Dear all,
there are 3 seats left for the "Generalised Linear Mixed Models in R":
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.
LEARNING OUTCOMES
1. Deepen understanding of fundamental regression concepts, including centering, scaling, interactions, contrasts, and ANOVA.
2. Understand the components of the GLMM framework (choice of distributions, random effects, variance structures, correlation structures)
3. Being able to choose the appropriate model structure in an applied analysis of experimental or observational data (focusing on the R packages lme4 and glmmTMB)
4. Know how to visualize fitted GLMMs (R package effects) and to check the assumptions of the model (R package DHARMa)
Best regards,
Carlo
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Carlo Pecoraro, Ph.D
Physalia-courses DIRECTOR
info at physalia-courses.org
mobile: +49 17645230846
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