Linear mixed models (using R)
Workshop series
Computational and Data Systems Initiative
Mixed models are a very useful modeling tool for situations in which there is some dependency among observations in the data, where the correlation typically arises from the observations being clustered in some way. The goal of the workshop is to provide a sense of when one would use linear mixed models and some standard techniques to implement them in R.
At the end of this workshop, you will be able to
> Know some basic information on how to identify if a data-set is ill-suited for standard linear models, and mixed models are better
> Understand the key concepts of linear mixed models, and know how to combine the strengths of random effects and fixed effects approaches into a single model
> Implement linear mixed models in R, interpret the results and visualize the model
Pre-requisites?
> Participants of the workshop need some basic knowledge of R. For example, they should be able to install packages, read in data, select subsets of the data, and estimate a linear regression model
> R packages necessary for the analysis (install with install.packages(“package”) at R prompt): lme4
> Optional R package: ggplot2 (for visualization)
> Install Rstudio
Date: Monday January 31, 2022
Time: 1PM to 3PM
Location: In person - Burnside Hall BH511 *Vaccine passport required
Instructor: Shou (Mila) Sun, PhD candidate in Biostatistics, McGill.
