Date:         Thu, 19 Apr 2012 11:33:10 -0500
Reply-To:     Warren Schlechte <Warren.Schlechte@TPWD.STATE.TX.US>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Warren Schlechte <Warren.Schlechte@TPWD.STATE.TX.US>
Subject:      Proc GLIMMIX - Estimate Question
Content-Type: text/plain; charset="us-ascii"

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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