def setup_class(cls):
super(TestGAMGamma, cls).setup_class() #initialize DGP
cls.family = family.Gamma(links.log())
cls.rvs = stats.gamma.rvs
cls.init()
def setup_class(cls):
super(TestGAMGaussianLogLink, cls).setup_class() # initialize DGP
cls.family = family.Gaussian(links.log())
cls.rvs = stats.norm.rvs
cls.scale = 5
cls.init()
def setup_class(cls):
# adjusted for Gamma, not in test_gee.py
vs = Independence()
family = families.Gamma(link=links.log())
np.random.seed(987126)
#Y = np.random.normal(size=100)**2
Y = np.exp(0.1 + np.random.normal(size=100)) # log-normal
X1 = np.random.normal(size=100)
X2 = np.random.normal(size=100)
X3 = np.random.normal(size=100)
groups = np.random.randint(0, 4, size=100)
D = pd.DataFrame({"Y": Y, "X1": X1, "X2": X2, "X3": X3})
mod1 = GEE.from_formula("Y ~ X1 + X2 + X3", groups, D,
family=family, cov_struct=vs)
cls.result1 = mod1.fit()
mod2 = GLM.from_formula("Y ~ X1 + X2 + X3", data=D, family=family)
cls.result2 = mod2.fit(disp=False)
def setup_class(cls):
# adjusted for Gamma, not in test_gee.py
vs = Independence()
family = families.Gamma(link=links.log())
np.random.seed(987126)
#Y = np.random.normal(size=100)**2
Y = np.exp(0.1 + np.random.normal(size=100)) # log-normal
X1 = np.random.normal(size=100)
X2 = np.random.normal(size=100)
X3 = np.random.normal(size=100)
groups = np.random.randint(0, 4, size=100)
D = pd.DataFrame({"Y": Y, "X1": X1, "X2": X2, "X3": X3})
mod1 = GEE.from_formula("Y ~ X1 + X2 + X3",
groups,
D,
family=family,
cov_struct=vs)
cls.result1 = mod1.fit()
mod2 = GLM.from_formula("Y ~ X1 + X2 + X3", data=D, family=family)
cls.result2 = mod2.fit(disp=False)