statsmodels.discrete.discrete_model.Probit.score_obs
-
Probit.score_obs(params)[source] -
Probit model Jacobian for each observation
Parameters: params : array-like
The parameters of the model
Returns: jac : ndarray, (nobs, k_vars)
The derivative of the loglikelihood for each observation evaluated at
params.Notes
![\frac{\partial\ln L_{i}}{\partial\beta}=\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/b2de64563b66f8c98646fc05a715a0b2773a1c44.png)
for observations
Where
. This simplification comes from the fact that the normal distribution is symmetric.
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