def __init__(self):
# Test Precisions
self.decimal_bic = DECIMAL_1
self.decimal_aic_R = DECIMAL_1
self.decimal_aic_Stata = DECIMAL_3
self.decimal_loglike = DECIMAL_1
self.decimal_resids = DECIMAL_3
nobs = 100
x = np.arange(nobs)
np.random.seed(54321)
y = 1.0 + 2.0 * x + x**2 + 0.1 * np.random.randn(nobs)
self.X = np.c_[np.ones((nobs,1)),x,x**2]
self.y_inv = (1. + .02*x + .001*x**2)**-1 + .001 * np.random.randn(nobs)
InverseLink_Model = GLM(self.y_inv, self.X,
family=sm.families.Gaussian(sm.families.links.inverse))
InverseLink_Res = InverseLink_Model.fit()
self.res1 = InverseLink_Res
from results.results_glm import GaussianInverse
self.res2 = GaussianInverse()
def __init__(self):
# Test Precision
self.decimal_aic_R = DECIMAL_0
self.decimal_aic_Stata = DECIMAL_2
self.decimal_loglike = DECIMAL_0
self.decimal_null_deviance = DECIMAL_1
nobs = 100
x = np.arange(nobs)
np.random.seed(54321)
# y = 1.0 - .02*x - .001*x**2 + 0.001 * np.random.randn(nobs)
self.X = np.c_[np.ones((nobs,1)),x,x**2]
self.lny = np.exp(-(-1.0 + 0.02*x + 0.0001*x**2)) +\
0.001 * np.random.randn(nobs)
GaussLog_Model = GLM(self.lny, self.X, \
family=sm.families.Gaussian(sm.families.links.log))
self.res1 = GaussLog_Model.fit()
from results.results_glm import GaussianLog
self.res2 = GaussianLog()
def __init__(self, endog, exog, smoothers=None, family=family.Gaussian()):
GLM.__init__(self, endog, exog, family=family)
AdditiveModel.__init__(self, exog, smoothers=smoothers)
