docstr: Fit a model using MICE.
  
  Parameters
  ----------
  n_burnin : int
      The number of burn-in cycles to skip.
  n_imputations : int
      The number of data sets to impute
docstr_tok: ["Fit","a","model","using","MICE",".","Parameters","--","--","--","--","--","n_burnin",":","int","The","number","of","burn-in","cycles","to","skip",".","n_imputations",":","int","The","number","of","data","sets","to","impute"]
code: def fit(self, n_burnin=10, n_imputations=10):
      """
      Fit a model using MICE.
  
      Parameters
      ----------
      n_burnin : int
          The number of burn-in cycles to skip.
      n_imputations : int
          The number of data sets to impute
      """
  
      # Run without fitting the analysis model
      self.data.update_all(n_burnin)
  
      for j in range(n_imputations):
          result = self.next_sample()
          self.results_list.append(result)
  
      self.endog_names = result.model.endog_names
      self.exog_names = result.model.exog_names
  
      return self.combine()
  
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 3 for calling the MICE obj's underlying member method code with example usage: obj.fit(n_burnin, n_imputations) and returns: self