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: Return predicted values for the mean structure.
  
  Parameters
  ----------
  exog : array_like
      The design matrix for the mean structure.  If None,
      use the model's design matrix.
  linear : bool
      If True, returns the linear predictor, otherwise
      transform the linear predictor using the link function.
  
  Returns
  -------
  A one-dimensional array of fitted values.
docstr_tok: ["Return","predicted","values","for","the","mean","structure",".","Parameters","--","--","--","--","--","exog",":","array_like","The","design","matrix","for","the","mean","structure",".","If","None",",","use","the","model","'s","design","matrix",".","linear",":","bool","If","True",",","returns","the","linear","predictor",",","otherwise","transform","the","linear","predictor","using","the","link","function",".","Returns","--","--","--","-","A","one-dimensional","array","of","fitted","values","."]
code: def predict(self, exog=None, linear=False):
      """
      Return predicted values for the mean structure.
  
      Parameters
      ----------
      exog : array_like
          The design matrix for the mean structure.  If None,
          use the model's design matrix.
      linear : bool
          If True, returns the linear predictor, otherwise
          transform the linear predictor using the link function.
  
      Returns
      -------
      A one-dimensional array of fitted values.
      """
  
      return self.model.predict(self.params, exog, linear)
  
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 5 for calling the BayesMixedGLMResults obj's underlying member method code with example usage: obj.predict(exog, linear) and returns: self