statsmodels.othermod.betareg.BetaModel.score_hessian_factor#
- BetaModel.score_hessian_factor(params, return_hessian=False, observed=True)[source]#
Derivatives of loglikelihood function w.r.t. linear predictors
This calculates score and hessian factors at the same time, because there is a large overlap in calculations.
- Parameters:
- params
ndarray Parameter at which score is evaluated.
- return_hessianbool,
optional If False, then only score_factors are returned If True, the both score and hessian factors are returned
- observedbool,
optional If True, then the observed Hessian is returned (default). If False, then the expected information matrix is returned.
- params
- Returns:
- (sf1, sf2)
tuple The score factors, as returned by
score_factor. Only returned if return_hessian is False.- (sf1, sf2), (-jbb, -jbg, -jgg)
tupleoftuples The score factors and a tuple with 3 hessian factors, corresponding to the upper triangle of the Hessian matrix. Only returned if return_hessian is True. TODO: check why there are minus
- (sf1, sf2)