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
