scipy linregress 函数错误的标准错误返回? [英] scipy linregress function erroneous standard error return?

问题描述

scipy.stats.linregress 出现了一个奇怪的情况，似乎返回了不正确的标准错误:

I have a weird situation with scipy.stats.linregress seems to be returning an incorrect standard error:

from scipy import stats
x = [5.05, 6.75, 3.21, 2.66]
y = [1.65, 26.5, -5.93, 7.96]
gradient, intercept, r_value, p_value, std_err = stats.linregress(x,y)
>>> gradient
5.3935773611970186
>>> intercept
-16.281127993087829
>>> r_value
0.72443514211849758
>>> r_value**2
0.52480627513624778
>>> std_err
3.6290901222878866

而 Excel 返回以下内容:

Whereas Excel returns the following:

 slope: 5.394

 intercept: -16.281

 rsq: 0.525

 steyX: 11.696

steyX 是 excel 的标准误差函数，返回 11.696 与 scipy 的 3.63.有人知道这里发生了什么吗?有没有其他方法可以在 Python 中获得回归的标准误差，无需使用 Rpy?

steyX is excel's standard error function, returning 11.696 versus scipy's 3.63. Anybody know what's going on here? Any alternative way of getting the standard error of a regression in python, without going to Rpy?

推荐答案

你可以试试 statsmodels 包:

In [37]: import statsmodels.api as sm

In [38]: x = [5.05, 6.75, 3.21, 2.66]

In [39]: y = [1.65, 26.5, -5.93, 7.96]

In [40]: X = sm.add_constant(x) # intercept

In [41]: model = sm.OLS(y, X)

In [42]: fit = model.fit()

In [43]: fit.params
Out[43]: array([  5.39357736, -16.28112799])

In [44]: fit.rsquared
Out[44]: 0.52480627513624789

In [45]: np.sqrt(fit.mse_resid)
Out[45]: 11.696414461570097

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