class_docstr: Class for results of tests of linear restrictions on coefficients in a model.
  
  This class functions mainly as a container for `t_test`, `f_test` and
  `wald_test` for the parameters of a model.
  
  The attributes depend on the statistical test and are either based on the
  normal, the t, the F or the chisquare distribution.
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docstr: Return the parameter table as a pandas DataFrame
  
  This is only available for t and normal tests
docstr_tok: ["Return","the","parameter","table","as","a","pandas","DataFrame","This","is","only","available","for","t","and","normal","tests"]
code: def summary_frame(self, xname=None, alpha=0.05):
      """Return the parameter table as a pandas DataFrame
  
      This is only available for t and normal tests
      """
      if self.effect is not None:
          # we have everything for a params table
          use_t = self.distribution == "t"
          yname = "constraints"  # Not used in params_frame
          if xname is None:
              xname = self.c_names
          from statsmodels.iolib.summary import summary_params_frame
  
          summ = summary_params_frame(
              (
                  self,
                  self.effect,
                  self.sd,
                  self.statistic,
                  self.pvalue,
                  self.conf_int(alpha),
              ),
              yname=yname,
              xname=xname,
              use_t=use_t,
              alpha=alpha,
          )
          return summ
      else:
          # TODO: create something nicer
          raise NotImplementedError("only available for t and z")
  
imports: import numpy
  from scipy.stats import f
  from scipy.stats import t
  from scipy import stats
  from statsmodels.tools.tools import clean0, fullrank
  from statsmodels.stats.multitest import multipletests
label_desc: Use image node_id 6 for calling the ContrastResults obj's underlying member method code with example usage: obj.summary_frame(xname, alpha) and returns: summ