Is this what you need??   data have; input var1 var2 var3; datalines; 23 12 133 94 45 99 99 32 54 ; run; proc means data=have noprint; var var1 var2 var3; output out=stats(drop=_type_ _freq_) max=max1 max2 max3; run; 　 data want; if _n_ = 1 then set stats; set have; array m {3} max1-max3; array v {3} var1-var3; do i = 1 to 3; if m{i} = 99 and v{i} = m{i} then v{i} = .; end; keep var1-var3; run; ... View more

Try this: Leave the code as is, but drop MATCH from WANT (or you can leave it in there, and have it over-written in the Merge below). You'll have a data set with 16x obs, the same number of obs in data set HAVE.   Then do a 1-1 merge:   Data Both; merge   WANT   HAVE; run;   In BOTH, column_name, etc. should be in the same order as HAVE, with variable Newval having the interpolated values between the 'a_xx' & 'a_16', with T1/Newval for  'a_16' unchanged. I don't know what you need for T1 at 'a_15' - is it the same as for 'a_16'???   The data set looks messy; that's on purpose so that you can check how the code is working, and make sure the data values are in the correct observations. Easy enough to clean up.       ... View more

Assuming the number of observations is multiple of 16, and the first observation is 'a_1', and data is sorted from 'a_1' to 'a_16' for each Match, and you want interpolated values for T1 on each obs between Match=1 and 'a_16'...   Give this code a try:   data want; length colname $ 4; set have(where = (Match = 1) rename = (T1 = firstval column_name = col1)); x=input(substr(col1,3),2.); set have(rename = (T1 = lastval column_name=col2) drop = match where = (col2 = 'a_16') ); interpol = (lastval - firstval)/(16-x); do i = 1 to 16; ColName = cats('a_',i); if i < x then do; newval=.; Xmatch=.; end; else if i = x then do; newval=firstval; Xmatch=1; end; else do; newval+interpol; Xmatch=.; end; output; end; run;     It calculates a straight-line interpolation value (Interpol) between Match=1 and 'a_16'. Change if needed. It then loops thru 'a_1' to 'a_16', putting the interpolated values in Newval, and outputting everything to data set WANT. I kept all the variables, look over the data carefully; making sure that Xmatch corresponds to Match (from HAVE), and that firstval, lastval match up with T1 (from HAVE).    ... View more

Any news on whether our old bookmarks are recoverable? ... View more

A quick additional note: I've cleared the cache a few times; doesn't seem to help. ... View more

My issues  mimic those described by ballardw (I'm using IE11 as well). One strange twist tho... whenever I start at sas.com, and click thru Community -> (any of the choices: Data Mgt, VA, Admin, All  etc.) it's hit-or-miss if I get thru. But if/when I do get thru, I almost always find myself logged out of communities.sas.com, even tho I was logged in at sas.com. AND I always get the 404 message when I try to sign in. I can start over at sas.com, log out, click thru to communities, and then I'm able to sign in. And it seems that once I'm signed in at communities.sas.com, I don't get the 404.   If I start at communities.sas.com, I'm usually still signed in, and typically have less problems. ... View more

Same problem/concern - no bookmarks under 'My Bookmarks'. And I too hope that they can be recovered. ... View more

I agree with SASKiwi... it's doubtful that you can install those products now. A suggestion: Ask SAS for a license extension (Haven't tried it; I think it lasts for only a week, tho): Go to... http://support.sas.com/techsup/license/index.html Click on... Request a temporary license extension for all SAS releases OR  at the bottom of that page is this: General License Questions For other questions about licenses for SAS software and other software products associated with SAS, invoices, or licensing terms, contact SAS Contracts Support I've talked to their Contracts Support before; they were very helpful. Good luck... ... View more

You said you wanted a macro, so here's my go at it.... /* create the 2 tables: */ data one; length VAR $ 1; input VAR  @@; cards; A B C D E F G H I run; data two; length VAR $ 1; input VAR @@; cards; A B C D E F G H run; /* create a table, ONE_ONLY, that has obs where VAR values are in ONE, but not TWO */ proc sql noprint; create table ONE_ONLY as select * from ONE LEFT JOIN TWO ON one.VAR = two.VAR WHERE one.VAR ^= two.VAR ; /* Now, put those values of VAR into a macro VAR1 */ select distinct VAR into :var1 separated by ' ' from work.ONE_ONLY ; quit; /* test to see if VAR1 exists. If so, then write the values that mis-matched. */ /* if not, then var1 not created, so no mis-matches                           */ %macro test; %let MACs = %symexist(var1); %if &MACs = 0 %then %put NO MISMATCH; %else %put These Values are in ONE, but not TWO: &var1 ; %mend test; %test  Some NOTES: 1) the code only looks for VAR values in ONE that are not in TWO. You can edit to make another table, TWO_ONLY, where VAR values are in TWO, but not ONE. (and even a table BOTH, where values are matched). 2) Make sure you don't have a macro variable &VAR1 already created. If you run this code multiple times to test it (changing the inputs in TWO),  you should probably do a        %symdel var1 ;          to remove/delete the macro before running PROC SQL. Hope this helps. ... View more

Well, that makes things interesting (I was wondering how you would evaluate >600 models). OK, Reeza is correct - if you can transpose the data then you'll end up with one output data set (I think you want the OUTEST= option), that gives the estimates for those IV's selected. If you'd rather keep your data in its original form, you could run the models as programmed earlier, getting an output data set for each model, and concatenating them one-at-a-time. Until you end up with one large data set with estimates for all 600+ models. One approach: put PROC APPEND in the macro, while using the OUTEST= option: %macro mylogit (dependent);      title "dependent variable is &dependent";      proc logistic data=test desc outest=ABC;      model &dependent=var1 var2 var3 var4/selection=forward;      run; PROC APPEND BASE=work.ALL data=work.ABC  FORCE; run; %mend; I'm a little worried about doing it this way because PROC APPEND will drop variables if they don't appear in the BASE table, but in the DATA table. And it might be that the OUTEST data set will change from one model to another, depending on how many (if any) IVs are significant. Give it a try, using a few runs (ie &start=1, &stop=10 and see what happens). OR... let's just make individual OUTEST data sets, and concatenate them outside the loop. %macro mylogit (dependent); title "dependent variable is &dependent";      proc logistic data=test desc outest=&dependent; *One output data set for each model, each named RULEnnnnn     ;      model &dependent=var1 var2 var3 var4/selection=forward;      run; %mend; So after the above loop runs, you'll have   &stop-&start+1   OUTEST data sets, named 'RULEnnnnn'. You don't want to do this: data all; set RULE000123 RULE1122 RULE.....; run; Instead make a macro list of the OUTEST data sets you just created: proc sql; select distinct name into :VARS separated by ' ' from work.var_names(where=(&start <= counter <= &stop)); quit; %put &vars=; /* the above code is using the COUNTER+1;  logic mentioned earlier */ data all; set  &vars; *or set ALL  &vars ;   if already have created data set ALL ; run; If you decide to try it this way, I'd advise starting slowly, with say 10 models. Look over the output, check it against all the OUTEST datasets, and then data ALL. If it looks OK, try again for another 20 or so models. NOTE: this is assuming it's the OUTEST= option you want. If not, find the one you need; the logic should hold for whatever OUT= you use. NOTE: you'll end up with 600+ OUTEST tables, so you should probably stop every so often and delete a bunch of them. ... View more

I agree with Reeza, because... 1) re-naming the variables will needlessly complicate things, and 2) the solution was pretty much exactly what *I* was going to post, but she beat me to it. One thought... 600+ regressions will create a LOT of output. If you want to limit the number of models analyzed, you can run  Reeza's  program multiple times, selecting a sub-set of DVs each time:      ... and name like 'RULE0%';     (analysis on only those variable names starting with 'RULE0'). Then re-run with:      ... and name like 'RULE1%';     (etc.)                .      ... and name like 'RULE9%'; (A similar approach: create 10 different var_names data sets (var_name0, var_name1, etc) and SET each one-at-a-time). OR if you want more control, you can put a counter variable in the full data set, and sub-set on that: data var_names; set var_names; counter+1; run; %let start=1; %let stop=50;  /* analysis/models  on first 50 rows (first 50 RULEs) of var_names data set */ data _null_; set var_names(where=(&start <= counter <= &stop)); ... (remainder of program).... Change the start, stop values and re-run the data _null_ step  until all 600+ models have been analyzed. Good luck on your research! ... View more