Hello NONMEM Users,
When I tried to print log-scaled VPC, there was an error message about negative values. It can be caused by an additive error and/or linearization of the
error model when NONMEM transforms
Y = F*DEXP(ERR(1)*SD1) into Y =
F+ F*ERR(1)*SD1. The error is inflated at small concentrations and removing the additive term
is not an option in my case.
Is there an easy way to
solve it? It can be something like Y = F**GAMMA*DEXP(ERR(1)*SD1) or
Y = F+ F*ERR(1)*SD1 + F**GAMMA*ERR(2)*SD2, where GAMMA<1.
Thanks,
Pavel
model of error, linearisation, additive error, and VPC
Hi Pavel,
I guess I would opt for using logtransformed concentrations as dv in your
model. That would deal with the negative model predictions.
Best,
Huub
Quoted reply history
________________________________________
From: [email protected] [[email protected]] on behalf of
Pavel Belo [[email protected]]
Sent: Friday, January 29, 2016 10:47 PM
Cc: '[email protected]'
Subject: [NMusers] model of error, linearisation, additive error, and VPC
Hello NONMEM Users,
When I tried to print log-scaled VPC, there was an error message about negative
values. It can be caused by an additive error and/or linearization of the
error model when NONMEM transforms Y = F*DEXP(ERR(1)*SD1) into Y = F+
F*ERR(1)*SD1. The error is inflated at small concentrations and removing the
additive term is not an option in my case.
Is there an easy way to solve it? It can be something like Y =
F**GAMMA*DEXP(ERR(1)*SD1) or
Y = F+ F*ERR(1)*SD1 + F**GAMMA*ERR(2)*SD2, where GAMMA<1.
Thanks,
Pavel