def fitNB(X, Y):
X = add_constant(X)
model = NegativeBinomial(Y, X).fit(disp=0, skip_hessian=True, maxiter=500)
if not model.mle_retvals['converged']:
model = NegativeBinomial(Y, X).fit_regularized(disp=0,
skip_hessian=True,
maxiter=500)
return model.params
def setupClass(cls):
data = sm.datasets.randhie.load()
exog = sm.add_constant(data.exog, prepend=False)
cls.res1 = NegativeBinomial(data.endog, exog, 'nb2').fit(method='newton', disp=0)
res2 = RandHIE()
res2.negativebinomial_nb2_bfgs()
cls.res2 = res2
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
