stepsel.modeling.stepwise_glm
=============================

.. py:module:: stepsel.modeling.stepwise_glm


Classes
-------

.. autoapisummary::

   stepsel.modeling.stepwise_glm.StepwiseGLM


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

.. py:class:: StepwiseGLM(formula: str, data: pandas.DataFrame, include: list[str] | None = None, slentry: float = 0.001, slstay: float = 0.001, model_fit_log: pandas.DataFrame = None, family: Any | None = None, offset: Any | None = None, exposure: Any | None = None, freq_weights: Any | None = None, var_weights: Any | None = None, missing: str = 'none', **kwargs: Any)

   Class for performing stepwise GLM.

   .. method:: fit()

      Fit stepwise GLM.



   .. py:attribute:: formula


   .. py:attribute:: data


   .. py:attribute:: include


   .. py:attribute:: slentry


   .. py:attribute:: slstay


   .. py:attribute:: family


   .. py:attribute:: offset


   .. py:attribute:: exposure


   .. py:attribute:: freq_weights


   .. py:attribute:: var_weights


   .. py:attribute:: missing


   .. py:attribute:: kwargs


   .. py:attribute:: full_formula


   .. py:attribute:: feature_ids


   .. py:attribute:: model_fit_log


   .. py:attribute:: current_model
      :value: None



   .. py:attribute:: current_model_features
      :value: None



   .. py:method:: _get_model_string(features: pandas.Series) -> str
      :staticmethod:


      Get model string from features.

      :param features: Features to include in model.
      :type features: pd.Series

      :returns: Model string.
      :rtype: str

      .. rubric:: Notes

      The model string is used as unique identifier for the model.
      Only the exogenous variables are included in the model string.
      The variables are sorted alphabetically.



   .. py:method:: _log_model_fit(features: pandas.Series, fit: statsmodels.api.GLM) -> None

      Log model fit.

      :param features: Features to include in model.
      :type features: pd.Series
      :param fit: Fitted model.
      :type fit: sm.GLM

      :returns: * *None*
                * *Modifies*
                * *--------*
                * **self.model_fit_log** (*pd.DataFrame*) -- Log of model fits - adds row for model.

      .. rubric:: Notes

      The model string is used as unique identifier for the model.



   .. py:method:: _model_fit(features: list[str]) -> None

      Fit model.

      :param features: Features to include in model.
      :type features: list[str]

      :returns: **fit** -- Fitted model.
      :rtype: sm.GLM



   .. py:method:: _add1(scope: list[str]) -> str | None

      Add one feature to the model if it improves the model fit.

      :param scope: List of features to consider for adding to the model.
      :type scope: list[str]

      :returns: * **best_new_eligible_feature** (*str | None*) -- Best feature to add to the model.
                  None if no feature improves the model fit.
                * *Modifies*
                * *--------*
                * **self.current_model** (*sm.GLM*) -- Fitted model if a feature is added.
                * **self.current_model_features** (*list[str]*) -- Features in the model if a feature is added.
                * **self.model_fit_log** (*pd.DataFrame*) -- Log of model fits - adds row for model.

      .. rubric:: Notes

      The model fit is improved if the p-value of the likelihood ratio test is below slentry.



   .. py:method:: _drop1(scope: list[str]) -> str | None

      Drop one feature from the model if it does not worsen the model fit.

      :param scope: List of features to consider for dropping from the model.
      :type scope: list[str]

      :returns: * **best_dropped_eligible_feature** (*str | None*) -- Best feature to drop from the model.
                  None if no feature can be dropped without worsening the model fit.
                * *Modifies*
                * *--------*
                * **self.current_model** (*sm.GLM*) -- Fitted model if a feature is dropped.
                * **self.current_model_features** (*list[str]*) -- Features in the model if a feature is dropped.
                * **self.model_fit_log** (*pd.DataFrame*) -- Log of model fits - adds row for model.

      .. rubric:: Notes

      The model fit is worsened if the p-value of the likelihood ratio test is above slstay.



   .. py:method:: fit()

      Fit the model using stepwise selection.

      Modifies
      --------
      self.current_model : sm.GLM
          Fitted model.
      self.current_model_features : list[str]
          Features in the model.
      self.model_fit_log : pd.DataFrame
          Log of model fits.

      .. rubric:: Notes

      Selection is done in a forward-backward fashion.
      It starts with the intercept and features in self.include.
      Then, it adds a feature if it improves the model fit.
      Then, it drops a feature if it does not worsen the model fit.
      Adding and dropping is repeated until no feature can be added or dropped.