class_docstr: Prediction results for GLM.
  
  This results class is used for backwards compatibility for
  `get_prediction` with GLM. The new PredictionResults classes dropped the
  `_mean` post fix in the attribute names.
class_docstr_tok: ["Prediction","results","for","GLM",".","This","results","class","is","used","for","backwards","compatibility","for","`","get_prediction","`","with","GLM",".","The","new","PredictionResults","classes","dropped","the","`","_mean","`","post","fix","in","the","attribute","names","."]
docstr: Summary frame
  
  Parameters
  ----------
  alpha : float, optional
      The significance level for the confidence interval.
      ie., The default `alpha` = .05 returns a 95% confidence interval.
  
  Returns
  -------
  pandas DataFrame with columns
  'mean', 'mean_se', 'mean_ci_lower', 'mean_ci_upper'.
docstr_tok: ["Summary","frame","Parameters","--","--","--","--","--","alpha",":","float",",","optional","The","significance","level","for","the","confidence","interval",".","ie.",",","The","default","`","alpha","`","=",".05","returns","a","95","%","confidence","interval",".","Returns","--","--","--","-","pandas","DataFrame","with","columns","'mean","'",",","'mean_se","'",",","'mean_ci_lower","'",",","'mean_ci_upper","'","."]
code: def summary_frame(self, alpha=0.05):
      """Summary frame
  
      Parameters
      ----------
      alpha : float, optional
          The significance level for the confidence interval.
          ie., The default `alpha` = .05 returns a 95% confidence interval.
  
      Returns
      -------
      pandas DataFrame with columns
      'mean', 'mean_se', 'mean_ci_lower', 'mean_ci_upper'.
      """
      # TODO: finish and cleanup
      ci_mean = self.conf_int(alpha=alpha)
      to_include = {}
      to_include["mean"] = self.predicted_mean
      to_include["mean_se"] = self.se_mean
      to_include["mean_ci_lower"] = ci_mean[:, 0]
      to_include["mean_ci_upper"] = ci_mean[:, 1]
  
      self.table = to_include
      # pandas dict does not handle 2d_array
      # data = np.column_stack(list(to_include.values()))
      # names = ....
      res = pd.DataFrame(
          to_include, index=self.row_labels, columns=to_include.keys()
      )
      return res
  
imports: import numpy
  from scipy import stats
  import pandas
label_desc: Use image node_id 6 for calling the PredictionResultsMean obj's underlying member method code with example usage: obj.summary_frame(alpha) and returns: res