statsmodels.imputation.mice.MICEData.set_imputer

MICEData.set_imputer(endog_name, formula=None, model_class=None, init_kwds=None, fit_kwds=None, predict_kwds=None, k_pmm=20, perturbation_method=None, regularized=False)[source]

Specify the imputation process for a single variable.

Parameters:
endog_namestr

Name of the variable to be imputed.

formulastr

Conditional formula for imputation. Defaults to a formula with main effects for all other variables in dataset. The formula should only include an expression for the mean structure, e.g. use ‘x1 + x2’ not ‘x4 ~ x1 + x2’.

model_classstatsmodels model

Conditional model for imputation. Defaults to OLS. See below for more information.

init_kwdsdit-like

Keyword arguments passed to the model init method.

fit_kwdsdict-like

Keyword arguments passed to the model fit method.

predict_kwdsdict-like

Keyword arguments passed to the model predict method.

k_pmmint

Determines number of neighboring observations from which to randomly sample when using predictive mean matching.

perturbation_methodstr

Either ‘gaussian’ or ‘bootstrap’. Determines the method for perturbing parameters in the imputation model. If None, uses the default specified at class initialization.

regularizeddict

If regularized[name]=True, fit_regularized rather than fit is called when fitting imputation models for this variable. When regularized[name]=True for any variable, perturbation_method must be set to boot.

Notes

The model class must meet the following conditions:
  • A model must have a ‘fit’ method that returns an object.

  • The object returned from fit must have a params attribute that is an array-like object.

  • The object returned from fit must have a cov_params method that returns a square array-like object.

  • The model must have a predict method.


Last update: May 17, 2023