statsmodels.imputation.mice.MICEData.set_imputer¶
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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_name
str Name of the variable to be imputed.
- formula
str 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
statsmodelsmodel 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_pmm
int Determines number of neighboring observations from which to randomly sample when using predictive mean matching.
- perturbation_method
str 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, perturbation_method must be set to boot.
- endog_name
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.