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)[source]¶

MI performs multiple imputation using a provided imputer object.

Parameters:¶

imp : object¶

An imputer class, such as BayesGaussMI.

model : model class¶

Any statsmodels model class.

model_args_fn : function¶

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_fn : function, optional¶

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

formula : str, optional¶

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

fit_args : list-like, optional¶

List of arguments to be passed to the fit method

fit_kwds : dict-like, optional¶

Keyword arguments to be passed to the fit method

xfunc : function mapping ndarray to ndarray¶

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

burn : int¶

Number of burn-in iterations

nrep : int¶

Number of imputed data sets to use in the analysis

skip : int¶

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.