def setup_class(cls): # here we don't need to check convergence from default start_params start_params = [13.1996, 0.8582, -2.8005, -1.5031, 2.3849, -8.5552, -2.88, 1.14] mod = NegativeBinomial(endog, exog) res = mod.fit(start_params=start_params, method='nm', maxiter=2000) marge = res.get_margeff(dummy=True) cls.res = res cls.margeff = marge cls.res1_slice = cls.res1_slice = [0, 1, 2, 3, 5, 6] cls.res1 = res_stata.results_negbin_margins_dummy cls.rtol_fac = 5e1
def setup_class(cls): # here we don't need to check convergence from default start_params start_params = [ 13.1996, 0.8582, -2.8005, -1.5031, 2.3849, -8.5552, -2.88, 1.14 ] mod = NegativeBinomial(endog, exog) res = mod.fit(start_params=start_params, method='nm', maxiter=2000) marge = res.get_margeff() cls.res = res cls.margeff = marge cls.res1_slice = slice(None, None, None) cls.res1 = res_stata.results_negbin_margins_cont cls.rtol_fac = 5e1