Predict values based on provided NLME project

This function takes an IQRnlmeProject and predicts values based on the individual parameter estimates. Simulation time can be chosen for each simulated subject independently. By default information from the NLME project will be used (individual observation times, the structural model, and the data). But individual simulation times, the structural model, and the data can also be customized.

pred_IQRnlmeProject(
  project,
  model = NULL,
  data = NULL,
  simtime = NULL,
  abs0inputs = NULL,
  abs0Tk0param = NULL,
  opt_abstol = 1e-06,
  opt_reltol = 1e-06
)

Arguments

project

NLME project

model

By default the IQRmodel stored within the NLNME project will be used. This can be overridden by providing a different IQRmodel. E.g., with additional integration odf the AUC.

data

By default the data from the NLME project will be used. This can be overridden by providing an altenative dataset. Elements taken from this dataset are the doses, regression parameters (time independent and time dependent) and by default the observation times for each individual (if simtime not provided).

simtime

A list with individual simulation time vectors. The list has to be named with the respective IDs in the dataset.

abs0inputs

Numeric vector with numbers of inputs (ADM) that are of type ABSORPTION0. If this argument is set also abs0Tk0param needs to be defined.

abs0Tk0param

Character vector with names of regression parameters that should be used as 0-order infusion time (by updating TINF). The regression parameters for these values are kept in the event table. Ordering has to be as in abs0inputs.

opt_abstol

Integratior absolute tolerance.

opt_reltol

Integrator relative tolerance.

Details

A typical use of this function would be in the prediction of individual exposures to then derive exposure metrics to use in an exposure-response analysis.

See also

Other IQRnlmeProject: IQRnlmeEst(), IQRnlmeProject(), addCovariateToModelSpec_IQRest(), addPar_modelSpec(), as_IQRnlmeProject(), as_IQRnlmeProjectMulti(), 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_IQRnlmeProject(), getResults_IQRnlmeProjectMulti(), hasrun_IQRnlmeProject(), hasrun_IQRnlmeProjectMulti(), informationContent_IQRnlmeProject(), is_IQRnlmeEst(), is_IQRnlmeProject(), is_IQRnlmeProjectMulti(), is_MONOLIX_IQRnlmeProject(), is_NLMIXR_IQRnlmeProject(), is_NONMEM_IQRnlmeProject(), modelSpec_IQRest(), outlier_IQRnlmeProject(), plot.IQRnlmeProject(), plot.IQRnlmeProjectMulti(), plotBLQVPC_IQRdataVPC(), plotConvergence_IQRnlmeProject(), plotETACOV_IQRnlmeProject(), plotETA_IQRnlmeProject(), plotGOF_IQRnlmeProject(), plotINDIVSIM_IQRnlmeProject(), plotINDIV_IQRnlmeProject(), plotMEANSIM_IQRnlmeProject(), plotVPC_IQRdataVPC(), print.IQRnlmeEst(), print.IQRnlmeProject(), print.IQRnlmeProjectMulti(), print_modelSpec(), run_IQRnlmeProject(), run_IQRnlmeProjectMulti(), sample_IQRnlmeProject(), scm_IQRnlmeProject(), summary.IQRnlmeProject(), summary.IQRnlmeProjectMulti(), summaryComments_IQRnlmeProjectMulti(), summaryCorrelations_IQRnlmeProjectMulti(), summaryCovariates_IQRnlmeProjectMulti(), summaryParameters_IQRnlmeProjectMulti(), vpc_IQRnlmeProject()