Hi, NM users:
I have a typical 2-compartment model that can describe my data quite well,
except the final gradient for the additive residual error is '0'. I
therefore fix the additive residual error to '0', but then NM would not
run. I tried different initial estimations for other parameters but the
additive residual error seems to be the one that decide whether NM will
run. Can anyone tell me why this would happen and how to solve it?
Thank you very much in advance for your help.
Siwei
Additive residual error
Hi Siwei,
In a similar situation previously, I've found fixing the additive error to a small value (~= 0.0001*LOQ) has provided a work-around for this. It usually arises from a zero measurement needing to be nonzero for estimation purposes. A better fix is to use the M2 method which should lower the constraint of needing additive error.
Thanks,
Bill
On Feb 11, 2013, at 7:27 PM, "siwei Dai" <ellen.siweidai
> Hi, NM users:
>
> I have a typical 2-compartment model that can describe my data quite well
, except the final gradient for the additive residual error is '0'. I therefore fix the additive residual error to '0', but then NM would not run. I tried different initial estimations for other parameters but the additive residual error seems to be the one that decide whether NM will run. Can anyone tell me why this would happen and how to solve it?
>
> Thank you very much in advance for your help.
>
> Siwei
Hi Siwei,
In a similar situation previously, I've found fixing the additive error to a
small value (~= 0.0001*LOQ) has provided a work-around for this. It usually
arises from a zero measurement needing to be nonzero for estimation purposes.
A better fix is to use the M2 method which should lower the constraint of
needing additive error.
Thanks,
Bill
Quoted reply history
On Feb 11, 2013, at 7:27 PM, "siwei Dai" <[email protected]> wrote:
> Hi, NM users:
>
> I have a typical 2-compartment model that can describe my data quite well,
> except the final gradient for the additive residual error is '0'. I therefore
> fix the additive residual error to '0', but then NM would not run. I tried
> different initial estimations for other parameters but the additive residual
> error seems to be the one that decide whether NM will run. Can anyone tell me
> why this would happen and how to solve it?
>
> Thank you very much in advance for your help.
>
> Siwei
Siwei,
A final gradient of zero is not necessarily pathological. It just means that the fit cannot be improved by changing that parameter. As usual you need to decide if the estimate of the residual error parameter is plausible of not. The gradient cannot tell you that.
A zero additive error will always cause an error if you have a predicted conc of zero. This is because the likelihood involves a division by the residual error. If this is zero then the "computer says no < http://en.wikipedia.org/wiki/Carol_Beer >". I assume that is what you mean when you say "NM would not run".
Nick
Quoted reply history
On 12/02/2013 12:53 p.m., siwei Dai wrote:
> Hi, NM users:
>
> I have a typical 2-compartment model that can describe my data quite well, except the final gradient for the additive residual error is '0'. I therefore fix the additive residual error to '0', but then NM would not run. I tried different initial estimations for other parameters but the additive residual error seems to be the one that decide whether NM will run. Can anyone tell me why this would happen and how to solve it?
>
> Thank you very much in advance for your help.
>
> Siwei
--
Nick Holford, Professor Clinical Pharmacology
Dept Pharmacology & Clinical Pharmacology, Bldg 503 Room 302A
University of Auckland,85 Park Rd,Private Bag 92019,Auckland,New Zealand
tel:+64(9)923-6730 fax:+64(9)373-7090 mobile:+64(21)46 23 53
email: [email protected]
http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford
The most common reason for zero gradient (if the code is correct and does not include parameters with no influence on objective function) is the bound on the parameter estimate. Even if you do not specify the bounds, Nonmem imposes internal bounds (initial value * / by 100 or something similar). I would try to run the initial model with
NOTHETABOUNDTEST NOOMEGABOUNDTEST NOSIGMABOUNDTEST
options.
Leonid
--------------------------------------
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web: www.quantpharm.com
e-mail: LGibiansky at quantpharm.com
tel: (301) 767 5566
Quoted reply history
On 2/11/2013 8:32 PM, Nick Holford wrote:
> Siwei,
>
> A final gradient of zero is not necessarily pathological. It just means
> that the fit cannot be improved by changing that parameter. As usual you
> need to decide if the estimate of the residual error parameter is
> plausible of not. The gradient cannot tell you that.
>
> A zero additive error will always cause an error if you have a predicted
> conc of zero. This is because the likelihood involves a division by the
> residual error. If this is zero then the "computer says no
> http://en.wikipedia.org/wiki/Carol_Beer". I assume that is what you
> mean when you say "NM would not run".
>
> Nick
>
> On 12/02/2013 12:53 p.m., siwei Dai wrote:
>
> > Hi, NM users:
> >
> > I have a typical 2-compartment model that can describe my data quite
> > well, except the final gradient for the additive residual error is
> > '0'. I therefore fix the additive residual error to '0', but then NM
> > would not run. I tried different initial estimations for other
> > parameters but the additive residual error seems to be the one that
> > decide whether NM will run. Can anyone tell me why this would happen
> > and how to solve it?
> >
> > Thank you very much in advance for your help.
> >
> > Siwei