Python拦截

Question

问题设置在状态模型分位数回归问题中，其最小绝对偏差摘要输出显示了中断。在这个例子中，他们使用了一个公式。

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 而不用使用statsmodels.formula.api as smf公式方法实现摘要输出？

Answer 1

当然，当我把这个问题放在一起的时候，我想明白了。与其删除它，我将分享，以防外面的人遇到这种情况。

正如我所怀疑的，我需要常数()，但我不知道怎么做。我做了一些愚蠢的事情，将常量添加到Y (endog)变量中，而不是X (exog)变量。

答案

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.

作为一个FYI，我发现有趣的是，add_constant()只是在您的数据中添加了一列1。有关add_constant()的更多信息可以是在这里发现的。