def setup_class(cls):
super(TestRlmBisquareHuber, cls).setup_class()
model = RLM(cls.data.endog, cls.data.exog, M=norms.TukeyBiweight())
results = model.fit(scale_est=HuberScale())
h2 = model.fit(cov="H2", scale_est=HuberScale()).bcov_scaled
h3 = model.fit(cov="H3", scale_est=HuberScale()).bcov_scaled
cls.res1 = results
cls.res1.h2 = h2
cls.res1.h3 = h3
def setup_class(cls):
super(TestRlmAndrewsHuber, cls).setup_class()
model = RLM(cls.data.endog, cls.data.exog, M=norms.AndrewWave())
results = model.fit(scale_est=HuberScale())
h2 = model.fit(cov="H2", scale_est=HuberScale()).bcov_scaled
h3 = model.fit(cov="H3", scale_est=HuberScale()).bcov_scaled
cls.res1 = results
cls.res1.h2 = h2
cls.res1.h3 = h3
def setup_class(cls):
cls.data = load_stackloss()
cls.data.exog = sm.add_constant(cls.data.exog, prepend=False)
model = RLM(cls.data.endog, cls.data.exog, M=norms.HuberT())
results = model.fit(scale_est=HuberScale())
h2 = model.fit(cov="H2", scale_est=HuberScale()).bcov_scaled
h3 = model.fit(cov="H3", scale_est=HuberScale()).bcov_scaled
cls.res1 = results
cls.res1.h2 = h2
cls.res1.h3 = h3