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, optional

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, optional

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

init_kwdsdict-like, optional

Keyword arguments passed to the model init method.

fit_kwdsdict-like, optional

Keyword arguments passed to the model fit method.

predict_kwdsdict-like, optional

Keyword arguments passed to the model predict method.

k_pmmint, optional

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

perturbation_methodstr, optional

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

regularizedbool, optional

If True, fit_regularized rather than fit is called when fitting the imputation model for this variable. If True, 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.