Linear and generalized linear mixed models

Abstract Generalized linear mixed models (GLMMs) are a powerful class of statistical models that combine the characteristics of generalized linear models and mixed models (models with both fixed and random predictor variables). This chapter: reviews the conceptual and theoretical background of GLMMs, focusing on the definition and meaning of random effects; gives basic guidelines and syntax for setting up a mixed model; and discusses the theoretical and practical details of estimating parameters, diagnosing problems with a model, and making statistical inferences (finding confidence intervals, estimating p values, and doing model selection) for GLMMs.