def __init__(self): from scikits.statsmodels.datasets.longley import load from results.results_regression import Longley data = load() data.exog = add_constant(data.exog) res1 = OLS(data.endog, data.exog).fit() res2 = Longley() res2.wresid = res1.wresid # workaround hack self.res1 = res1 self.res2 = res2
def setupClass(cls): from results.results_regression import Longley data = longley.load() data.exog = add_constant(data.exog, prepend=False) res1 = OLS(data.endog, data.exog).fit() res2 = Longley() res2.wresid = res1.wresid # workaround hack cls.res1 = res1 cls.res2 = res2 res_qr = OLS(data.endog, data.exog).fit(method="qr") cls.res_qr = res_qr
def setupClass(cls): from results.results_regression import Longley data = longley.load() data.exog = add_constant(data.exog, prepend=False) res1 = OLS(data.endog, data.exog).fit() res2 = Longley() res2.wresid = res1.wresid # workaround hack cls.res1 = res1 cls.res2 = res2 res_qr = OLS(data.endog, data.exog).fit(method="qr") model_qr = OLS(data.endog, data.exog) Q, R = np.linalg.qr(data.exog) model_qr.exog_Q, model_qr.exog_R = Q, R model_qr.normalized_cov_params = np.linalg.inv(np.dot(R.T, R)) model_qr.rank = np_matrix_rank(R) res_qr2 = model_qr.fit(method="qr") cls.res_qr = res_qr cls.res_qr_manual = res_qr2
def setupClass(cls): from results.results_regression import Longley data = longley.load() data.exog = add_constant(data.exog, prepend=False) res1 = OLS(data.endog, data.exog).fit() res2 = Longley() res2.wresid = res1.wresid # workaround hack cls.res1 = res1 cls.res2 = res2 res_qr = OLS(data.endog, data.exog).fit(method="qr") model_qr = OLS(data.endog, data.exog) Q, R = np.linalg.qr(data.exog) model_qr.exog_Q, model_qr.exog_R = Q, R model_qr.normalized_cov_params = np.linalg.inv(np.dot(R.T, R)) model_qr.rank = rank(R) res_qr2 = model_qr.fit(method="qr") cls.res_qr = res_qr cls.res_qr_manual = res_qr2