Proc Glimmix random intercept large estimates

yuchinher — Tue, 20 Apr 2021 08:50:40 GMT

Hi,

I am trying to model a three level logistic regression using PROC GLIMMIX. When I first added only my level 3 clustering variable into the model, the estimates and odds ratio I got look fine and reasonable. However, when I added both the level 3 and level 2 clustering variable to the model, the estimates and odds ratio I got became really big that are probably unreal (e.g., odds ratio of 40 to odds ratio of 300). I wonder if anyone knows the reasons behind and how I can fix it? Thanks for the help!

The code I used for having both level 3 and level 2 clusters, which gave me really large parameters:

proc glimmix data=new noclprint method=laplace;

class clustervar_L3 clustervar_L2;

model event(event='1')=dur dur2 income incouple rent houseprice age sex

/solution cl link=logit dist=binary

ddfm=bw oddsratio(DIFF=LAST LABEL);

random intercept / solution type = un subject= clustervar_L3;

random intercept / solution type = un subject= clustervar_L2(clustervar_L3);

covtest / wald;

run;

Re: Proc Glimmix random intercept large estimates

PaigeMiller — Tue, 20 Apr 2021 11:27:34 GMT

@yuchinher wrote:

I am trying to model a three level logistic regression using PROC GLIMMIX. When I first added only my level 3 clustering variable into the model, the estimates and odds ratio I got look fine and reasonable. However, when I added both the level 3 and level 2 clustering variable to the model, the estimates and odds ratio I got became really big that are probably unreal (e.g., odds ratio of 40 to odds ratio of 300).

My first thought is that adding additional class variables into the model causes quasi-complete separation, or maybe even complete separation.

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Proc Glimmix random intercept large estimates

Re: Proc Glimmix random intercept large estimates