在Python中轻松访问标准化残差,厨师价值,帽子价值(杠杆)等？

Question

我在拟合线性回归后寻找影响统计数据.在RI中可以像这样获得它们(例如):

hatvalues(fitted_model) #hatvalues (leverage)
cooks.distance(fitted_model) #Cook's D values
rstandard(fitted_model) #standardized residuals
rstudent(fitted_model) #studentized residuals

等等

在拟合这样的模型后,如何在Python中使用statsmodel时获得相同的统计信息:

#import statsmodels
import statsmodels.api as sm

#Fit linear model to any dataset
model = sm.OLS(Y,X)
results = model.fit()

#Creating a dataframe that includes the studentized residuals
sm.regression.linear_model.OLSResults.outlier_test(results)

编辑:见下面的答案......

Answer 1

尽管接受的答案是正确的，但我发现statsmodels.regression.linear_model.OLSResults.get_influence在拟合模型后将统计信息作为影响实例 ( ) 的实例属性单独访问会很有帮助。这使我不必索引，summary_frame因为我只对其中一个统计数据感兴趣，而不是对所有统计数据感兴趣。所以也许这对其他人有帮助：

import statsmodels.api as sm

#Fit linear model to any dataset
model = sm.OLS(Y,X)
results = model.fit()

#create instance of influence
influence = results.get_influence()

#leverage (hat values)
leverage = influence.hat_matrix_diag

#Cook's D values (and p-values) as tuple of arrays
cooks_d = influence.cooks_distance

#standardized residuals
standardized_residuals = influence.resid_studentized_internal

#studentized residuals
studentized_residuals = influence.resid_studentized_external

Answer 2

我在这里找到它：

http://www.statsmodels.org/dev/generated/statsmodels.stats.outliers_influence.OLSInfluence.summary_frame.html

OLSInfluence.summary_frame()