Hello I have what I am sure is a rather inane query relating to proc logistic, specifically, how to create a resampling program/macro involving a training and testing data set? Please find the program for the train/test phases below, which appears to work fine. What I would like to do now is reiterate the process (say 500 times) so that essentially the the training sets are different each time (and therefore the predicted probabilities inputted into the test phase) and then obtain an overall summary of misclassification and various other output statistics.

The data set I am using is relatively small (n = 441) and I worry about the spectrum of 'goodness of fit'and maximum likelihood results I get each time I run the procedure. Any suggestions and advice would be very much appreciated Thanks for your time, Rob

DATA FItrain FItest; SET Fraser2; IF RANUNI(0) < = 1/2 then OUTPUT FItrain; ELSE OUTPUT FItest; RUN; title 'Severe Incidents Model'; proc logistic data = FItrain order = data outest = PARMS descending simple ; class Walking (ref = FIRST) TOD jmale (ref = FIRST) admale (ref = FIRST) submale (ref = FIRST) season; model absolute = season TOD Walking admale submale jmale / rsq lackfit EXPB SELECTION = FORWARD SLE = 0.20 SLS = 0.10 CTABLE; output out=out1 p=p; run; proc logistic data = FItest order = data inest = PARMS descending simple ; class Walking (ref = FIRST) TOD jmale (ref = FIRST) admale (ref = FIRST) submale (ref = FIRST) season; model absolute = season TOD Walking admale submale jmale / rsq lackfit maxiter = 0 EXPB; output out=out2 p=p; run; proc print data=out2;