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

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

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