MNLogit.score()

statsmodels.discrete.discrete_model.MNLogit.score

MNLogit.score(params) [source]

Score matrix for multinomial logit model log-likelihood

Parameters:

Parameters:	params : array The parameters of the multinomial logit model.
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`.

params : array

The parameters of the multinomial logit model.

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.

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.

Links:

http://statsmodels.sourceforge.net/stable/generated/statsmodels.discrete.discrete_model.MNLogit.score.html

doc_statsmodels

2017-01-18 16:12:23

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