Dates: online, 9-13 October 2023
This course is aimed at graduate students and researchers who have experience with generalized linear regression models in R and want to extend their knowledge by learning how to add random effects, correlation structures, and variance models (to account for heteroscedasticity) to these models. The basics of LM, GLM, and ANOVA are reviewed at the beginning of the course
- Monday– Classes from 2-8 PM Berlin time
On day 1, we will recap regression basics that would typically be covered in an introductory stats course: 1) the linear model: interpretation, diagnostics, multiple regression, scaling and centering, omitted-variable bias, interactions 2) contrasts for categorical predictors 3) ANOVA
- Tuesday– Classes from 2-8 PM Berlin time
On day 2, we will introduce random effects and variance structures (GLS), and talk about model selection and causal inference.
- Wednesday– Classes from 2-8 PM Berlin time
On day 3, we will merge the topics from day 2 (variance structures and random effects) with the GLM framework, which means that we arrive at using GLMMs. We will talk about specification, diagnostics, and common issues when working with GLMMs, in particular variance partitioning and model selection for fixed and random effects. If we have time, we will also introduce cross-validation and the bootstrap.
- Thursday– Classes from 2-8 PM Berlin time
Day 4 is about correlation structure in the data, other than random effects. Specifically, we'll talk about spatial, temporal and phylogenetic correlation structures and how to account for them in regression models.
- Friday– Classes from 2-6 PM Berlin time
On day 5, we will speak about some advanced topics, in particular non-parametric methods, including null models, the boostrap and cross-validation, and how they relate to parametric methods, Bayesian estimation vs. frequentist estimation of GLMMs, or structural equation models. Emphasis can be changed depending on the interest of the group.