26-30 September 2022


Due to the COVID-19 outbreak, this course will be held online


Course overview

The framework of (generalised) linear mixed models (GLMMs) is the de-facto standard for the statistical analyses of experimental and observational data. The GLMM framework extends the standard LM/GLM models with a) random effects, to account for grouped data, b) variance structures, to account for non-constant variances c) correlation structures, e.g. to account for residual spatial / temporal autocorrelation. In this course, you will learn to specify, interpret and validate linear and generalized linear mixed models. The key focus will be to produce a valid and defensible analysis of experimental or observational data in an applied research context in R, with a focus on the regression packages lme4 and glmmTMB.


Target audience and assumed background

The course is aimed at graduate students and researchers that have previously run regression models in R, and that want to broaden their knowledge about advanced options in this context, in particular how to solve problems that typically occur in the analysis of real experimental or observational data (random effects, heteroskedasticity, correlations, …).  

Learning outcomes

1.    Understand the components of the GLMM framework (choice of distributions, random effects, variance structures, correlation structures)
2.    Being able to choose the appropriate model structure in an applied analysis of experimental or observational data (focusing on the R packages lme4 and glmmTMB)
3.    Know how to visualise fitted GLMMs (R package effects) and to check the assumptions of the model (R package DHARMa)


Sessions from 13:00 to 19:00 (Monday to Thursday), 13:00 to 18:00 on Friday (Berlin time).  From Tuesday to Friday, the first hour will be dedicated to Q&A and working through practical exercises or students’ own analyses over Slack and Zoom. Sessions will interweave mix lectures, in-class discussion/ Q&A, and practical exercises.



Monday– Classes from 1-7 PM Berlin time

On day 1, we will recap regression basics that would typically be covered in an introductory stats course: 1) the linear model: interpretation, diagnostics, multiple regression, scaling and centering, OVB, interactions 2) contrasts for categorial predictors 3) ANOVA 4) GLMs + diagnostics 5) Model selection, comparison and validation methods 6) causal inference, confounders and all that

Tuesday– Classes from 1-7 PM Berlin time

On day 2, we will talk about modelling variance in the linear model. We will introduce the the GLS framework as well as random and mixed effect models.

Wednesday– Classes from 1-7 PM Berlin time

On day 3, we will merge the topics from day 2 (variance structures and random effects) with the GLM framework, which means that we arrive at using GLMMs. We will talk about specification, diagnostics, and common issues when working with GLMMs, in particular variance partitioning and model selection for fixed and random effects. If we have time, we will also introduce cross-validation and the bootstrap.

Thursday– Classes from 1-7 PM Berlin time

Day 4 is about correlation structure in the data, other than random effects. Specifically, we'll talk about spatial, temporal and phylogenetic correlation structures and how to account for them in regression models.

Friday– Classes from 1-6 PM Berlin time


On day 5, we will speak about some advanced topics, in particular non-parametric methods, including null models, the boostrap and cross-validation, and how they relate to parametric methods, Bayesian estimation vs. frequentist estimation of GLMMs, or structural equation models. Emphasis can be changed depending on the interest of the group.





Cost overview

Package 1


480 €

Should you have any further questions, please send an email to info@physalia-courses.org

Cancellation Policy:


> 30  days before the start date = 30% cancellation fee

< 30 days before the start date= No Refund.


Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.