Hybrid (onsite and online): Multivariate Analysis
- Integrating Classical Techniques with multivariate GLMM -
Centro Oceanográfico de Málaga. Explanada de San Andrés (Muelle 9). 29002 Puerto de Málaga - Málaga, Spain
3 - 7 March 2025
Course flyer
This is an onsite course, but you can also participate online via a Zoom connection (same price).
This course offers a journey through classical multivariate analysis techniques, progressing into advanced, recently developed tools for multivariate generalised linear models (GLM) and generalised linear mixed models (GLMM).
We begin with classical multivariate techniques such as principal component analysis (PCA) and redundancy analysis, laying a solid foundation. From there, we transition into generalised linear latent variable models (GLLVM), a powerful approach for analysing multiple response variables simultaneously. GLLVMs account for dependencies among response variables and between observations, providing a flexible framework for complex data.
The course also covers extensions of GLLVMs, including reduced rank regression (constrained latent variables), concurrent ordination and models that incorporate spatial or temporal dependency structures.
On Friday, participants are invited to apply the methods learned throughout the course to their own data (time allowing). This practical session provides a unique opportunity to receive personalised feedback and guidance from the instructors, ensuring that participants leave with tailored insights and a deeper understanding of how to implement these techniques in their own work.
This is an applied and non-technical course that focuses on the practical implementation in R.
Pre-required knowledge
Participants should be familiar with data exploration, linear regression and basic GLM and GLMM (i.e. Poisson and negative binomial GLM) in R. The course website contain revision/preparation material with on-demand videos covering these topics. 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 principal component analysis (PCA) and redundancy analysis (RDA)
- Exercise on PCA and RDA.
- Theory presentation on generalised linear latent variable models (GLLVM).
Tuesday:
- Catching up.
- Two exercises on GLLVM using Poisson and negative binomial models for count data.
Wednesday:
- Theory presentation on constrained GLLVM (reduced rank regression and concurrent ordination).
- Two exercises on constrained GLLVM.
Thursday:
- We will apply exercises using GLLVM with various distributions, including Tweedie, Gamma, Bernoulli, Gaussian, and Beta. While we may not cover all these distributions during the course, solution files will be provided.
Friday:
- Adding spatial and temporal dependency structures to GLLVM.
- Time allowing: Applying the methods to your own data and receiving constructive feedback from the instructors.
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: