cat2cat.summary

Functions

summary_c2c(→ pandas.DataFrame)

Adjust regression summaries fitted on replicated cat2cat data.

Module Contents

cat2cat.summary.summary_c2c(model: Any, df_old: float, df_new: float | None = None) → pandas.DataFrame

Adjust regression summaries fitted on replicated cat2cat data.

Parameters:

model – A fitted statsmodels-like result object with params, bse, and tvalues attributes.
df_old – Residual degrees of freedom on the original observation scale.
df_new – Residual degrees of freedom on the replicated data scale. Defaults to model.df_resid.

Returns:

coefficient table with corrected standard errors, corrected statistics, corrected p-values, and reference distribution.

Return type:

pandas.DataFrame

Examples

>>> from pandas import DataFrame, concat
>>> import statsmodels.api as sm
>>> from cat2cat import summary_c2c
>>> data = DataFrame({
...     "y": [2.0, 3.0, 5.0, 7.0, 11.0, 13.0, 17.0, 19.0],
...     "x1": [1.0, 1.5, 2.0, 2.7, 3.2, 4.1, 4.8, 5.2],
...     "x2": [0, 1, 0, 1, 0, 1, 0, 1],
... })
>>> model = sm.OLS.from_formula("y ~ x1 + x2", data=data).fit()
>>> model_rep = sm.OLS.from_formula(
...     "y ~ x1 + x2", data=concat([data, data])
... ).fit()
>>> out = summary_c2c(model_rep, df_old=model.df_resid, df_new=model_rep.df_resid)
>>> all(col in out.columns for col in ["std.error_c", "statistic_c", "p.value_c"])
True