cat2cat.summary
===============

.. py:module:: cat2cat.summary


Functions
---------

.. autoapisummary::

   cat2cat.summary.summary_c2c


Module Contents
---------------

.. py:function:: summary_c2c(model: Any, df_old: float, df_new: Optional[float] = None) -> pandas.DataFrame

   Adjust regression summaries fitted on replicated cat2cat data.

   :param model: A fitted statsmodels-like result object with ``params``, ``bse``,
                 and ``tvalues`` attributes.
   :param df_old: Residual degrees of freedom on the original observation scale.
   :param 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.
   :rtype: pandas.DataFrame

   .. rubric:: 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