This is an open online live course
Open online live course: Introduction to linear mixed-effects models, multivariate GLMM and GLLVM.
- Dates: 7, 8, 9, 10, and 11 October 2024 (5 days).
- Times: 13.00 - 19.00 UK time (08.00 - 14.00 EST).
- Included: 1 hour face-to-face video chat about your data. See flyer for details and conditions.
Course format
- Live online teaching is from 13.00-19.00 UK time (BST).
- 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.
Brief outline
The central theme of this course is the analysis of multiple correlated response (or dependent) variables using GLMs and GLMMs. Instead of applying multiple univariate GLMMs, we will discuss multivariate GLMMs for datasets with a relatively small number of response variables and generalised linear latent variable models (GLLVMs) for datasets with a relatively large number of response variables.
During the course, we cover a large number of exercises with examples such as trait variables from turtle hatchlings from multiple clutches, biomass data from fish species sampled at multiple sites, count data from 250 freshwater benthic species sampled at 200 sites, abundances of multiple parasite species on fish, counts of 60 different debris types in water samples, abundances of multiple spider species in traps, multiple morphometric variables sampled from honeybees, and absence/presence of diet variables from faecal samples of brown bears.
In all these examples, we can analyse each variable with a univariate GLMM. Although these analyses are relatively simple, there are also some problems:
- Extra Work: Individual analyses are computationally less efficient and require separate validation, interpretation, and reporting.
- Lack of Multivariate Relationships: Analysing the variables individually neglects the interconnected relationships and interactions between them.
- No Shared Variation: Univariate models might overlook consistent residual patterns across species, while multivariate models can capture shared variations due to common environmental factors.
- Multiple Testing: Conducting separate analyses increases the risk of Type I errors, especially when the response variables are highly correlated.
- Loss of Community-Level Insights: Analysing species separately misses out on a comprehensive, community-level viewpoint and can lead to inconsistent conclusions.
For more information, see the course flyer: Flyer2024_10_MultivariateGLMMGLLVM.pdf