def setup_class(cls):
endog = mm.m_marietta
exog = add_constant(mm.CRSP)
mod = TLinearModel(endog, exog)
res = mod.fit(method='bfgs', disp=False)
modf = TLinearModel.from_formula("price ~ CRSP",
data={"price":mm.m_marietta, "CRSP":mm.CRSP})
resf = modf.fit(method='bfgs', disp=False)
from .results_tmodel import res_t_dfest as res2
cls.res2 = res2
cls.res1 = res # take from module scope temporarily
cls.resf = resf
"""L1""" dfx = pd.DataFrame(x, columns = ['x']) dfy = pd.DataFrame(y, columns = ['y']) exog = sm.add_constant(dfx['x']) endog = dfy['y'] dft = pd.DataFrame(x_test, columns = ['test']) qrmodel = QuantReg(endog, exog) result = qrmodel.fit(q=0.5) ypred_qr = np.dot(dft, result.params[1]) + result.params[0] #results.predict(dft) """Student-t""" tmodel = TLinearModel(endog, exog) results = tmodel.fit(df=0.6) ypred_t = np.dot(dft, results.params[1]) + results.params[0] #results.predict(dft) """Plot""" plt.xlim(xmin, xmax) plt.ylim(ymin, ymax) plt.yticks(np.arange(ymin, ymax, 1.0)) plt.scatter(x, y, color="none", edgecolor="black") plt.plot(x_test, y_pred1, '-.', color='black') #Least squares plt.plot(x_test, y_pred2, '--', color='green') #Huber plt.plot(x_test, ypred_t, color='red') #student plt.plot(x_test, ypred_qr, '--', color='blue') plt.legend(["Least squares", "Huber, \u0394 =1", "Student-t, \u03BD =0.6", "Laplace"])