问题设置 在statsmodels Quantile Regression问题中，它们的最小绝对偏差摘要输出显示截距。在该示例中，他们使用公式
from __future__ import print_function
import patsy
import numpy as np
import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
from statsmodels.regression.quantile_regression import QuantReg
data = sm.datasets.engel.load_pandas().data
mod = smf.quantreg('foodexp ~ income', data)
res = mod.fit(q=.5)
print(res.summary())
QuantReg Regression Results
==============================================================================
Dep. Variable: foodexp Pseudo R-squared: 0.6206
Model: QuantReg Bandwidth: 64.51
Method: Least Squares Sparsity: 209.3
Date: Fri, 09 Oct 2015 No. Observations: 235
Time: 15:44:23 Df Residuals: 233
Df Model: 1
==============================================================================
coef std err t P>|t| [95.0% Conf. Int.]
------------------------------------------------------------------------------
Intercept 81.4823 14.634 5.568 0.000 52.649 110.315
income 0.5602 0.013 42.516 0.000 0.534 0.586
==============================================================================
The condition number is large, 2.38e+03. This might indicate that there are
strong multicollinearity or other numerical problems.
问题
如何使用Intercept 使用statsmodels.formula.api as smf公式方法，使用from __future__ import print_function
import patsy
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.regression.quantile_regression import QuantReg
data = sm.datasets.engel.load_pandas().data
data = sm.add_constant(data)
mod = QuantReg(data['foodexp'], data[['const', 'income']])
res = mod.fit(q=.5)
print(res.summary())
QuantReg Regression Results
==============================================================================
Dep. Variable: foodexp Pseudo R-squared: 0.6206
Model: QuantReg Bandwidth: 64.51
Method: Least Squares Sparsity: 209.3
Date: Fri, 09 Oct 2015 No. Observations: 235
Time: 22:24:47 Df Residuals: 233
Df Model: 1
==============================================================================
coef std err t P>|t| [95.0% Conf. Int.]
------------------------------------------------------------------------------
const 81.4823 14.634 5.568 0.000 52.649 110.315
income 0.5602 0.013 42.516 0.000 0.534 0.586
==============================================================================
The condition number is large, 2.38e+03. This might indicate that there are
strong multicollinearity or other numerical problems.
获得摘要输出？
答案 0 :(得分：6)
当然，当我把这个问题放在一起时，我想出来了。我会分享，而不是删除它，以防有人在那里遇到过这种情况。
我怀疑，我需要add_constant()，但我不确定如何。我正在做一些愚蠢的事情并将常量添加到Y（endog）变量而不是X（exog）变量。
答案
add_constant()作为一个仅供参考，我觉得有趣的是1只会在您的数据中添加一列add_constant() s。有关{{1}}的更多信息可以是found here。