class_docstr: A results class for multiple imputation (MI).
  
  Parameters
  ----------
  mi : MI instance
      The MI object that produced the results
  model : instance of statsmodels model class
      This can be any instance from the multiple imputation runs.
      It is used to get class information, the specific parameter
      and data values are not used.
  params : array_like
      The overall multiple imputation parameter estimates.
  normalized_cov_params : array_like (2d)
      The overall variance covariance matrix of the estimates.
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docstr: Summarize the results of running multiple imputation.
  
  Parameters
  ----------
  title : str, optional
      Title for the top table. If not None, then this replaces
      the default title
  alpha : float
      Significance level for the confidence intervals
  
  Returns
  -------
  smry : Summary instance
      This holds the summary tables and text, which can be
      printed or converted to various output formats.
docstr_tok: ["Summarize","the","results","of","running","multiple","imputation",".","Parameters","--","--","--","--","--","title",":","str",",","optional","Title","for","the","top","table",".","If","not","None",",","then","this","replaces","the","default","title","alpha",":","float","Significance","level","for","the","confidence","intervals","Returns","--","--","--","-","smry",":","Summary","instance","This","holds","the","summary","tables","and","text",",","which","can","be","printed","or","converted","to","various","output","formats","."]
code: def summary(self, title=None, alpha=0.05):
      """
      Summarize the results of running multiple imputation.
  
      Parameters
      ----------
      title : str, optional
          Title for the top table. If not None, then this replaces
          the default title
      alpha : float
          Significance level for the confidence intervals
  
      Returns
      -------
      smry : Summary instance
          This holds the summary tables and text, which can be
          printed or converted to various output formats.
      """
  
      from statsmodels.iolib import summary2
  
      smry = summary2.Summary()
      float_format = "%8.3f"
  
      info = {}
      info["Method:"] = "MI"
      info["Model:"] = self.mi.model.__name__
      info["Dependent variable:"] = self._model.endog_names
      info["Sample size:"] = "%d" % self.mi.imp.data.shape[0]
      info["Num. imputations"] = "%d" % self.mi.nrep
  
      smry.add_dict(info, align="l", float_format=float_format)
  
      param = summary2.summary_params(self, alpha=alpha)
      param["FMI"] = self.fmi
  
      smry.add_df(param, float_format=float_format)
      smry.add_title(title=title, results=self)
  
      return smry
  
file_path: statsmodels/statsmodels/imputation/bayes_mi.py
imports: import numpy
  import pandas
  from statsmodels.base.model import LikelihoodModelResults
label_desc: Use image node_id 2 for calling the MIResults obj's underlying member method code with example usage: obj.summary(title, alpha) and returns: smry