Perform a non-parametric bootstrap on an IQRnlmeProject

Runs a non-parametric bootstrap analysis on the provided NLME project folder. The provided NLME fit is used as the reference. NSAMPLES bootstrap fits are generated and run. Stratification for dataset resampling is possible along a single column in the dataset. The results are plotted and stored as a PDF. 95% confidence intervals and medians are shown for the bootstrap results. For the reference model the point estimate and its 95% confidence interval are shown. Additionally to the plots a text file (results_bootstrap.txt) with tabular information is generated in the bootstrapPath. Fits that lead to an objective function of NaN are assumed to be "crashed fits" and are excluded from the bootstrap result generation.

  bootstrapPath = "bootstrap",
  Nsamples = 200,
  group = NULL,
  FLAGanalysisOnly = FALSE,
  FLAGgenerateModelsOnly = FALSE,
  Nparallel = 1,
  ncores = 1,
  SIGDIG = 4,
  FLAGreturnOutput = FALSE,



Path to an NLME project folder.


Path where to store the bootstrap projects


Number of bootstrap samples


String with group name to use for stratification (some column name in the dataset that contains categorical information). The sampling of the dataset then is done to approximately keep the proportion of the group categories the same as in the original dataset. Can be vector of column names in the dataset. Stratification will then be done by all of the columns.


If set to TRUE then the function does not re-run the parameter estimation but assumes that bootstrap models already have been executed and only produces the output


If set to TRUE then the function only generates the models


Number of parallel model runs


Number of cores to parallelize single run


Number of significant digits for table information


Logical. If TRUE then the resulting plots and tables are returned as list of ggplot and IQRoutputTable objects


Logical. If TRUE then the RESULTSORIG folder will be removed. this will save a considerable amount of space on disk.


See FLAGreturnOutput

See also

Other IQRnlmeProject: IQRnlmeEst(), IQRnlmeProject(), addCovariateToModelSpec_IQRest(), addPar_modelSpec(), as_IQRnlmeProjectMulti(), as_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(), vpc_IQRnlmeProject()