Question

我一直在尝试寻找一种在Python的OLS / GLS回归中指定预定义系数的方法。我可以使用public interface UnitStackTranslation<F extends UnitStack, T extends UnitStack, FV extends ValuePair, TV extends ValuePair> { TV translateFrom(FV value, F from, T to); FV translateTo(TV value, T to, F from); }在R中做到这一点，但是Python中似乎没有类似的东西。

R当量：

offset

因此，在此示例中，x和z是我们的自变量，并且x是由模型预测的，但我们已指定z的影响为0.2

Answer 1

使用statsmodels，您可以在Python中使用style similar to R进行回归分析。在回归函数的 some 中，您会发现offset作为自变量。一个示例是GLM。

具有这样的数据集：

import statsmodels.formula.api as smf
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(8, 3), columns=list('yxz'))
df

#           y         x         z
# 0  0.091761 -1.987569 -0.219672
# 1  0.357113  1.477894 -0.518270
# 2 -0.808494 -0.501757  0.915402
# 3  0.328751 -0.529760  0.513267
# 4  0.097078  0.968645 -0.702053
# 5 -0.327662 -0.392108 -1.463515
# 6  0.296120  0.261055  0.005113
# 7 -0.234587 -1.415371 -0.420645

您可以这样做：

known = 0.2

res1 = smf.glm('y ~ x', data = df, offset=known*df['z']).fit()
print(res1.summary())

# ==============================================================================
# Dep. Variable:                      y   No. Observations:                    8
# Model:                            GLM   Df Residuals:                        6
# .
# .
# ==============================================================================
#                  coef    std err          z      P>|z|      [0.025      0.975]
# ------------------------------------------------------------------------------
# Intercept      0.0614      0.165      0.373      0.709      -0.261       0.384
# x              0.1478      0.148      0.995      0.320      -0.143       0.439
# ==============================================================================

您还可以通过手动执行相同的操作来运行健全性检查。您可以这样创建一个偏移量：

offset = known*df['z']
y_offset = df['y']-offset
df2  = pd.concat([pd.Series(y_diff), df['x']], axis = 1)
df2.columns = ['y_diff', 'x']

res2 = smf.glm('y_offset ~ x', data = df2).fit()
print(res2.summary())

# ==============================================================================
# Dep. Variable:               y_offset   No. Observations:                    8
# Model:                            GLM   Df Residuals:                        6
# .
# .
# ==============================================================================
#                  coef    std err          z      P>|z|      [0.025      0.975]
# ------------------------------------------------------------------------------
# Intercept      0.0614      0.165      0.373      0.709      -0.261       0.384
# x              0.1478      0.148      0.995      0.320      -0.143       0.439
# ==============================================================================

我们可以在Python中设置/固定回归方程中的系数吗

1 个答案: