class_docstr: Asymptotic covariance by Deltamethod
  
  The function is designed for 2d array, with rows equal to
  the number of equations or constraints and columns equal to the number
  of parameters. 1d params work by chance ?
  
  fun: R^{m*k) -> R^{m}  where m is number of equations and k is
  the number of parameters.
  
  equations follow Greene
  
  This class does not use any caching. The intended usage is as a helper
  function. Extra methods have been added for convenience but might move
  to calling functions.
  
  The naming in this class uses params for the original random variable, and
  cov_params for it's covariance matrix. However, this class is independent
  of the use cases in support of the models.
  
  Parameters
  ----------
  func : callable, f(params)
      Nonlinear function of the estimation parameters. The return of
      the function can be vector valued, i.e. a 1-D array.
  params : ndarray
      Parameters at which function `func` is evaluated.
  cov_params : ndarray
      Covariance matrix of the parameters `params`.
  deriv : function or None
      First derivative or Jacobian of func. If deriv is None, then a
      numerical derivative will be used. If func returns a 1-D array,
      then the `deriv` should have rows corresponding to the elements
      of the return of func.
  func_args : None
      Not yet implemented.
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docstr: Summarize the Results of the nonlinear transformation.
  
  This provides a parameter table equivalent to `t_test` and reuses
  `ContrastResults`.
  
  Parameters
  -----------
  xname : list of strings, optional
      Default is `c_##` for ## in p the number of regressors
  alpha : float
      Significance level for the confidence intervals. Default is
      alpha = 0.05 which implies a confidence level of 95%.
  title : string, optional
      Title for the params table. If not None, then this replaces the
      default title
  use_t : boolean
      If use_t is False (default), then the normal distribution is used
      for the confidence interval, otherwise the t distribution with
      `df` degrees of freedom is used.
  df : int or float
      degrees of freedom for t distribution. Only used and required if
      use_t is True.
  
  Returns
  -------
  smry : string or Summary instance
      This contains a parameter results table in the case of t or z test
      in the same form as the parameter results table in the model
      results summary.
      For F or Wald test, the return is a string.
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code: def summary(
      self, xname=None, alpha=0.05, title=None, use_t=False, df=None
  ):
      """Summarize the Results of the nonlinear transformation.
  
      This provides a parameter table equivalent to `t_test` and reuses
      `ContrastResults`.
  
      Parameters
      -----------
      xname : list of strings, optional
          Default is `c_##` for ## in p the number of regressors
      alpha : float
          Significance level for the confidence intervals. Default is
          alpha = 0.05 which implies a confidence level of 95%.
      title : string, optional
          Title for the params table. If not None, then this replaces the
          default title
      use_t : boolean
          If use_t is False (default), then the normal distribution is used
          for the confidence interval, otherwise the t distribution with
          `df` degrees of freedom is used.
      df : int or float
          degrees of freedom for t distribution. Only used and required if
          use_t is True.
  
      Returns
      -------
      smry : string or Summary instance
          This contains a parameter results table in the case of t or z test
          in the same form as the parameter results table in the model
          results summary.
          For F or Wald test, the return is a string.
      """
      # this is an experimental reuse of ContrastResults
      from statsmodels.stats.contrast import ContrastResults
  
      predicted = self.predicted()
      se = self.se_vectorized()
      # TODO check shape for scalar case, ContrastResults requires iterable
      predicted = np.atleast_1d(predicted)
      if predicted.ndim > 1:
          predicted = predicted.squeeze()
      se = np.atleast_1d(se)
  
      statistic = predicted / se
      if use_t:
          df_resid = df
          cr = ContrastResults(
              effect=predicted, t=statistic, sd=se, df_denom=df_resid
          )
      else:
          cr = ContrastResults(
              effect=predicted,
              statistic=statistic,
              sd=se,
              df_denom=None,
              distribution="norm",
          )
  
      return cr.summary(xname=xname, alpha=alpha, title=title)
  
file_path: statsmodels/statsmodels/stats/_delta_method.py
imports: import numpy
  from scipy import stats
label_desc: Use image node_id 9 for calling the NonlinearDeltaCov obj's underlying member method code with example usage: obj.summary(xname, alpha, title, use_t, df) and returns: cr