[Statsmodels]：如何让 statsmodel 返回 OLS 对象的 pvalue？

我对编程还很陌生，我正在尝试使用 Python 来熟悉数据分析和机器学习。

我正在学习有关多元线性回归的向后消除的教程。这是现在的代码：

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('50_Startups.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 4].values

#Taking care of missin' data
#np.set_printoptions(threshold=100) 
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
imputer = imputer.fit(X[:, 1:3])
X[:, 1:3] = imputer.transform(X[:, 1:3]) 

#Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelEncoder_X = LabelEncoder()
X[:, 3] = labelEncoder_X.fit_transform(X[:, 3])
onehotecnoder = OneHotEncoder(categorical_features = [3])
X = onehotecnoder.fit_transform(X).toarray()

#Avoid the Dummy Variables Trap
X = X[:, 1:]

#Splitting data in train and test
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)

#Fitting multiple Linear Regression to Training set
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)

#Predict Test set
regressor = regressor.predict(X_test)

#Building the optimal model using Backward Elimination
import statsmodels.formula.api as sm
a = 0
b = 0
a, b = X.shape
X = np.append(arr = np.ones((a, 1)).astype(int), values = X, axis = 1)
print (X.shape)

X_optimal = X[:,[0,1,2,3,4,5]]
regressor_OLS = sm.OLS(endog = y, exog = X_optimal).fit()
regressor_OLS.summary()
X_optimal = X[:,[0,1,3,4,5]]
regressor_OLS = sm.OLS(endog = y, exog = X_optimal).fit()
regressor_OLS.summary()
X_optimal = X[:,[0,3,4,5]]
regressor_OLS = sm.OLS(endog = y, exog = X_optimal).fit()
regressor_OLS.summary()
X_optimal = X[:,[0,3,5]]
regressor_OLS = sm.OLS(endog = y, exog = X_optimal).fit()
regressor_OLS.summary()
X_optimal = X[:,[0,3]]
regressor_OLS = sm.OLS(endog = y, exog = X_optimal).fit()
regressor_OLS.summary()

现在，执行消除的方式对我来说似乎确实是手动的，我想将其自动化。为了做到这一点，我想知道是否有办法让我以某种方式返回回归量的 pvalue（例如，是否有一种方法可以在 statsmodels 中执行此操作）。这样我认为我应该能够循环 X_optimal 数组的特征并查看 pvalue 是否大于我的 SL 并消除它。

谢谢你！

遇到了同样的问题。

您可以通过以下方式访问 p 值

regressor_OLS.pvalues 

它们以科学记数法存储为 float64 数组。我对 python 有点陌生，我确信有更干净、更优雅的解决方案，但这是我的：

sigLevel = 0.05

X_opt = X[:,[0,1,2,3,4,5]]
regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
regressor_OLS.summary()
pVals = regressor_OLS.pvalues

while np.argmax(pVals) > sigLevel:
    droppedDimIndex = np.argmax(regressor_OLS.pvalues)
    keptDims = list(range(len(X_opt[0])))
    keptDims.pop(droppedDimIndex)
    print("pval of dim removed: " + str(np.argmax(pVals)))
    X_opt = X_opt[:,keptDims]
    regressor_OLS = sm.OLS(endog = y, exog = X_opt).fit()
    pVals = regressor_OLS.pvalues
    print(str(len(pVals)-1) + " dimensions remaining...")
    print(pVals)

regressor_OLS.summary()