Course flyer
HYBRID: This is an onsite course, but you can also join online via Zoom
Location: James Cook University, Townsville Campus, Australia
Dates and times: 2 - 6 March 2026. 09.00 - 16.00 (local time)
This course is a hands-on introduction to generalised linear mixed-effects models and generalised additive mixed models in R.
We’ll start with a quick refresher on multiple linear regression, then move on to generalised additive models (GAMs) — a flexible way to handle non-linear relationships. You’ll get to see how they work through practical
exercises, not heavy maths. Next, we’ll look at mixed-effects models, which are great for data that come from grouped or repeated measurements — like several observations from the same site or individual. We’ll then combine the two approaches to create generalised additive mixed models (GAMMs) and work through examples using GLMMs and GAMMs.
In the final part of the course, we’ll apply these models to spatial and spatio-temporal data, using different types of response variables — continuous, counts, and more — with Gaussian, Poisson, and negative binomial distributions. We’ll use R throughout, mainly with the mgcv and glmmTMB packages.
It’s a practical, non-technical course designed to help you understand how to fit, interpret, and visualise these models in R
The course includes a 1-hour face-to-face video chat with the instructors.
Pre-required knowledge
Working knowledge of R, data exploration, linear regression and GLM (Poisson, negative binomial) is required. The course website provides preparatory materials, including on-demand videos and R scripts covering multiple linear regression, basic matrix notation, generalised linear models, model validation using DHARMa, and the explanation of variograms.
1 hour face-to-face
The course includes a 1-hour face-to-face video chat with the instructors (to be used after the course). You are invited to apply the statistical techniques discussed during the course on your own data and if you encounter any problems, you can ask questions during the 1-hour face-to-face chat.
A discussion board (access for 12 months) allows for interaction on course content between instructors and participants.
Course content
Preparation material (in case you are not familiar with the required knowledge). Estimated time: 2 hours
- Exercise on linear regression (with on-demand video).
- Exercise on Poisson GLM (with on-demand video).
- Short explanation o DHARma (with on-demand video).
- Short explanation of a variogram (with on-demand video).
Module 1:
- General introduction.
- Theory presentation on GAM.
- Two introductory GAM exercises.
- GAM exercise discussing model selection and smoother interactions.
Module 2:
- Catching up
- Theory presentation on linear-mixed effects models.
- One exercise using linear mixed-effects models.
- One exercise on Gaussian additive mixed-effects models (GAM with random effects).
Module 3:
- Short theory presentation on hierarchical GAMs
- Exercise using hierarchical GAMs (the GAM equivalent of random slope models).
- Exercise using hierarchical GAMs to estimate common trends in time series.
- One exercise on negative binomial GAM.
Module 4:
- One exercise on negative binomial GLMM.
- Two exercises on Poisson and negative binomial GAMM.
Module 5:
- One exercise on (Gaussian) GAM applied to spatial data.
- One exercise on GAM applied to spatial count data.
- One exercise on GAM(M) applied to spatial-temporal count data.
- Time allowing: Guidance for the analysis of binary, continuous, proportional and continuous data (Bernoulli, binomial, beta, Tweedie and Gamma distributions).
The course website provides preparatory materials, including on-demand videos and R scripts covering multiple linear regression, basic matrix notation, generalised linear models, model validation using DHARMa, and the explanation of variograms. If you are not familiar with these methods, please review them before the course begins.
We reserve the right to change the exercises. Pdf files of all theory material will be provided. All exercises consist of data sets and annotated R scripts. Access to the course website is for 12 months.
For terms and conditions, see: