Thank you for your reply and your suggestion to check out Rick's posts. I will read through them later when I have time. For now, if there is a way to use proc logistic to utilize saved binary parameters from the store statement, I would like to see an example of the syntax. If logistic scoring of new data using this saved data is currently missing functionality, that's fine. I would just like to know one way or the other before I finish my final report so I can be confident I know what I am talking about when I am demonstrating SAS abilities. I come from a background of Software Development / Engineering (mostly in C/C++) and dealing with the data involved, so the main thing I am adding to my skill set is the use of the advanced statistics. If you can answer whether it is possible / practical to use the saved parameters for logistic analysis on new data sets with an example of the syntax (if so), then I am satisfied for the moment. In case you are curious, my project involves 2-3 yearly data sets each with over 2 million records where the target of my hypothesis is available in all 3. There is actually several more data set years available but I am limiting myself to those. I am splitting the first year for training and validation and testing with a different year by applying the scoring. Since I can always compare the prediction to the actual value of the target, I used a few sas procedures (data step and proc means / proc freq) to see how well it did. In the process I also computed a value to consider using for cutoff for the predicted values. It appeared I could use proc plm results to come somewhat close to the proc logistic results if I used a different cutoff for both. However the predicted values from proc plm could go negative etc while the proc logistic results were nicely bounded between 0 and 1, so it was obvious proc plm doesn't use logistic analysis unless there is a option I don't know about yet. If you want to see the steps here, I have to switch laptops. I am not running SAS University edition on this one.
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