Using R and lme/lmer to fit different two- and three- level longitudinal models

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I often get asked how to fit different multilevel models (or individual growth models, hierarchical linear models or linear mixed-models, etc.) in R. In this guide I have compiled some of the more common and/or useful models (at least common in clinical psychology), and how to fit them using nlme::lme() and lme4::lmer(). I will cover the common two-level random intercept-slope model, and three-level models when subjects are clustered due to some higher level grouping (such as therapists), partially nested models were there are clustering in one group but not the other, and different level 1 residual covariances (such as AR(1)). The point of this post is to show how to fit these longitudinal models in R, not to cover the statistical theory behind them, or how to interpret them.

Link to resource:

Type of resources: Reading, R code

Education level(s): Graduate / Professional

Primary user(s): Student, Teacher, Researcher

Subject area(s): Education, Life Science, Math & Statistics, Social Science

Language(s): English