Introduction to mixed-effects models using R
[5 half days] - [English]
Does your data carry a complex structure that does not allow the use of traditional models ? Maybe do they feature random factors or repeated measures ? Time to switch to the mixed-effects models !
Mixed models are a family of models with both fixed and random effects. They make it possible to take into account the correlation between several observations, in particular the temporal aspect of the data.
Training aims
After this training, participants will understand some of the theoretical concepts beyond the mixed-effects models and will know how to create their mixed-effects models using R with the packages lme4 and nlme.
There are many exercises based on true data in order to get familiar with these models and their use in R.
There are many exercises based on true data in order to get familiar with these models and their use in R.
Prerequisites
By registering for this training, you commit to a level of knowledge equivalent to the following training(s):
Content
- Simple structures :
- Models with a random intercept
- Models with a random intercept and a random slope
- More specific structure of variance-covariance matrix useful for longitudinal or spatial data
- Hierachical structures with more than 2 levels
Rate
Methods and method families discussed
Mixed models
Linear mixed model
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