statsmodels.discrete.discrete_model.Probit.score¶

Probit.score(params)[source]¶

Probit model score (gradient) vector

Parameters:¶

paramsarray_like

The parameters of the model

Returns:¶

scorendarray, 1-D

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

Notes

$$\frac{\partial \ln L}{\partial \beta }=\sum _{i=1}^n\left[\frac{q_i\varphi \left(q_i{x}_i^{\prime }\beta \right)}{\mathrm{\Phi }\left(q_i{x}_i^{\prime }\beta \right)}\right]x_i$$

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