Worked R examples

Mixed effect models

These are all the mixed effect model examples from two chapters of my book Extending the Linear Model with R. Each model is fit using several different methods:

• lme4
• INLA
• STAN
• BRMS
• MGCV

I have focused on the computation rather than the interpretation of the models.

Examples

• Single Random Effect - the pulp data
• Randomized Block Design - the penicillin data
• Split Plot Design - the irrigation data
• Nested Effects - the eggs data
• Crossed Effects - the abrasion data
• Multilevel Models - the jsp data
• Longitudinal Models - the psid data
• Repeated Measures - the vision data
• Multiple Response Models - the jsp data
• Poisson reponse model - the nitrofen data
• Binary response model - the ohio data

Mixed effect models using Frequentist methods

The comparison above is focused on Bayesian methods but there is also some choice in the Frequentist approach which we explore below using the following packages:

• lme4
• nlme
• mmrm
• glmmTMB

Examples

• Single Random Effect - the pulp data
• Randomized Block Design - the penicillin data
• Split Plot Design - the irrigation data
• Another Split Plot example - the steelbar data
• Nested Effects - the eggs data
• Crossed Effects - the abrasion data
• Multilevel Models - the jsp data
• Longitudinal Models - the psid data
• Repeated Measures - the vision data
• Multiple Response Models - the jsp data
• Poisson reponse model - the nitrofen data
• Binary response model - the ohio data
• Overall conclusion