Add residual variability
The best way to simulate with residual variability is to include the
it in the estimation control stream as described in this vignette.
NMsim currently does not provide any automated way to add
simulation of residual variability with Nonmem. It does provide a method
to simulate residual variability in R, based on the Nonmem parameter
estimates. This should only be used in case one has an existing Nonmem
without residual variability simulated, and it is not feasible to modify
the model control stream for some reason. The function is called
addResVar() and supports additive, proportional, and
combined (additive and proportional) error models. It can also add the
residual error on log scale (exponential error model).
addResVar supports both estimation using
$SIGMA and $THETA (in Nonmem). The user has to
specify which of the two methods were used in the Nonmem model using the
par.type argument. The other thing that must be specified
is the parameter numbers for the standard deviations or variances. The
model simulated in this vignette has this combined error model estimated
using the $SIGMA matrix:
Y=F+F*ERR(1)+ERR(2)We now specify for addResVar to find the variance for
the proportional component in $SIGMA[1,1] and the one for
the additive component in $SIGMA[2,2]. In this case where
SIGMA is used, the off-diagonal (covariance) elements of
the $SIGMA matrix are also used for the simulation.
file.mod <- file.project("nonmem/xgxr021.mod")
simres <- NMsim(file.mod=file.mod,
data=dat.sim)
simres.with.resvar <- addResVar(simres,path.ext=fnExtension(file.mod,"ext"),par.type="SIGMA",prop=1,add=2)If par.type="THETA" the default assumption is that the
thetas represent standard deviation (in contrast to when using
par.type="SIGMA"). This can be modified using the
scale.par argument. There are arguments to avoid negative
observations and several other features. But again, this should be the
last resort.