Thanks for your input and Ideas. I encountered the following scenario. I would like to know if this is possible.
I am running a PROC mixed procedure, before I ran, I am checking for Model structure type that fits for the data, if the convergence criteria won't meet then I am doing the alternative procedures.
Since I wanted a clean log, is it possible when convergence criteria won't meet, I want that log need to go into the different file, and the remaining should go to one file. is It possible to achieve. My aim is keep the log clean and at the same time have another log file. I am open to alternative options.
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Code to create the QC1 dataset
***************************************************************;
***---MMRM ---***
%macro mmrm(var =);
*** Check if TYPE =UN works or not***;
/*options nonotes nosource DKROCOND = nowarn;*/
ods output convergencestatus=cs LSmeans=lsmn Diffs = LSMDiff FitStatistics = fit;
/*ods exclude all;*/
proc mixed data = qc1 method = reml;
class usubjid trtn avisitn agegroup;
model &var = trtn avisitn trtn*avisitn agegroup base /ddfm = kenwardroger solution;
repeated avisitn/subject=usubjid type=un;
lsmeans trtn*avisitn avisitn/diff cl;
run;
/*ods exclude none; /* Resume output */*/;
/*options notes source DKROCOND = warn;*/
******* ****************************************************************
Looking the above MMRM log have to be in different file
If convergence criteria met, then no issue; if not it is generating
Warnings in LOG
************************************************************************;
proc sql noprint;
select strip(upcase(reason)) into: criteria separated by "" from work.cs;
quit;
%put &criteria.;
%if %index(&criteria.,CONVERGENCE CRITERIA MET.) %then
%do;
ods output LSmeans=lsmn Diffs = LSMDiff FitStatistics = fit;
proc mixed data = qc1 method = reml;
class usubjid trtn avisitn;
model &var = trtn avisitn trtn*avisitn agegroup base /ddfm = kr;
repeated avisitn/subject=usubjid type=un;
lsmeans trtn*avisitn avisitn/diff cl;
run;
%end;
%else %do;
%let type = %str(CS TOEP AR);
%do ord = 1 %to 3;
ods output convergencestatus=cs LSmeans=lsmn Diffs = LSMDiff FitStatistics = fit;
proc mixed data = qc1 method = reml;
class usubjid trtn avisitn;
model &var = trtn avisitn trtn*avisitn agegroup base /ddfm = kr;
repeated avisitn/subject=usubjid type=%scan(&type,&ord) %if &ord=3 %then(1);;
lsmeans trtn*avisitn avisitn/diff cl;
run;
proc sql noprint;
select strip(upcase(reason)) into :criteria separated by "" from work.cs;
quit;
data data⩝
length type $20;
set fit;
if Descr='AIC (Smaller is Better)';
criteria="&criteria";
type="%scan(&type,&ord)";
run;
%end;
%end;
data all0;
set data1 data2 data3;
if index(criteria, 'CONVERGENCE CRITERIA MET.');
run;
proc sql;
select count(distinct criteria) into :nobs from all0;
quit;
proc sort data = all0;
by criteria value type;
run;
data all;
set all0;
by criteria value type;
if first.criteria;
run;
proc sql noprint;
select strip(upcase(type)) into :typemin separated by "" from work.all;
quit;
ods output LSmeans=lsmn Diffs = LSMDiff;
proc mixed data = qc1 method = reml;
class usubjid trtn avisitn;
model &var = trtn avisitn trtn*avisitn agegroup base /ddfm = kr;
%IF &typemin=%STR(AR) %THEN %LET typemin = ar(1);
repeated avisitn/subject=usubjid type=&typemin.;
lsmeans trtn*avisitn avisitn/diff cl;
run;
%mend;
%mmrm(var = aval);
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