docstr: Confidence interval for the predicted value.
  
  This is currently only available for t and z tests.
  
  Parameters
  ----------
  method : {"endpoint", "delta"}
      Method for confidence interval, "m
      If method is "endpoint", then the confidence interval of the
      linear predictor is transformed by the prediction function.
      If method is "delta", then the delta-method is used. The confidence
      interval in this case might reach outside the range of the
      prediction, for example probabilities larger than one or smaller
      than zero.
  alpha : float, optional
      The significance level for the confidence interval.
      ie., The default `alpha` = .05 returns a 95% confidence interval.
  kwds : extra keyword arguments
      currently ignored, only for compatibility, consistent signature
  
  Returns
  -------
  ci : ndarray, (k_constraints, 2)
      The array has the lower and the upper limit of the confidence
      interval in the columns.
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code: def conf_int(self, method="endpoint", alpha=0.05, **kwds):
      """Confidence interval for the predicted value.
  
      This is currently only available for t and z tests.
  
      Parameters
      ----------
      method : {"endpoint", "delta"}
          Method for confidence interval, "m
          If method is "endpoint", then the confidence interval of the
          linear predictor is transformed by the prediction function.
          If method is "delta", then the delta-method is used. The confidence
          interval in this case might reach outside the range of the
          prediction, for example probabilities larger than one or smaller
          than zero.
      alpha : float, optional
          The significance level for the confidence interval.
          ie., The default `alpha` = .05 returns a 95% confidence interval.
      kwds : extra keyword arguments
          currently ignored, only for compatibility, consistent signature
  
      Returns
      -------
      ci : ndarray, (k_constraints, 2)
          The array has the lower and the upper limit of the confidence
          interval in the columns.
      """
      tmp = np.linspace(0, 1, 6)
      # TODO: drop check?
      is_linear = (self.func(tmp) == tmp).all()
      if method == "endpoint" and not is_linear:
          ci_linear = self._conf_int_generic(
              self.linpred,
              self.linpred_se,
              alpha,
              dist_args=self.dist_args,
          )
          ci = self.func(ci_linear)
      elif method == "delta" or is_linear:
          ci = self._conf_int_generic(
              self.predicted, self.se, alpha, dist_args=self.dist_args
          )
  
      return ci
  
imports: import numpy
  from scipy import stats
  import pandas
label_desc: Use image node_id 3 for calling the PredictionResultsMonotonic obj's underlying member method code with example usage: obj.conf_int(method, alpha) and returns: ci