Defines a data list for use in setting up estimation objects.

Can be used for syspharm and NLME type of estimation objects. This is a convenience function to allow better documentation of the elements that can be in the data argument for IQRnlmeEst and IQRsysEst. No consistency checks are done at this point. This is conducted in the IQRnlmeEst and IQRsysEst functions.

data_IQRest(datafile, covNames = NULL, catNames = NULL, regressorNames = NULL)

Arguments

datafile

"datafile" needs to be the path to the dataset. Can be absolute or relative (from current working directory). For flexibility, instead of a path to the "datafile" also a data.frame can be provided. In this case this dataset will always be stored inside the IQRnlmeProject.

covNames

Character vector with names of continuous candidate covariates in the dataset.

catNames

Character vector with names of categorical candidate covariates in the dataset.

regressorNames

Character vector with names of regressors in the dataset. Regressor names can be provided in any desired order.

Value

Returns a data list for IQRnlmeEst and IQRsysEst

See also

Other IQRnlmeProject: IQRnlmeEst(), IQRnlmeProject(), addCovariateToModelSpec_IQRest(), addPar_modelSpec(), as_IQRnlmeProject(), as_IQRnlmeProjectMulti(), bootstrap_IQRnlmeProject(), compareModels_IQRnlmeProjectMulti(), convertETAINDIVPRED_IQRnlmeProject(), covariateEffect_IQRnlmeProject(), 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(), pred_IQRnlmeProject(), 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()

Author

Daniel Lill, IntiQuan