statsmodels 的 OLS 不适用于成反比的数据?

OLS of statsmodels does not work with inversely proportional data?(statsmodels 的 OLS 不适用于成反比的数据?)

问题描述

我正在尝试使用一些成反比的数据执行普通最小二乘回归，但似乎拟合结果是错误的?

I'm trying to perform a Ordinary Least Squares Regression with some inversely proportional data, but seems like the fitting result is wrong?

import statsmodels.formula.api as sm
import numpy as np
import matplotlib.pyplot as plt

y = np.arange(100, 0, -1)
x = np.arange(0, 100)

result = sm.OLS(y, x).fit()
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(20, 4), sharey=True)
ax.plot(x, result.fittedvalues, 'r-')
ax.plot(x, y, 'x')

fig.show()

推荐答案

您没有添加常量作为 文档建议，因此它试图将整个内容设为 y = mx.您最终会得到一个 m 大约为 0.5，因为它正在尽其所能，因为您必须在 0 处为 0，等等.

You're not adding a constant as the documentation suggests, so it's trying to fit the whole thing as y = m x. You wind up with an m which is roughly 0.5 because it's doing the best it can, given that you have to be 0 at 0, etc.

以下代码(注意导入的变化)

The following code (note the change in import as well)

import statsmodels.api as sm
import numpy as np
import matplotlib.pyplot as plt

y = np.arange(100, 0, -1)
x = np.arange(0, 100)
x = sm.add_constant(x)

result = sm.OLS(y, x).fit()
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 8), sharey=True)
ax.plot(x[:,1], result.fittedvalues, 'r-')
ax.plot(x[:,1], y, 'x')

plt.savefig("out.png")

生产

系数为 [ 100. -1.].

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