Course flyer
This is an onsite course for IMR staff and students, with the option for non-IMR participants to join online via Zoom.
This (live) online course consists of 5 modules representing a total of approximately 40 hours of work. Each module consists of live teaching, followed by exercises using real data sets. All the material is also available as on-demand video files. A discussion board allows for daily interaction between instructors and participants.
The course includes a 1-hour face-to-face video chat with the instructors.
Course content We begin with a short introduction to R and provide a protocol for data exploration to avoid common statistical problems. We will discuss how to detect outliers, deal with collinearity and transformations. An important statistical tool is multiple linear regression. Various basic linear regression topics will be explained from a biological point of view. We will discuss potential problems and show how generalised linear models (GLM) can be used to analyse count data, continuous data, presence-absence data and proportional data.
Sometimes, parametric models (linear regression, GLM) do not quite fit the data and in such cases generalised additive models (GAM; a smoothing technique) can be used.
Pre-required knowledge
Basic statistics.
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
Day 1:
- General introduction.
- Introduction to R.
- Theory presentation on data exploration (outliers, collinearity, relationships, interactions, etc.).
- One exercise.
Day 2:
- Theory presentation on linear regression.
- Different strategies for model selection.
- Interactions.
- Dealing with categorical covariates.
- Visualising covariate effects.
- Two exercises.
Days 3 and 4:
- Theory presentations on Poisson, negative binomial, generalised Poisson, Tweedie, Gamma,
- Bernoulli, binomial and beta distributions.
- Exercise 1: Poisson/generalised Poisson/negative binomial GLM for count data.
- Exercise 2: Bernoulli GLM for absence/presence data.
- Exercise 3: Gamma or Tweedie GLM for continuous (e.g. biomass) data.
- Time allowing: Exercises on beta GLM (for proportional data) and binomial GLM (proportional data). On-demand video files are available.
Day 5
- Theory presentation on GAM.
- Two exercises using Gaussian GAM and Poisson and negative binomial GAMs.
- Based on various chapters in Zuur (2012).
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.
For terms and conditions, see: