statsmodels.discrete.discrete_model.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
paramsNotes
![\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}](http://statsmodels.sourceforge.net/stable/_images/math/d0921c2cd412905de14c1f1c872d5a24cba7606e.png)
Where
. This simplification comes from the fact that the normal distribution is symmetric.
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