def setup_class(cls):
super(TestPolySmoother1, cls).setup_class() #initialize DGP
y, x, exog = cls.y, cls.x, cls.exog
#use order = 2 in regression
pmod = smoothers.PolySmoother(2, x)
pmod.fit(y) #no return
cls.res_ps = pmod
cls.res2 = OLS(y, exog[:,:2+1]).fit()
def __init__(self):
super(self.__class__, self).__init__() #initialize DGP
y, x, exog = self.y, self.x, self.exog
#use order = 2 in regression
pmod = smoothers.PolySmoother(2, x)
pmod.fit(y) #no return
self.res_ps = pmod
self.res2 = OLS(y, exog[:,:2+1]).fit()
def setup_class(cls):
super(TestPolySmoother2, cls).setup_class() #initialize DGP
y, x, exog = cls.y, cls.x, cls.exog
#use order = 3 in regression
pmod = smoothers.PolySmoother(3, x)
#pmod.fit(y) #no return
pmod.smooth(y) #no return, use alias for fit
cls.res_ps = pmod
cls.res2 = OLS(y, exog[:,:3+1]).fit()
def __init__(self):
super(self.__class__, self).__init__() #initialize DGP
y, x, exog = self.y, self.x, self.exog
#use order = 3 in regression
pmod = smoothers.PolySmoother(3, x)
#pmod.fit(y) #no return
pmod.smooth(y) #no return, use alias for fit
self.res_ps = pmod
self.res2 = OLS(y, exog[:,:3+1]).fit()
def setup_class(cls):
super(TestPolySmoother3, cls).setup_class() #initialize DGP
y, x, exog = cls.y, cls.x, cls.exog
nobs = y.shape[0]
weights = np.ones(nobs)
weights[:nobs//3] = 0.1
weights[-nobs//5:] = 2
#use order = 2 in regression
pmod = smoothers.PolySmoother(2, x)
pmod.fit(y, weights=weights) #no return
cls.res_ps = pmod
cls.res2 = WLS(y, exog[:,:2+1], weights=weights).fit()
def __init__(self):
super(self.__class__, self).__init__() #initialize DGP
y, x, exog = self.y, self.x, self.exog
nobs = y.shape[0]
weights = np.ones(nobs)
weights[:nobs//3] = 0.1
weights[-nobs//5:] = 2
#use order = 2 in regression
pmod = smoothers.PolySmoother(2, x)
pmod.fit(y, weights=weights) #no return
self.res_ps = pmod
self.res2 = WLS(y, exog[:,:2+1], weights=weights).fit()
from statsmodels.sandbox.nonparametric import smoothers, kernels from statsmodels.regression.linear_model import OLS, WLS #DGP: simple polynomial order = 3 sigma_noise = 0.5 nobs = 100 lb, ub = -1, 2 x = np.linspace(lb, ub, nobs) x = np.sin(x) exog = x[:, None]**np.arange(order + 1) y_true = exog.sum(1) y = y_true + sigma_noise * np.random.randn(nobs) #xind = np.argsort(x) pmod = smoothers.PolySmoother(2, x) pmod.fit(y) #no return y_pred = pmod.predict(x) error = y - y_pred mse = (error * error).mean() print(mse) res_ols = OLS(y, exog[:, :3]).fit() print(np.squeeze(pmod.coef) - res_ols.params) weights = np.ones(nobs) weights[:nobs // 3] = 0.1 weights[-nobs // 5:] = 2 pmodw = smoothers.PolySmoother(2, x) pmodw.fit(y, weights=weights) #no return y_predw = pmodw.predict(x)
ax2.plot(KSx, KVar, "-o")
ax3 = fig.add_subplot(223)
ax3.plot(x, y, "+")
ax3.plot(KSx, KS2y, "-o")
ax3.set_ylim(-20, 30)
ax4 = fig.add_subplot(224)
ax4.plot(KSx, K2Var, "-o")
fig2 = plt.figure()
ax5 = fig2.add_subplot(111)
ax5.plot(x, y, "+")
ax5.plot(KSConfIntx, KSConfInty, "-o")
from statsmodels.nonparametric import lowess as lo
ys = lo.lowess(y, x)
ax5.plot(ys[:, 0], ys[:, 1], 'b-')
ys2 = lo.lowess(y, x, frac=0.25)
ax5.plot(ys2[:, 0], ys2[:, 1], 'b--', lw=2)
#need to sort for matplolib plot ?
xind = np.argsort(x)
pmod = smoothers.PolySmoother(5, x[xind])
pmod.fit(y[xind])
yp = pmod(x[xind])
ax5.plot(x[xind], yp, 'k-')
ax5.set_title('Kernel regression, lowess - blue, polysmooth - black')
#plt.show()
