This course is only for DFO Canada employees
Onlive live course: Generalised Additive Models for the analysis of spatial and spatial-temporal data.
- Dates: 9 and 30 January, 5, 6, 12 and 13 February 2024.
- Times: 15.00 - 20.00 UK time (10.00-15.00 EST).
- Included: 2 hours face-to-face video chat about your data. See flyer for details and conditions.
- Live online teaching is from 15.00-20.00 UK time (10.00-15.00 EST).
There are six 5-hour sessions over a 3-week period, representing a total of 30 hours of work.
- 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.
- Please note that this is not a self-study course.
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, and spatial-temporal data. To allow for natural barriers (e.g. an island in the sea), soap-film smoothers are used.
Throughout the course we will use the mgcv package in R. This is a non-technical, and easy-to-follow course.
For more information, see the course flyer: Flyer2024_02_DFOGAMSpatTemp.pdf