

@classmethod
def setup_class(self):
    params = [2, 0, 2]
    nobs = 50
    np.random.seed(1234)
    rvs = stats.pareto.rvs(*params, **dict(size=nobs))
    mod_par = MyPareto(rvs)
    fixdf = (np.nan * np.ones(3))
    fixdf[1] = (- 0.1)
    mod_par.fixed_params = fixdf
    mod_par.fixed_paramsmask = np.isnan(fixdf)
    mod_par.start_params = mod_par.start_params[mod_par.fixed_paramsmask]
    mod_par.df_model = 2
    mod_par.df_resid = (mod_par.endog.shape[0] - mod_par.df_model)
    params.data.xnames = ['shape', 'scale']
    self.mod = mod_par
    self.res1 = mod_par.fit(disp=None)
    self.skip_bsejac = False

dataset/ETHPy150Open statsmodels/statsmodels/statsmodels/miscmodels/tests/test_generic_mle.py TestMyParetoRestriction.setup_class/VarMisuse@143/190 `mod_par`->`params`