# statsmodels.discrete.discrete_model.Probit.score_factor¶

Probit.score_factor(params)[source]

Probit model Jacobian for each observation

Parameters:
paramsarray_like

The parameters of the model

Returns:
score_factorarray_like (nobs,)

The derivative of the loglikelihood function for each observation with respect to linear predictor evaluated at params

Notes

$\frac{\partial\ln L_{i}}{\partial\beta}=\left[\frac{q_{i}\phi\left(q_{i}x_{i}^{\prime}\beta\right)}{\Phi\left(q_{i}x_{i}^{\prime}\beta\right)}\right]x_{i}$

for observations $$i=1,...,n$$

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