GLM - 14 - Mixed models (LMM, GLMM)

In mixed models we add random effects to the linear predictor, to account for the within-group correlation. This complicates the likelihood and the estimating equations quite a bit.
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“GLM in R” Course Outline:
Administration
Administration
Up to Scratch
Notebook - Introduction
Notebook - Linear Models
Notebook - Intro to R
Intro to GLM’s
Linear Models vs. Generalized Linear Models
Least Squares vs. Maximum Likelihood
Saturated vs. Constrained Model
Link Functions
Exponential Family
Definition and Examples
More Examples
Notebook - Exponential Family
Mean and Variance
Notebook - Mean-Variance Relationship
Deviance
Deviance
Notebook - Deviance
Likelihood Analysis
Likelihood Analysis
Numerical Solution
Notebook - GLM’s in R
Notebook - Fitting the GLM
Inference
Code Examples:
Notebook - Binary/Binomial Regression
Notebook - Poisson & Negative Binomial Regression
Notebook - Gamma & Inverse Gaussian Regression
Advanced Topics:
Quasi-Likelihood
Generalized Estimating Equations (GEE)
Mixed Models (GLMM)
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