Hello everyone,
I am fitting a multilevel (5 levels , 3 random intercept) of longitudinal data ( binary outcome) using proc glimmix. When I test a random slope ( age) on the lowest level. I got the following message:
‘Estimated G matrix is not positive definite ‘ and the covariance parameter estimate of slope is zero.
It does mean that I don’t need the random slope age? ( that’s one of the suggestions for Proc mixed, not sure if the same applies to Proc glimmix).
Any help would be appreciated ,
Alda
Code:
proc glimmix data=PENPIG10 method=RSPL noclprint=20 ;;
class Age FarmID CohortID_C PenID_C PigID Nursc Envc Nursc4 Rxpc Subjpc Mortpc HthP Gender Season PenCat visit Tlw1wk_rtb;
model PigSalmCult (event='Pos')= Tlw1wk_rtb Nursc4 Mortpc
Age FarmID /solution link=logit dist=binary oddsratio ddfm=Kr; ;
nloptions tech=nrridg;;
random intercept/subject= CohortID_C;
random intercept/subject=PenID_C (CohortID_C);
random intercept Age /subject= PigID (PenID_C CohortID_C)type=VC;
covtest/wald;
Title' MT5.11.-Tlw72h_rtb Trend ';
format Nursc Envc HthP Tlw1wk_rtb YN. Nursc4 Rxpc Subjpc Mortpc YNM. Gender gender. PigSalmCult Cult. Season Season. PenCat PenCat. FarmID FarmID. TNZ12h TNZ.;
run;
Basically, this is a convergence issue in maximum likelihood estimation...most likely there is a quasi-complete separation of points where one, some or many of the values for Age fall 100% on one or the other of the values for PigSalmCult. Try grouping Age into buckets.
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