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_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 (ditlike) – Keyword arguments passed to the model init method.

fit_kwds (dictlike) – Keyword arguments passed to the model fit method.

predict_kwds (dictlike) – 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 arraylike object.  The object returned from
fit
must have a cov_params method that returns a square arraylike object.  The model must have a
predict
method.