glmer: Fitting Generalized Linear Mixed-Effects Models. In lme4: Linear Mixed-Effects Models using ’Eigen’ and S4. Fit a generalized linear mixed-effects model (GLMM). Both fixed effects and random effects are specified via the model formula. Fit a generalized linear mixed model, which incorporates both fixed-effects parameters and random effects in a linear predictor, via maximum likelihood. The linear predictor is related to the conditional mean of the response through the inverse link function defined in the GLM family. The expression for the likelihood of a mixed-effects model is an integral over the random effects space. For a linear mixed-effects model (LMM), as fit by lmer, this integral can be evaluated exactly. For a GLMM the integral must be approximated. The most reliable approximation for GLMMs is adaptive Gauss-Hermite quadrature, at present implemented only for models with a single scalar random effect. The nAGQ argument controls the number of nodes in the quadrature formula. A model with a single, scalar random-effects term could reasonably use up to 25 quadrature points per scalar integral.

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  1. Hu, Xinyu; Qian, Min; Cheng, Bin; Cheung, Ying Kuen: Personalized policy learning using longitudinal mobile health data (2021)