Author: kuekawa

When running PROC GLIMMIX (SAS) in a macro-driven way (e.g., running similar models 100 times), what gets annoying is some HLM models do not converge and you have to comb through output and decide which models to convert to fixed effect models, which is simpler and is easier to converge.   The following allows the execution of a fixed model (non-HLM) when a random effect model (HLM) fails.

The following macro (%mend checkds;) checks if the first random effect model produces one of the result files (parameter estimates) and if it doesn't exist (i.e., random effect model did not converge), it will run the model without the random effect statement.

 

proc glimmix data=asdf METHOD=RSPL;
class CAMPUS_14;
model &out = &main

stud_char
interX

&predictors
/dist=binomial link=logit s ddfm=kr STDCOEF;
random int / subject = CAMPUS_14;
covtest /wald;
ods output
ParameterEstimates=kaz1
CovParms=uekawa1
ModelInfo=estes
dimensions=diminfo
ConvergenceStatus=concon
FitStatistics=FITSTAT

;
run;

data hlm1;
hlm1="HLM ";
run;

/*Check if converged and if not run fixed model*/
%macro checkds(dsn);
%if %sysfunc(exist(&dsn)) %then %do;
/*there is concon created*/
%end;
%else %do;
/*delete imcomplete data from the previous proc
that did not converge*/
proc datasets;
delete kaz1 estes diminfo concon FITSTAT hlm1;
run;

proc glimmix data=asdf METHOD=RSPL;
class CAMPUS_14;
model &out = &main
stud_char
interX
&predictors
/dist=binomial link=logit s ddfm=kr;
ods output
ParameterEstimates=kaz1
/*CovParms=uekawa1*/
/*nobs=jeana */
ModelInfo=estes
dimensions=diminfo
/*ConvergenceStatus=concon*/
FitStatistics=FITSTAT;
run;

data hlm1;
hlm1="Fixed";
run;

%end;
%mend checkds;
/* Invoke the macro, pass a non-existent data set name to test */
*%checkds(work.concon);
*%checkds(work.uekawa1);
%checkds(work.FITSTAT);