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()
Esempio n. 3
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 def __init__(self, endog, exog, smoothers=None, family=family.Gaussian()):
     GLM.__init__(self, endog, exog, family=family)
     AdditiveModel.__init__(self, exog, smoothers=smoothers)