statsmodels.genmod.bayes_mixed_glm.PoissonBayesMixedGLM.fit_map

PoissonBayesMixedGLM.fit_map(method='BFGS', minim_opts=None, scale_fe=False, rng=None)

Construct the Laplace approximation to the posterior distribution.

Parameters:

methodstr

Optimization method for finding the posterior mode.

minim_optsdict

Options passed to scipy.minimize.

scale_febool

If True, the columns of the fixed effects design matrix are centered and scaled to unit variance before fitting the model. The results are back-transformed so that the results are presented on the original scale.

rng{None, int, array_like[int], numpy.random.Generator, numpy.random.RandomState}, optional

If rng is None, a new Generator is created using fresh entropy from the operating system. If rng is an int or array of ints, a new Generator is created, seeded with rng. If rng is already a Generator or RandomState instance, that instance is used.

Returns:

BayesMixedGLMResults instance.