Tim,

you seem to be butting heads with GLM's 'dirty lil secret' which is that it considers ALL factors fixed; whether or not declared RANDOM; and is not, in fact, unknown to spew out erroneous EMS and therefore incorrect tests -- in the 'right' circumstances. Of course, this is no secret since SAS is at pains to inform the user.

what to do? I'd forget about GLM and use MIXED which overcomes many, but not all, of GLM's shortcomings. it is quite well documented (other than an occassional typo here and there) in SAS/STAT C&E 6.12 manual and in SAS System for Mixed Models (SSMM). Be sure to download the latest errata sheet for smmm. if you are already in the brave new world of V7, documentation is online ...

---Tim Phelps <phelpst@PSU.EDU> wrote: > > Hello, > I have been trying, without much success, to analyze a hierarchial > (nested) random effects model using PROC GLM and PROC VARCOMP. My > effects are all random and look something like this: X, D, D(C), C(B), > B(A). I need to test each variable based on EMS and variance > components. The RANDOM statement under PROC GLM does a good job of this > up to certain point. Basically, I can successfully analyze X, D, X*D, > D(C), C using this statement. However, when I add the nested effect C > within B (i.e. C(B)) I get nothing. Logistically, this makes sense > since the variability has already been explained by D(C) and C. Is > there someway I can isolate the nested variables to analyze them > seperately The problem is that EMS for each nested variable should be > somewhat of an additive, so they all need to be included in the model > statement (or so it seems). I'd sure hate to do this by hand. > > Here's an example of a program that works and one that does not: > proc glm data=XXXX.XXXX; > class X D C B A; | > . | > . | > works fine > random X D(C) X*D(C) C / test; | > > > random X D(C) X*D(C) C C(B) B B(A) A / test; | doesn't work > > Any suggestion or ideas would be greatly appreciated. > > Thanks, > Tim >

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