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Probit.score()

statsmodels.discrete.discrete_model.Probit.score

Probit.score(params) [source]

Probit model score (gradient) vector

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

Notes

$\frac{\partial\ln L}{\partial\beta}=\sum_{i=1}^{n}\left[\frac{q_{i}\phi\left(q_{i}x_{i}^{\prime}\beta\right)}{\Phi\left(q_{i}x_{i}^{\prime}\beta\right)}\right]x_{i}$

Where $q=2y-1$ . This simplification comes from the fact that the normal distribution is symmetric.

© 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.Probit.score.html