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Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists

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Doi: 10.20982/tqmp.12.3.p175

Sorensen, Tanner , Hohenstein, Sven , Vasishth, Shravan
Keywords: Bayesian data analysis , linear mixed models
Tools: Stan, R
(no sample data)   (no appendix)

With the arrival of the R packages nlme and lme4, linear mixed models (LMMs) have come to be widely used in experimentally-driven areas like psychology, linguistics, and cognitive science. This tutorial provides a practical introduction to fitting LMMs in a Bayesian framework using the probabilistic programming language Stan. We choose Stan (rather than WinBUGS or JAGS) because it provides an elegant and scalable framework for fitting models in most of the standard applications of LMMs. We ease the reader into fitting increasingly complex LMMs, using a two-condition repeated measures self-paced reading study.

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