Model Selection and Model Simplification (COURSE FULL)
Model Selection and Simplification in R – a live online course covering model fit, nested model comparison, cross-validation, information criteria (AIC/BIC), and variable selection methods including stepwise, ridge, Lasso, and elastic net.
Duration: 2 Days, 4 hours per day
Next Date: November 3-4, 2025
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.
£250Registration Fee
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- Overview
- Instructors
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Course Description
This 2-day course provides a practical and theoretical introduction to statistical model evaluation, comparison, selection, and simplification. These concepts are central to almost every form of applied statistical analysis, yet they are often poorly understood or inconsistently applied. Through lectures, worked examples, and hands-on coding in R, participants will explore measures of model fit, techniques for nested model comparison, and approaches to assessing predictive performance. The course will also cover variable selection methods (including stepwise regression, ridge regression, Lasso, and elastic nets). Examples will be drawn from linear models, generalised linear models, and mixed-effects models, ensuring broad relevance across disciplines.
What You’ll Learn
- Understand fundamental measures of model fit, including likelihood, log-likelihood, deviance, and residual sums of squares.
- Perform nested model comparisons in linear, generalised linear, and mixed effects models.
- Recognise the risks of overfitting and assess models using out-of-sample predictive performance.
- Apply cross-validation methods and interpret information criteria such as AIC.
- Compare and implement variable selection techniques, from stepwise regression to penalised regression (ridge, Lasso, elastic net).
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 material is intended for data analysts, postgraduate students, and researchers who use statistical models in their work. A basic familiarity with R and RStudio is assumed, such as loading data, running scripts, and working with packages. While prior knowledge of linear regression and generalized linear models (GLMs) is recommended, it is not strictly required. Some experience with data wrangling in R (for example using packages like dplyr or tidyr) and basic use of modeling functions (such as lm() or glm()) will be helpful but is not essential.
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 – 01:15:00 – Measuring Model Fit
Likelihood, log-likelihood, deviance, residual sums of squares, RMSE, and deviance residuals as tools for evaluating fit.
Session 2 – 01:15:00 – Nested Model Comparison
Defining nested models; F-tests in linear models; deviance-based tests in GLMs and mixed-effects models.
Session 3 – 01:15:00 – Out-of-Sample Predictive Performance
Overfitting and generalisation; cross-validation approaches; information criteria (AIC, BIC, WAIC) and their interpretation.
Session 4 – 01:15:00 – Variable Selection
Stepwise regression, ridge regression, Lasso, and elastic net. Advantages, limitations, and implementation in R.
Session 5 – 01:15:00 – Model Averaging
Principles of model averaging, weighting candidate models by predictive performance, and practical implementation using information criteria.
Session 6 – 01:15:00 – Best Practices and Applications
Comparing approaches; when to simplify vs. when to average; recommendations for robust practice.
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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