Numpy 连接statsmodels.OLS的数据帧列

如果我想建立一个基于Y和X2的对数的模型，我会：

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

d = {'Y': [1,2,3,4], 'X1': [5,6,7,8], 'X2': [9,10,11,12]}
df = pd.DataFrame(d)
model = smf.ols(formula='np.log(Y) ~ X1 + np.log(X2)', data=df).fit()

如何对statsmodels.api执行相同的操作？我知道我可以连接df，但肯定有一个更简单的方法

import statsmodels.api as sm
import numpy as np
import pandas as pd

d = {'Y': [1,2,3,4], 'X1': [5,6,7,8], 'X2': [9,10,11,12]}
df = pd.DataFrame(d)
y = np.log(df['Y'])
x = pd.DataFrame()
x['X1'] = d['X1']
x['logX2'] = np.log(d['X2'])
#x = df[['X1', np.log('X2')]] # I'd like to type sth like this
x = sm.add_constant(x)
model = sm.OLS(y, x).fit()
model.summary()

在

x=df…

（注释行）我得到：

---

您可以使用

pd.DataFrame

构建

x

：

x = pd.DataFrame({'X1': df['X1'], 'log(X2)': np.log(df['X2'])})

而不是

x = pd.DataFrame()
x['X1'] = d['X1']
x['logX2'] = np.log(d['X2'])

---

x = pd.DataFrame()
x['X1'] = d['X1']
x['logX2'] = np.log(d['X2'])

import numpy as np
import pandas as pd
import statsmodels.api as sm

d = {'Y': [1,2,3,4], 'X1': [5,6,7,8], 'X2': [9,10,11,12]}
df = pd.DataFrame(d)
y = np.log(df['Y'])
x = pd.DataFrame({'X1': df['X1'], 'log(X2)': np.log(df['X2'])})
x = sm.add_constant(x)
model = sm.OLS(y, x).fit()
print(model.summary())