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_hessian : bool¶

If False, then only score_factors are returned If True, the both score and hessian factors are returned

observed : bool¶

If True, then the observed Hessian is returned (default). If False, then the expected information matrix is returned.

Returns:¶

• score_factor (ndarray, 2-D) – A 2d weight vector used in the calculation of the score_obs.

• (-jbb, -jbg, -jgg) (tuple) – A tuple with 3 hessian factors, corresponding to the upper triangle of the Hessian matrix. TODO: check why there are minus