Online course with on-demand video and live Zoom meetings: Introduction to Linear Mixed Effects Models and GLMM with R

This online course consists of 4 modules representing a total of approximately 40 hours of work. Each module consists of multiple video files with short theory presentations, followed by exercises using real data sets, and video files discussing the solutions. All video files are on-demand and can be watched online, as often as you want, at any time of the day, within a 6 month period.

A discussion board allows for daily interaction between instructors and participants. 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 on the Discussion Board. The course includes a 1-hour face-to-face video chat with the instructors.

A detailed outline of the course is provided below. All exercises consist of a data set, R solution code, and a 20-60 minutes video describing the data, the questions, and a detailed discussion of the R solution code.

The course starts with a short revision of multiple linear regression and generalised linear models, followed by an introduction to linear mixed-effects models and generalised linear mixed-effects models (GLMM) to analyse hierarchical or clustered data, e.g. multiple observations from the same animal, site, area, nest, patient, hospital, vessel, lake, hive,
transect, etc. These statistical techniques are designed to take care of dependency in your data. In the second part of the course GLMMs are applied on continuous (e.g. biomass), binary (e.g. absence/presence of a disease), proportional
(e.g. % coverage) and count data using the Gaussian, Poisson, negative binomial, Bernoulli, binomial, beta, and gamma distributions.

This is a non-technical, and easy-to-follow course.

Module 1: Introduction, revision linear regression and linear mixed-effects models.

  • 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: A series of linear-mixed effects modeling exercises.

  • One exercises 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 or two exercises using GLS

Module 3: Revision GLM and three GLMM exercises.

  • Brief revision generalised linear models (Poisson, negative binomial, and Bernoulli GLMs).
  • Exercise showing how to execute a Poisson GLM and negative binomial GLM.
  • A series of exercises covering Poisson GLMM, negative binomial GLMM, and Poisson and negative binomial GLMMs with two-way nested and crossed random effects.

Module 4: A series of GLMM exercises

  • 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


Free 1-hour face-to-face video meeting: The course includes a 1-hour face-to-face meeting with one or both instructors. The meeting needs to take place within 3 months after the last live zoom meeting. You can discuss your own data but the statistical topics need to be within the content of the course. The 1-hour needs to be consumed in one session and will take place at a mutually convenient time.

Web meetings: Web meetings are hosted on Click here for recommended internet speed (see the text under 'Recommended bandwidth for Webinar Attendees'). We will record the meetings and make them available on the course website.

Discussion Board: You can use the Discussion Board to ask any questions related to the course material. 

Pre-required knowledge: Basic statistics (e.g. mean, variance, normality). No R knowledge is required. You will learn R ‘on the fly’. This is a non-technical course.

Cancellation policy: What if you are not able to participate? Once participants are given access to course exercises with R solution codes, pdf files of certain book chapters, pdf files of PowerPoint or Prezi presentations and video solution files, all course fees are non-refundable and non-transferable to another participant.

Copyright: Sharing the access details of the course website or the pdf files of our course material is prohibited. Video files cannot be downloaded, but they can be watched in the same way as on Netflix.