'Log-likelihood of logit model for each observation.
Parameters
params : array-like
The parameters of the logit model.
Returns
loglike : ndarray
The log likelihood for each observation of the model evaluated
at `params`. See Notes
Notes
.. math:: \ln L=\sum_{i}\ln\Lambda\left(q_{i}x_{i}^{\prime}\beta\right)
for observations :math:`i=1,...,n`
where :math:`q=2y-1`. This simplification comes from the fact that the
logistic distribution is symmetric.'

def loglikeobs(self, params):

    q = ((2 * self.endog) - 1)
    X = self.exog
    return np.log(self.cdf((q * np.dot(X, params))))
