This is an open online live course
Open online live course: GLMs and GAMs with Spatial, Temporal or Spatial-Temporal Correlation using R-INLA.
- Dates: 6 - 10 October 2025 (5 days).
- Times: 08.30 - 16.00 UK time (BST)
- Included: 1 hour face-to-face video chat about your data.
For more information, see the course flyer: Flyer2025_10_SpatTempGLMGAM.pdf
Price:
- Early bird registration (May and June): 450 GBP
- July - October: 500 GBP
Course format
- Live online teaching is from 08.30 - 16.00 UK time.
- The course includes a few theory presentations along with a large number of exercises using real data sets.
- Detailed, annotated R code will be provided, and a brief period will be set aside for practice before each exercise is discussed in depth.
Brief outline
This course offers a practical introduction to the analysis of spatial, temporal, and spatial-temporal data using generalised linear models (GLMs) and generalised additive models (GAMs) in R-INLA.
We begin with how to add spatial structure to regression models using frequentist techniques, and then introduce Bayesian methods, focusing on how to implement models with spatial and temporal dependency using R-INLA. The course covers a range of data types and distributions, including Gaussian, Poisson, generalised Poisson, negative binomial, Bernoulli, and Tweedie.
Participants will learn how to build models that incorporate spatial correlation, temporal trends, and spatio-temporal structure, and how to address practical challenges such as modelling in the presence of natural barriers (e.g., coastlines, forests) that prevent spatial correlation from extending freely across space, such as marine/terrestrial boundaries or fragmented habitats. We will also cover the use of more complex spatial meshes and multivariate likelihoods to accommodate study areas with isolated groups of sites.
Through hands-on exercises, you will gain experience in fitting and interpreting models for continuous, count, and binary data, and understand how to adapt your models to the specific structure and distribution of your data.