Certara.RsNLME (version 3.1.0.1)

residualError: Assign residual error model to model object

Description

Use to change or update residual error model for model object

Usage

residualError(
  .Object,
  predName = "C",
  errorType = NULL,
  SD = NULL,
  isFrozen = FALSE,
  isBQL = FALSE,
  staticLLOQ = NULL,
  EObsBQL = NULL,
  CObsBQL = NULL,
  C1ObsBQL = NULL,
  A0ObsBQL = NULL,
  exponent = NULL
)

Value

Modified NlmePmlModel object

Arguments

.Object

Model object

predName

Name of the predicted variable as returned in residualEffectNames.

errorType

Options are "Additive", "LogAdditive", "Multiplicative", "AdditiveMultiplicative", "MixRatio", "Power".

SD

Value for the standard deviation of the residual error variable.

isFrozen

Set to TRUE to freeze the standard deviation to the value specified for SD.

isBQL

Set to TRUE if BQL values present in the observation data.

staticLLOQ

Optional LLOQ value if isBQL = TRUE

EObsBQL

Column mapping argument that represents the input dataset column that contains the BQL flag for observation values corresponding to EObs. Only applicable to isBQL = TRUE.

CObsBQL

Column mapping argument that represents the input dataset column that contains the BQL flag for observation values corresponding to CObs. Only applicable to isBQL = TRUE.

C1ObsBQL

Column mapping argument that represents the input dataset column that contains the BQL flag for observation values corresponding to C1Obs. Only applicable to isBQL = TRUE.

A0ObsBQL

Column mapping argument that represents the input dataset column that contains the BQL flag for observation values corresponding to AObs. Only applicable to isBQL = TRUE.

exponent

Value of exponent. Only applicable to errorType = "Power".

Examples

model <- pkindirectmodel(indirectType = "LimitedInhibition", isBuildup = FALSE,
 data = pkpdData, ID = "ID", Time = "Time", A1 = "Dose", CObs = "CObs", EObs = "EObs")

residualEffectNames(model)

# Change error type to "Multiplicative" and value of SD to 0.1 for "E"
model <- residualError(model, predName = "E", errorType = "Multiplicative", SD = 0.1)

# Change error type to "Power", value of SD to 0.15, and set exponent = 2 for "C"
model <- residualError(model, predName = "C", errorType = "Power", SD = 0.15, exponent = 2)