Yes, and that's what I'm doing in handling the count=0 code; attempt to run it again with a different parameter, and if that still doesn't get it to run, then save the model info to a running data set so that I've got all the ones that finally didn't run together. This means I've got lots of things in both blocks of code- attempts at reruns if the code did not run, and saving of model info, in addition to the output of the things that did run in my block of code if it did work.

It is legal in many statistical procedures to just not get ODS output on a procedure even though the procedure worked- such as odds ratios may not be appropriate for that particular run, and I'm accumulating a final spreadsheet of data in which that cell would just be blank, and appropriately so; it isn't an error in the analysis. The trouble is, normal SAS output for something like a Proc Logistic can be up to 3 pages long, and therefore 30,000 pages for 10,000 runs, whereas I want just one row of output for each run, with perhaps 10,000 rows down. Using the macro If statements in conjunction with ODS can do this, and it is one of the reasons why so many genetics people are going over to R, because they don't think only R can do this, but SAS can, so that's why I brought it up in the SAS vs. R discussion.

-Mary ----- Original Message ----- From: data _null_, To: Mary Cc: SAS-L@listserv.uga.edu Sent: Monday, July 07, 2008 4:13 PM Subject: Re: Re: SET with OBS=0

On 7/7/08, Mary <mlhoward@avalon.net> wrote: > The problem with the call execute statement is that it is only one thing, > instead of allowing me to just handle the condition and move on- usually I'm > trying to aggregate 10,000 runs or so- I **don't** want to stop just because > one didn't work.

The call execute(endsas) was an example I don't know exactly what you are trying to do. I am not following the R thread closely and I did not read your contributions.

To me it would seem more efficient to fit all 10,000 "runs" using by group processing. Then examine the "misfits" and refit them using different parameters then examine the "misfits" and refit those etc.