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Logit.score()

statsmodels.discrete.discrete_model.Logit.score

Logit.score(params) [source]

Logit 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}

doc_statsmodels
doc_statsmodels
2017-01-18 16:11:43
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