Arguments for VPC runs

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.

Examples

if (FALSE) { # \dontrun{

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)

# Observation dataset predcheck0.csv
dt_ObsData <- finalVPCJob$predcheck0

# Simulation output dataset predout.csv
dt_SimData <- finalVPCJob$predout

# Add PRED from REPLICATE = 0 of simulation output dataset to observed input dataset
dt_ObsData$PRED <- dt_SimData[REPLICATE == 0]$PRED

# tidyvpc package VPC example:
library(magrittr)
library(tidyvpc)
# Create a regular VPC plot with binning method set to be "jenks"
binned_VPC <- tidyvpc::observed(dt_ObsData, x = IVAR, yobs = DV) %>%
  tidyvpc::simulated(dt_SimData, ysim = DV) %>%
  tidyvpc::binning(bin = "jenks") %>%
  tidyvpc::vpcstats()

plot_binned_VPC <- plot(binned_VPC)

# Create a pcVPC plot with binning method set to be "jenks"
binned_pcVPC <- tidyvpc::observed(dt_ObsData, x = IVAR, yobs = DV) %>%
  tidyvpc::simulated(dt_SimData, ysim = DV) %>%
  tidyvpc::binning(bin = "jenks") %>%
  tidyvpc::predcorrect(pred = PRED) %>%
  tidyvpc::vpcstats()

plot_binned_pcVPC <- plot(binned_pcVPC)
} # }

Arguments

See also

Examples