The author said, *************** Stepwise Regression Using SAS • In this example, the lung function data will be used again, with two separate analyses. o Analysis 1: Determining which independent variables for the father (**bleep**e, fheight, fweight) significantly contribute to the variability in the father’s (ffev1)? o SAS commands are: Proc STEPWISE; Model ffev1=**bleep**e fheight fweight; Title 'Stepwise regression of father data'; Run;   another report: https://www.stat.wisc.edu/~yandell/software/sas/linmod.html Stepwise Regression There is a stepwise model selection regression method. It works something like doing a series of proc regs, but the computer automatically makes the model choices of entry and elimination. Watch out! Be sure you know what this is doing for you (and to you). proc stepwise; model y = x1 x2 x3; Here are model options for the means of selection and elimination: model y = x1 x2 x3 / forward; /* forward selection */ model y = x1 x2 x3 / backward; /* backward elimination */ model y = x1 x2 x3 / stepwise; /* forward in & backward out */ model y = x1 x2 x3 / maxr stop=4; /* like stepwise, but using R^2 */ The cheapest methods are backward (or b) and forward (or f). The stepwise option (the default) is not much more costly, and a good idea in practice, as it checks back and forth. The maxr option is much more expensive, but does consider pairs of variables in ways possibly missed by stepwise; the stop=4 option to maxr only considers models with 4 or fewer variables, at considerable time savings. There is an alternative to maxr (called minr) which is even more costly. model y = x1 x2 x3 / noint; /* no intercept */ model y = x1 x2 x3 / slentry=0.5; /* signif. level for selection */ model y = x1 x2 x3 / slstay=0.1; /* signif. level for elimination */ model y = x1 x2 x3 / include=2; /* force in first 2 variables */ model y = x1 x2 x3 / start=2; /* start with 2 variables */ model y = x1 x2 x3 / details; /* more details of R^2, F stats */   ... View more

https://www.ndsu.edu/faculty/horsley/Stepwise_regression_(HZAU).pdf ... View more

proc transpose data=toprint out=transp; by unique _obs_; id _type_; run; proc print data=transp(where=(base^=compare)); run; i saw the similar one online. actually i only it lists obs with their ids   should i modify as id id? ... View more

thank you when i think it over, i feel my knowledge is not enough data is unlimited, so there should be some other options, so the value might be icd-9, or icd-10, or both, or other 3. by using your code, i find there are around 60% of obs using icd10, 40% using icd9. Now I think I would ask for advice, if there are four options, listed above, other than two options only. I do not know how it looks like if the code is both, and how to develop a program to read that. any advice? Thanks.         if the value of icd is more than 5 characters but less than 8, I can call it is icd10, if the value has less than 3 character or more than 8 characters, i will call it "other" ... ... View more