def _derivative_exog(self, params, exog=None, transform='dydx',
            dummy_idx=None, count_idx=None):
        """
        For computing marginal effects, returns dF(XB) / dX where F(.)
        is the fitted mean.

        transform can be 'dydx', 'dyex', 'eydx', or 'eyex'.

        Not all of these make sense in the presence of discrete regressors,
        but checks are done in the results in get_margeff.
        """
        # This form should be appropriate for group 1 probit, logit,
        # logistic, cloglog, heckprob, xtprobit.
        offset_exposure = None
        if exog is None:
            exog = self.exog
            offset_exposure = self._offset_exposure

        margeff = self.mean_deriv_exog(exog, params, offset_exposure)

        if 'ex' in transform:
            margeff *= exog
        if transform in 'ey':
            margeff /= self.predict(params, exog)[:, None]
        if count_idx is not None:
            from statsmodels.discrete.discrete_margins import (
                    _get_count_effects)
            margeff = _get_count_effects(margeff, exog, count_idx, transform,
                    self, params)
        if dummy_idx is not None:
            from statsmodels.discrete.discrete_margins import (
                    _get_dummy_effects)
            margeff = _get_dummy_effects(margeff, exog, dummy_idx, transform,
                    self, params)
        return margeff
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/genmod/generalized_estimating_equations.py/GEE._derivative_exog/swapped operands of `in`