- Self-study course.
- On-demand access to all video content online within a 12-month period.
- Daily interaction on the Discussion Board for detailed questions.
- Live chat for quick queries.
- Course fee includes a 1-hour video chat with instructors for personalized questions and data assistance.
The course begins with a brief review of multiple linear regression and generalized linear models. This is followed by an introduction to linear mixed-effects models and generalized linear mixed-effects models (GLMMs) for analyzing hierarchical or clustered data. Such data may include multiple observations from entities like animals, sites, areas, nests, patients, hospitals, vessels, lakes, hives, transects, etc.
In the second part of the course, GLMMs are employed to analyze various types of data: continuous data (e.g., biomass), binary data (e.g., disease absence/presence), proportional data (e.g., % coverage), and count data. This is accomplished through distributions such as Gaussian, Poisson, negative binomial, Bernoulli, binomial, beta, and gamma distributions.
Module 1 consists of 5 on-demand videos:
- General introduction.
- One exercise revising data exploration and multiple linear regression in R.
- Introduction to matrix notation.
- Theory presentation for linear mixed-effects models for nested data.
- Two exercises on linear mixed-effects models with random intercepts.
- Comparing lme4/nlme/glmmTMB results.
Module 2 consists of 4 on-demand videos:
- One exercise showing how to apply a two-way nested linear mixed-effects model.
- One exercise on linear mixed-effects models with random intercepts and slopes.
- Using multiple variances (Generalised Least Squares) to deal with heterogeneity.
- One exercise using GLS.
Module 3 consists of 5 on-demand video files:
- Brief revision generalised linear models (GLM)
- Exercise showing how to execute a Poisson GLM and negative binomial GLM
- Three GLMM exercises
- Poisson GLMM
- Negative binomial GLMM
- Poisson and negative binomial GLMMs with two-way nested and crossed random effects
Module 4 consists of 4 on-demand video files:
- Exercise showing how to apply a Bernoulli GLMM for the analysis of absence-presence data.
- Exercise showing how to apply a binomial GLMM for the analysis of proportional data.
- Exercise showing how to apply a beta GLMM for the analysis of coverage data.
- Exercise showing how to apply a gamma GLMM for the analysis of continuous positive data.
Zoom recordings from previous courses are also available to watch.
We will use the packages lme4, nlme and glmmTMB in R.
Course material is partly based on:
- Zuur, Hilbe, Ieno (2013). Beginner’s Guide to GLM and GLMM with R.