使用 DF 预测器进行 Pandas Statsmodels ols 回归预测? [英] Pandas Statsmodels ols regression prediction using DF predictor?

问题描述

使用 Pandas OLS，我可以按如下方式拟合和使用模型:

Using Pandas OLS I am able to fit and use a model as follows:

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 以获得一些额外的功能(主要是残差图 See(问题在这里)

I needed to switch to statsmodels to get some additional functionality (mainly the residual plots See(question here)

所以现在我有:

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

还有:

model=fit_line2(merged2[:-1].lastqu,merged2[:-1].Units)
print fit.summary()

但是我无法得到

yrahead2=model.predict(merged2.lastqu[-1:]) 

或任何变体给我一个预测?请注意，pd.ols 使用相同的 merge2.lastqu[-1:] 来获取我想要预测"的数据，无论我在 () 中放入什么来预测我都没有任何乐趣.似乎statsmodels 想要在 () 中特定的东西而不是熊猫 DF 单元格我什至试图在那里放一个数字，例如 2696 但仍然没有......我目前的错误是

or any variant to give me a prediction? Note that the pd.ols uses the same merged2.lastqu[-1:] to grab the data I want to 'predict" from, no matter what I put into the () for predict I'm not having any joy. It seems statsmodels wants something specific in the () other than a pandas DF cell I even tried to just put a number eg 2696 there but still nothing... My current error is

----> 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 

推荐答案

您的 merged2.lastqu[-1:] 不包含常量

yrahead2=model.predict(sm.add_constant(merged2.lastqu[-1:], prepend=True))

应该可以.

另一种方法是以与模型中的 X 相同的方式将常量添加到数据框，并使用数据框的适当列 df[['const', my_other_X]]

An alternative is to add the constant to the dataframe in the same way as to the X in the model, and use the appropriate columns of the dataframe df[['const', my_other_X]]

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