Create data for VPC

Simulates trial data for producing VPCs

  project = NULL,
  dataVPC = NULL,
  gpf = NULL,
  dosingInfo = NULL,
  regressors = NULL,
  model = NULL,
  modelsSample = NULL,
  FLAGpreparePC = FALSE,
  simtimeOption = c("obs", "simTFD", "simTAD"),
  simtimeN = 200,
  simtimes = NULL,
  simtimeMax = NULL,
  FLAGindivSimtimeMax = FALSE,
  FLAGaddResidualNoise = TRUE,
  FLAGsampleUncertainty = TRUE,
  FLAGlogOutput = FALSE,
  factorMult = NULL,
  ncores = 1,
  seed = NULL,
  FLAGkeepParameters = FALSE,
  opt_initstep = 0,
  opt_abstol = 1e-06,
  opt_reltol = 1e-06,
  opt_maxstep = 0,
  opt_minstep = 0,
  par_lower = NULL,
  par_upper = NULL,
  FLAGfixDoseOverlap = TRUE,
  FLAGstopFailedSim = FALSE



Path to IQRnlmeProject to be used to generate the VPC. The stuctural model of this project will be used and parameters will be sampled from this project.


Dataset for VPC generation. If undefined, the modeling dataset from modelSampleSimulate will be used. Mandatory if sampling from GPF.


Number of trials to simulate for the VPC


GPF object or path to GPF as xlsx file.


Required for information on lagtimes and zero order absorption when no project is provided. A list with one field per input in the model. Fields need to be named according to the input names in the model. Each field is a named vector with the required name "type" and the optional names "Tlag" and "Tk0". "type" can be "BOLUS", "INFUSION", or "ABSORPTION0". Only difference between INFUSION and ABSORPTION0 is that in the first case the infusion RATE is provided in the data and in the second the absorption time is sampled. Lag times can be added to each dosing input by defining "Tlag" as vector element. The value of this element needs to be the parameter named for this lag time. If type="ABSORPTION0" then also "Tk0" needs to be present as element in the vector. The value is a parameter name that also needs to appear in the model. For examples see dosing_IQRest


Required for information on regressors if no project is given. Input is a vector with the names of the regressors taken from the dataset.


Required structural model (IQRmodel) if no project is given


If sequential model building has been done (e.g. PKPD) model parameters might need to be sampled from additional IQRnlmeProjects. modelSample can be a vector with paths to these additional IQRnlmeProjects. It is not allowed that in these projects and modelSampleSimulate estimated parameter names overlap


Flag whether to prepare prediction correction by corresponding additional simulations


Select for which times, outputs are simulated: 'obs': respective indivdual observation time, 'simTFD': Equidistant or user-profided times after first dose, or 'simTAD': equidistant or user-provided times after last dose (these will be repeated for each dose)


Define number of simulation time steps to add between smallest and largest event time in the dataset. Equidistant sampling will be used.


Vector of simulation times. If not NULL, this vector of simulation times overrules simtimeN


Define maximum simulation time for VPC for all subjects.


If TRUE then the simulation time of a simulated subject will be limited to the max event time for this subject in the dataset. A subject here basically defines the dosing schedule and the covariates. If set to FALSE then the simulation results will not be truncated.


logical. If TRUE then residual noise will be added to the simulations


logical. If set to TRUE then for each new trial new population parameters are drawn from the uncertainty distribution


Logical. If set to TRUE then the model outputs are assumed to be log transformed data and will be back-transformed. The data in dataVPC are assumed to be in the normal domain - to simplify dose-normalization. If FALSE then data and model outputs are assumed to be in the same domain.


named vector allowing to define multiplicative factors to modulate by name selected sampled parameters for simulation


Number of cores for parallelization.


Set seed for reproducible sampling. If the simulations are parallelized, the seed will be set for the simulation of each population to seed + population number - 1.


Logical. If set to TRUE, sampled parameters will be added to returned simulation results. Defaults to FALSE


Double value for initial step-size to be attempted for simulation


Double value for absolute tolerance for simulation


Double value for relative tolerance for simulation


Double value for maximal integrator step-size for simulation


Double value for minimal integrator step-size for simulation


Named vector of lower limits for sampled individual parameters. Applies to individual parameters after potential modulation by factorMult argument.


Named vector of upper limits for sampled individual parameters. Applies to individual parameters after potential modulation by factorMult argument.


Input argument for IQReventTable. If TRUE (default) then ensure non-overlapping dosing intervals if these occur by re-defining the dosing events. If FALSE in case of overlapping dose intervals, error is issued. Please inspect correct behavior. If correction does not work, par_upper could be an alternative to avoid overlapping dosing events.


Logical. If TRUE execution will be stopped if simulation fails for any of the repeated trials. Defaults to FALSE. Execution will stop anyways if all simulations fail.


List with data frames sim and obs. If not prediction correction preparation is required, sim contains individual predictions and obs the observation data, including dosing records. Otherwise sim and obs contains typical predictions in addition.


Model from IQR nlme project is used to simulate trial population defined either in contained dataset or given dataset as dataVPC. It will also perform additional simulations to performed prediction correction for the VPCs if required. For the observations, a typical prediction will also be performed using the population parameter point estimates. The VPC can subsequently e plotted using plotVPC_IQRdataVPC.

See also

Other IQRnlmeProject: IQRnlmeEst(), IQRnlmeProject(), addCovariateToModelSpec_IQRest(), addPar_modelSpec(), as_IQRnlmeProjectMulti(), as_IQRnlmeProject(), bootstrap_IQRnlmeProject(), compareModels_IQRnlmeProjectMulti(), convertETAINDIVPRED_IQRnlmeProject(), covariateEffect_IQRnlmeProject(), data_IQRest(), dosing_IQRest(), duplicate_IQRnlmeProject(), eigenvalues_IQRnlmeProject(), exportVirtualSubjects_IQRnlmeProject(), getData_IQRnlmeProject(), getETAs_IQRnlmeProject(), getEst_IQRnlmeProject(), getHeader_IQRnlmeProject(), getIndivParameters_IQRnlmeProject(), getIndivPredictions_IQRnlmeProject(), getModel_IQRnlmeProject(), getObsPred_IQRnlmeProject(), getPopParameters_IQRnlmeProject(), getResults_IQRnlmeProjectMulti(), getResults_IQRnlmeProject(), hasrun_IQRnlmeProjectMulti(), hasrun_IQRnlmeProject(), informationContent_IQRnlmeProject(), is_IQRnlmeEst(), is_IQRnlmeProjectMulti(), is_IQRnlmeProject(), is_MONOLIX_IQRnlmeProject(), is_NLMIXR_IQRnlmeProject(), is_NONMEM_IQRnlmeProject(), modelSpec_IQRest(), outlier_IQRnlmeProject(), plot.IQRnlmeProjectMulti(), plot.IQRnlmeProject(), plotBLQVPC_IQRdataVPC(), plotConvergence_IQRnlmeProject(), plotETACOV_IQRnlmeProject(), plotETA_IQRnlmeProject(), plotGOF_IQRnlmeProject(), plotINDIVSIM_IQRnlmeProject(), plotINDIV_IQRnlmeProject(), plotVPC_IQRdataVPC(), pred_IQRnlmeProject(), print.IQRnlmeEst(), print.IQRnlmeProjectMulti(), print.IQRnlmeProject(), print_modelSpec(), run_IQRnlmeProjectMulti(), run_IQRnlmeProject(), sample_IQRnlmeProject(), scm_IQRnlmeProject(), summary.IQRnlmeProjectMulti(), summary.IQRnlmeProject(), summaryComments_IQRnlmeProjectMulti(), summaryCorrelations_IQRnlmeProjectMulti(), summaryCovariates_IQRnlmeProjectMulti(), summaryParameters_IQRnlmeProjectMulti()

Other VPC: plotBLQVPC_IQRdataVPC(), plotVPC_IQRdataVPC(), plotVPC_IQRtteProject()