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