**Date:** Mon, 17 Nov 2008 16:41:09 -0600
**Reply-To:** Robin R High <rhigh@UNMC.EDU>
**Sender:** "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
**From:** Robin R High <rhigh@UNMC.EDU>
**Subject:** Re: Proc Mixed help
**In-Reply-To:** <200811172007.mAHGhL20025477@malibu.cc.uga.edu>
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Brad,

Work through this example dataset with each MODEL statement run in turn
may help demonstrate how you can dummy code the continuous variable of
interest.

DATA tst;
cls=1; do x = 1 to 15; x1=x; x2=0; x3=0; y = 5 + 2.5*x + 1.2*rannor(929);
OUTPUT; END;
cls=2; do x = 1 to 15; x1=0; x2=x; x3=0; y = 7 + .10*x + 1.2*rannor(0);
OUTPUT; END;
cls=3; do x = 1 to 15; x1=0; x2=0; x3=x; y = 8 + .15*x + 1.2*rannor(0);
OUTPUT; END;

ods select solutionF;

PROC MIXED;
CLASS cls;
MODEL Y = cls x(cls) / solution ;
* MODEL y = cls x1 x2 x3/ solution ;
* MODEL y = cls x1 / solution ;
run;

Robin High
UNMC

Brad Heins <hein0106@UMN.EDU>
Sent by: "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
11/17/2008 02:10 PM
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Brad Heins <hein0106@UMN.EDU>

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Subject
Proc Mixed help

I have a question about proc mixed at if there is a trick I can do with my
model statement or not.

I have a variable aocm that is continuous and is nested within a class
variable (LNR) that has 3 levels. From the output below you can see that
at
LNR 2 and 3 the regression coefficient is not significant, but it for LNR
1.

Is there a way that I can tell Proc mixed to just adjust for only LNR 1
and
not for LNR 2 or 3 since they are not significant. I want to leave LNR 1
is
the model and adjust for those records, but not for LNR 2 or 3 because the
regression coefficients make no biological sense to the data that I have.

Any help would be appreciated.
Thanks.
Brad

proc mixed statement:
class group lnr hy;
model y= group lnr hy aocm(lnr) ;

Effect Estimate Error DF t Value Pr > |t|
aocm(LNR) 1 2.1697 0.2378 9660 4.92 <.0001
aocm(LNR) 2 -1.0259 0.2432 9660 8.33 <.6538
aocm(LNR) 3 -1.4974 0.2572 9660 5.82 <.8520