---

1. Raue, Bioinformatics 31(21), 2015↩︎

2. Kaschek, dMod, 2016 (https://cran.r-project.org/package=dMod)↩︎

Model specification for IQRsysEst

modelSpec_IQRsysEst.Rd

Definition of estimation settings. See documentation for IQRnlmeEst

modelSpec_IQRsysEst(
  POPvalues0 = NULL,
  POPestimate = NULL,
  IIVdistribution = NULL,
  IIVvalues0 = NULL,
  IIVestimate = NULL,
  errorModel = NULL,
  covarianceModel = NULL,
  covariateModel = NULL,
  covariateModelValues = NULL,
  COVestimate = NULL,
  COVcentering = NULL,
  PriorVarPOP = NULL,
  PriorVarCovariateModelValues = NULL,
  PriorDFerrorModel = NULL,
  PriorIIV = NULL,
  PriorDFIIV = NULL,
  LOCmodel = NULL,
  LOCvalues0 = NULL,
  LOCestimate = NULL,
  LOCdistribution = NULL
)

Arguments

POPvalues0

named vector. Names are names of the parameters set to "estimate" in the model. A value of 0 indicates that the fixed effect of this parameter is fixed on its initial guess. A value of 1 indicates that this parameter is estimated.

POPestimate

A named vector. Names are names of the parameters set to "estimate" in the model. Values are the initial guesses for the fixed effects. This is a required entry, as the function needs to know at least which parameters are in scope for the estimation!

IIVdistribution

A named vector. Names are names of the parameters set to "estimate" in the model. Elements can be "N", "L", and "G". "N" indicates a normal distribution of the corresponding individual parameters. "L": logNormal, and "G": logitNormal.

IIVvalues0

A named vector. Names are names of the parameters set to "estimate" in the model. A value of 0 indicates that the random effect of this parameter is fixed to 0. A value of 1 indicates that the random effect is estimated. A value of 2 indicates that this random effect is fixed to its initial guess and not estimated.

IIVestimate

A named vector. Names are names of the parameters set to "estimate" in the model. Values are the initial guesses for the random effects.

errorModel

A list with as many fields as OUTPUT* definitions in the model. Names of these fields need to be the names of the OUTPUT* definitions. Each field is defined by a vector with the following elements: First element: "type" and "guess". "type" can be "abs" for absolute/additive error model, "rel" for relative/proportional error model, and "absrel" for absolute/additive : relative/proportional error model. Following elements (optional): initial guesses for error parameters. For "abs" and "rel" only one additional element needs to be present. For "absrel" two, whereby the first element is the initial guess for the absolute error and the second for the relative error. In addition it is possible to choose "event" for the error model type. This allows Joint Modeling with a TTE outcome. In this case no initial guess needs to be provided but the name of the variable in the model that describes the hazard function value.

covarianceModel

Vector with definitions of blocks. Syntax is as follows: covarianceModel = c("ka,CL,Vc","Q1,Q2"). If covarianceModel=NULL or covarianceModel = "diagonal" then no off diagonal elements will be estimated. IMPORTANT: If NONMEM BAYES algorithm is planned to be used then covariance model needs to be full! This means that if any of the prior information is provided, the covarianceModel setting will be disregarded and et to full. The user is warned about that.

covariateModel

List with definition of covariate model. Elements are named with the names of the parameters on which to add a covariate. Values are vectors with names of covariates to add. By default continuous covariates will be added as *(COV/REF)^THETA and categorical covariates multiplicative as well. Reference values are medians of continuous and smallest categories of categorical covariates. More complex desired covariate models can be coded in the model itself - then covariates become "regression" parameters. Example: covariateModel = list( CL = c("WT0","SEX"), ka = c("SEX"), Vc = c("WT0") )

covariateModelValues

List with definition of covariate model initial guesses. Elements are named with the names of the parameters on which to add a covariate. Values are named vectors with names of covariates and values as the initial guesses. For categorical covariates the same initial guess is used for all categories. covariateModelValues = list( CL = c(WT0=0.75, SEX=0.3), ka = c(SEX=0.5), Vc = c(WT0=1) )

COVestimate

List with definition of covariate model estimation settings. Elements are named with the names of the parameters on which to add a covariate. Values are 1 for estimation and 0 for fixing the covariate coefficient. For categorical covariates only all category coefficients or none can be estimated or fixed. Example: COVestimate = list( CL = c(WT0=0, SEX=1), ka = c(SEX=1), Vc = c(WT0=0) )

COVcentering

Named vector, defining the centering/reference values for covariates. Example: COVcentering = c(WT0=70, SEX=2)

PriorVarPOP

Not used in IQR Tools QSP modeling

PriorVarCovariateModelValues

Not used in IQR Tools QSP modeling

PriorDFerrorModel

Not used in IQR Tools QSP modeling

PriorIIV

Not used in IQR Tools QSP modeling

PriorDFIIV

Not used in IQR Tools QSP modeling

LOCmodel

Deprecated in IQR Tools QSP modeling

LOCvalues0

Deprecated in IQR Tools QSP modeling

LOCestimate

Deprecated in IQR Tools QSP modeling

LOCdistribution

Deprecated in IQR Tools QSP modeling

Value

IQRsysEst object

See also

Other QSP: IQRsysEst(), IQRsysModel(), IQRsysProject(), addOutputs_IQRmodel(), addRelations_IQRmodel(), as_IQRsysEst(), as_IQRsysProjectMulti(), as_IQRsysProject(), comparePars_IQRsysModel(), duplicate_IQRsysProject(), exportVariant_IQRmodel(), exportVirtualSubjects_IQRnlmeProject(), export_IQRsysData(), getDosing_IQRsysModel(), getEmpiricalETACovarianceMatrix_IQRsysProject(), getOptTrace_IQRsysProject(), getPars_IQRoptTrace(), getPars_IQRsysModel(), getPrediction_IQRsysModel(), hasrun_IQRsysProject(), importVariant_IQRmodel(), import_IQRsysData(), is_IQRsysEst(), is_IQRsysModel(), is_IQRsysProjectMulti(), is_IQRsysProject(), load_IQRsysProject(), model_IQRsysModel(), offsetTIME_IQRsysData(), plotDVPRED_IQRsysModel(), plotFit_IQRsysModel(), plotPars_IQRsysModel(), plotPred_IQRsysModel(), plotProfile_IQRsysModel(), plotWRES_IQRsysModel(), plotWaterfall_IQRsysModel(), plot_IQRoptTrace(), plot_IQRsysModel(), print.IQRsensitivity(), print.IQRsysProject(), profile_IQRsysModel(), replaceTerms_IQRmodel(), run_IQRsysProjectMulti(), run_IQRsysProject(), sample_IQRsysModel(), sensitivity_IQRmodel(), setPars_IQRsysModel(), sim_IQRsysModel(), sim_IQRvirtualSubjects(), simbio_CSV2namedVector(), simbio_updateParamIC_IQRmodel(), spmIIV_IQRsysProject(), spm_IQRsysProject(), switchOpt_IQRsysModel(), tablePars_IQRsysModel()

Author

Daniel Lill, IntiQuan