python - 属性错误: 'OLSResults' object has no attribute 'norm_resid'

Question

当我运行此程序时，出现以下错误:

AttributeError: 'OLSResults' object has no attribute 'norm_resid'

我有最新版本的 OLS，因此属性 norm_resid 应该在那里。

有什么想法吗？

from scipy import stats
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
from sklearn import datasets, linear_model
from statsmodels.formula.api import ols
"""
Data Management
"""
data = pd.read_csv("TestExer1-sales-round1.csv")
X_train = data["Advertising"]
Y_train  = data["Sales"]


# use of linregregress
model = ols("Y_train ~ X_train", data).fit()
print(model.summary()) 

plt.plot(X_train,Y_train , 'ro')
plt.plot(X_train, model.fittedvalues, 'b')
plt.legend(['Sales', 'Advertising'])
plt.ylim(0, 70)
plt.xlim(5, 18)
plt.hist(model.norm_resid())
plt.ylabel('Count')
plt.xlabel('Normalized residuals')

plt.xlabel('Temperature')
plt.ylabel('Gas')
plt.title('Before Insulation')

Answer 1

我遇到了同样的问题，但以下方法有效:

plt.hist(model.resid_pearson)

因此您的解决方案应如下所示:

from scipy import stats
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
from sklearn import datasets, linear_model
from statsmodels.formula.api import ols
"""
Data Management
"""
data = pd.read_csv("TestExer1-sales-round1.csv")
X_train = data["Advertising"]
Y_train  = data["Sales"]


# use of linregregress
model = ols("Y_train ~ X_train", data).fit()
print(model.summary()) 

plt.plot(X_train,Y_train , 'ro')
plt.plot(X_train, model.fittedvalues, 'b')
plt.legend(['Sales', 'Advertising'])
plt.ylim(0, 70)
plt.xlim(5, 18)
plt.hist(model.resid_pearson)
plt.ylabel('Count')
plt.xlabel('Normalized residuals')

plt.xlabel('Temperature')
plt.ylabel('Gas')
plt.title('Before Insulation')

当使用 statsmodel 版本 0.8.0 或更高版本时。

注意: PIL 逊残差仅将每个残差值除以残差的标准误差。而归一化还将每个残差除以所有残差的总和。更多信息请参见here

来自docs .