def fit(self, start_params=None, method='newton', maxiter=35,
            full_output=1, disp=1, callback=None, **kwargs):
        if callback is None:
            callback = self._check_perfect_pred
        else:
            pass # make a function factory to have multiple call-backs

        mlefit = super(DiscreteModel, self).fit(start_params=start_params,
                method=method, maxiter=maxiter, full_output=full_output,
                disp=disp, callback=callback, **kwargs)

        return mlefit # up to subclasses to wrap results
Fit the model using maximum likelihood.
dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/discrete/discrete_model.py/DiscreteModel.fit