Course flyer
HYBRID: This is an onsite course, but you can also join online via Zoom
Location: Murdoch University, Perth, Australia
Dates and times: 9 - 13 February 2026. 09.00 - 16.00 (AWST)
We will begin with a step-by-step revision exercise on data exploration and linear regression. Next, we will introduce linear mixed-effects models, which are essential for analysing hierarchical or clustered data, such as multiple observations from the same vessel, catch, pen in a fish farm, animal, site, lake, or area. Keywords at this stage are dependency, pseudo replication, model diagnostics, model interpretation and model visualisation.
In the second part of the course, GLMMs are applied to count data, binary data (e.g. absence/presence of a disease), proportional data (e.g. % coverage), and continuous data with or without zeros, using the Gaussian, Poisson, negative binomial, generalised Poisson, Bernoulli, binomial, beta, gamma, and Tweedie distributions.
Time allowing, we also discuss power analysis to determine the optimal number of observations per
cluster and the optimal number of clusters.
The course includes a 1-hour face-to-face video chat with the instructors.
Pre-required knowledge
A working knowledge of R, multiple linear regression, and basic GLMs (such as Poisson, negative binomial, and Bernoulli GLMs) is recommended. This course is designed to be accessible and non-technical, with revision material available through on-demand videos to support your learning.
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
Monday:
- General introduction.
- Exercise revising data exploration and multiple linear regression in R.
- Theory presentation for linear mixed-effects models for nested data.
- Two exercises on linear mixed-effects models with random intercepts.
Tuesday:
- Catching up.
- Theory presentation on models with random intercepts and random slopes
- One exercise on linear mixed-effects models with random intercepts and slopes.
- Using multiple variances (Generalised Least Squares) to deal with heterogeneity.
- One exercise using GLS.
Wednesday:
- Three GLMM exercises:
- Poisson GLMM for the analysis of count data.
- Negative binomial GLMM for the analysis of count data.
- Negative binomial GLMMs with two-way nested and crossed random effects.
Thursday:
- Catching up
- Exercise showing how to apply a Bernoulli GLMM for the analysis of absence-presence data.
- Exercise showing how to apply a beta GLMM for the analysis of coverage data.
- Exercise showing how to apply a binomial GLMM for the analysis of proportional data.
Friday:
- Gamma GLMM exercise for the analysis of continuous positive data (without zeros).
- Tweedie GLMM exercise for the analysis of continuous positive data (with zeros).
- Time allowing: Determining optimal number of observations per cluster and the number of clusters using power analysis.
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: