Create data for VPC
createDataVPC_IQRnlmeProject.RdSimulates trial data for producing VPCs
createDataVPC_IQRnlmeProject(
project,
dataVPC = NULL,
Ntrials,
ABSORPTION0names = NULL,
modelsSample = NULL,
FLAGpreparePC = FALSE,
simtimeOption = c("obs", "simTFD", "simTAD")[1],
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
)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.
- dataVPC
Dataset for VPC generation. If undefined, the modeling dataset from modelSampleSimulate will be used.
- Ntrials
Number of trials to simulate for the VPC
- ABSORPTION0names
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.
- modelsSample
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
- FLAGpreparePC
Flag whether to prepare prediction correction by corresponding additional simulations
- simtimeOption
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)
- simtimeN
Define number of simulation time steps to add between smallest and largest event time in the dataset. Equidistant sampling will be used.
- simtimes
Vector of simulation times. If not NULL, this vector of simulation times overrules simtimeN
- simtimeMax
Define maximum simulation time for VPC for all subjects.
- FLAGindivSimtimeMax
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.
- FLAGaddResidualNoise
logical. If TRUE then residual noise will be added to the simulations
- FLAGsampleUncertainty
logical. If set to TRUE then for each new trial new population parameters are drawn from the uncertainty distribution
- FLAGlogOutput
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.
- factorMult
named vector allowing to define multiplicative factors to modulate by name selected sampled parameters for simulation
- ncores
Number of cores for parallelization.
- seed
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.
- FLAGkeepParameters
Logical. If set to TRUE, sampled parameters will be added to returned simulation results. Defaults to FALSE
- opt_initstep
Double value for initial step-size to be attempted for simulation
- opt_abstol
Double value for absolute tolerance for simulation
- opt_reltol
Double value for relative tolerance for simulation
- opt_maxstep
Double value for maximal integrator step-size for simulation
- opt_minstep
Double value for minimal integrator step-size for simulation
- par_lower
Named vector of lower limits for sampled individual parameters. Applies to individual parameters after potential modulation by factorMult argument.
- par_upper
Named vector of upper limits for sampled individual parameters. Applies to individual parameters after potential modulation by factorMult argument.
- FLAGfixDoseOverlap
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.
- FLAGstopFailedSim
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.
Value
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.
Details
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(),
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(),
vpc_IQRnlmeProject()
Other VPC:
plotVPC_IQRdataVPC(),
plotVPC_IQRtteProject(),
vpc_IQRnlmeProject()