some Etas estimated to be around 2 or 3, but since I am fitting a quite noisy 
PD data,  I think they are actually reasonable
no Etas close to 0
cov% esimated in the range of 10-90%.should those small ones like 10%  be taken 
out? 

 

________________________________
From: "Bachman, William" <william.bach...@iconplc.com>
To: Ethan Wu <ethan.w...@yahoo.com>; nmusers@globomaxnm.com
Sent: Wednesday, April 15, 2009 12:12:56 PM
Subject: RE: [NMusers] OMEGA selection


Well, the first thing that I would do is look at the magnitude of the estimates 
of the etas.  I would eliminate those etas that are poorly estimated 
(essentially the very large values or those approaching zero).


________________________________
From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On 
Behalf Of Ethan Wu
Sent: Wednesday, April 15, 2009 11:47 AM
To: nmusers@globomaxnm.com
Subject: [NMusers] OMEGA selection


Dear all,
  I am fitting a PD response, and the equation goes like this:
total response = baseline+f(placebo response) +f(drug response)
first, I tried full omega block, and model was able to converge, but $COV stop 
failed.
To me, this indicates that too many parameters in the model. The structure 
model is rather simple one, so I think probably too many Etas. 
I wonder is there a good principle of Eta reduction that I could implement 
here. Any good reference?

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