Generalized Linear Mixed Effects Models in StatsModels library

Recipe Objective - What are Generalized Linear Mixed Effects Models in the StatsModels library?

The generalized linear mixed-effects (GLIMMIX) model is a generalized linear model with a random effect on the linear predictor. statsmodels currently supports binomial and Poisson GLIMMIX model estimation using two Bayesian methods: the rear Laplace approximation and the rear variational Bayesian approximation. Both ways provide point estimates (posterior mean) and uncertainty estimates (posterior standard deviation). The current implementation only supports independent random effects. In contrast to the mixed linear model of the statistical model, the performance of GLIMMIX is not group-based. Groups are created by interacting all random products with categorical variables. This creates a large and sparse design matrix with the random effect exog_vc. Internally, exog_vc is converted to a Scipy-Sparse matrix. If the arguments are passed directly to the class initializer, you can give a sparse matrix. A dense matrix is ​​created and converted to a sparse matrix when using formulas. For substantial problems, the size of this dense intermediate matrix can make formulas unusable.

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