yDiv/HIGRADE course: Introduction to Generalized Linear Models and Mixed Effects Models in R
This course is for doctoral and postdoctoral researchers with prior knowledge in importing, formatting and transforming data in R, producing histograms, boxplots and scatterplots in R as well as fitting and checking linear models in R (Regression, ANOVA, ANCOVA).
You will learn about the analyses of studies with random effects (e.g. blocked designs, panel data, repeated measures, or replicate groups, clusters, plots…), and the background on generalized linear models (error distributions, link functions) and mixed-effects models (fixed effects vs. random effects). Focus will be on the application of the models, including model fitting, model selection, hypothesis testing and visualization of data and model predictions.
In the end, you will be able to understand and choose appropriate error distributions and link functions for a generalized linear model (GLM), understand and define appropriate random intercepts and random slopes for a mixed-effects model (LME), check and interpret GLM and LME models appropriately and generate and visualize predictions from GLM and LME.
Active participation in the lectures, hands-on sessions, exercises and project work and the presentation of exercise results are expected from the participants.