class_docstr: Get marginal effects of a Discrete Choice model.
  
  Parameters
  ----------
  results : DiscreteResults instance
      The results instance of a fitted discrete choice model
  args : tuple
      Args are passed to `get_margeff`. This is the same as
      results.get_margeff. See there for more information.
  kwargs : dict
      Keyword args are passed to `get_margeff`. This is the same as
      results.get_margeff. See there for more information.
docstr: Returns a DataFrame summarizing the marginal effects.
  
  Parameters
  ----------
  alpha : float
      Number between 0 and 1. The confidence intervals have the
      probability 1-alpha.
  
  Returns
  -------
  frame : DataFrames
      A DataFrame summarizing the marginal effects.
  
  Notes
  -----
  The dataframe is created on each call and not cached, as are the
  tables build in `summary()`
docstr_tok: ["Returns","a","DataFrame","summarizing","the","marginal","effects",".","Parameters","--","--","--","--","--","alpha",":","float","Number","between","0","and","1",".","The","confidence","intervals","have","the","probability","1-alpha",".","Returns","--","--","--","-","frame",":","DataFrames","A","DataFrame","summarizing","the","marginal","effects",".","Notes","--","--","-","The","dataframe","is","created","on","each","call","and","not","cached",",","as","are","the","tables","build","in","`","summary","(",")","`"]
code: def summary_frame(self, alpha=0.05):
      """
      Returns a DataFrame summarizing the marginal effects.
  
      Parameters
      ----------
      alpha : float
          Number between 0 and 1. The confidence intervals have the
          probability 1-alpha.
  
      Returns
      -------
      frame : DataFrames
          A DataFrame summarizing the marginal effects.
  
      Notes
      -----
      The dataframe is created on each call and not cached, as are the
      tables build in `summary()`
      """
      _check_at_is_all(self.margeff_options)
      results = self.results
      model = self.results.model
      from pandas import DataFrame, MultiIndex
  
      names = [
          _transform_names[self.margeff_options["method"]],
          "Std. Err.",
          "z",
          "Pr(>|z|)",
          "Conf. Int. Low",
          "Cont. Int. Hi.",
      ]
      ind = (
          self.results.model.exog.var(0) != 0
      )  # True if not a constant
      exog_names = self.results.model.exog_names
      k_extra = getattr(model, "k_extra", 0)
      if k_extra > 0:
          exog_names = exog_names[:-k_extra]
      var_names = [name for i, name in enumerate(exog_names) if ind[i]]
  
      if self.margeff.ndim == 2:
          # MNLogit case
          ci = self.conf_int(alpha)
          table = np.column_stack(
              [
                  i.ravel("F")
                  for i in [
                      self.margeff,
                      self.margeff_se,
                      self.tvalues,
                      self.pvalues,
                      ci[:, 0, :],
                      ci[:, 1, :],
                  ]
              ]
          )
  
          _, yname_list = results._get_endog_name(
              model.endog_names, None, all=True
          )
          ynames = np.repeat(yname_list, len(var_names))
          xnames = np.tile(var_names, len(yname_list))
          index = MultiIndex.from_tuples(
              list(zip(ynames, xnames)), names=["endog", "exog"]
          )
      else:
          table = np.column_stack(
              (
                  self.margeff,
                  self.margeff_se,
                  self.tvalues,
                  self.pvalues,
                  self.conf_int(alpha),
              )
          )
          index = var_names
  
      return DataFrame(table, columns=names, index=index)
  
imports: from statsmodels.compat.python import lzip
  import numpy
  from scipy.stats import norm
  from statsmodels.tools.decorators import cache_readonly
label_desc: Use image node_id 4 for calling the DiscreteMargins obj's underlying member method code with example usage: obj.summary_frame(alpha) and returns: DataFrame