statsmodels.imputation.mice.MICE#
- class statsmodels.imputation.mice.MICE(model_formula, model_class, data, n_skip=3, init_kwds=None, fit_kwds=None)[source]#
Multiple Imputation with Chained Equations
This class can be used to fit most statsmodels models to data sets with missing values using the ‘multiple imputation with chained equations’ (MICE) approach.
- Parameters:
- model_formula
str The model formula to be fit to the imputed data sets. This formula is for the ‘analysis model’.
- model_class
statsmodelsmodel The model to be fit to the imputed data sets. This model class is for the ‘analysis model’.
- data
MICEDatainstance MICEData object containing the data set for which missing values will be imputed
- n_skip
int,optional The number of imputed datasets to skip between consecutive imputed datasets that are used for analysis.
- init_kwdsdict-like,
optional Dictionary of keyword arguments passed to the init method of the analysis model.
- fit_kwdsdict-like,
optional Dictionary of keyword arguments passed to the fit method of the analysis model.
- model_formula
Methods
combine()Pool MICE imputation results
fit([n_burnin, n_imputations])Fit a model using MICE
Perform one complete MICE iteration
Examples
Run all MICE steps and obtain results:
>>> imp = mice.MICEData(data) >>> fml = 'y ~ x1 + x2 + x3 + x4' >>> mice = mice.MICE(fml, sm.OLS, imp) >>> results = mice.fit(10, 10) >>> print(results.summary())
Results: MICE ================================================================= Method: MICE Sample size: 1000 Model: OLS Scale 1.00 Dependent variable: y Num. imputations 10 ----------------------------------------------------------------- Coef. Std.Err. t P>|t| [0.025 0.975] FMI ----------------------------------------------------------------- Intercept -0.0234 0.0318 -0.7345 0.4626 -0.0858 0.0390 0.0128 x1 1.0305 0.0578 17.8342 0.0000 0.9172 1.1437 0.0309 x2 -0.0134 0.0162 -0.8282 0.4076 -0.0451 0.0183 0.0236 x3 -1.0260 0.0328 -31.2706 0.0000 -1.0903 -0.9617 0.0169 x4 -0.0253 0.0336 -0.7520 0.4521 -0.0911 0.0406 0.0269 =================================================================
Obtain a sequence of fitted analysis models without combining to obtain summary:
>>> imp = mice.MICEData(data) >>> fml = 'y ~ x1 + x2 + x3 + x4' >>> mice = mice.MICE(fml, sm.OLS, imp) >>> results = [] >>> for k in range(10): >>> x = mice.next_sample() >>> results.append(x)
Methods