code: def __load_omitted_predictions(self):
          
          # if there are no base predictions
          if self.predictions is None:
              raise Exception("ErrVar.__load_omitted_predictions(): " +
                              "no 'included' predictions is None")
          if self.omitted_predictions_arg is None and \
                          self.omitted_par_arg is None:
              raise Exception("ErrVar.__load_omitted_predictions: " +
                              "both omitted args are None")
          # try to set omitted_predictions by
          # extracting from existing predictions
          if self.omitted_predictions_arg is None and \
                          self.omitted_par_arg is not None:
              # check to see if omitted par names are in each predictions
              found = True
              missing_par,missing_pred = None, None
              for par_name in self.omitted_jco.col_names:
                  for prediction in self.predictions_iter:
                      if par_name not in prediction.row_names:
                          found = False
                          missing_par = par_name
                          missing_pred = prediction.col_names[0]
                          break
              if found:
                  opreds = []
                  # need to access the attribute directly,
                  # not a view of attribute
                  opred_mat = self._LinearAnalysis__predictions.extract(
                              row_names=self.omitted_jco.col_names)
                  opreds = [opred_mat.get(col_names=name) for name in self.forecast_names]
  
                  #for prediction in self._LinearAnalysis__predictions:
                  #    opred = prediction.extract(self.omitted_jco.col_names)
                  #    opreds.append(opred)
                  self.__omitted_predictions = opreds
              else:
                  raise Exception("ErrVar.__load_omitted_predictions(): " +
                                  " omitted parameter " + str(missing_par) +\
                                  " not found in prediction vector " +
                                  str(missing_pred))
          elif self.omitted_parcov_arg is not None:
              raise NotImplementedError()
docs: private: set the omitted_predictions attribute