class_docstr: Class to hold results from a Bayesian estimation of a Mixed GLM model.
  
  Attributes
  ----------
  fe_mean : array_like
      Posterior mean of the fixed effects coefficients.
  fe_sd : array_like
      Posterior standard deviation of the fixed effects coefficients
  vcp_mean : array_like
      Posterior mean of the logged variance component standard
      deviations.
  vcp_sd : array_like
      Posterior standard deviation of the logged variance component
      standard deviations.
  vc_mean : array_like
      Posterior mean of the random coefficients
  vc_sd : array_like
      Posterior standard deviation of the random coefficients
class_docstr_tok: ["Class","to","hold","results","from","a","Bayesian","estimation","of","a","Mixed","GLM","model",".","Attributes","--","--","--","--","--","fe_mean",":","array_like","Posterior","mean","of","the","fixed","effects","coefficients",".","fe_sd",":","array_like","Posterior","standard","deviation","of","the","fixed","effects","coefficients","vcp_mean",":","array_like","Posterior","mean","of","the","logged","variance","component","standard","deviations",".","vcp_sd",":","array_like","Posterior","standard","deviation","of","the","logged","variance","component","standard","deviations",".","vc_mean",":","array_like","Posterior","mean","of","the","random","coefficients","vc_sd",":","array_like","Posterior","standard","deviation","of","the","random","coefficients"]
code: def __init__(self, model, params, cov_params, optim_retvals=None):
      self.model = model
      self.params = params
      self._cov_params = cov_params
      self.optim_retvals = optim_retvals
  
      self.fe_mean, self.vcp_mean, self.vc_mean = model._unpack(params)
  
      if cov_params.ndim == 2:
          cp = np.diag(cov_params)
      else:
          cp = cov_params
      self.fe_sd, self.vcp_sd, self.vc_sd = model._unpack(cp)
      self.fe_sd = np.sqrt(self.fe_sd)
      self.vcp_sd = np.sqrt(self.vcp_sd)
      self.vc_sd = np.sqrt(self.vc_sd)
  
imports: import numpy
  from scipy.optimize import minimize
  from scipy import sparse
  import statsmodels.base.model
  from statsmodels.iolib import summary2
  from statsmodels.genmod import families
  import pandas
  import warnings
  import patsy
label_desc: Use image node_id 1 to create a new BayesMixedGLMResults object  with example: obj = BayesMixedGLMResults(model, params, cov_params, optim_retvals)