Fitting Linear Mixed-Effects Models Usinglme4

Fitting Linear Mixed-Effects Models using lme4

Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixedand random-effects terms. The formula and data together determine a numerical repr...

Adaptive Fitting of Linear Mixed-Effects Models with Correlated Random-effects.

Linear mixed-effects model has been widely used in longitudinal data analyses. In practice, the fitting algorithm can fail to converge due to boundary issues of the estimated random-effects covariance matrix G, i.e., being near-singular, non-positive definite, or both. Current available algorithms are not computationally optimal because the condition number of matrix G is unnecessarily increase...

Fitting linear mixed models using JAGS and Stan: A tutorial

This tutorial is aimed at psycholinguists and psychologists interested in fitting linear mixed models using JAGS and Stan.

A tutorial on fitting Bayesian linear mixed models using Stan

With the arrival of the R packages nlme and lme4, linear mixed models (LMMs) have come to be widely used in psychology, cognitive science, and related areas. In this tutorial, we provide a practical introduction to fitting LMMs in a Bayesian framework using the probabilistic programming language Stan. Although the Bayesian framework has several important advantages, specifying a Bayesian model ...

Fitting Generalized Linear Mixed Models For Point-Referenced Spatial Data