RE: 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).
Quoted reply history
________________________________
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?