- 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 revision of multiple linear regression, followed by an introduction to Bayesian analysis and how to execute regression models in R-INLA. We then explain linear mixed effects models to analyse nested data, followed by a series of mixed modelling exercises in R-INLA. Nested data means multiple observations from the same animal, site, area, nest, patient, hospital, vessel, lake, hive, transect, etc.
In the second part of the course GLMMs are applied to count data, binary data (e.g. absence/presence of a disease), proportional data (e.g. % coverage), and continuous data (e.g. biomass or distance) using the Poisson, negative binomial, Bernoulli, binomial, beta and gamma distributions.
Module 1 consists of 7 on-demand videos
- General introduction.
- Revision exercise multiple linear regression in R.
- Introduction to matrix notation.
- Brief introduction to Bayesian analysis.
- Brief theory presentation on INLA.
- Exercise how to fit a regression model in R-INLA.
Module 2 consists of 4 on-demand videos
- Theory presentation on linear mixed-effects models
- Two exercises showing how to apply (one-way and two-way nested) linear mixed effects models in R-INLA
- Exercise on random intercept and slope models
Module 3 consists of 7 on-demand video files
- Using multiple variances (Generalised Least Squares) in R-INLA
One exercise using GLS
Brief revision generalised linear models (GLM)
Exercise showing how to execute a Poisson GLM and negative binomial GLM in R-INLA
Three GLMM exercises in R-INLA
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
- A series of exercises covering the applications of Bernoulli, binomial, gamma and beta GLMMs in R-INLA
Module 5 consists of 3 on-demand video files
- Theory presentation on temporal trends and residual auto-correlation
- Two exercises showing the analysis of (univariate and multivariate) time series using GLMs in R-INLA
Recorded Zoom summary sessions (2 hours each) from a previous course are available to watch.
Working knowledge of R, data exploration, linear regression and GLM (Poisson, negative binomial, Bernoulli). This is a non-technical course.