A Hands-On Crash Course in R
Hannah Lam and Veranika Puhacheuskaya, University of Alberta
In recent years, linear mixed modeling has become one of the primary forms of analysis for experimental data. This hands-on course equips participants with both the conceptual understanding and practical skills needed to perform mixed-effects regression (linear and logistic) using lme4 in R. Participants will develop confidence in 1) data verification and visualization; 2) implementing linear mixed-effects regression; and 3)
interpreting and reporting the results. Tentative course topics:
- Introduction to the course
- Introduction to linear and linear mixed models
- Building, fitting, and interpreting a linear mixed model:
- Random & fixed effects (types of random effects; crossed random effects vs. nested random effects)
- Random slopes
- Categorical predictors and variable coding (dummy, sum, contrast)
- Emmeans and post hoc analysis
- Troubleshooting and convergence issues (common warnings; handling non-convergence; overfitting vs. underfitting)
- Model diagnostics & assumptions (checking assumptions – residuals, normality, and homoscedasticity; solutions to violated assumptions; checking fit and validity)
- Model comparison
- Logistic mixed models and other types of mixed models
- How to report your model for a journal paper
- Hands-on exercises (our data or your data)
Prerequisites:
Basic knowledge of R
Please bring your own laptop with R and RStudio installed
https://posit.co/download/rstudio-desktop/