1.2.15.1.15. statsmodels.api.Probit.score¶
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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.