/au-viborg-mixed-modelling-course

MIT LicenseMIT

Applied mixed modelling with R

Aarhus University PhD Course

  • Autumn 2024 running: August 13th – 15th

  • ECTS credits: 1.5 ECTS

  • Language: English

  • Fee: 350 DKK

Name of course leader

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

Registration

To register for the course, please contact Julie Jensen on jsj@anivet.au.dk.

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

After completing the course, participants will

  1. have a good introductory understanding of the concepts of fixed and random effects and mixed or hierarchical modelling in general,
  2. be able to choose an appropriate method to use to analyse a data set,
  3. know how to diagnose problems with fitted models,
  4. be able to use the R statistical software to analyse multivariate data
  5. 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 (23rd August).

Course content

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 masters 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 course leader

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