3.2.2.3.32.1.16. statsmodels.discrete.discrete_model.Poisson.score¶

Poisson.score(params)[source]¶

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

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 params

Notes

$$\frac{\partial\ln L}{\partial\beta}=\sum_{i=1}^{n}\left(y_{i}-\lambda_{i}\right)x_{i}$$

where the loglinear model is assumed

$$\ln\lambda_{i}=x_{i}\beta$$