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Simulations for Visual Predictive Checks (VPC)

This vignette shows how to generate simulations for generation of VPC plots. While NMsim does not include any functionality for summarizing quantiles or plotting, it provides powerful ways to obtain the simulated data needed. We shall see how the tidyvpc package easily creates VPC plots based on the simulation results.

Default option: reuse estimation data for simulation

Normally, the two main arguments to NMsim are the path to the input control stream (file.mod) and the simulation input data set (data). But if we leave out the the data argument, NMsim will re-use the estimation data for the simulation. That is the simulation we need for a VPC. We will use an example model included with NMsim:

file.project <- function(...)file.path(system.file("examples",package="NMsim"),...)
file.mod <- file.project("nonmem/xgxr032.mod")
           allow.unknown=TRUE ## necessary for dir.sims and dir.res
           ## until NMdata 0.1.5
## notice the data argument is not used.
simres.vpc <- NMsim(file.mod,
                    table.vars=c("PRED","IPRED", "Y"),

The performed simulation is similar to the one produced by the VPC function in PSN. However, there are some important differences.

  • The simulation results are automatically read into R.

  • The table.vars argument allows the user to narrow down the variables to be written to disk. This can speed up the simulation considerably and reduce the amount of disk space the Nonmem simulation results require.

  • No postprocessing of the results is being done by NMsim. See below how to easily do that.

Plotting using tidyvpc

As mentioned, NMsim does not postprocess the simulation for generation of a VPC plot, nor does it offter any plotting functions. The R package called tidyvpc offer those two things and is moreover implemented in data.table, so it’s fast. The following simple code shows how to get from the results from NMsim to the VPC plot with tidyvpc.

#> tidyvpc is part of Certara.R!
#> Follow the link below to learn more about PMx R package development at Certara.
## read the data as it was used in the Nonmem model
res <- NMscanData(file.mod,"data.table",quiet=TRUE)
## only plot observation events from estimation data set
data.obs <- res[EVID==0]
## Only plot simulated observation events
data.sim <- simres.vpc[EVID==0]

## run vpc
vpc1 <-
    observed(data.obs, x = TIME, y = DV) |>
    simulated(data.sim, y = Y) |>
    stratify(~DOSE) |>
    binning(bin = "ntile", nbins = 9) |>


Use a different input data set

In the first example we used the exact same data as was used for the estimation. This is a common way to produce a VPC, and we saw the advantage that the user does not risk making mistakes in preparing the data set for the simulations. However, it may be of interest to include additional data or even a different data set in the simulation. It could be including data points that were excluded in estimation (like samples below the quantification limit) or a separate study that was not included in the model. All you have to do is to read the data you want and provide it in NMsim’s data argument.

Make use of the cluster

We will repeat the same as above, but now 500 times (subproblems). We make use of a few more arguments for efficiency. sge means that the jobs will be sent to the cluster. The nc argument is now used meaning only one core will be used per job. If each node on the cluster has 16 cores, this could engage 500/16 ~ 32 nodes in parallel, with all jobs executed at the same time. We supply the path to the Nonmem executable. With PSN this should work without specifying the Nonmem path, but PSN for some reason takes more time submitting the jobs to the cluster. If nodes are available, the following simulation should not take more than a couple of minutes to execute.

file.res <- "simulate-results/simpaths-vpc.rds"
## notice the data argument is not used.
sim.vpc.sge <- NMsim(file.mod,
                     table.vars=c("PRED","IPRED", "Y"),
                     ## ,path.nonmem="/opt/nonmem/nm751/run/nmfe75"