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)