docstr: Return endog and exog for imputation of a given variable.
  
  Parameters
  ----------
  vname : str
     The variable for which the split data is returned.
  
  Returns
  -------
  endog_obs : DataFrame
      Observed values of the variable to be imputed.
  exog_obs : DataFrame
      Current values of the predictors where the variable to be
      imputed is observed.
  exog_miss : DataFrame
      Current values of the predictors where the variable to be
      Imputed is missing.
  init_kwds : dict-like
      The init keyword arguments for `vname`, processed through Patsy
      as required.
  fit_kwds : dict-like
      The fit keyword arguments for `vname`, processed through Patsy
      as required.
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code: def get_split_data(self, vname):
      """
      Return endog and exog for imputation of a given variable.
  
      Parameters
      ----------
      vname : str
         The variable for which the split data is returned.
  
      Returns
      -------
      endog_obs : DataFrame
          Observed values of the variable to be imputed.
      exog_obs : DataFrame
          Current values of the predictors where the variable to be
          imputed is observed.
      exog_miss : DataFrame
          Current values of the predictors where the variable to be
          Imputed is missing.
      init_kwds : dict-like
          The init keyword arguments for `vname`, processed through Patsy
          as required.
      fit_kwds : dict-like
          The fit keyword arguments for `vname`, processed through Patsy
          as required.
      """
  
      formula = self.conditional_formula[vname]
      endog, exog = patsy.dmatrices(
          formula, self.data, return_type="dataframe"
      )
  
      # Rows with observed endog
      ixo = self.ix_obs[vname]
      endog_obs = np.require(endog.iloc[ixo], requirements="W")
      exog_obs = np.require(exog.iloc[ixo, :], requirements="W")
  
      # Rows with missing endog
      ixm = self.ix_miss[vname]
      exog_miss = np.require(exog.iloc[ixm, :], requirements="W")
  
      predict_obs_kwds = {}
      if vname in self.predict_kwds:
          kwds = self.predict_kwds[vname]
          predict_obs_kwds = self._process_kwds(kwds, ixo)
  
      predict_miss_kwds = {}
      if vname in self.predict_kwds:
          kwds = self.predict_kwds[vname]
          predict_miss_kwds = self._process_kwds(kwds, ixo)
  
      return (
          endog_obs,
          exog_obs,
          exog_miss,
          predict_obs_kwds,
          predict_miss_kwds,
      )
  
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 8 for calling the MICEData obj's underlying member method code with example usage: obj.get_split_data(vname) and returns: endog_obs,  exog_obs,  exog_miss,  predict_obs_kwds,  predict_miss_kwds