statsmodels.genmod.bayes_mixed_glm.BayesMixedGLMResults

class statsmodels.genmod.bayes_mixed_glm.BayesMixedGLMResults(model, params, cov_params, optim_retvals=None)

Class to hold results from a Bayesian estimation of a Mixed GLM model.

Attributes:

model_BayesMixedGLM

The model instance used to obtain this result.

paramsndarray

The full vector of posterior parameter estimates (fixed effects, variance component parameters, and random effect realizations, in that order).

fe_meanndarray

Posterior mean of the fixed effects coefficients.

fe_sdndarray

Posterior standard deviation of the fixed effects coefficients

vcp_meanndarray

Posterior mean of the logged variance component standard deviations.

vcp_sdndarray

Posterior standard deviation of the logged variance component standard deviations.

vc_meanndarray

Posterior mean of the random coefficients

vc_sdndarray

Posterior standard deviation of the random coefficients

optim_retvalsoptional

The return value of the numerical optimization routine used to obtain the fit (e.g. the OptimizeResult from scipy.optimize.minimize), if available.

Methods

cov_params()	Return the covariance matrix of the posterior parameter estimates.
predict([exog, linear])	Return predicted values for the mean structure.
random_effects([term])	Posterior mean and standard deviation of random effects.
summary()	Summarize the posterior parameter estimates.

Methods

cov_params()	Return the covariance matrix of the posterior parameter estimates.
predict([exog, linear])	Return predicted values for the mean structure.
random_effects([term])	Posterior mean and standard deviation of random effects.
summary()	Summarize the posterior parameter estimates.