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Introduction to mixed-effects models using R

[5 half days] - [starts on 19-05-2025 at 09:30] - [English] - [Louvain-la-Neuve]

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.

Prerequisites
By registering for this training, you commit to a level of knowledge equivalent to the following training(s):
Introduction to data analysis with R

Content

Location
Louvain-la-Neuve : CV09 - Find the location on Google Maps
(This is an air-conditioned room so please cover yourself!)

- Voie du Roman Pays, 20 - Niveau 0, local C045 -

Attendance
The training is open to all but requires prior registration.

Time slots for training
19-05-2025 from 09:30 to 12:30
20-05-2025 from 09:30 to 12:30
21-05-2025 from 09:30 to 12:30
22-05-2025 from 09:30 to 12:30
23-05-2025 from 09:30 to 12:30

Rate
UCLouvain member (with an internal account), UCLouvain student : Training available free of charge but registration in advance is required.
Administrative fees will be applied in case of late cancellation.
Invoice paid by university, UCLouvain clinic member, SHS Namur member : 150 euros per day for a total of 375 euros
Company, Researcher, Non-UCLouvain member : 250 euros per day for a total of 625 euros

Financial support
This training course is recognized by the IABE, enabling participants to earn CPD points.
(Note that this is true for all SMCS courses.)
IABE logo

For more information (open)

Methods and method families discussed
Mixed models
   Linear mixed model


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