docstr: Impute missing values for a single variable.
  
  This is a two-step process in which first the parameters are
  perturbed, then the missing values are re-imputed.
  
  Parameters
  ----------
  vname : str
      The name of the variable to be updated.
docstr_tok: ["Impute","missing","values","for","a","single","variable",".","This","is","a","two-step","process","in","which","first","the","parameters","are","perturbed",",","then","the","missing","values","are","re-imputed",".","Parameters","--","--","--","--","--","vname",":","str","The","name","of","the","variable","to","be","updated","."]
code: def update(self, vname):
      """
      Impute missing values for a single variable.
  
      This is a two-step process in which first the parameters are
      perturbed, then the missing values are re-imputed.
  
      Parameters
      ----------
      vname : str
          The name of the variable to be updated.
      """
  
      self.perturb_params(vname)
      self.impute(vname)
  
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 20 for calling the MICEData obj's underlying member method code with example usage: obj.update(vname) without return types