Poisson.score()

statsmodels.discrete.discrete_model.Poisson.score

Poisson.score(params) [source]

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

Parameters:

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`

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

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

where the loglinear model is assumed

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

© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.discrete.discrete_model.Poisson.score.html