statsmodels.discrete.discrete_model.MNLogit.score¶
- MNLogit.score(params)[source]¶
Score matrix for multinomial logit model log-likelihood
- Parameters:
- params
ndarray
The parameters of the multinomial logit model.
- params
- Returns:
- score
ndarray
, (K
* (J-1),) The 2-d score vector, i.e. the first derivative of the loglikelihood function, of the multinomial logit model evaluated at params.
- score
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 the score matrix is K x J-1 but is returned as a flattened array to work with the solvers.