rlm_model之后的statsmodels摘要不匹配所使用的规范

Question

我正在使用以下代码执行数据X和Y的TukeyBiWeight（Bisquare）鲁棒线性模型回归。

from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
import statsmodels.api as sm
X = sm.add_constant(X)
rlm_model = sm.RLM(y, X, m=sm.robust.norms.TukeyBiweight())
rlm_results = rlm_model.fit()

尽管代码无问题运行，但总结是我的担心。运行以下内容之后：

rlm_results.summary()

输出以下内容。

                   Robust linear Model Regression Results                    
==============================================================================
Dep. Variable:                      y   No. Observations:               106721
Model:                            RLM   Df Residuals:                   106719
Method:                          IRLS   Df Model:                            1
Norm:                          HuberT                                         
Scale Est.:                       mad                                         
Cov Type:                          H1                                         
Date:                Wed, 08 Jun 2022                                         
Time:                        19:44:34                                         
No. Iterations:                    50                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
const          0.0377   9.14e-06   4117.608      0.000       0.038       0.038
x1            -0.0076   1.58e-05   -478.404      0.000      -0.008      -0.008
==============================================================================

If the model instance has been used for another fit with different fit
parameters, then the fit options might not be the correct ones anymore .

模型和方法是正确的，但是它说的规范不是我喂入M估计器字段的规范。有人知道为什么会发生这种情况吗？