statsmodels.discrete.discrete_model.Probit.score

Probit.score(params)[source]

Probit model score (gradient) vector

Parameters:

params : array-like

The parameters of the model
Returns:

score : ndarray, 1-D

The score vector of the model, i.e. the first derivative of the loglikelihood function, evaluated at params

Notes

\frac{\partial\ln L}{\partial\beta}=\sum_{i=1}^{n}\left[\frac{q_{i}\phi\left(q_{i}x_{i}^{\prime}\beta\right)}{\Phi\left(q_{i}x_{i}^{\prime}\beta\right)}\right]x_{i}

Where q=2y-1. This simplification comes from the fact that the normal distribution is symmetric.