`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 ¶

Bases: ResultProtocolMixin

Results from MICE imputation.

Examples:

>>> import statspai as sp
>>> import numpy as np, pandas as pd
>>> rng = np.random.default_rng(7)
>>> x1 = rng.normal(size=120)
>>> x2 = 0.5 * x1 + rng.normal(size=120)
>>> y = 1.0 + x1 + x2 + rng.normal(size=120)
>>> df = pd.DataFrame({"y": y, "x1": x1, "x2": x2})
>>> df.loc[rng.choice(120, size=15, replace=False), "x2"] = np.nan
>>> res = sp.mice(df, m=3, method="pmm", seed=0)
>>> type(res).__name__
'MICEResult'
>>> res.n_imputations
3
>>> bool(res.complete(0)["x2"].notna().all())
True

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: Any, **kwargs: Any) -> 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`	`Any`	Arguments passed to the estimator.	`{}`

Returns:

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