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statsmodels.discrete.discrete_model.MNLogit.score

MNLogit.score(params)[source]

Score matrix for multinomial logit model log-likelihood

Parameters :

params : array

The parameters of the multinomial logit model.

Returns :

The 2-d score vector of the multinomial logit model evaluated at :

`params`. :

Notes

\frac{\partial\ln L}{\partial\beta_{j}}=\sum_{i}\left(d_{ij}-\frac{\exp\left(\beta_{j}^{\prime}x_{i}\right)}{\sum_{k=0}^{J}\exp\left(\beta_{k}^{\prime}x_{i}\right)}\right)x_{i}

for j=1,...,J

In the multinomial model ths score matrix is K x J-1 but is returned as a flattened array to work with the solvers.

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