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.
class_docstr_tok: ["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","."]
code: def __init__(self, mi, model, params, normalized_cov_params):
      super().__init__(model, params, normalized_cov_params)
      self.mi = mi
      self._model = model
  
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 1 to create a new MIResults object from inherited base classes: LikelihoodModelResults with example: obj = MIResults(mi, model, params, normalized_cov_params)