www.highstat.com
Highland Statistics Ltd

On demand: Introduction to Linear Mixed Effects Models and GLMM with R-INLA

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

This online course consists of 5 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. The course also contains 5 2-hour live web meetings (max. 12 participants per meeting) in which we summarise the theory and the exercises. Attending these live web meetings is optional. We will run the web meetings in different time zones. You can also use a Discussion board to ask any questions on the course material. The course fee includes a 1-hour face-to-face video chat with the instructors. You can use this video chat to ask questions about your own data.

A detailed outline of the course is provided below. All exercises consist of a data set, a video describing the data and the questions, R solution code and a video discussing the R solution file.

Course content

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 modeling 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 on 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. In the third part of the course, we show how R-INLA can be used to execute GLMs with temporal dependency for the analysis of univariate and multivariate time series.

A detailed outline of the course is provided below. All exercises consist of a data set, a video describing the data and the questions, R solution code and a video discussing the R solution file.

 

Module 1: Introduction, revision linear regression and an introduction to Bayesian analysis

  • A video with a general introduction.
  • Revision exercise for multiple linear regression.
  • A short video explaining basic matrix algebra.
  • A video presentation with an introduction to Bayesian statistics and the role of priors.
  • A video presentation explaining the basic principles of INLA.
  • One exercise showing how to execute a linear regression model in INLA.

Web meeting 1: A 2-hour web-meeting will be scheduled. In this web meeting, we will summarise module 1.

 

Module 2: Linear mixed-effects models.

  • A video presentation with an introduction to linear mixed-effects models.
  • One exercise showing how to execute a one-way nested linear mixed-effects model in INLA.
  • One exercise showing how to execute a two-way nested linear mixed-effects model in INLA.
  • One exercise showing how to execute a linear mixed-effects model with random intercepts and random slopes in INLA.

Web meeting 2: A 2-hour web-meeting will be scheduled. In this web meeting, we will summarise module 2.

 

Module 3: Dealing with heterogeneity, revision GLM, and applying GLMM in R-INLA.

  • A video presentation with an explanation of how to use multiple variances in a linear regression model (Generalised Least Squares) in R-INLA.
  • One exercise showing how to execute a GLS model in INLA.
  • A video presentation with brief revision of generalised linear models (GLM).
  • One exercise showing how to execute a Poisson GLM and negative binomial GLM in R-INLA.
  • Three GLMM exercises: Poisson GLMM, Negative binomial GLMM, and Poisson and negative binomial GLMMs with two-way nested and crossed random effects.

Web meeting 3: A 2-hour web-meeting will be scheduled. In this web meeting, we will summarise module 3.

 

Module 4: GLMM exercises.

  • Exercise showing how to execute a Bernoulli GLMM in INLA.
  • Exercise showing how to execute a binomial GLMM in INLA.
  • Exercise showing how to execute a gamma GLMM in INLA.
  • Exercise showing how to execute a beta GLMM in INLA.

Web meeting 4: A 2-hour web-meeting will be scheduled. In this web meeting, we will summarise module 4.

 

Module 5: Using R-INLA for time series.

  • A video with a 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.

Web meeting 5: A 2-hour web-meeting will be scheduled. In this web meeting, we will summarise module 5.

 

Free 1-hour face-to-face video meeting: The course fee includes a 1-hour face-to-face video 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 zoom.us. 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: Working knowledge of R, data exploration, linear regression and GLM (Poisson, negative binomial, Bernoulli). Short revisions are provided. 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.