statspai.imputation

imputation

Missing data handling and multiple imputation.

Provides MICE (Multiple Imputation by Chained Equations), EM imputation, and analysis tools for multiply-imputed data.

MICEResult

Results from MICE imputation.

complete

complete(m: int = 0) -> DataFrame

Return the m-th completed dataset (0-indexed).

combine

combine(estimates: List[Dict[str, Any]]) -> Dict[str, Any]

Apply Rubin's rules to combine estimates across imputations.

mi_estimate

mi_estimate(mice_result: MICEResult, estimator, **kwargs) -> Dict[str, Any]

Run an estimator on each imputed dataset and combine using Rubin's rules.

Parameters:

Name	Type	Description	Default
mice_result	MICEResult	Result from mice().	required
estimator	callable	Estimation function that returns an EconometricResults object.	required
**kwargs		Arguments passed to the estimator.	{}

Returns:

Type	Description
dict	Combined estimates (Rubin's rules).

Examples:

>>> import statspai as sp
>>> mice_res = sp.mice(df, m=5)
>>> combined = sp.mi_estimate(mice_res, sp.regress, formula="y ~ x1 + x2")