Perform visual predictive check for NLME models
vpcmodel.RdPerform visual predictive check for NLME models
Arguments
- model
PK/PD model class object.
- vpcParams
VPC argument setup. See
NlmeVpcParams. Ifmissing, default values generated by NlmeVpcParams() are used.- params
Engine argument setup. See
engineParams. The following arguments are the subject of interest: sort, ODE, rtolODE, atolODE, maxStepsODE. Ifmissing, default values generated by engineParams(model) are used.- hostPlatform
Host definition for model execution. See
hostParams. Ifmissing, simple local host is used.- runInBackground
Set to
TRUEto run in background and return prompt.- ...
Additional class initializer arguments for
NlmeVpcParamsorhostParams, or arguments available insideengineParamsfunctions. IfengineParamsarguments are supplied through bothparamsargument and additional argument (i.e., ellipsis), then the arguments inparamswill be ignored and only the additional arguments will be used with warning. IfhostParamsarguments are supplied through bothhostPlatformargument and additional argument, then its values will be overridden by additional arguments. In addition, ifNlmeVpcParamsarguments are supplied through bothvpcParamsargument and additional argument, then its slots will be overridden by additional arguments.
Value
if runInBackground is TRUE, it returns job properties.
Otherwise,
If the function is called in an interactive mode, the resulting simulated tables and summary statistics tables will be loaded and presented as a list;
If the function is called in a non-interactive mode, it returns the full paths of the tables generated
Examples
# \donttest{
model <- pkmodel(
numComp = 1,
absorption = "Extravascular",
ID = "Subject",
Time = "Act_Time",
CObs = "Conc",
Aa = "Amount",
data = pkData,
modelName = "PkModel",
workingDir = tempdir()
)
host <- hostParams(
sharedDirectory = tempdir(),
parallelMethod = "NONE",
hostName = "local",
numCores = 1
)
job <- fitmodel(model = model,
hostPlatform = host)
#>
#> NLME Job
#>
#> Compiling 1 of 1 NLME models
#> TDL5 version: 25.7.1.1
#>
#> Status: OK
#> License expires: 2026-08-06
#> Refresh until: 2025-09-05 07:01:25
#> Current Date: 2025-08-06
#> The model compiled
#>
#>
#> Iteration -2LL tvKa tvV tvCl nSubj nObs
#> 1 1.994694e+09 1.000000 1.000000 1.000000 16 112
#> 2 1.105887e+09 1.000000 1.000000 1.000000 16 112
#> 3 5.289122e+08 1.000000 1.000000 1.000000 16 112
#> 4 2.683141e+08 1.000000 1.000000 1.000000 16 112
#> 5 1.331841e+08 1.000000 1.000000 1.000000 16 112
#> 6 6.670444e+07 1.000000 1.000000 1.000000 16 112
#> 7 3.330637e+07 1.000000 1.000000 1.000000 16 112
#> 8 1.665502e+07 1.000000 1.000000 1.000000 16 112
#> 9 8.325699e+06 1.000000 1.000000 1.000000 16 112
#> 10 4.163575e+06 1.000000 1.000000 1.000000 16 112
#> 11 4.163575e+06 1.000000 1.000000 1.000000 16 112
#> 12 3.030272e+06 0.986951 0.526228 1.000040 16 112
#> 13 2.414036e+06 0.986078 0.552713 1.091940 16 112
#> 14 2.197233e+06 1.008050 0.556518 1.117300 16 112
#> 15 1.248481e+06 1.018500 0.541097 1.195840 16 112
#> 16 1.248481e+06 1.018500 0.541097 1.195840 16 112
#> 17 5.155618e+05 1.031220 0.546366 0.906401 16 112
#> 18 2.602570e+05 1.021620 0.552988 0.951693 16 112
#> 19 1.163796e+05 1.017350 0.555889 0.995225 16 112
#> 20 6.653264e+04 1.021310 0.558975 1.033720 16 112
#> 21 3.316336e+04 1.021200 0.565210 1.080030 16 112
#> 22 1.835841e+04 1.020950 0.569509 1.116360 16 112
#> 23 1.007382e+04 1.020870 0.574333 1.159190 16 112
#> 24 6.305650e+03 1.020930 0.578809 1.198910 16 112
#> 25 4.388810e+03 1.020910 0.583362 1.239470 16 112
#> 26 3.479240e+03 1.020910 0.587750 1.278590 16 112
#> 27 3.050900e+03 1.020920 0.591980 1.316400 16 112
#> 28 2.864540e+03 1.020990 0.595878 1.351220 16 112
#> 29 2.794160e+03 1.021040 0.599139 1.380520 16 112
#> 30 2.773150e+03 1.021090 0.601567 1.402290 16 112
#> 31 2.769420e+03 1.021140 0.603770 1.421790 16 112
#> 32 2.769240e+03 1.021150 0.603450 1.418950 16 112
#> 33 2.769230e+03 1.021170 0.603483 1.419190 16 112
#> 34 2.769120e+03 1.021300 0.603526 1.419270 16 112
#> 35 2.763770e+03 1.033130 0.607641 1.429080 16 112
#> 36 2.762090e+03 1.034870 0.608505 1.433630 16 112
#> 37 2.760000e+03 1.038610 0.610145 1.440780 16 112
#> 38 2.760000e+03 1.038610 0.610145 1.440780 16 112
#> 39 2.702990e+03 1.038610 0.610145 1.440780 16 112
#> 40 2.699260e+03 1.038610 0.610145 1.440780 16 112
#> 41 2.611010e+03 1.038610 0.610014 1.440780 16 112
#> 42 2.611010e+03 1.038610 0.610014 1.440780 16 112
#> 43 2.562180e+03 1.038610 0.608207 1.440780 16 112
#> 44 2.562180e+03 1.038610 0.608207 1.440780 16 112
#> 45 2.546930e+03 1.149980 0.693723 2.074670 16 112
#> 46 2.546930e+03 1.149980 0.693723 2.074670 16 112
#> 47 2.543730e+03 1.149980 0.693723 2.074670 16 112
#> 48 2.543730e+03 1.149980 0.693723 2.074670 16 112
#>
#> Trying to generate job results...
#>
#> Generating Overall.csv
#> Generating EtaEta.csv
#> Generating Eta.csv
#> Generating EtaStacked.csv
#> Generating bluptable.dat
#> Generating ConvergenceData.csv
#> Generating initest.csv
#> Generating doses.csv
#> Generating omega.csv
#> Generating theta.csv
#> Generating thetaCorrelation.csv
#> Generating thetaCovariance.csv
#> Generating Covariance.csv
#> Generating Residuals.csv
#> Generating posthoc.csv
#>
#> Finished summarizing results. Transferring data and loading the results...
#> Done generating job results.
finalModelVPC <- copyModel(model,
acceptAllEffects = TRUE,
modelName = "model_VPC",
workingDir = tempdir())
# View the model
print(finalModelVPC)
#>
#> Model Overview
#> -------------------------------------------
#> Model Name : model_VPC
#> Working Directory : C:\Users\jcraig\AppData\Local\Temp\RtmpI3HPMy
#> Is population : TRUE
#> Model Type : PK
#>
#> PK
#> -------------------------------------------
#> Parameterization : Clearance
#> Absorption : FirstOrder
#> Num Compartments : 1
#> Dose Tlag? : FALSE
#> Elimination Comp ?: FALSE
#> Infusion Allowed ?: FALSE
#> Sequential : FALSE
#> Freeze PK : FALSE
#>
#> PML
#> -------------------------------------------
#> test(){
#> cfMicro(A1,Cl/V, first = (Aa = Ka))
#> dosepoint(Aa)
#> C = A1 / V
#> error(CEps=628.509289505977)
#> observe(CObs=C * ( 1 + CEps))
#> stparm(Ka = tvKa * exp(nKa))
#> stparm(V = tvV * exp(nV))
#> stparm(Cl = tvCl * exp(nCl))
#> fixef( tvKa = c(,1.14998472307097,))
#> fixef( tvV = c(,0.693723326857944,))
#> fixef( tvCl = c(,2.07466660878669,))
#> ranef(diag(nKa,nV,nCl) = c(1.12055347272733,1.86975623088902,1.48746566512628))
#>
#> }
#>
#> Structural Parameters
#> -------------------------------------------
#> Ka V Cl
#> -------------------------------------------
#> Observations:
#> Observation Name : CObs
#> Effect Name : C
#> Epsilon Name : CEps
#> Epsilon Type : Multiplicative
#> Epsilon frozen : FALSE
#> is BQL : FALSE
#> -------------------------------------------
#> Column Mappings
#> -------------------------------------------
#> Model Variable Name : Data Column name
#> id : Subject
#> time : Act_Time
#> Aa : Amount
#> CObs : Conc
#>
# 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, hostPlatform = host)
#> Using localhost without parallelization.
#>
#> NLME Job
#>
#> Compiling 1 of 1 NLME models
#> TDL5 version: 25.7.1.1
#>
#> Status: OK
#> License expires: 2026-08-06
#> Refresh until: 2025-09-05 07:01:25
#> Current Date: 2025-08-06
#> The model compiled
#>
#> Trying to generate job results...
#>
#> Copying tables predcheck0.csv, predcheck0_cat.csv, predcheck1.csv, predcheck1_cat.csv, predcheck1_npd.csv, predcheck2.csv, predcheck2_cat.csv, predcheck2_npd.csv, predcheck_bql.csv, predout.csv
#>
#> Finished summarizing results. Transferring data and loading the results...
#> Done generating job results.
#>
#> VPC/Simulation results are ready in C:/Users/jcraig/AppData/Local/Temp/RtmpI3HPMy
#> Loading the results
#> Loading predcheck_bql.csv
#> Loading predcheck0.csv
#> Loading predcheck0_cat.csv
#> Loading predcheck1.csv
#> Loading predcheck1_cat.csv
#> Loading predcheck2.csv
#> Loading predcheck2_cat.csv
#> Loading predout.csv
# the same as:
# finalVPCJob <- vpcmodel(model = finalModelVPC, outputPRED = TRUE)
# Observed 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(tidyvpc)
# library(magrittr)
# Create a regular VPC plot with binning method set to be "jenks"
# binned_VPC <- observed(dt_ObsData, x = IVAR, yobs = DV) %>%
# simulated(dt_SimData, ysim = DV) %>%
# binning(bin = "jenks") %>%
# vpcstats()
# plot_binned_VPC <- plot(binned_VPC)
# Create a pcVPC plot with binning method set to be "jenks"
# binned_pcVPC <- observed(dt_ObsData, x = IVAR, yobs = DV) %>%
# simulated(dt_SimData, ysim = DV) %>%
# binning(bin = "jenks") %>%
# predcorrect(pred = PRED) %>%
# vpcstats()
# plot_binned_pcVPC <- plot(binned_pcVPC)
# }