RE: PARAMETER ESTIMATE IS NEAR ITS BOUNDARY with NONMEM v6
Durairaj,
My comments are as follows:
a) Is there any option to overcome this issue without suppressing the message
as suggested earlier?
In my experience, changing initial estimates to estimates nearer the final
estimate may cause this problem to disappear.
Also changing the units on your clearance, volume of distribution, etc., can
cause this problem to disappear.
b) What causes the above problem? Do I need to avoid specifying any boundary
for the thetas?
Your scaled parameter estimate is near the boundary of what was specified.
When you expect the estimate or large to be small, this problem can occur.
c) What does this message indicate? a poor selection of model? poor initial
estimates? narrow boundary? or any correlation issues with the estimates?
This message indicates that one of your parameters is either too small or too
large, which sometimes may be taken care of with changing initial estimates.
The rest of the questions are more easily answered in context of your model,
what is expected.
Matt.
Quoted reply history
-----Original Message-----
From: [email protected] [mailto:[email protected]] On
Behalf Of Durairaj Chandrasekar
Sent: Thursday, September 24, 2009 3:19 PM
To: [email protected]
Subject: [NMusers] PARAMETER ESTIMATE IS NEAR ITS BOUNDARY with NONMEM v6
Dear group,
Recently I was running a sparse sampling animal data (one data per animal)
using NONMEM v6 & WFN and ran into this trouble:
"PARAMETER ESTIMATE IS NEAR ITS BOUNDARY
THIS MUST BE ADDRESSED BEFORE THE COVARIANCE STEP CAN BE IMPLEMENTED".
I searched the forum for any solution and found the message from Mark Sale
(Next Level Solutions).
My question: a) Is there any option to overcome this issue without suppressing
the message as suggested earlier?
b) What causes the above problem? Do I need to avoid specifying any boundary
for the thetas?
c) What does this message indicate? a poor selection of model? poor initial
estimates? narrow boundary? or any correlation issues with the estimates?
I highly appreciate your input. Thanks in advance
Regards,
Chandra Durairaj,
Post-doctoral Fellow,
Univ of Colorado Denver
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