Hybrid (onsite and online): Data Exploration, Regression, GLM & GAM with an introduction to R. Marine Research Institute, Klaipeda University, Lithuania
Course flyer
This is an onsite course, but you can also participate online via a Zoom connection (same price).
We will begin with a brief intro to R and provide a step-by-step guide for data exploration, helping you avoid common statistical mistakes. You’ll learn how to identify outliers, manage collinearity, visualise relationships, and understand dependency structures. We will also explain why checking for normality at this stage is unnecessary.
We will then move on to multiple linear regression, a key statistical tool. We’ll cover the basics of linear regression using examples from biology, discuss potential challenges, and introduce the concept of interactions.
We will demonstrate how generalised linear models (GLMs) can be used to analyse count data, presence-absence data, proportional data, and continuous data. Some key distributions will be explored in detail, along with a clear overview of all GLMs.
We will also discuss and apply generalised additive models (GAMs) to handle non-linear relationships. We will cover smoother-factor interactions and explore model selection within GAMs.
Throughout the course, there will be around 15 exercises to help you apply what you’ve learned.
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
Monday:
- General introduction.
- Introduction to R.
- Theory presentation on data exploration (outliers, collinearity, transformations, relationships, interactions),
based on Zuur et al. (2010) and Ieno and Zuur (2015).
- Two exercises.
Tuesday & Wednesday morning:
- Theory presentation on linear regression.
- Different strategies for model selection.
- Interactions.
- Dealing with categorical covariates.
- Sketching model fit.
- Two exercises.
Wednesday afternoon and Thursday:
- Theory presentation on Poisson, negative binomial, Bernoulli, and binomial distributions.
- Theory presentation on GLM.
- Poisson GLM: How to deal with overdispersion.
- Negative binomial GLM.
- DHARMa for model validation.
- Two exercises on Poisson and negative binomial GLM.
- A general overview discussing Bernoulli, binomial, Tweedie, Gamma, and beta GLM.
- One or two GLM exercises (time permitting).
Friday:
- Theory presentation on GAM.
- Two or three exercises using Gaussian GAM, and Poisson and negative binomial GAMs
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: