# VPC for NLME projects

This is an alias for the createDataVPC_IQRnlmeProject function, generating trial data for producing VPCs. Plotting then is done using the plotVPC_IQRnlmeProject() function.

vpc_IQRnlmeProject(
project,
dataVPC = NULL,
Ntrials,
ABSORPTION0names = NULL,
modelsSample = NULL,
FLAGpreparePC = FALSE,
simtimeN = 1000,
simtimeMax = NULL,
FLAGindivSimtimeMax = FALSE,
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
)

## Arguments

project 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. Number of trials to simulate for the VPC Character vector with names of parameters that are used as Tk0 parameters for the dosing inputs that are 0 order absorptions. For each "DOSINGTYPE" of "ABSORPTION0" in the modelSampleSimulate NLME project an entry has to be made. 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. All event times in the dataset will be added additionally. 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 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

## Value

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

## Details

Model from IQRnlmeProject 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.

Other IQRnlmeProject: IQRnlmeEst(), IQRnlmeProject(), addCovariateToModelSpec_IQRest(), addPar_modelSpec(), as_IQRnlmeProjectMulti(), as_IQRnlmeProject(), bootstrap_IQRnlmeProject(), compareModels_IQRnlmeProjectMulti(), convertETAINDIVPRED_IQRnlmeProject(), covariateEffect_IQRnlmeProject(), createDataVPC_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(), 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(), summary.IQRnlmeProjectMulti(), summary.IQRnlmeProject(), summaryComments_IQRnlmeProjectMulti(), summaryCorrelations_IQRnlmeProjectMulti(), summaryCovariates_IQRnlmeProjectMulti(), summaryParameters_IQRnlmeProjectMulti()
Other VPC: createDataVPC_IQRnlmeProject(), plotVPC_IQRdataVPC(), plotVPC_IQRtteProject()