In this two day course, we provide a comprehensive practical and theoretical introduction to multilevel models, also known as hierarchical or mixed effects models. We will focus primarily on multilevel linear models, but also cover multilevel generalized linear models. Likewise, we will also describe Bayesian approaches to multilevel modelling. On Day 1, we will begin by focusing on random effects multilevel models. These models make it clear how multilevel models are in fact models of models. In addition, random effects models serve as a solid basis for understanding mixed effects, i.e. fixed and random effects, models. In this coverage of random effects, we will also cover the important concepts of statistical shrinkage in the estimation of effects, as well as intraclass correlation. We then proceed to cover linear mixed effects models, particularly focusing on varying intercept and/or varying slopes regresssion models. On Day 2, we cover further aspects of linear mixed effects models, including multilevel models for nested and crossed data data, and group level predictor variables. On Day 2, we also cover Bayesian approaches to multilevel levels using the brms R package.
ONLINE COURSE – Introduction To Mixed Models Using R And Rstudio (IMMR06) This course will be delivered live
https://www.prstatistics.com/course/introduction-to-mixed-models-using-r-and-rstudio-immr06/
Please feel free to share!
ABOUT THIS COURSE
In this two day course, we provide a comprehensive practical and theoretical introduction to multilevel models, also known as hierarchical or mixed effects models. We will focus primarily on multilevel linear models, but also cover multilevel generalized linear models. Likewise, we will also describe Bayesian approaches to multilevel modelling. On Day 1, we will begin by focusing on random effects multilevel models. These models make it clear how multilevel models are in fact models of models. In addition, random effects models serve as a solid basis for understanding mixed effects, i.e. fixed and random effects, models. In this coverage of random effects, we will also cover the important concepts of statistical shrinkage in the estimation of effects, as well as intraclass correlation. We then proceed to cover linear mixed effects models, particularly focusing on varying intercept and/or varying slopes regresssion models. On Day 2, we cover further aspects of linear mixed effects models, including multilevel models for nested and crossed data data, and group level predictor variables. On Day 2, we also cover Bayesian approaches to multilevel levels using the brms R package.
Please email oliverhooker@prstatistics.com