Is it possible to produce residuals of a regression model source dataset in SAS

[FeliciaFung]

Hi everyone!

I am using SAS miner workstation.

I need to extract the residuals of a regression model and feed them into a new model. Is it possible?

Many thanks!

[Reeza]

Yes it's possible. There's usually an option to create an output data set and that should have the predicted values and residuals or you could score the data in another node again to get the predicted vs residuals and then pipe that into your next step.

[FeliciaFung]

Thanks for answering. But I could not find the option which can create the output data set of residuals?

[Reeza]

Try the scoring option then? I'm very surprised it wouldn't be in the default output data set. What variables are in that data set?

[FeliciaFung]

Those are financial data such as CAR and turnovers

[FeliciaFung]

And I would like to ask how to obtain output data set?

[Reeza]

Residuals are listed as included in the output data set from the documentation:

https://documentation.sas.com/?docsetId=emref&docsetTarget=n1jqzz8cssr9m2n1ktx2iyv87q56.htm&docsetVe...

The Regression node must run before you can view the Regression node output data sets. After a successful run, ensure that the Regression node is selected in the Diagram Workspace, then select the button to the right of the Exported Data property. This opens the Exported Data — Regression window, which lists the data sets that the Regression node outputs.

Two types of data sets are output from the Regression node:

• Scored Data Sets — are the scored training, validation, and test data set that contains original inputs and scores (prediction, residuals, classification results, and so on).

  Here is a list of the name prefixes of the scores in the scored data sets:

  • BL_ or BP_ — best possible profit or loss for any of the decisions
  • CL_ or CP_ — profit or loss that is computed from the target value
  • D_ — level of the decision that is chosen by the model
  • E_ — error function
  • EL_ or EP_ — expected profit or loss that is chosen by the model
  • F_ — normalized category that the case comes from
  • I_ — normalized category that the case is classified into
  • IC_ — investment cost
  • P_ — posterior probabilities for categorical targets or predicted values for interval targets
  • R_ — residuals
  • ROI_ — return on investment
  • U_ — un-normalized category that the case is classified into.

  When the model selection methods of forward, backward, or stepwise are applied, the Regression node automatically changes the model roles from input to rejected for variables that are not included in the regression model.

[FeliciaFung]

Thanks, I opened the exported data property after running the basic regression but the scored data are not in the default output data set. I will try to add the scored node between the regression and next model as you mentioned then.

[Reeza]

Are you saying you don't see a data set that has a variable named R_ in it?

[FeliciaFung]

yes

[FeliciaFung]

Oh sorry I saw the variable residual but I am not able to extract it

[Reeza]

What do you mean by 'extract it'?

[FeliciaFung]

which means to produce those residuals into a new dataset or to have it in the original dataset

[Reeza]

You can use a task on the output data set to filter/extract only columns of interest? Use the output dataset name as your input or add it to the process flow or a new process?

[FeliciaFung]

But I am not sure why I cannot save the output. I can only 'plot' after opening the exported data property