pklinearmodel: Create PK linear model
In Certara.RsNLME: Pharmacometric Modeling

pklinearmodel

R Documentation

Create PK linear model

Description

Use to create a PK/PD model with PD described by either constant, linear, or quadratic model

Usage

pklinearmodel(
  isPopulation = TRUE,
  parameterization = "Clearance",
  absorption = "Intravenous",
  numCompartments = 1,
  isClosedForm = TRUE,
  isTlag = FALSE,
  hasEliminationComp = FALSE,
  isFractionExcreted = FALSE,
  isSaturating = FALSE,
  infusionAllowed = FALSE,
  isDuration = FALSE,
  isSequential = FALSE,
  isPkFrozen = FALSE,
  hasEffectsCompartment = FALSE,
  linearType = "Constant",
  isLinearFrozen = FALSE,
  data = NULL,
  columnMap = TRUE,
  modelName = "",
  workingDir = "",
  ...
)

Arguments

`isPopulation`	Is this a population model `TRUE` or individual model `FALSE`?
`parameterization`	Type of parameterization. Options are `"Clearance"`, `"Micro"`, `"Macro"`, or `"Macro1"`.
`absorption`	Type of absorption. Options are `"Intravenous"`, `"FirstOrder"`, `"Gamma"`, `"InverseGaussian"`, `"Weibull"` .
`numCompartments`	Value of either `1`, `2`, or `3`.
`isClosedForm`	Set to `TRUE` to convert model from a differential equation to close form.
`isTlag`	Set to `TRUE` to add a lag time parameter to the model.
`hasEliminationComp`	Set to `TRUE` to add an elimination compartment to the model.
`isFractionExcreted`	Set to `TRUE` if elimination compartment (`hasEliminationComp = TRUE`) contains a fraction excreted parameter.
`isSaturating`	Set to `TRUE` to use Michaelis-Menten kinetics for elimination. Only applicable to models with `paramteterization = "Clearance"`
`infusionAllowed`	Set to `TRUE` if infusions allowed.
`isDuration`	Set to `TRUE` if infusions use duration instead of rate (must also set `infusionAllowed = TRUE`).
`isSequential`	Set to `TRUE` to freeze PK fixed effects and convert the corresponding random effects into covariates as well as remove the PK observed variable from the model.
`isPkFrozen`	Set to `TRUE` to freeze PK fixed effects and remove the corresponding random effects as well as the PK observed variable from the model.
`hasEffectsCompartment`	Set to `TRUE` to include an effect compartment into the model.
`linearType`	Type of PD model; Options are `"Constant"`, `"Linear"`, `"Quadratic"`.
`isLinearFrozen`	Set to `TRUE` to freeze PD fixed effects and remove the corresponding random effects as well as the PD observed variable from the model.
`data`	Input dataset
`columnMap`	If `TRUE` (default) column mapping arguments are required. Set to `FALSE` to manually map columns after defining model using `colMapping`.
`modelName`	Model name for subdirectory created for model output in current working directory.
`workingDir`	Working directory to run the model. Current working directory will be used if `workingDir` not specified.
`...`	Arguments passed on to `pkindirectmodel_MappingParameters` `ID` Column mapping argument for input dataset column(s) that identify individual data profiles. Only applicable to population models `isPopulation = TRUE`. `Time` Column mapping argument that represents the input dataset column for the relative time used in a study and only applicable to time-based models. `A1` Column mapping argument that represents the input dataset column for the amount of drug administered. Only applicable to the following types of models: Models with `absorption = "Intravenous"` and parameterization set to either `"Clearance"`,`"Micro"`, or `"Macro"` Models with `absorption` set to either `"Gamma"`, `"InverseGaussian"`, or `"Weibull"` `Aa` Column mapping argument that represents the input dataset column for the amount of drug administered and only applicable to models with `absorption = "FirstOrder"`. `A` Column mapping argument that represents the input dataset column for the amount of drug administered and only applicable to models with `absorption = "Intravenous"` and `parameterization = "Macro1"`. `A1_Rate` Column mapping argument that represents the input dataset column for the rate of drug administered. Only applicable to the following types of models: Models with `absorption = "Intravenous"`, `infusionAllowed = TRUE` and parameterization set to either `"Clearance"`,`"Micro"` or `"Macro"` Models with `absorption` set to either `"Gamma"`, `"InverseGaussian"`, or `"Weibull"` and `infusionAllowed = TRUE` `A1_Duration` Column mapping argument that represents the input dataset column for the duration of drug administered. Only applicable to the following types of models: Models with `absorption = "Intravenous"`, `infusionAllowed = TRUE` with `isDuration = TRUE` and parameterization set to either `"Clearance"`,`"Micro"` or `"Macro"` Models with `absorption` set to either `"Gamma"`, `"InverseGaussian"`, or `"Weibull"` and `infusionAllowed = TRUE` with `isDuration = TRUE` `Aa_Rate` Column mapping argument that represents the input dataset column for the rate of drug administered and only applicable to models with `absorption = "FirstOrder"`, `infusionAllowed = TRUE`. `Aa_Duration` Column mapping argument that represents the input dataset column for the duration of drug administered and only applicable to models with `absorption = "FirstOrder"`, `infusionAllowed = TRUE`, and `isDuration = TRUE`. `A_Rate` Column mapping argument that represents the input dataset column for the rate of drug administered and only applicable to models with `absorption = "Intravenous"`, `infusionAllowed = TRUE`, and `parameterization = "Macro1"`. `A_Duration` Column mapping argument that represents the input dataset column for the duration of drug administered and only applicable to models with `absorption = "Intravenous"`, `infusionAllowed = TRUE`, `isDuration = TRUE`, and `parameterization = "Macro1"`. `A1Strip` Column mapping argument that represents the input dataset column for the stripping dose and only applicable to models with `parameterization = "Macro"`. `CObs` Column mapping argument that represents the input dataset column for the observations of drug concentration in the central compartment and only applicable to models with `parameterization` being either set to either `"Clearance"` or `"Micro"`. `C1Obs` Column mapping argument that represents the input dataset column for the observations of drug concentration in the central compartment and only applicable to models with `parameterization` being either set to either `"Macro"` or `"Macro1"`. `A0Obs` Column mapping argument that represents the input dataset column for the observed amount of drug in the elimination compartment. (`hasEliminationComp = TRUE`). `EObs` Column mapping argument that represents the input dataset column for the observed drug effect. `nV` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nV`. `nV2` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nV2`. `nV3` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nV3`. `nCl` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nCl`. `nCl2` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nCl2`. `nCl3` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nCl3`. `nKa` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nKa`. `nA` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nA`. `nAlpha` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nAlpha`. `nB` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nB`. `nBeta` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nBeta`. `nC` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nC`. `nGamma` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nGamma`. `nKe` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nKe`. `nK12` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nK12`. `nK21` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nK21`. `nK13` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nK13`. `nK31` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nK31`. `nTlag` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nTlag`. `nKm` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nKm`. `nVmax` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nVmax`. `nFe` If `isSequential = TRUE` and `isFractionExcreted = TRUE`, mapped to the input dataset column that lists the values for random effect `nFe`. `nMeanDelayTime` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nMeanDelayTime`. `nShapeParam` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nShapeParam`. `nShapeParamMinusOne` If `isSequential = TRUE`, mapped to the input dataset column that lists the values for random effect `nShapeParamMinusOne`.

Value

NlmePmlModel object

Column mapping

Note that quoted and unquoted column names are supported. Please see colMapping.

Examples

model <- pklinearmodel(
  parameterization = "Clearance",
  linearType = "Constant",
  data = pkpdData,
  ID = "ID",
  Time = "Time",
  A1 = "Dose",
  CObs = "CObs",
  EObs = "EObs"
)

# View the model as well as its associated column mappings
print(model)

Certara.RsNLME documentation built on April 3, 2025, 11:04 p.m.