class to calculate outlier and influence measures for OLS result
Parameters : | results : Regression Results instance
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Notes
One part of the results can be calculated without any auxiliary regression (some of which have the _internal postfix in the name. Other statistics require leave-one-observation-out (LOOO) auxiliary regression, and will be slower (mainly results with _external postfix in the name). The auxiliary LOOO regression only the required results are stored.
Using the LOO measures is currently only recommended if the data set is not too large. One possible approach for LOOO measures would be to identify possible problem observations with the _internal measures, and then run the leave-one-observation-out only with observations that are possible outliers. (However, this is not yet available in an automized way.)
This should be extended to general least squares.
The leave-one-variable-out (LOVO) auxiliary regression are currently not used.
Methods
get_resid_studentized_external([sigma]) | calculate studentized residuals |
summary_frame() | Creates a DataFrame with all available influence results. |
summary_table([float_fmt]) | create a summary table with all influence and outlier measures |