statsmodels.discrete.discrete_model.Poisson.score_obs¶

Poisson.score_obs(params)[source]¶

Poisson model Jacobian of the log-likelihood for each observation

Parameters:

params : array-like

The parameters of the model

Returns:

score : ndarray (nobs, k_vars)

The score vector of the model evaluated at params

Notes

$\frac{\partial\ln L_{i}}{\partial\beta}=\left(y_{i}-\lambda_{i}\right)x_{i}$

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

where the loglinear model is assumed

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