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 cov_params(self):
      if hasattr(self.model.data, "frame"):
          # Return the covariance matrix as a dataframe or series
          na = (
              self.model.fep_names
              + self.model.vcp_names
              + self.model.vc_names
          )
          if self._cov_params.ndim == 2:
              return pd.DataFrame(
                  self._cov_params, index=na, columns=na
              )
          else:
              return pd.Series(self._cov_params, index=na)
  
      # Return the covariance matrix as a ndarray
      return self._cov_params
  
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 2 for calling the BayesMixedGLMResults obj's underlying member method code with example usage: obj.cov_params() and returns: self, pd, pd