Introduction to Generalised Linear Mixed Models for Ecologists (MMIE02)
Learn to build and interpret linear, generalised linear, and multilevel models for ecological data using R, lme4, and rstanarm in this five day applied training course.
Duration: 5 Days, 5 hours per day
Next Date: February 2-6, 2026
Format: Live Online Format
UK (GMT) local time - All sessions will be recorded and made available to ensure accessibility for attendees across different time zones.
£450Registration Fee
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Course Description
This 5-day course offers a practical and theoretical introduction to statistical models, through the most fundamental and versatile statistical procedure in ecology: the linear model. We’ll begin by looking at simple linear models with both continuous and discrete predictors – this includes regression, ANOVA, ANCOVA, and all classic ANOVA designs. We’ll also look at extending these linear models to include counts and proportions as responses, and to include data that naturally falls into groups – such as repeated measures or spatial plots – which require a multilevel or hierarchical model.
Participants will learn to work with ecological data from both experiments and designs, where factors we’re interested in may be nested, crossed or in some way structured. We’ll cover ways to add flexibility into models, such as adding group-level predictors and overdispersion. We’ll learn and contrast both Bayesian and frequentist approaches to modelling. Along the way we’ll practice essential skills such as diagnostic plots, model selection, and predictions. We’ll do this by applying the models we’re studying to real-world data.
This course is ideal for ecologists and applied scientists working with grouped, hierarchical, or repeated-measures data.
The course will be taught using R and Rstudio, and the packages lme4 (for frequentist statistics) and rstanarm for Bayesian statistics.
What You’ll Learn
During the course we will cover the following:
- Understand the linear model that underlies regression, ANOVA, ANCOVA, and all traditional designed experiments.
- How to understand a model using plotted predictions.
- Evaluate data using simulations.
- Fit and interpret linear models, generalised linear models (GLMs) and hierarchical models using both lme4 and rstanarm.
- Model count and binary data appropriately, including overdispersion and zero-inflation.
- Communicate uncertainty and model results using plots and statistical summaries.
Course Format
Interactive Learning Format
Each day features a well-balanced combination of lectures and hands-on practical exercises, with dedicated time for discussing participants’ own data, time permitting.
Global Accessibility
All live sessions are recorded and made available on the same day, ensuring accessibility for participants across different time zones.
Collaborative Discussions
Open discussion sessions provide an opportunity for participants to explore specific research questions and engage with instructors and peers.
Comprehensive Course Materials
All code, datasets, and presentation slides used during the course will be shared with participants by the instructor.
Personalized Data Engagement
Participants are encouraged to bring their own data for discussion and practical application during the course.
Post-Course Support
Participants will receive continued support via email for 30 days following the course, along with on-demand access to session recordings for the same period.
Who Should Attend / Intended Audiences
This course is aimed at quantitative scientists who work with data to test hypotheses, estimate causal effects, or build predictive models. It is designed for participants who already have some experience in R, including installing and managing packages, organising data with the tidyverse, and creating visualisations with ggplot2, and who have a working installation of a recent R version with the lme4 and rstanarm packages. A basic foundation in statistics is recommended, as the course begins with a review of simple regression and then progressively builds towards more advanced modelling concepts and techniques.
Equipment and Software requirements
A laptop or desktop computer with a functioning installation of R and RStudio is required. Both R and RStudio are free, open-source programs compatible with Windows, macOS, and Linux systems.
A working webcam is recommended to support interactive elements of the course. We encourage participants to keep their cameras on during live Zoom sessions to foster a more engaging and collaborative environment.
While not essential, using a large monitor—or ideally a dual-monitor setup—can significantly enhance your learning experience by allowing you to view course materials and work in R simultaneously.
A comprehensive list of required packages will also be shared with participants ahead of the course to allow for pre-installation. Participants are required to install rstanarm in advance of the course, and to test it by running an example from the package vignette.
Dr Andrew MacDonald
Andrew is a community ecologist and entomologist, and an applied statistician also. It is the passion for learning, practicing, and talking about statistics that led him into collaborations with scientists from many different fields. Andrew has extensive experience in collaboration and consulting, and now has experience building statistical models of animal behaviour, community composition, occupancy modelling, tree growth, and survey data from humans – to name just a few. He’s especially passionate about helping ecologists connect their theories and their data through expressive statistical models. His favourite tools to do this with are R, brms, and Stan. His goal is to improve our ability to make predictions for ecological systems, and to build a community of practice around statistical methods in ecology.
Education & Career
• PhD in Zoology (Experimental community ecology)
• MSc in Botany (Invasive species ecology)
• Instructor and consultant on a wide range of ecological topics.
Teaching & Skills
• Teaching topics including GLMs, nonlinear models, and hierarchical models – usually with a Bayesian perspective.
• Training and advice regarding reproducible workflows and open science.
• Experienced in collaboration and in developing statistical methods to combine theory with a specific dataset.
Links
Google scholar
Session 1 – 01:40:00 – Introduction to the Linear model. A review of the linear model; why it is important to ecologists, and the strengths and weaknesses of this approach.
Session 2 – 01:40:00 – Building and plotting a linear model – data simulation. The power of simulation for building understanding, how to plot a model, fitted vs predicted values.
Session 3 – 01:40:00 – Building and plotting a linear model – published data. Introduction to the penguins dataset, building and plotting a model with these data, including a second predictor – simpson’s paradox, comparison of bayesian approach with frequentist.
Session 4 – 01:40:00 – Adding linear interactions to models. Theory and code for adding interactions, visualizing continuous interactions, the importance of centering predictor variables.
Session 5 – 01:40:00 – Introduction to generalized linear models. Link functions and why we use them, a bestiary of statistical distributions that ecologists use, studying a GLM with simulations
Session 6 – 01:40:00 – Fitting GLMs to data. Understanding GLM syntax in lme4 and brms, Interpreting coefficients from summaries and plots.
Session 7 – 01:40:00 – Why Multilevel Models? Grouped data is everywhere in ecology, pseudoreplication and why we must avoid it, bias-variance trade off and partial pooling, mathematical definition of hyperparameters.
Session 8 – 01:40:00 – Simulating random effects models. Generating simulated datasets for simple and crossed designs, recovering parameters and hyperparameters with lme4 and brms, plots for hierarchical models – exploring shrinkage.
Session 9 – 01:40:00 – Model Diagnostics for Multilevel Models. Troubleshooting hierarchical models, strengths and weaknesses of lme4 and brms, two kinds of predictions (at least) in multilevel models.
Session 10 – 01:40:00 – Data simulation for GLMMs in ecology. Numbers on the link scale are larger (or smaller) than they appear!, simulating from hyperparameters, random slopes and intercepts
Session 11 – 01:40:00 – Fitting and plotting hierarchical GLMs. Interpretation of summary tables, visualizing a model object.
Session 12 – 01:40:00 – GLMMs for designed experiments & surveys. GLMMs for different experimental designs – 2-way ANOVA, nested or crossed designs, repeated measures, In-class work on student models or designs.
Session 13 – 01:40:00 – Extending Count Models: overdispersion . How to identify overdispersion, poisson models with observation-level random effects, Negative Binomial models, and Beta-binomial models.
Session 14 – 01:40:00 – Binary and Proportional Data – accounting for unequal sampling effort. Using link functions and the offset() function for counts and proportions, why correcting for exposure by dividing can often multiply your problems.
Session 15 – 01:40:00 – Review and questions. Review of model building, simulation, fitting and checking, time for questions from participants and independent projects.
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Testimonials
PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.
Rolando O. Santos
PhD Assistant Professor, Florida International University
Courses attended
SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07
Frequently asked questions
Everything you need to know about the product and billing.
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
When will I receive instructions on how to join?
You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.
Do I need administrator rights on my computer?
I’m attending the course live — will I also get access to the session recordings?
I can’t attend every live session — can I join some sessions live and catch up on others later?
I’m in a different time zone and plan to follow the course via recordings. When will these be available?
I can’t attend live — how can I ask questions?
Will I receive a certificate?
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