docs/source/mixed_glm.rst
.. currentmodule:: statsmodels.genmod.bayes_mixed_glm
Generalized Linear Mixed Effects (GLIMMIX) models are generalized linear models with random effects in the linear predictors. statsmodels currently supports estimation of binomial and Poisson GLIMMIX models using two Bayesian methods: the Laplace approximation to the posterior, and a variational Bayes approximation to the posterior. Both methods provide point estimates (posterior means) and assessments of uncertainty (posterior standard deviation).
The current implementation only supports independent random effects.
Unlike statsmodels mixed linear models, the GLIMMIX implementation is
not group-based. Groups are created by interacting all random effects
with a categorical variable. Note that this creates large, sparse
random effects design matrices exog_vc. Internally, exog_vc is
converted to a scipy sparse matrix. When passing the arguments
directly to the class initializer, a sparse matrix may be passed.
When using formulas, a dense matrix is created then converted to
sparse. For very large problems, it may not be feasible to use
formulas due to the size of this dense intermediate matrix.
References ^^^^^^^^^^
Blei, Kucukelbir, McAuliffe (2017). Variational Inference: A review for Statisticians https://arxiv.org/pdf/1601.00670.pdf
.. module:: statsmodels.genmod.bayes_mixed_glm :synopsis: Bayes Mixed Generalized Linear Models
The model classes are:
.. autosummary:: :toctree: generated/
BinomialBayesMixedGLM PoissonBayesMixedGLM
The result class is:
.. autosummary:: :toctree: generated/
BayesMixedGLMResults