Wed, 2026-09-30 10:00 - 11:00
Thu, 2026-10-01 10:00 - 11:00
Fri, 2026-10-02 10:00 - 11:00