Python 在statsmodels的摘要中保留变量名
我使用的是statsmodel的OLS，链接是 你可以看到X在摘要中显示为USD，这就是我想要的。 但是，在添加新变量之后Python 在statsmodels的摘要中保留变量名,python,statsmodels,Python,Statsmodels,我使用的是statsmodel的OLS，链接是 你可以看到X在摘要中显示为USD，这就是我想要的。 但是，在添加新变量之后 #JPY + USD X = sm.add_constant(JPY) X = np.column_stack((X, USD)) model = sm.OLS(y, X) results = model.fit() print(results.summary()) OLS Regression Results
#JPY + USD
X = sm.add_constant(JPY)
X = np.column_stack((X, USD))
model = sm.OLS(y, X)
results = model.fit()
print(results.summary())
OLS Regression Results
========================================================================================
Dep. Variable: All Ordinaries closing price R-squared: 0.641
Model: OLS Adj. R-squared: 0.640
Method: Least Squares F-statistic: 868.8
Date: Tue, 23 Oct 2018 Prob (F-statistic): 2.80e-217
Time: 17:39:19 Log-Likelihood: -7669.4
No. Observations: 977 AIC: 1.534e+04
Df Residuals: 974 BIC: 1.536e+04
Df Model: 2
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const -1559.5880 149.478 -10.434 0.000 -1852.923 -1266.253
x1 78.6589 2.466 31.902 0.000 73.820 83.497
x2 -366.5850 178.672 -2.052 0.040 -717.211 -15.958
==============================================================================
Omnibus: 24.957 Durbin-Watson: 0.031
Prob(Omnibus): 0.000 Jarque-Bera (JB): 27.278
Skew: 0.353 Prob(JB): 1.19e-06
Kurtosis: 3.415 Cond. No. 743.
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
它显示的不是美元和日元，而是x1-x2。有办法解决吗？我试过谷歌，但什么也没找到 因为我的问题都是关于显示的，因此，如果我保留标题，那么问题就解决了，所以我会发布我的解决方案，以防有人有同样的问题
#JPY + USD
X = JPY.join(USD)
X = sm.add_constant(X)
#X = np.column_stack((X, USD))
model = sm.OLS(y, X)
results = model.fit()
print(results.summary())
OLS Regression Results
========================================================================================
Dep. Variable: All Ordinaries closing price R-squared: 0.641
Model: OLS Adj. R-squared: 0.640
Method: Least Squares F-statistic: 868.8
Date: Tue, 23 Oct 2018 Prob (F-statistic): 2.80e-217
Time: 22:51:43 Log-Likelihood: -7669.4
No. Observations: 977 AIC: 1.534e+04
Df Residuals: 974 BIC: 1.536e+04
Df Model: 2
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const -1559.5880 149.478 -10.434 0.000 -1852.923 -1266.253
JPY 78.6589 2.466 31.902 0.000 73.820 83.497
USD -366.5850 178.672 -2.052 0.040 -717.211 -15.958
==============================================================================
Omnibus: 24.957 Durbin-Watson: 0.031
Prob(Omnibus): 0.000 Jarque-Bera (JB): 27.278
Skew: 0.353 Prob(JB): 1.19e-06
Kurtosis: 3.415 Cond. No. 743.
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
美元和日元到底是什么意思？当您将它们作为常量添加时，它们的值是多少？问题是，我发现当我使用np.column_stack时，它只会返回值，因此不包括标题日元和美元。然而，我还没有找到解决问题的方法，这不是我的问题；你只是在重复你原来的问题。当然，statsmodels很好地跟踪了常数的名称，并且尽可能地跟踪它们。当使用NumPy数组作为输入时，statsmodels不能再这样做了。我怀疑你是否可以手动检索这些名称，因为如果你可以的话，它很可能已经被编程到statsmodels中了。真正的问题是：这有什么关系？也许真正的解决方案是，
USDJPYpandas.Dataframe#JPY + USD
X = JPY.join(USD)
X = sm.add_constant(X)
#X = np.column_stack((X, USD))
model = sm.OLS(y, X)
results = model.fit()
print(results.summary())
OLS Regression Results
========================================================================================
Dep. Variable: All Ordinaries closing price R-squared: 0.641
Model: OLS Adj. R-squared: 0.640
Method: Least Squares F-statistic: 868.8
Date: Tue, 23 Oct 2018 Prob (F-statistic): 2.80e-217
Time: 22:51:43 Log-Likelihood: -7669.4
No. Observations: 977 AIC: 1.534e+04
Df Residuals: 974 BIC: 1.536e+04
Df Model: 2
Covariance Type: nonrobust
==============================================================================
coef std err t P>|t| [0.025 0.975]
------------------------------------------------------------------------------
const -1559.5880 149.478 -10.434 0.000 -1852.923 -1266.253
JPY 78.6589 2.466 31.902 0.000 73.820 83.497
USD -366.5850 178.672 -2.052 0.040 -717.211 -15.958
==============================================================================
Omnibus: 24.957 Durbin-Watson: 0.031
Prob(Omnibus): 0.000 Jarque-Bera (JB): 27.278
Skew: 0.353 Prob(JB): 1.19e-06
Kurtosis: 3.415 Cond. No. 743.
==============================================================================
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.