Question

我“使用”Statsmodel的时间不到2天，并且完全不熟悉导入命令等。我想从here运行一个简单的variance_inflation_factor但我有一些问题。我的代码如下：

from numpy import *
import numpy as np
import pandas as pd
from pandas import DataFrame, Series
import statsmodels.formula.api as sm
from sklearn.linear_model import LinearRegression
import scipy, scipy.stats
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('ggplot')
from statsmodels.api import add_constant
from numpy import linalg as LA
import statsmodels as sm

## I have been adding libraries and modules/packages with the intention of erring on the side of caution 

a = df1.years_exp
b = df1.leg_totalbills
c = df1.log_diff_rgdp
d = df1.unemployment
e = df1.expendituresfor
f = df1.direct_expenditures
g = df1.indirect_expenditures

sm.variance_inflation_factor((['a', 'b', 'c', 'd', 'e', 'f']), g)

then I get the following error:

AttributeError                            Traceback (most recent call last)
<ipython-input-61-bb126535eadd> in <module>()
----> 1 sm.variance_inflation_factor((['a', 'b', 'c', 'd', 'e', 'f']), g)

AttributeError: module 'statsmodels' has no attribute 'variance_inflation_factor'

有人可以指导我加载和执行此模块的正确语法吗？如果我发布链接到某些源代码更方便，请询问。但是，我觉得这只是一个简单的语法问题。

Answer 1

variance_inflation_factor中的statsmodels.stats.outlier_influence功能可见in the docs，因此要使用它，您必须正确导入，选项可以是

from statsmodels.stats import outliers_influence
# code here 
outliers_influence.variance_inflation_factor((['a', 'b', 'c', 'd', 'e', 'f']), g)

Answer 2

感谢您提出这个问题！我今天也有同样的问题，除了我想计算每个特征的方差膨胀系数。这是一种编程方式：

from patsy import dmatrices
from statsmodels.stats.outliers_influence import variance_inflation_factor

# 'feature_1 + feature_2 ... feature_p'
features_formula = "+".join(df1.columns - ["indirect_expenditures"])

# get y and X dataframes based on this formula:
# indirect_expenditures ~ feature_1 + feature_2 ... feature_p
y, X = dmatrices('indirect_expenditures ~' + features_formula, df1, return_type='dataframe')

# For each Xi, calculate VIF and save in dataframe
vif = pd.DataFrame() 
vif["vif"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]
vif["features"] = X.columns
vif

请注意，只有在您导入pandas且df1为pandas DataFrame

的情况下，上述代码才有效

Answer 3

a = df1.years_exp
b = df1.leg_totalbills
c = df1.log_diff_rgdp
d = df1.unemployment
e = df1.expendituresfor
f = df1.direct_expenditures
g = df1.indirect_expenditures

ck=np.array([a,b,c,d,e,f,g])
outliers_influence.variance_inflation_factor(ck, 6)

熊猫＆gt; Statsmodel：实现variance_inflation_factor的语法错误

3 个答案: