Introduction to Generalised Linear Models using R (GLMG01) SOLD OUT!
Learn Generalised Linear Models (GLMs) in R with this live online course. Covers Poisson regression, logistic regression, multinomial, ordinal, Gamma models, mixed-effects, and Bayesian GLMs. Ideal for researchers, postgraduate students, and data analysts.
Duration: 5 Days, 6 hours per day
Next Date: January 12-16, 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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- Overview
- Instructors
- Schedule
Course Description
This 5-day intensive course provides a comprehensive and practical introduction to Generalised Linear Models (GLMs) using R. GLMs extend the classical linear model to accommodate response variables that are not normally distributed – including counts, binary outcomes, proportions, categorical responses, and strictly non-negative data.
The course begins with linear models as the foundation of GLMs, then introduces data preparation and visualisation strategies for model fitting. We move on to Poisson regression (a natural first step into GLMs), then cover logistic regression for binary and grouped binomial data. From there, we expand into more complex categorical outcomes (multinomial and ordinal regression), before finishing with Gamma GLMs, mixed-effects extensions, zero-inflation, and an introduction to Bayesian approaches.
Each day blends theory with hands-on practice using R, including live coding, exercises, and opportunities to discuss participants’ own data. By the end of the course, participants will be confident in applying GLMs to real-world data problems across diverse disciplines.
What You’ll Learn
By the end of the course participants should:
- Understand the theoretical foundations of GLMs and their relationship to linear models.
- Choose appropriate error structures and link functions for different types of data.
- Fit, interpret, and evaluate Poisson, logistic, multinomial, ordinal, and Gamma GLMs in R.
- Diagnose model fit, check assumptions, and handle issues such as overdispersion.
- Apply GLMs to real-world datasets across a range of disciplines.
- Communicate model results effectively with visualisations and clear interpretation.
- Recognise best practices and common pitfalls in applied GLM analysis.
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 resource is aimed at data analysts, postgraduate students, and early-career researchers who are working with statistical models. It assumes basic experience with R and RStudio, such as importing data and running simple functions. A foundational understanding of statistics—including concepts like mean, variance, correlation, and linear regression—is expected, though prior experience with linear models, while helpful, is not essential as key ideas will be reviewed. Additional skills such as data wrangling with packages like dplyr or tidyr, creating basic plots with ggplot2, and interpreting model output are not required but will make the material easier to follow.
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 or linux simultaneously.
All necessary R packages will be introduced and installed during the workshop. A comprehensive list of required packages will also be shared with participants ahead of the course to allow for optional pre-installation.
Dr. Niamh Mimnagh
Niamh is a statistician working at the interface of ecology, epidemiology, and data science. Her research focuses on applying and developing statistical and machine learning methods to address real-world challenges such as estimating species population sizes from count and trace data and predicting livestock disease re-emergence using sparse or imbalanced datasets. She works with a wide array of statistical approaches, including Bayesian hierarchical models, N-mixture models, anomaly detection algorithms, and spatial analysis techniques.
Niamh earned her PhD in Statistics, with a focus on multispecies abundance modelling, and holds a first-class MSc in Data Science. Alongside her research, she is actively engaged in science communication and education, running a popular blog on applied statistics for non-specialists, and regularly delivering workshops and guest lectures on topics such as GLMs and machine learning with imbalanced data.
Education & Career
- PhD in Statistics (Multispecies Abundance Modelling)
- MSc in Data Science (First Class Honours)
- Instructor, consultant, and science communicator in statistical ecology and epidemiology
Research Focus
Niamh’s work centres on extracting meaningful insights from complex ecological and epidemiological data. She is particularly interested in population estimation techniques and predictive modelling for conservation and disease management, using advanced statistical tools and reproducible workflows.
Current Projects
- Development of Bayesian and ML approaches for estimating species abundance from imperfect data
- Modelling livestock disease risk using spatial and temporal predictors
- Creating accessible educational materials for teaching applied statistics in R
Professional Consultancy
Niamh provides expert statistical support to academic and applied research projects, with a focus on ecological monitoring, conservation planning, and disease modelling. She also advises on study design and data workflows for interdisciplinary teams.
Teaching & Skills
- Teaches topics including GLMs, Bayesian statistics, machine learning for imbalanced data, and spatial statistics in R
- Advocates for reproducibility, open science, and accessible statistical training
- Experienced in communicating complex methods to broad audiences
Links
Session 1 – 01:30:00 – Introduction to Linear Models
The normal linear model as the foundation for GLMs: assumptions, model structure, categorical predictors, and limitations.
Session 2 – 01:30:00 – From Linear Models to GLMs
Why GLMs are needed; exponential family distributions; link functions; GLM structure.
Session 3 – 01:30:00 – GLMs in R
Using the glm() function; understanding model objects and outputs.
Session 4 – 01:30:00 – Data Preparation and Visualisation
Preparing data for GLMs: handling factors, scaling and centering predictors, dealing with missing data.
Session 5 – 01:30:00 – Poisson Regression I
Introduction to Poisson regression for count data; interpreting log link coefficients.
Session 6 – 01:30:00 – Poisson Regression II
Practical applications: offsets, exposure variables, prediction and visualisation of count model outputs.
Session 7 – 01:30:00 – Residuals and Diagnostics
Checking model fit using residuals (including DHARMa); identifying patterns, influence, and outliers in GLMs.
Session 8 – 01:30:00 – Model Selection and Evaluation
Model comparison using AIC, BIC, likelihood ratio tests, and cross-validation. Principles of simplification vs. overfitting; assessing predictive performance.
Session 9 – 01:30:00 – Binary Logistic Regression I
The logit link and binary outcomes; fitting and interpreting logistic regression models.
Session 10 – 01:30:00 – Binary Logistic Regression II
Predicted probabilities, odds ratios, visualising predictions.
Session 11 – 01:30:00 – Grouped Binomial Models
Modelling grouped binary/proportion data; weights; comparing logit, probit, and cloglog links.
Session 12 – 01:30:00 – Multinomial Logistic Regression
Theory and practice of modelling unordered categorical outcomes; interpreting and visualising category probabilities.
Session 13 – 01:30:00 – Ordinal Logistic Regression
Ordered categorical outcomes; cumulative logit models; assumptions (e.g., proportional odds).
Session 14 – 01:30:00 – Gamma GLMs
Modelling skewed, strictly non-negative responses; log vs. identity links; comparing Gamma and log-normal approaches.
Session 15 – 01:30:00 – Overdispersion in GLMs
Identifying and diagnosing overdispersion in Poisson and binomial models; quasi-likelihood approaches; negative binomial extensions.
Session 16 – 01:30:00 – Full-Length Case Study
End-to-end application of GLMs: data preparation, model fitting, diagnostics, interpretation, and presentation.
Session 17 – 01:30:00 – Introduction to Bayesian GLMs
Bayesian inference in the GLM framework: priors, posteriors, uncertainty, and interpretation.
Session 18 – 01:30:00 – Bayesian GLMs in Practice
Fitting Bayesian GLMs in R (e.g., brms); posterior predictive checks; comparison with frequentist GLMs.
Session 19 – 01:30:00 – Mixed Effects GLMs (Intro to GLMMs)
Why random effects are useful; fitting random intercepts with lme4::glmer(); interpreting group-level effects.
Session 20 – 01:30:00 – Extensions and Wrap-Up
Random slopes, challenges with convergence and overfitting; best practices and pitfalls.
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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