使用Pandas OLS,我能够拟合并使用以下模型:
ols_test = pd.ols(y=merged2[:-1].Units, x=merged2[:-1].lastqu) #to exclude current year, then do forecast method
yrahead=(ols_test.beta['x'] * merged2.lastqu[-1:]) + ols_test.beta['intercept']
我需要切换到 statsmodels 来获得一些额外的功能(主要是残差图,请参见这里)。
因此,现在我有:
def fit_line2(x, y):
X = sm.add_constant(x, prepend=True) #Add a column of ones to allow the calculation of the intercept
model = sm.OLS(y, X,missing='drop').fit()
"""Return slope, intercept of best fit line."""
X = sm.add_constant(x)
return model
并且:
model=fit_line2(merged2[:-1].lastqu,merged2[:-1].Units)
print fit.summary()
但我无法获取
yrahead2=model.predict(merged2.lastqu[-1:])
或者任何其他变体可以给我一个预测?请注意,pd.ols使用相同的merged2.lastqu[-1:]来获取我想要“预测”的数据,无论我将什么放入()中进行预测,都没有成功。似乎statsmodels在()中需要特定的内容,而不是pandas DF单元格,我甚至尝试只在那里放置一个数字,例如2696,但仍然没有结果...我的当前错误是
----> 3 yrahead2=model.predict(merged2.lastqu[-1:])
/usr/lib/pymodules/python2.7/statsmodels/base/model.pyc in predict(self, exog, transform, *args, **kwargs)
1004 exog = np.atleast_2d(exog) # needed in count model shape[1]
1005
-> 1006 return self.model.predict(self.params, exog, *args, **kwargs)
1007
1008
/usr/lib/pymodules/python2.7/statsmodels/regression/linear_model.pyc in predict(self, params, exog)
253 if exog is None:
254 exog = self.exog
--> 255 return np.dot(exog, params)
256
257 class GLS(RegressionModel):
ValueError: objects are not aligned
> /usr/lib/pymodules/python2.7/statsmodels/regression/linear_model.py(255)predict()
254 exog = self.exog
--> 255 return np.dot(exog, params)
256