statsmodels.discrete.discrete_model.Logit.score¶

Logit.score(params)[source]¶

Logit model score (gradient) vector of the log-likelihood

Parameters:¶

paramsarray_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

$$\frac{\partial \ln L}{\partial \beta }=\sum _{i=1}^n(y_i-{\mathrm{\Lambda }}_i)x_i$$