在Python中轻松访问标准化残差，cook值，hatvalues(杠杆)等? [英] Access standardized residuals, cook's values, hatvalues (leverage) etc. easily in Python?

问题描述

在拟合线性回归后，我正在寻找影响力统计数据.在R中，我可以像这样获得它们:

I am looking for influence statistics after fitting a linear regression. In R I can obtain them (e.g.) like this:

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

等

在拟合如下模型后，如何在Python中使用statsmodels时获得相同的统计信息:

How can I obtain the same statistics when using statsmodels in Python after fitting a model like this:

#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)

请参见下面的答案...

See answer below...

推荐答案

我在这里找到它:

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

OLSInfluence.summary_frame()

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