The following is the coding for a model I have run:

proc glimmix data=data2b ; class ndens tank arch; model out/in = arch ndens days /s dist=binomial link=logit e3 oddsratio; random _residual_; random intercept/subject=tank; output out=binom_pout predicted(ilink noblup) =p resid(ilink noblup)=r; estimate "S125" intercept 1 ndens 1 0 0 arch 1 0/ilink cl; run;

Some items to notice: * ndens, and arch are fixed categorical variables * tank is a random categorical variable * days is a fixed continuous variable * the random residual statement is included to help capture overdispersion in the binomial response.

What I am most interested in is this: Is the estimate statement giving me the predicted value for my first treatment of density and architecture at the average of the variable "days", and averaged across all "tanks"? Is that a correct assumption, and if not, how do I change the code to reflect the "average" conditions.

The reason I ask is, if I use the estimate statement, I get one estimate of the predicted outcome. If instead, I use the output statement, then create the summary statistics of the predicted values, I get a substantially different answer. The value from the estimate statement seems to reflect the estimate when days=0, not at the mean of days.

There is some unbalancedness within the design, but not so severe I would expect to see the differences I see.

Thanks.

Warren Schlechte HOH Fisheries Science Center 5103 Junction Hwy Mt. Home, TX 78058 Phone 830.866.3356 x214 Fax 830.866.3549

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