Introduction to GAMM and GLMM with R - With GAM applications to spatial, and spatial-temporal data -. University of Essex, Colchester, UK. 24 - 28 June 2024
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This course offers an introduction to generalised additive models (GAMs), linear mixed-effects models, generalised linear mixed-effects models (GLMMs), the combination of GAMs and GLMMs, and also includes examples of GAMMs applied to spatial and spatio-temporal data.
The course begins with a review of multiple linear regression, followed by a non-technical introduction to GAMs. Through a series of exercises, we demonstrate the application of GAMs to accommodate non-linear covariate effects.
The second part of the course introduces linear mixed-effects models, which are suitable for data with complex structures, such as observations from clustered or hierarchical sources (e.g., multiple measurements from the same subject). These models extend traditional models by incorporating random effects to account for the non-independence of data points.
Once familiar with mixed models, we will integrate them with GAMs, resulting in generalised additive mixed-effects models (GAMMs). We will do various exercises using GLMMs and GAMMs.
In the third part of the course, we will apply GAMs and GAMMs to various spatial and spatio-temporal datasets. Throughout the course, we will utilise GAMMs and GLMMs on different types of data (continuous and count), employing Gaussian, Poisson, and negative binomial distributions.
Pre-required knowledge
Working knowledge of R, data exploration, linear regression and GLM (Poisson, negative binomial). This is a non-technical course.
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.
- Theory presentation on GAM.
- Two introductory GAM exercises.
Tuesday
- GAM exercise discussing model selection and smoother interactions.
- Theory presentation on linear-mixed effects models.
- One exercise using linear mixed-effects models.
- One exercise on Gaussian additive mixed-effects models (GAM with random effects).
Module 3
- Time allowing: Exercise using hierarchical GAMs (the GAM equivalent of random slope models).
- Two revision exercise on Poisson and negative binomial GLMs.
- One exercise on negative binomial GAM.
Module 4
- One exercise on Poisson GLMM.
- Time allowing: One exercise on negative binomial GLMM.
- Two exercises on Poisson and negative binomial GAMM.
Module 5
- One exercise on (Gaussian) GAM applied to spatial data.
- One exercise on GAM applied to spatial count data.
- One exercise on GAM(M) applied to spatial-temporal count data.
- Time allowing: Guidance for the analysis of binary, continuous, proportional and continuous data (Bernoulli, binomial, beta, Tweedie and Gamma distributions).
We will predominately use the R packages mgcv and glmmTMB for the exercises.
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. The Monday-Friday material does not contain on-demand video.
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