statsmodels.discrete.discrete_model.Probit.score¶
-
Probit.score(params)[source]¶ Probit model score (gradient) vector
- Parameters
- paramsarray_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
- score
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.