Run proc print and proc contents on your data set.  Look at the results. Is it a TYPE=SUMMARY data set (or some other special type)?  Does it have the right stuff ?     proc means data=sashelp.class; var age; class sex; output out=m; run; proc print; run; proc contents varnum; run; proc ttest data=m; var age; class sex; run; ... View more

Hi Michellel, Did you resolve the issue? error in gamma GLM ERROR: Libref SUBFUNC is not assigned. ERROR: Libref SUBFUNC is not assigned. WARNING: Unable to load prototypes from 'subfunc.cmplib'.     ... View more

Here's a much simpler approach.  It involves little to no macro language, and certainly doesn't require the first macro at all.   data merged_1_samples; do sample=1 to 1000;    do recno=1 to _nobs_;       get_this_one = ceil(_nobs_ * ranuni(12345));       set merged_1 nobs=_nobs_ point=get_this_one;       output;    end; end; stop; run;   You can always vary the seed to RANUNI if you would like.  But NOBS= retrieves the number of observations in the data set (no macro variable needed) as part of the same DATA step that performs the sampling. ... View more

You get the confidence intervals of the Log(IRR) in the parmest dataset. They can just be expontiated and you will have confidence intervals of IRR. If you want the confidence intervals calculated within proc genmod you can use the estimate statement. data abc;   do i=1 to 100;     group=int(5*ranuni(-1));     y=rand('poisson',exp(group));     output;   end; run; ods output parameterestimates=parmest; proc genmod data=abc;   class group;   model y=group/dist=poisson link=log; run; data parmest;   set parmest;   text=put(exp(estimate),5.2)||'('||put(exp(lowerwaldcl),5.2)||','||put(exp(upperwaldcl),5.2)||')';   put parameter= @25 level1= text=; run; *and if you want the intervals calculated by genmod:; ods output estimates=estimates; proc genmod data=abc;   class group;   model y=group/dist=poisson link=log;   estimate 'group 0 vs 4' group 1 0 0 0 -1/exp;   estimate 'group 1 vs 4' group 0 1 0 0 -1/exp;   estimate 'group 2 vs 4' group 0 0 1 0 -1/exp;   estimate 'group 3 vs 4' group 0 0 0 1 -1/exp; run; ... View more

Have a look at the CSV file in a text editor like Notepad. What does it show for the column being truncated? Is it showing correctly and what is the format for it? ... View more

it works. Thanks ballardw! ... View more

OU. I just realize what you are looking for . Code: Program.sas data have; informat id $4.  surgery_date  medication_date mmddyy10.   medication_name $4.; format   surgery_date  medication_date mmddyy10.   ; input id   surgery_date   medication_date   medication_name; datalines; 1 03/15/2010 05/01/2010 A 1 03/15/2010 05/01/2010 B 1 03/15/2010 09/01/2010 C 1 03/15/2010 11/01/2010 D 1 05/01/2011 09/01/2011 E 1 05/01/2011 09/01/2011 F 1 05/01/2011 09/01/2011 G 1 05/01/2011 12/01/2011 H ; run; proc sql; select max(count) into : n separated by ' '   from (select count(*) as count from have group by id,surgery_date,medication_date); quit; proc summary data=have nway; class id surgery_date medication_date; output out=temp(drop=_:) idgroup(out[&n]  (medication_name )=); run; proc sql; select max(count) into : m separated by ' '   from (select count(*) as count from temp group by id,surgery_date); quit; proc summary data=temp nway; class id surgery_date; output out=want(drop=_:) idgroup(out[&m]  (medication_date medication_name_1-medication_name_&n )=); run; proc sql; select name into : list separated by ' '   from dictionary.columns    where libname='WORK' and memname='WANT' and lowcase(name) like 'medication%'    order by input(scan(name,-1,'_'),best8.),name; quit; data final_want; retain id surgery_date &list ; set want; run; Xia Keshan ... View more

The gamma distribution has two parameters, mu (which may be a function of covariates and treatments, and associated parameters) and a scale parameter. The scale parameter is like a standard deviation or variance (but not quite); at least it serves that purpose (a measure of variability, similar to sigma with normal data). But with the gamma distribution, the variance is a function of the mean. var(Y) = scale*(mu^2).  ... View more