docstr: Gaussian perturbation of model parameters.
  
  The normal approximation to the sampling distribution of the
  parameter estimates is used to define the mean and covariance
  structure of the perturbation distribution.
docstr_tok: ["Gaussian","perturbation","of","model","parameters",".","The","normal","approximation","to","the","sampling","distribution","of","the","parameter","estimates","is","used","to","define","the","mean","and","covariance","structure","of","the","perturbation","distribution","."]
code: def _perturb_gaussian(self, vname):
      """
      Gaussian perturbation of model parameters.
  
      The normal approximation to the sampling distribution of the
      parameter estimates is used to define the mean and covariance
      structure of the perturbation distribution.
      """
  
      endog, exog, init_kwds, fit_kwds = self.get_fitting_data(vname)
  
      klass = self.model_class[vname]
      self.models[vname] = klass(endog, exog, **init_kwds)
      self.results[vname] = self.models[vname].fit(**fit_kwds)
  
      cov = self.results[vname].cov_params()
      mu = self.results[vname].params
      self.params[vname] = np.random.multivariate_normal(
          mean=mu, cov=cov
      )
  
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 17 for calling the MICEData obj's underlying member method code with example usage: obj._perturb_gaussian(vname) without return types