docstr: Pools MICE imputation results.
  
  This method can only be used after the `run` method has been
  called.  Returns estimates and standard errors of the analysis
  model parameters.
  
  Returns a MICEResults instance.
docstr_tok: ["Pools","MICE","imputation","results",".","This","method","can","only","be","used","after","the","`","run","`","method","has","been","called",".","Returns","estimates","and","standard","errors","of","the","analysis","model","parameters",".","Returns","a","MICEResults","instance","."]
code: def combine(self):
      """
      Pools MICE imputation results.
  
      This method can only be used after the `run` method has been
      called.  Returns estimates and standard errors of the analysis
      model parameters.
  
      Returns a MICEResults instance.
      """
  
      # Extract a few things from the models that were fit to
      # imputed data sets.
      params_list = []
      cov_within = 0.0
      scale_list = []
      for results in self.results_list:
          results_uw = results._results
          params_list.append(results_uw.params)
          cov_within += results_uw.cov_params()
          scale_list.append(results.scale)
      params_list = np.asarray(params_list)
      scale_list = np.asarray(scale_list)
  
      # The estimated parameters for the MICE analysis
      params = params_list.mean(0)
  
      # The average of the within-imputation covariances
      cov_within /= len(self.results_list)
  
      # The between-imputation covariance
      cov_between = np.cov(params_list.T)
  
      # The estimated covariance matrix for the MICE analysis
      f = 1 + 1 / float(len(self.results_list))
      cov_params = cov_within + f * cov_between
  
      # Fraction of missing information
      fmi = f * np.diag(cov_between) / np.diag(cov_params)
  
      # Set up a results instance
      scale = np.mean(scale_list)
      results = MICEResults(self, params, cov_params / scale)
      results.scale = scale
      results.frac_miss_info = fmi
      results.exog_names = self.exog_names
      results.endog_names = self.endog_names
      results.model_class = self.model_class
  
      return results
  
imports: import pandas
  import numpy
  import patsy
  from statsmodels.base.model import LikelihoodModelResults
  from statsmodels.regression.linear_model import OLS
  from collections import defaultdict
label_desc: Use image node_id 4 for calling the MICE obj's underlying member method code with example usage: obj.combine() and returns: results