statsmodels.imputation.bayes_mi.MI

class statsmodels.imputation.bayes_mi.MI(imp, model, model_args_fn=None, model_kwds_fn=None, formula=None, fit_args=None, fit_kwds=None, xfunc=None, burn=100, nrep=20, skip=10)

MI performs multiple imputation using a provided imputer object.

Parameters:

impobject

An imputer class, such as BayesGaussMI.

modelmodel class

Any statsmodels model class.

model_args_fnfunction

A function taking an imputed dataset as input and returning endog, exog. If the model is fit using a formula, returns a DataFrame used to build the model. Optional when a formula is used.

model_kwds_fnfunction, optional

A function taking an imputed dataset as input and returning a dictionary of model keyword arguments.

formulastr, optional

If provided, the model is constructed using the from_formula class method, otherwise the __init__ method is used.

fit_argslist-like, optional

List of arguments to be passed to the fit method

fit_kwdsdict-like, optional

Keyword arguments to be passed to the fit method

xfuncfunction mapping ndarray to ndarray

A function that is applied to the complete data matrix prior to fitting the model

burnint

Number of burn-in iterations

nrepint

Number of imputed data sets to use in the analysis

skipint

Number of Gibbs iterations to skip between successive multiple imputation fits.

Notes

The imputer object must have an ‘update’ method, and a ‘data’ attribute that contains the current imputed dataset.

xfunc can be used to introduce domain constraints, e.g. when imputing binary data the imputed continuous values can be rounded to 0/1.

Methods

fit([results_cb])

Impute datasets, fit models, and pool results.