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)

Specify the imputation process for a single variable.

Parameters:

• endog_name (string) – Name of the variable to be imputed.
• formula (string) – 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_class (statsmodels model) – Conditional model for imputation. Defaults to OLS. See below for more information.
• init_kwds (dit-like) – Keyword arguments passed to the model init method.
• fit_kwds (dict-like) – Keyword arguments passed to the model fit method.
• predict_kwds (dict-like) – Keyword arguments passed to the model predict method.
• k_pmm (int) – Determines number of neighboring observations from which to randomly sample when using predictive mean matching.
• perturbation_method (string) – Either ‘gaussian’ or ‘bootstrap’. Determines the method for perturbing parameters in the imputation model. If None, uses the default specified at class initialization.
• regularized (dict) – 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, pertrurbation_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.