Run the two code samples from SAS Help Center , both of them failed. The first one: %macro setvar; %let dsid=%sysfunc(open(sasuser.houses, i)); /* No leading ampersand with %SYSCALL */ %syscall set(dsid); %let rc=%sysfunc(fetchobs(&dsid, 10)); %let rc=%sysfunc(close(&dsid)); %mend setvar; %global price style; %setvar %put _global_; Log shows: WARNING: Argument 1 to function SET referenced by the %SYSFUNC or %QSYSFUNC macro function is out of range. GLOBAL PRICE GLOBAL STYLE The second one: data mydata; /* create variables for assignment */ /*by CALL SET */ length style $8 sqfeet bedrooms baths 8 street $16 price 8; drop rc dsid; dsid=open("sasuser.houses", "i"); call set (dsid); do i=1 to 10; rc=fetchobs(dsid, i); output; end; run; Log shows: NOTE: Variable style is uninitialized. NOTE: Variable sqfeet is uninitialized. NOTE: Variable bedrooms is uninitialized. NOTE: Variable baths is uninitialized. NOTE: Variable street is uninitialized. NOTE: Variable price is uninitialized. NOTE: Argument 1 to function SET(0) at line 18 column 9 is invalid. style= sqfeet=. bedrooms=. baths=. street= price=. dsid=0 i=11 rc=70021 _ERROR_=1 _N_=1 NOTE: The data set WORK.MYDATA has 10 observations and 7 variables. And dataset WORK.MYDATA are all missing values.   I use 9.4M7 and 9.4M8, see the same result. What happend to CALL SET routine? ... View more

I'm a beginner to SAS. I'm trying to have nested counts presented cleanly as follows.   This is code to generate toy data, as well as 3 attempts I tried (from hours of googling how to do this) that give the correct info, but none are as clean as the above. Imagine how awful they'd look when I need more than 2 layers of nesting. and multiplying together the total number of categories per variables is large, but the actual number of their combinations in the data is small. data toy; input A $ B $; datalines; A1 B1 A1 B2 A1 B2 A2 B1 A2 B1 A2 B1 A2 B2 A2 B2 A2 B2 A2 B2 ; proc tabulate data=toy; class A B; table A*(B all) all; run; proc summary data=toy print; class A B; run; proc freq data=toy; tables A*B / nopercent norow nocol; run; ... View more

Dear SAS Community,   I am running a proc logistic model to compare the rotting percentage between avocado varieties. Since the outcome variable can only take two values (0 or 100%) I am analyzing it as a binomial var. I am using the firth option because otherwise I will get this warning: There is possibly a quasi-complete separation of data points. The maximum likelihood estimate may not exist.   title2 'PercStemEndRot: Comparing varieties within Weeks for each Harvest across Season'; proc logistic data=one desc; class Harvest Variety Wks/param=glm; model PercStemEndRot=Harvest*Variety*Wks/firth; slice Harvest*Variety*Wks/sliceby=Harvest*Wks  adjust=simulate(seed=1); run;   When using the firth option I got this warning: Ridging has failed to improve the loglikelihood. You may want to use a different ridging technique (RIDGING= option), or switch to using linesearch to reduce the step size (RIDGING=NONE), or specify a new set of initial estimates (INEST= option).   Is there anything I could do to bypass this issue other than eliminating dep variables or interactions?   Thank you very much!   ... View more