def setup_class(cls):
        cls.res2 = results_st.results_poisson_clu
        mod = smd.Poisson(endog, exog)
        cls.res1 = res1 = mod.fit(disp=False)

        debug = False
        if debug:
            # for debugging
            cls.bse_nonrobust = cls.res1.bse.copy()
            cls.res1 = res1 = mod.fit(disp=False)
            cls.get_robust_clu()
            cls.res3 = cls.res1
            cls.bse_rob3 = cls.bse_rob.copy()
            cls.res1 = res1 = mod.fit(disp=False)

        from statsmodels.base.covtype import get_robustcov_results

        #res_hc0_ = cls.res1.get_robustcov_results('HC1')
        get_robustcov_results(cls.res1._results, 'cluster',
                                                  groups=group,
                                                  use_correction=True,
                                                  df_correction=True,  #TODO has no effect
                                                  use_t=False, #True,
                                                  use_self=True)
        cls.bse_rob = cls.res1.bse

        nobs, k_vars = res1.model.exog.shape
        k_params = len(res1.params)
        #n_groups = len(np.unique(group))
        corr_fact = (nobs-1.) / float(nobs - k_params)
        # for bse we need sqrt of correction factor
        cls.corr_fact = np.sqrt(corr_fact)

    def setup_class(cls):
        cls.res2 = results_st.results_poisson_clu
        mod = smd.Poisson(endog, exog)
        cls.res1 = res1 = mod.fit(disp=False)

        debug = False
        if debug:
            # for debugging
            cls.bse_nonrobust = cls.res1.bse.copy()
            cls.res1 = res1 = mod.fit(disp=False)
            cls.get_robust_clu()
            cls.res3 = cls.res1
            cls.bse_rob3 = cls.bse_rob.copy()
            cls.res1 = res1 = mod.fit(disp=False)

        from statsmodels.base.covtype import get_robustcov_results

        #res_hc0_ = cls.res1.get_robustcov_results('HC1')
        get_robustcov_results(cls.res1._results, 'cluster',
                                                  groups=group,
                                                  use_correction=True,
                                                  df_correction=True,  #TODO has no effect
                                                  use_t=False, #True,
                                                  use_self=True)
        cls.bse_rob = cls.res1.bse

        cls.corr_fact = cls.get_correction_factor(cls.res1)

    def __init__(self, model, params, normalized_cov_params=None, scale=1.,
                 **kwargs):
        super(LikelihoodModelResults, self).__init__(model, params)
        self.normalized_cov_params = normalized_cov_params
        self.scale = scale

        # robust covariance
        # We put cov_type in kwargs so subclasses can decide in fit whether to
        # use this generic implementation
        if 'use_t' in kwargs:
            use_t = kwargs['use_t']
            if use_t is not None:
                self.use_t = use_t
        if 'cov_type' in kwargs:
            cov_type = kwargs.get('cov_type', 'nonrobust')
            cov_kwds = kwargs.get('cov_kwds', {})

            if cov_type == 'nonrobust':
                self.cov_type = 'nonrobust'
                self.cov_kwds = {'description': 'Standard Errors assume that the ' +
                                 'covariance matrix of the errors is correctly ' +
                                 'specified.'}
            else:
                from statsmodels.base.covtype import get_robustcov_results
                if cov_kwds is None:
                    cov_kwds = {}
                use_t = self.use_t
                # TODO: we shouldn't need use_t in get_robustcov_results
                get_robustcov_results(self, cov_type=cov_type, use_self=True,
                                      use_t=use_t, **cov_kwds)

    def _get_robustcov_results(self, cov_type='nonrobust', use_self=True,
                               use_t=None, **cov_kwds):
        from statsmodels.base.covtype import get_robustcov_results
        if cov_kwds is None:
            cov_kwds = {}

        if cov_type == 'nonrobust':
            self.cov_type = 'nonrobust'
            self.cov_kwds = {'description': 'Standard Errors assume that the ' +
                             'covariance matrix of the errors is correctly ' +
                             'specified.'}
        else:
            # TODO: we shouldn't need use_t in get_robustcov_results
            get_robustcov_results(self, cov_type=cov_type, use_self=True,
                                  use_t=use_t, **cov_kwds)

    def __init__(self, model, params, normalized_cov_params, scale,
                 cov_type='nonrobust', cov_kwds=None, use_t=None):
        super(GLMResults, self).__init__(model, params,
                                         normalized_cov_params=
                                         normalized_cov_params, scale=scale)
        self.family = model.family
        self._endog = model.endog
        self.nobs = model.endog.shape[0]
        self.mu = model.mu
        self._data_weights = model.data_weights
        self.df_resid = model.df_resid
        self.df_model = model.df_model
        self.pinv_wexog = model.pinv_wexog
        self._cache = resettable_cache()
        # are these intermediate results needed or can we just
        # call the model's attributes?

        # for remove data and pickle without large arrays
        self._data_attr.extend(['results_constrained'])
        self.data_in_cache = getattr(self, 'data_in_cache', [])
        self.data_in_cache.extend(['null'])

        # robust covariance
        from statsmodels.base.covtype import get_robustcov_results
        if use_t is None:
            self.use_t = False    # TODO: class default
        else:
            self.use_t = use_t
        if cov_type == 'nonrobust':
            self.cov_type = 'nonrobust'
            self.cov_kwds = {'description' : 'Standard Errors assume that the ' +
                             'covariance matrix of the errors is correctly ' +
                             'specified.'}

        else:
            if cov_kwds is None:
                cov_kwds = {}
            get_robustcov_results(self, cov_type=cov_type, use_self=True,
                                       use_t=use_t, **cov_kwds)