statsmodels.discrete.discrete_model.Poisson.score_obs¶

Poisson.score_obs(params)[source]¶

Poisson model Jacobian of the log-likelihood for each observation

Parameters:¶

paramsarray_like

The parameters of the model

Returns:¶

scorearray_like

The score vector (nobs, k_vars) of the model evaluated at params

Notes

$$\frac{\partial \ln L_i}{\partial \beta }=(y_i-{\lambda }_i)x_i$$

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

where the loglinear model is assumed

$$\ln {\lambda }_i=x_i\beta$$