'Fit a proportional hazards regression model.
Parameters
groups : array-like
Labels indicating groups of observations that may be
dependent.  If present, the standard errors account for
this dependence. Does not affect fitted values.
Returns a PHregResults instance.'

def fit(self, groups=None, **args):

    if (groups is not None):
        if (len(groups) != len(self.endog)):
            msg = ('len(groups)    =    %d    and    len(endog)    =    %d    differ' % (len(groups), len(self.endog)))
            raise ValueError(msg)
        self.groups = np.asarray(groups)
    else:
        self.groups = None
    if ('disp' not in args):
        args['disp'] = False
    fit_rslts = super(PHReg, self).fit(**args)
    if (self.groups is None):
        cov_params = fit_rslts.cov_params()
    else:
        cov_params = self.robust_covariance(fit_rslts.params)
    results = PHRegResults(self, fit_rslts.params, cov_params)
    return results
