def _deriv_mean_dparams(self, params):
    '\n        Derivative of the expected endog with respect to the parameters.\n\n        not verified yet\n\n        Parameters\n        ----------\n        params : ndarray\n            parameter at which score is evaluated\n\n        Returns\n        -------\n        The value of the derivative of the expected endog with respect\n        to the parameter vector.\n        '
    link = self.link
    lin_pred = self.predict(params, which='linear')
    idl = link.inverse_deriv(lin_pred)
    dmat = (self.exog * idl[:, None])
    return np.column_stack((dmat, np.zeros(self.exog_precision.shape)))
Derivative of the expected endog with respect to the parameters.

not verified yet

Parameters
----------
params : ndarray
    parameter at which score is evaluated

Returns
-------
The value of the derivative of the expected endog with respect
to the parameter vector.
def _deriv_mean_dparams(self, params):
    '\n        Derivative of the expected endog with respect to the parameters.\n\n        not verified yet\n\n        Parameters\n        ----------\n        params : ndarray\n            parameter at which score is evaluated\n\n        Returns\n        -------\n        The value of the derivative of the expected endog with respect\n        to the parameter vector.\n        '
    link = self.link
    lin_pred = self.predict(params, which='linear')
    idl = link.inverse_deriv(lin_pred)
    dmat = (self.exog * idl[:, None])
    return np.column_stack((dmat, np.zeros(self.exog_precision.shape)))