Logit.score_obs()

statsmodels.discrete.discrete_model.Logit.score_obs

Logit.score_obs(params) [source]

Logit model Jacobian of the log-likelihood for each observation

Parameters:

Parameters:	params: array-like The parameters of the model
Returns:	jac : ndarray, (nobs, k_vars) The derivative of the loglikelihood for each observation evaluated at `params`.

params: array-like

The parameters of the model

Returns:

jac : ndarray, (nobs, k_vars)

The derivative of the loglikelihood for each observation evaluated at params.

$\frac{\partial\ln L_{i}}{\partial\beta}=\left(y_{i}-\Lambda_{i}\right)x_{i}$

for observations $i=1,...,n$

© 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.Logit.score_obs.html