Onsite course :
Generalised Additive Models for the analysis of spatial and spatial-temporal data
This online course consists of 4 modules representing a total of approximately 36 hours of work. We will start with a non-technical introduction to generalised additive models (GAM). Using a series of exercises, we show how GAMs can be used to allow for non-linear covariate effects. Once we are familiar with GAM, we will apply them to various spatial, and spatial-temporal data sets.
During the course, GAMs are applied to count data, absence-presence data, proportional data, and continuous data using the Gaussian, Poisson, negative binomial, Bernoulli, beta, gamma and Tweedie distributions. We will apply GAMs with 2-dimensional smoothers to analyse spatial data. To allow for natural barriers (e.g. an island in the sea), soap-film smoothers are used. On the 4th day of the course, spatial-temporal data sets are analysed.
The course contains a short revision of generalised linear models (GLM). During the course, we will explain the beta, Gamma, and Tweedie distributions. Preparation material on data exploration and linear regression with on-demand video is supplied. Throughout the course we will use the mgcv package in R. This is a non-technical, and easy-to-follow course.
The course website contains the following modules.
Preparation material (containing on-demand video):
- Revision exercise on multiple linear regression.
- Introduction to matrix notation.
- Introduction to DHARMa.
- What is a variogram.
- General introduction.
- Theory presentation on GAM.
- Three introductory GAM exercises.
- Key phrases: How to fit a GAM using mgcv, how to read its output, model selection, model validation,
smoother interactions, what to present in a paper.
- Revision of Poisson and negative binomial GLM.
- Two revision exercises on Poisson and negative binomial GLM.
- One exercise on negative binomial GAM.
- Two exercises on GAMs applied to spatial data
- Catching up.
- Two exercises showing how to use GAM with a spatial smoother in case the study area contains a natural
barrier (e.g. an island in the sea).
- Time allowing: GAM applied to areal data.
- Time allowing: GAM applied to data measured on a sphere.
- Four exercises GAM applied to spatial-temporal data using a variety of distributions (e.g. Poisson, negative
binomial, Bernoulli, Tweedie, Gamma, beta).
The course material consists of relevant pdf files of presentations, data sets, and clearly documented R code. Course participants will be given access to the course website with all data sets, R solution code, and course material 2 weeks before the start of the course.
Working knowledge of R and linear regression. This is a non-technical course.