ECTS credits: 1.5 ECTS

Course parameters:

Language: English

Level of course: PhD Course

Time of year: 7-9 January 2025

Capacity limits: 30

Course fee: DKK 350

Objectives of the course:

The course will provide an applied introduction to generalized linear mixed modelling in R for biologists. The course will equip participants to fit appropriate models to data using R and the lme4 and glmmTMB packages, how to test the assumptions of the fitted model and assess the adequacy of fit, and how to use the model to estimate quantities of interest or test hypotheses of interest using the marginaleffects package.

Learning outcomes and competences:
At the end of the course, the student should be able to:

• have a good introductory understanding of the concepts of fixed and random effects and mixed or hierarchical modelling in general,
• be able to choose an appropriate method to use to analyse a data set,
• know how to diagnose problems with fitted models,
• know how to test specific hypotheses and estimate quantities of interest using fitted models,
• be able to use the R statistical software and in particular the lme4, glmmTMB, and marginaleffects packages to fit and analyse generalized linear mixed effects models.

Compulsory programme:

Active participation in the course including attendance at lectures and completion of computer-based classes and exercises. Completion of short, computer-based assessments testing their understanding of a topic and the practical skills taught. For credit, students must complete a data analysis exercise to be submitted one week after the end of the course (23 August).

Course contents:

The course is based on a series of lectures and computer-based practical classes led by an international expert in biological data analysis, who has expertise in mixed and hierarchical modelling.

The course covers the following topics:

• Generalized linear models for data that are not Gaussian
• Fixed and random effects in Generalized linear mixed models (GLMMs)
• Fitting GLMMs with the lme4 and glmmTMB packages
• Model diagnostics and assessment
• Estimating marginal effects and adjusted predictions with GLMMs
• Hypothesis testing using GLMMs
• Displaying model estimates and reporting results

Prerequisites:

This course is suitable for Phd students (including senior thesis-based MSc students) and researchers working with biological data where observations are correlated or grouped in some way, such as longitudinal data, or experimental data with blocking. The course will be of particular interest to PhD candidates and researchers in inter alia biology, animal science, ecology, agriculture. Some prior knowledge of R is required, and some prior knowledge of generalized linear modelling in R would be an advantage.

Name of lecturer:

Assistant Professor Gavin Simpson, Department of Animal and Veterinary Sciences, Aarhus University gavin@anivet.au.dk

Literature:

Open access teaching resources prepared by the course leader will be supplemented by original literature (papers). Electronic copies of the open access teaching resources will be provided to each participant before the course starts.

Course homepage:

https://github.com/gavinsimpson/au-viborg-mixed-modelling-course

Course assessment:

The course will be assessed through a data analysis exercise (take home).

Provider:

The course is provided by the Department of Animal and Veterinary Sciences, Aarhus University

Time:

3 days of teaching in a single block (7-9 January 2025). Classes are held from 09.30 to 16.00 each day.

Place:

The course will be taught at AU Campus Viborg

Course fee:

DKK 350

Registration:

Registration deadline is 3 January 2025. Please sign up for the course here: https://events.au.dk/phdcourseappliedmixedmodellingwithr

If you have any questions, please contact Assistant Professor Gavin Simpson, Department of Animal and Veterinary Sciences, Aarhus University, e-mail: gavin@anivet.au.dk