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Performing Simulations

The Pmetrics simulator is a powerful Monte Carlo engine that is smoothly integrated with Pmetrics inputs and outputs. Unlike NPAG and IT2B, it is run from within R. No batch file is created or terminal window opened. However, the actual simulator is a Fortran executable compiled and run in an OS shell.


To complete a simulator run you must include data and a model object. These can be previously loaded objects with PM_data() and PM_model() or loaded on the fly by specifying file names of appropriate files in the current working directory (check with getwd() and list.files()), e.g., .csv for data and .txt for models, at the time of simulation. The model dictates the equations and the data file serves as the template specifying doses, observation times, covariates, etc.

The other mandatory item is a prior probability distribution for all random parameter values in the model. This is referenced by the poppar argument, detailed below.

You run the simulator in two ways.

  • Use the $sim() method for PM_result() objects. This takes poppar from the PM_result$final field and the model from the PM_result$model field, so only a data object needs to be specified at minimum.
  • Use PM_sim()$run. This takes a manually specified values for poppar, model, and data. These can be other Pmetrics objects, or de novo, which can be useful for simulating from models reported in the literature.


To complete a simulator run you must include the names of a data and a model file in the working directory. The only way to run the simulator is with SIMrun(). The poppar argument must also be supplied.

Model and data details

The structures of the model and data objects when used by the simulator are identical to those used by NPAG and IT2B. Of course, the model parameters must match the parameters in the poppar argument. Any covariates must match between model and data. The data object contains the template dosing and observation history as well as any covariates. Observation values (the OUT column) for EVID=0 events can be any number; they will be replaced with the simulated values. However, do not use -99, as this will simulate a missing value, which might be useful if you are testing the effects of such values. A good choice for the OUT value in the simulator data template is -1 to remind you that it is being simulated, but this choice is optional.

You can have any number of subject records within a data object, each with its own covariates if applicable. Each subject will cause the simulator to run one time, generating as many simulated profiles as you specify from each template subject. This is controlled by the include, exclude, and nsim arguments to the simulator (see below). The first two specify which subjects in the data object will serve as templates for simulation. The second specifies how many profiles are to be generated from each included subject.

Simulation options

Details of all arguments available to modify simulations are available by typing ?SIMrun into the R console or following the hyperlink SIMrun(). A few are highlighted here.

Simulation from a non-parametric prior distribution (from NPAG) can be done in one of two ways. The first is simply to take the mean and covariance matrix of the distribution and perform a standard Monte Carlo simulation. This is accomplished by setting split = FALSE as an argument to SIMrun(), whether accessed directly or through an R6 method.

The second way is what we call semi-parametric (Goutelle et al. 2022). In this method, the non-parametric “support points” in the population model, each a vector of one value for each parameter in the model and the associated probability of that set of parameter values, serve as the mean of one multi-variate normal distribution in a multi-modal, multi-variate joint distribution. The weight of each multi-variate distribution is equal to the probability of the point. The overall population covariance matrix is divided by the number of support points and applied to each distribution for sampling.

Below are illustrations of simulations with a single mean and covariance for two parameters (left, split = FALSE), and with multi-modal sampling (right, split = TRUE).

Limits may be specified for truncated parameter ranges to avoid extreme or inappropriately negative values. The simulator will report values for the total number of simulated profiles needed to generate nsim profiles within the specified limits, as well as the means and standard deviations of the simulated parameters to check for simulator accuracy.

Output from the simulator can be controlled by further arguments to R6 $sim, $run or Legacy SIMrun(). If makecsv is not missing, a .csv file with the simulated profiles will be created with the name as specified by makecsv; otherwise, there will be no .csv file created. If outname is not missing, the simulated values and parameters will be saved in a .txt file whose name is that specified by outname; otherwise the filename will be “simout”. In either case, integers 1 to the number of subjects will be appended to outname or “simout”, e.g. “simout1.txt”, “simout2.txt”.

Simulation output


Simulation output files (e.g. simout1.txt, simout2.txt) are automatically read by SIMparse() and returned as the new PM_sim object that was assigned to contain the results of the simulation. All files will remain on the hard drive.


Output files from the simulator can be read into R using the SIMparse() command. Note that SIMparse() returns the parsed output of a simulator run as a PMsim object, so use the command like this:

simdata <- SIMparse(...)

so that simdata will contain the results. The arguments to SIMparse() are detailed in the help for the function: ?SIMparse(). NOTE: combine in R6 becomes a argument to the simulator run methods, which is passed through to SIMparse(), since the parsing function is no longer called by the user generally.

Plotting simulation output


Output from PM_result$sim() and PM_sim$run() is a PM_sim object. The object can be plotted with the attached plot method: PM_sim$plot() which in turn calls the plot.PM_sim() function. See the help for the function and vignette("plotly") for further details.


Load the output of SIMrun() with SIMparse() as described above. This creates a PMsim object which can be plotted.

sim <- SIMparse(...)
plot(sim) #see plot.PMsim for help


Goutelle, Sylvain, Jean‐Baptiste Woillard, Michael Neely, Walter Yamada, and Laurent Bourguignon. 2022. “Nonparametric Methods in Population Pharmacokinetics.” The Journal of Clinical Pharmacology 62 (2): 142–57.