def setupClass(cls):
from results.results_discrete import RandHIE
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 setupClass(cls):
from results.results_discrete import RandHIE
data = sm.datasets.randhie.load()
exog = sm.add_constant(data.exog.view((float, 9)))
cls.res1 = Poisson(data.endog, exog).fit(method='newton', disp=0)
res2 = RandHIE()
res2.poisson()
cls.res2 = res2
def setupClass(cls):
from results.results_discrete import RandHIE
data = sm.datasets.randhie.load()
exog = sm.add_constant(data.exog, prepend=False)
cls.res1 = Poisson(data.endog, exog).fit(method='newton', disp=0)
res2 = RandHIE()
res2.poisson()
cls.res2 = res2
def __init__(self):
from results.results_discrete import RandHIE
data = sm.datasets.randhie.load()
nobs = len(data.endog)
exog = sm.add_constant(data.exog.view(float).reshape(nobs,-1))
self.res1 = Poisson(data.endog, exog).fit(method='newton', disp=0)
res2 = RandHIE()
res2.poisson()
self.res2 = res2