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
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docstr: Posterior mean and standard deviation of random effects.
  
  Parameters
  ----------
  term : int or None
      If None, results for all random effects are returned.  If
      an integer, returns results for a given set of random
      effects.  The value of `term` refers to an element of the
      `ident` vector, or to a position in the `vc_formulas`
      list.
  
  Returns
  -------
  Data frame of posterior means and posterior standard
  deviations of random effects.
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code: def random_effects(self, term=None):
      """
      Posterior mean and standard deviation of random effects.
  
      Parameters
      ----------
      term : int or None
          If None, results for all random effects are returned.  If
          an integer, returns results for a given set of random
          effects.  The value of `term` refers to an element of the
          `ident` vector, or to a position in the `vc_formulas`
          list.
  
      Returns
      -------
      Data frame of posterior means and posterior standard
      deviations of random effects.
      """
  
      z = self.vc_mean
      s = self.vc_sd
      na = self.model.vc_names
  
      if term is not None:
          termix = self.model.vcp_names.index(term)
          ii = np.flatnonzero(self.model.ident == termix)
          z = z[ii]
          s = s[ii]
          na = [na[i] for i in ii]
  
      x = pd.DataFrame({"Mean": z, "SD": s})
  
      if na is not None:
          x.index = na
  
      return x
  
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 4 for calling the BayesMixedGLMResults obj's underlying member method code with example usage: obj.random_effects(term) and returns: x