statsmodels.discrete.discrete_model.Logit.score¶
method
-
Logit.score(params)[source]¶ Logit model score (gradient) vector of the log-likelihood
- Parameters
- params: array-like
The parameters of the model
- Returns
- scorendarray, 1-D
The score vector of the model, i.e. the first derivative of the loglikelihood function, evaluated at params
Notes
\[\frac{\partial\ln L}{\partial\beta}=\sum_{i=1}^{n}\left(y_{i}-\Lambda_{i}\right)x_{i}\]