NlmeVpcParams-class: Arguments for VPC runs

In Certara.RsNLME: Pharmacometric Modeling

Arguments for VPC runs

Description

Class initializer for arguments of visual predictive check (VPC) runs

Arguments

numReplicates	Integer; Number of replicates to simulate the model
seed	Integer; Random number generator seed
predCorrection	Character; Type of correction to use when calculating a prediction-corrected observation. Options are "none", "proportional", "additive". This option is ignored for discontinuous observed variables (categorical, count, and time-to-event).
predVarCorr	Logical; Set to TRUE to use Prediction Variance Correction. Only applicable to the case where predCorrection is set to either "proportional" or "additive".
outputPRED	Logical; Set to TRUE to include population prediction (PRED) results for continuous observed variables in output.
stratifyColumns	Character or character vector; Names of categorical covariates (up to 3) used to stratify modeling simulation results.
observationVars	NlmeObservationVar class instance or list of these instances
simulationTables	Optional list of simulation tables. NlmeSimTableDef class instance or a list of such instances. Could be generated by tableParams wrapper function or by NlmeSimTableDef class instance initializing directly.

See Also

tableParams, NlmeSimTableDef, NlmeObservationVar

Examples

job <- fitmodel(model)

# View estimation results
print(job)

finalModelVPC <- copyModel(model, acceptAllEffects = TRUE, modelName = "model_VPC")

# View the model
print(finalModelVPC)

# Set up VPC arguments to have PRED outputted to simulation output dataset "predout.csv"
vpcSetup <- NlmeVpcParams(outputPRED = TRUE)

# Run VPC using the default host, default values for the relevant NLME engine arguments
finalVPCJob <- vpcmodel(model = finalModelVPC, vpcParams = vpcSetup)

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