Thank you so much, @ballardw , @FreelanceReinh and @Tom! These are very helpful solutions! I used the code chunk right above this message. ... View more

OK. But that is not so easy to score a new dataset with category variables. data raw_tbl; input sex $ age subs @@; cards; Female 35 0 Male 44 0 Male 45 1 Female 47 1 Female 51 0 Female 47 0 Male 54 1 Male 47 1 Female 35 0 Female 34 0 Female 48 0 Female 56 1 Male 46 1 Female 59 1 Female 46 1 Male 59 1 Male 38 1 Female 39 0 Male 49 1 Male 42 1 Male 50 1 Female 45 0 Female 47 0 Female 30 1 Female 39 0 Female 51 0 Female 45 0 Female 43 1 Male 39 1 Male 31 0 Female 39 0 Male 34 0 Female 52 1 Female 46 0 Male 58 1 Female 50 1 Female 32 0 Female 52 1 Female 35 0 Female 51 0 ; Run; data test_tbl; input sex $ age; cards; Female 35 Male 19 Male 70 ; run; options validvarname=v7; ods output parameterestimates=RonLogitModel2; proc genmod data=raw_tbl namelen=60 descending ; class sex ; model subs=sex age/ dist=binomial link=logit type3 wald ; output out=xxx p=P_subscibe xbeta=logit; run; /*create design matrix for scoring*/ data test; set test_tbl; subs=1; run; proc glmselect data=test outdesign=outdesign(drop=subs) noprint; class sex; model subs=sex age/selection=none; /*same model as PROC GENMOD*/ quit; /*make a parameter dataset*/ data RonLogitModel2; set RonLogitModel2 end=last; vname=catx('_',Parameter,Level1); if last then delete; /*only keep variables which are used to score*/ run; proc transpose data=RonLogitModel2 out=RonLogitModel22 ; var Estimate ; id vname; run; data RonLogitModel22; set RonLogitModel22; _type_='PARM'; run; /*score dataset test_tbl*/ proc score data=outdesign out=want score=RonLogitModel22 type=parm; run; data want; set want(rename=(Estimate=xbeta)); predicted=logistic(xbeta); run; proc print;run;   ... View more

Great! One thing - if your STRATVAR has more than 2 levels (looks like you have 3), the macro will not calculate p-values (only SMDs). If you really need p-values for each pair-wise comparison of the STRATVAR, you would need to run the macro multiple times (once for each pair of cohorts) and merge them together. Very awkward, but works. But SMDs should work regardless of how many columns - there will be one for each pair. ... View more

SAS_kernel depends on SASPy, so when you install it, it will install SASPy if it's not already there; so that part should be ok. The error implies that you haven't configured saspy to have any idea what SAS deployment it's supposed to be connecting to. The documentation for configuring SASPy is here: https://sassoftware.github.io/saspy/configuration.html There are different access methods for connecting to different kinds of SAS deployments. w/in that configuration doc will be a section that helps you identify which access method to configure depending upon where your client is and where/what kind of SAS deployment you're trying to connect to. ... View more

Great, thank you! Happy Solar Eclipse! ... View more

This solved the error for me :). ... View more

The following link provide the location of the log files SAS 9.4 https://go.documentation.sas.com/doc/en/bicdc/9.4/bisag/p1ausbmrrybuynn1xnxb6jmdfarz.htm  https://documentation.sas.com/doc/en/bicdc/9.4/bimtag/n1ih0exzxu35f3n1xfec5udk1l3f.htm  In a typical organization, there are policies for log retention/archiving. If there is none, then create a policy, document it and follow it.   ... View more

Thank you for the recommendation.  ... View more

Break dataset AIR into 10 chunks (_imputation 1-10, 11-20, 21-30, etc.) and feed them to a macro: /*Define macro named arbitrarily, e.g., "piggie" */ %macro piggie (chunk); %DO I = 1 %TO 10; --- your code here --- --- done on chunk&i --- %END; %mend piggie; /*Call macro piggie*/ %piggie(chunk1) %piggie(chunk2) ... %piggie(chunk10) ... View more

You should normally avoid using RETAIN for variables that you are actually READING in with an INPUT statement.  The only case where that makes sense is when the INPUT statement is executed CONDITIONALLY so values are only read in some times so that the retained value can be used in the other cases.   A better way to see what is happening is to look at the values during the data step itself. DATA demo; LENGTH a b c d ac bc cc dc 8.; RETAIN a ; *same as RETAIN a .; RETAIN b 0; RETAIN c 10; *NOTE: doing nothing to variable d (default); if _n_=1 then put ' A B C D AC BC CC DC'; put _n_=; put (_all_) (3.) ' <- START'; /* Initialization proceeds as follows */ ac = a + 1; *_N_ = 1: a = . (the value you provided initially); *_N_ = 2: a = . (the value retained from previous step); *_N_ = 3: a = 5 (the value retained from previous step); bc = b + 1; *_N_ = 1: b = 0 (the value you provided initially); *_N_ = 2: b = . (the value retained from previous step); *_N_ = 3: b = 5 (the value retained from previous step); cc = c + 1; *_N_ = 1: c = 10 (the value you provided initially); *_N_ = 2: c = . (the value retained from previous step); *_N_ = 3: c = 5 (the value retained from previous step); dc = d + 1; *_N_ = 1: d = . (default behavior: initialized to . beginning every step); *_N_ = 2: d = . (default behavior: initialized to . beginning every step); *_N_ = 3: d = . (default behavior: initialized to . beginning every step); put (_all_) (3.) ' <- AFTER IF'; INPUT a b c d; put (_all_) (3.) ' <- AFTER INPUT'; DATALINES; . . . . 5 5 5 5 . . . . ; Result A B C D AC BC CC DC _N_=1 . 0 10 . . . . . <- START . 0 10 . . 1 11 . <- AFTER IF . . . . . 1 11 . <- AFTER INPUT _N_=2 . . . . . . . . <- START . . . . . . . . <- AFTER IF 5 5 5 5 . . . . <- AFTER INPUT _N_=3 5 5 5 . . . . . <- START 5 5 5 . 6 6 6 . <- AFTER IF . . . . 6 6 6 . <- AFTER INPUT _N_=4 . . . . . . . . <- START . . . . . . . . <- AFTER IF Notice also that the data steps ends on the 4th iteration when the INPUT statement reads past the end of the datalines. ... View more

https://support.sas.com/kb/62/362.html https://support.sas.com/kb/37/344.html https://support.sas.com/kb/37/228.html ... View more

Just try my original code. No need any change. ... View more

Yes, this is exactly what happened, thank you! ... View more