Models

Multiple-imputation pooling.

pool

pool(models: list[Any], *, conf_level: float = 0.95) -> ModelSummary

Pool a list of fitted models via Rubin's rules.

Parameters:

Name	Type	Description	Default
models	list[Any]	A list of two or more fitted models, each fit on a separate imputed dataset. Every model must be one of the families recognised by :func:pysofra.models.extract.extract — statsmodels (Logit, OLS, GLM, Poisson), lifelines (CoxPHFitter, AFT family), or scikit-learn linear models. All models in the list must share the same coefficient names.	required
conf_level	float	Confidence level for the pooled CIs, in the open interval (0, 1). Default 0.95.	0.95

Returns:

Type	Description
ModelSummary	A summary whose estimates, ci_lo, ci_hi and pvalues reflect Rubin's-rule pooling across the imputed-dataset fits. Pass this directly into :func:pysofra.tbl_regression to render a pooled regression table.

Notes

The pooled point estimate is the across-imputation mean of the per-imputation estimates. The total variance T = Ū + (1 + 1/m)·B combines the average within-imputation variance Ū and the between-imputation variance B (with the small-sample correction 1 + 1/m). Confidence intervals use a t distribution with Rubin's original degrees-of-freedom df = (m − 1)·(1 + Ū / ((1 + 1/m)·B))². The newer Barnard–Rubin (1999) df refinement is not yet implemented; for very small per-imputation df it slightly narrows the CI relative to mice::pool.

References

Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. Wiley. Barnard, J., & Rubin, D. B. (1999). Small-sample degrees of freedom with multiple imputation. Biometrika, 86(4), 948–955.