This is an open course
Open online live course: Introduction to Linear Mixed-Effects Models,GLMM and Multivariate GLMM with R.
- Dates: 28 and 30 November, 1, 4, 5, 6 and 7 December 2023.
- Times: 14.00 - 19.00 UK time (09.00-14.00 EST).
- Included: 2 hours face-to-face video chat about your data. See flyer for details and conditions.
- Live online teaching is from 14.00-19.00 UK time (09.00-14.00 EST).
- There are seven 5-hour sessions over a 2-week period.
- 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.
- Please note that this is not a self-study course.
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 (GLMM) for analyzing hierarchical or clustered data. Examples of such data include multiple observations from the same animal, site, area, nest, patient, hospital, vessel, lake, hive, transect, and so forth. These statistical techniques are designed to address dependency within your data.
In the second part of the course, we apply GLMMs to various types of data. This includes continuous data (e.g., biomass), binary data (e.g., the presence or absence of a disease), proportional data (e.g., % coverage), and count data. For these analyses, we employ several distributions: Poisson, negative binomial, Bernoulli, binomial, beta, ordered beta, Tweedie, and gamma.
In the third part of the course, we delve into multivariate GLMMs. These models allow for the analysis of multiple response variables within a single model using the 'gllvm' package.
For more information, see the course flyer: Flyer2023_11_GLMM.pdf