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lyton80
Obsidian | Level 7

I have a code snippet shown below which is embedded in a model building macro:
....
proc glimmix data=&indata;
class study;
model &outcome = &covariates / s oddsratio covb;
random intercept / subject=study;
by _Imputation_;
ods select ParameterEstimates CovB FitStatistics;
ods output ParameterEstimates=lgparms CovB=lgcovb FitStatistics=FitStats;
run;
.....
my response/outcome variable is binary so I am inclined to specifiy a binary distribution and a logit link to keep the code consistent to my modeling expectations, However a change to this:

proc glimmix data=&indata;
class study;
model &outcome = &covariates / dist=bin s oddsratio covb;
random intercept / subject=study;
by _Imputation_;
ods select ParameterEstimates CovB FitStatistics;
ods output ParameterEstimates=lgparms CovB=lgcovb FitStatistics=FitStats;
run;

gives me this error:
ERROR: Within-imputation observations not found for _Imputation_= 6 in the input PARMS= data set.

Can anyone decode this error?

1 REPLY 1
lyton80
Obsidian | Level 7

 

On close examination, proc glimmix is not producing ParameterEstimates because some imputed datasets are not converging. This is causing the proc mianalyze to throw the error.  ("Within-imputation observations not found for _Imputation_= .. in the input PARMS= data set")

 

Is there any way to skip over non-converging datasets?

 

 

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