23313a
Lecture
SoSe 17: V Advanced Statistical Applications: from LM to GLMM using R
Alexandre Courtiol
Information for students
open for Master students who took part in the course Introduction to R for statistical applications / Einführung in R für statistische Anwendungen or equivalent courses and open for PhD students
Comments
1. In-depth understanding of the Linear Models
1. general definition of LM, assumptions
2. creating one‘s own LM fitting function from scratch
2. In-depth analysis of the LM
1. testing assumptions
2. testing hypothesis (predictions, intervals, model comparison)
3. Generalized LM
1. general definition of GLM
2. logistic model, Poisson model
3. creating one‘s own GLM fitting function from scratch
4. Modelling heteroscedasticity
1. overdispersion
2. spatial and temporal autocorrelation
5. Linear Mixed effects Models
1. standard LMM
2. LMM with multiple responses
6. Generalized LMM
1. standard GLMM
2. GLMM with non Gaussian random effects
7. Some mixed model applications
1. comparative analyses controling for phylogeny
2. meta-analysis
3. heritability estimation
8. Further discussions and exercises
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