def score_obs(self, params):
    '\n        Score, first derivative of the loglikelihood for each observation.\n\n        Parameters\n        ----------\n        params : ndarray\n            Parameter at which score is evaluated.\n\n        Returns\n        -------\n        score_obs : ndarray, 2d\n            The first derivative of the loglikelihood function evaluated at\n            params for each observation.\n        '
    (sf1, sf2) = self.score_factor(params)
    d1 = (sf1[:, None] * self.exog)
    d2 = (sf2[:, None] * self.exog_precision)
    return np.column_stack((d1, d2))
Score, first derivative of the loglikelihood for each observation.

Parameters
----------
params : ndarray
    Parameter at which score is evaluated.

Returns
-------
score_obs : ndarray, 2d
    The first derivative of the loglikelihood function evaluated at
    params for each observation.
def score_obs(self, params):
    '\n        Score, first derivative of the loglikelihood for each observation.\n\n        Parameters\n        ----------\n        params : ndarray\n            Parameter at which score is evaluated.\n\n        Returns\n        -------\n        score_obs : ndarray, 2d\n            The first derivative of the loglikelihood function evaluated at\n            params for each observation.\n        '
    (sf1, sf2) = self.score_factor(params)
    d1 = (sf1[:, None] * self.exog)
    d2 = (sf2[:, None] * self.exog_precision)
    return np.column_stack((d1, d2))