RE: OMEGA selection
In my opinion, I would not remove those in the 10-90% range. I would be
suspect of anything over 100%, even with noisy data, they are being poorly
estimated.
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Quoted reply history
From: [email protected] [mailto:[email protected]] On
Behalf Of Ethan Wu
Sent: Wednesday, April 15, 2009 1:15 PM
To: Bachman, William; [email protected]
Subject: Re: [NMusers] OMEGA selection
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?
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From: "Bachman, William" <[email protected]>
To: Ethan Wu <[email protected]>; [email protected]
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).
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From: [email protected] [mailto:[email protected]] On
Behalf Of Ethan Wu
Sent: Wednesday, April 15, 2009 11:47 AM
To: [email protected]
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?