This example script is intended to illustrate how to use the 'ddmore' R package to perform a M&S workflow using the DDMoRe Standalone Execution Environment (SEE).
The following steps are implemented in this workflow:
To run a task, select with the cursor any code lines you wish to execute and press CTRL+R+R in your keyboard. An HTML file containing the commands in this file and associated output will be provided to allow the user to compare the results
Clear workspace and set working directory under 'UsesCasesDemo' project
rm(list=ls(all=FALSE))
mydir <- file.path(Sys.getenv("MDLIDE_WORKSPACE_HOME"),"UseCasesDemo")
setwd(mydir)
Set name of .mdl file and dataset for future tasks
uc<-"UseCase5"
datafile <- "warfarin_conc_sexf.csv"
mdlfile <- paste0(uc,".mdl")
Create a new folder to be used as working directory and where results will be stored
wd <- file.path(mydir,uc)
dir.create(wd)
Copy the dataset and the .mdl file available under “models” into the working directory
file.copy(file.path(mydir,"models", datafile),wd)
## [1] TRUE
file.copy(file.path(mydir,"models",mdlfile),wd)
## [1] TRUE
Set the working directory.
setwd(file.path(mydir,uc))
The working directory needs to be set differently when knitr R package is used to spin the file
library(knitr)
opts_knit$set(root.dir = file.path(mydir,uc))
List files available in working directory
list.files()
## [1] "UseCase5.mdl" "warfarin_conc_sexf.csv"
Use 'ddmore' function getMDLObjects() to retrieve model object(s) from an existing .mdl file. This function reads the MDL in an .mdl file and parses the MDL code for each MDL Object into an R list of objects of appropriate types with names corresponding to the MDL Object names given in the file.
myMDLObj <- getMDLObjects(mdlfile)
length(myMDLObj)
## [1] 4
names(myMDLObj)
## [1] "warfarin_PK_covariate_dat" "warfarin_PK_covariate_par"
## [3] "warfarin_PK_covariate_mdl" "warfarin_PK_ODE_task"
Use 'ddmore' function getDataObjects() to retrieve only data object(s) from an existing .mdl file This function returns a list of Parameter Object(s) from which we select the first element Hoover over the variable name to see its structure
myDataObj <- getDataObjects(mdlfile)[[1]]
Use 'ddmore' function getParameterObjects() to retrieve only parameter object(s) from an existing .mdl file
myParObj <- getParameterObjects(mdlfile)[[1]]
Use 'ddmore' function getModelObjects() to retrieve only object(s) from an existing .mdl file.
myModObj <- getModelObjects(mdlfile)[[1]]
Use 'ddmore' function getTaskPropertiesObjects() to retrieve only task properties object(s) from an existing .mdl file
myTaskObj <- getTaskPropertiesObjects(mdlfile)[[1]]
Recall that getDataObjects only reads the MDL code from the .mdl file. Use 'ddmore' function readDataObj() to create an R object from the MDL data object.
myData <- readDataObj(myDataObj)
Let's look at the first 6 lines of the data set
head(myData)
## ID TIME WT AGE SEXF AMT DVID DV MDV
## 1 1 0.0 66.7 50 male 100 0 NA 1
## 2 1 0.5 66.7 50 male NA 1 0.0 0
## 3 1 1.0 66.7 50 male NA 1 1.9 0
## 4 1 2.0 66.7 50 male NA 1 3.3 0
## 5 1 3.0 66.7 50 male NA 1 6.6 0
## 6 1 6.0 66.7 50 male NA 1 9.1 0
Extract only observation records
myEDAData<-myData[is.na(myData$AMT),]
Open an R window to record and access all your plots
windows(record=TRUE)
Now plot the data using xyplot from the lattice library
plot1 <- xyplot(DV~TIME,groups=ID,data=myEDAData,type="b",ylab="Conc. (mg/L)",xlab="Time (h)")
print(plot1)
plot2 <- xyplot(DV~TIME|ID,data=myEDAData,type="b",layout=c(3,4),ylab="Conc. (mg/L)",xlab="Time (h)",scales=list(relation="free"))
print(plot2)
Export results to a PDF file
pdf(paste0(uc,"_EGA.pdf"))
print(plot1)
print(plot2)
dev.off()
## png
## 2
#
The ddmore “estimate” function translates the contents of the .mdl file to a target language and then estimates parameters using the target software. After estimation, the output is converted to a Standardised Output object which is saved in a .SO.xml file.
Translated files and Monolix output will be returned in the ./Monolix subfolder. The Standardised Output object (.SO.xml) is read and parsed into an R object called “mlx” of (S4) class “StandardOutputObject”.
mlx <- estimate(mdlfile, target="MONOLIX", subfolder="Monolix")
## -- Tue Aug 16 12:41:31 2016
## New
## Submitted
## Job 908f1b6e-5043-4792-a38b-be3a09f88613 progress:
## Running [ .... ]
## Importing Results
## Copying the result data back to the local machine for job ID 908f1b6e-5043-4792-a38b-be3a09f88613...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job356474a1bfe to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase5/Monolix
## Done.
##
##
## The following main elements were parsed successfully:
## ToolSettings
## RawResults
## Estimation::PopulationEstimates::MLE
## Estimation::PrecisionPopulationEstimates::MLE
## Estimation::IndividualEstimates::Estimates
## Estimation::IndividualEstimates::RandomEffects
## Estimation::Residuals::ResidualTable
## Estimation::Predictions
## Estimation::OFMeasures::IndividualContribToLL
## Estimation::OFMeasures::InformationCriteria
## Estimation::OFMeasures::LogLikelihood
##
## Completed
## -- Tue Aug 16 12:42:53 2016
slotNames(mlx)
## [1] "ToolSettings" "RawResults" "TaskInformation"
## [4] "Estimation" "ModelDiagnostic" "Simulation"
## [7] "OptimalDesign" ".pathToSourceXML"
The ddmore “LoadSOObj” reads and parsed existing Standardise Output Objects
mlx <- LoadSOObject(“Monolix/UseCase5.SO.xml”)
#'
The ddmore function “getPopulationParameters” extracts the Population Parameter values from an R object of (S4) class “StandardOutputObject” and returns the estimates. See documentation for getPopulationParameters to see other arguments and settings for this function.
parameters_mlx <- getPopulationParameters(mlx, what="estimates")$MLE
print(parameters_mlx)
## POP_V BETA_V_WT POP_KA POP_CL BETA_CL_WT BETA_CL_AGE
## 8.06842 1.00000 1.67953 0.14160 0.75000 0.00854
## POP_FCL_FEM POP_TLAG PPV_V PPV_KA PPV_CL PPV_TLAG
## 0.13702 0.95115 0.12803 1.04272 0.24162 0.10000
## CORR_CL_V RUV_ADD RUV_PROP
## 0.18183 0.21156 0.06749
print(getPopulationParameters(mlx, what="precisions"))
## $MLE
## Parameter MLE SE RSE
## 1 BETA_CL_AGE 0.00854 0.00421 49.30
## 2 BETA_CL_WT 0.75000 0.00000 0.00
## 3 BETA_V_WT 1.00000 0.00000 0.00
## 4 CORR_CL_V 0.18183 0.20386 112.12
## 5 POP_CL 0.14160 0.00884 6.24
## 6 POP_FCL_FEM 0.13702 0.11958 87.27
## 7 POP_KA 1.67953 0.65148 38.79
## 8 POP_TLAG 0.95115 0.05304 5.58
## 9 POP_V 8.06842 0.21243 2.63
## 10 PPV_CL 0.24162 0.03156 13.06
## 11 PPV_KA 1.04272 0.28688 27.51
## 12 PPV_TLAG 0.10000 0.00000 0.00
## 13 PPV_V 0.12803 0.02146 16.77
## 14 RUV_ADD 0.21156 0.04303 20.34
## 15 RUV_PROP 0.06749 0.00907 13.44
print(getEstimationInfo(mlx))
## $OFMeasures
## $OFMeasures$LogLikelihood
## $OFMeasures$LogLikelihood[[1]]
## [1] -330.15
##
##
## $OFMeasures$IndividualContribToLL
## Subject ICtoLL
## 1 1 -23.24
## 2 2 -5.64
## 3 3 -12.82
## 4 4 -12.33
## 5 5 -12.46
## 6 6 -7.47
## 7 7 -17.73
## 8 8 -20.95
## 9 9 -30.82
## 10 10 -5.57
## 11 12 -19.57
## 12 13 -19.80
## 13 14 -19.04
## 14 15 -10.61
## 15 16 -14.60
## 16 17 -5.44
## 17 18 -5.06
## 18 19 -6.76
## 19 20 -5.93
## 20 21 -5.62
## 21 22 -6.03
## 22 23 -7.87
## 23 24 -4.62
## 24 25 -7.40
## 25 26 -6.99
## 26 27 -5.00
## 27 28 -6.58
## 28 29 -5.44
## 29 30 -4.63
## 30 31 -4.86
## 31 32 -4.51
## 32 33 -4.75
##
## $OFMeasures$InformationCriteria
## $OFMeasures$InformationCriteria$AIC
## [1] 684.3
##
## $OFMeasures$InformationCriteria$BIC
## [1] 701.89
##
##
##
## $Messages
## list()
Use 'ddmore' function as.xpdb() to create an Xpose database object from the Standardised Output object, regardless of target software used for estimation.
mlx.xpdb<-as.xpdb(mlx,datafile)
##
## Removed dose rows in rawData slot of SO to enable merge with Predictions data.
We can then call Xpose functions referencing this mlx.xpdb object as the input. Perform some basic goodness of fit (graphs are exported to PDF file)
print(basic.gof(mlx.xpdb))
print(ind.plots(mlx.xpdb))
Export graphs to a PDF file
pdf("GOF_MLX.pdf")
print(basic.gof(mlx.xpdb))
print(ind.plots(mlx.xpdb))
dev.off()
## png
## 2
By default, a covariance step is not run when estimating in NONMEM. To see how it can be requested, see UseCase1_1.mdl
NM <- estimate(mdlfile, target="NONMEM", subfolder="NONMEM")
## -- Tue Aug 16 12:42:58 2016
## New
## Submitted
## Job 5060ad68-955d-4004-b0f3-ae7ab4281d72 progress:
## Running [ .... ]
## Importing Results
## Copying the result data back to the local machine for job ID 5060ad68-955d-4004-b0f3-ae7ab4281d72...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job3564470829c0 to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase5/NONMEM
## Done.
##
##
## The following main elements were parsed successfully:
## RawResults
## Estimation::PopulationEstimates::MLE
## Estimation::IndividualEstimates::Estimates
## Estimation::IndividualEstimates::RandomEffects
## Estimation::Residuals::ResidualTable
## Estimation::Predictions
## Estimation::OFMeasures::Deviance
##
## The following MESSAGEs were raised during the job execution:
## estimation_successful: 1
## covariance_step_run: 0
## rounding_errors: 0
## estimate_near_boundary: 0
## s_matrix_singular: 0
## nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.27
##
## Completed
## -- Tue Aug 16 12:44:21 2016
Load previous results
NM <- LoadSOObject(“NONMEM/UseCase5.SO.xml”)
The ddmore function “getPopulationParameters” extracts the Population Parameter values from an R object of (S4) class “StandardOutputObject” and returns the estimates. See documentation for getPopulationParameters to see other arguments and settings for this function.
parameters_nm <- getPopulationParameters(NM, what="estimates")
print(getPopulationParameters(NM, what="estimates",block="structural"))
## $MLE
## POP_CL POP_V POP_KA POP_TLAG BETA_CL_AGE POP_FCL_FEM
## 0.14087600 8.09520000 1.67554000 0.92918900 0.00834105 0.13954700
## BETA_CL_WT BETA_V_WT
## 0.75000000 1.00000000
print(parameters_nm)
## $MLE
## POP_CL POP_V POP_KA POP_TLAG RUV_PROP RUV_ADD
## 0.14087600 8.09520000 1.67554000 0.92918900 0.07632220 0.16968600
## BETA_CL_AGE POP_FCL_FEM BETA_CL_WT BETA_V_WT PPV_CL CORR_CL_V
## 0.00834105 0.13954700 0.75000000 1.00000000 0.24275000 0.22216900
## PPV_V PPV_KA PPV_TLAG
## 0.13633900 0.94410400 0.10000000
print(getEstimationInfo(NM))
## $OFMeasures
## $OFMeasures$Deviance
## $OFMeasures$Deviance[[1]]
## [1] -267.5108
##
##
##
## $Messages
## $Messages$Info
## $Messages$Info$estimation_successful
## [1] "1"
##
## $Messages$Info$covariance_step_run
## [1] "0"
##
## $Messages$Info$rounding_errors
## [1] "0"
##
## $Messages$Info$estimate_near_boundary
## [1] "0"
##
## $Messages$Info$s_matrix_singular
## [1] "0"
##
## $Messages$Info$nmoutput2so_version
## [1] "This SOBlock was created with nmoutput2so version 4.5.27"
nm.xpdb<-as.xpdb(NM,datafile)
##
## Removed dose rows in rawData+Predictions slot of SO to enable merge with Residuals data.
##
## Residuals data does not currently contain dose rows in output from Nonmem executions.
Perform some basic goodness of fit (graphs are exported to PDF file)
print(basic.gof(nm.xpdb))
print(ind.plots(nm.xpdb))
print(parm.hist(nm.xpdb))
Export graphs to a PDF file
pdf("GOF_NM.pdf")
print(basic.gof(nm.xpdb))
print(ind.plots(nm.xpdb))
print(parm.hist(nm.xpdb))
dev.off()
## png
## 2
MDL Objects can be manipulated from R to change for example the estimation algorithm
myTaskProperties <- getTaskPropertiesObjects(mdlfile)[[1]]
myNewTaskProperties <- myTaskProperties
myNewTaskProperties@ESTIMATE$algo <- "focei"
Assembling the new Modelling Object Group (MOG). Note that we reuse the data, parameters and model from the MOG.
myNewerMOG <- createMogObj(dataObj = getDataObjects(mdlfile)[[1]],
parObj = getParameterObjects(mdlfile)[[1]],
mdlObj = getModelObjects(mdlfile)[[1]],
taskObj = myNewTaskProperties)
We can then write the MOG back out to an .mdl file.
mdlfile.FOCEI <- paste0(uc,"_FOCEI.mdl")
writeMogObj(myNewerMOG,mdlfile.FOCEI)
Test estimation using this new MOG.
By default, a covariance step is not run when estimating in PsN. To see how it can be requested, see UseCase1_1.mdl
NM.FOCEI <- estimate(mdlfile.FOCEI, target="PsN", subfolder="NONMEM_FOCEI")
## -- Tue Aug 16 12:44:36 2016
## New
## Submitted
## Job 299f1c43-73f4-4b13-a407-89ef334efaf9 progress:
## Running [ .. ]
## Importing Results
## Copying the result data back to the local machine for job ID 299f1c43-73f4-4b13-a407-89ef334efaf9...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job35643009563e to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase5/NONMEM_FOCEI
## Done.
##
##
## The following main elements were parsed successfully:
## RawResults
## Estimation::PopulationEstimates::MLE
## Estimation::IndividualEstimates::Estimates
## Estimation::IndividualEstimates::RandomEffects
## Estimation::Residuals::ResidualTable
## Estimation::Predictions
## Estimation::OFMeasures::Deviance
##
## The following MESSAGEs were raised during the job execution:
## estimation_successful: 1
## covariance_step_run: 0
## rounding_errors: 0
## hessian_reset: 0
## zero_gradients: 0
## final_zero_gradients: 0
## estimate_near_boundary: 0
## s_matrix_singular: 0
## significant_digits: 3.2
## nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.27
##
## Completed
## -- Tue Aug 16 12:45:19 2016
Load previous results
NM.FOCEI <- LoadSOObject(“NONMEM_FOCEI/UseCase5_FOCEI.SO.xml”)
Results from NONMEM should be comparable to previous results
print(getPopulationParameters(NM.FOCEI, what="estimates"))
## $MLE
## POP_CL POP_V POP_KA POP_TLAG RUV_PROP RUV_ADD
## 0.1416380 8.1059900 1.5625700 0.9676060 0.0708693 0.1971440
## BETA_CL_AGE POP_FCL_FEM BETA_CL_WT BETA_V_WT PPV_CL CORR_CL_V
## 0.0084406 0.1384690 0.7500000 1.0000000 0.2391590 0.2197880
## PPV_V PPV_KA PPV_TLAG
## 0.1347520 0.9333550 0.1000000
print(parameters_nm)
## $MLE
## POP_CL POP_V POP_KA POP_TLAG RUV_PROP RUV_ADD
## 0.14087600 8.09520000 1.67554000 0.92918900 0.07632220 0.16968600
## BETA_CL_AGE POP_FCL_FEM BETA_CL_WT BETA_V_WT PPV_CL CORR_CL_V
## 0.00834105 0.13954700 0.75000000 1.00000000 0.24275000 0.22216900
## PPV_V PPV_KA PPV_TLAG
## 0.13633900 0.94410400 0.10000000
nmfocei.xpdb<-as.xpdb(NM.FOCEI,datafile)
##
## Removed dose rows in rawData+Predictions slot of SO to enable merge with Residuals data.
##
## Residuals data does not currently contain dose rows in output from Nonmem executions.
Basic diagnostics for NONMEM fit.
print(basic.gof(nmfocei.xpdb))
Export graphs to a PDF file
pdf("GOF_NM_FOCEI.pdf")
print(basic.gof(nmfocei.xpdb))
dev.off()
## png
## 2
The ddmore “bootstrap.PsN” function is a wrap up function that calls Bootstrap PsN functionality using as input an MDL file that will be translated to NMTRAN as first step. Additional PsN arguments can be specified under the “bootstrapOptions” attribute. After task execution, the output from PsN is converted to a Standardised Output object which is saved in a .SO.xml file. Translated files and PsN output will be returned in the ./Bootstrap subfolder
bootstrapResults <- bootstrap.PsN(mdlfile.FOCEI, samples=20, seed=123456,
bootstrapOptions=" -no-skip_minimization_terminated -threads=2",
subfolder="Bootstrap", plot=TRUE)
## -- Tue Aug 16 12:45:22 2016
## New
## Submitted
## Job 2b2f867d-3d34-43fb-8204-e4c223e4cc18 progress:
## Running [ ..... ]
## Importing Results
## Copying the result data back to the local machine for job ID 2b2f867d-3d34-43fb-8204-e4c223e4cc18...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job35645a831133 to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase5/Bootstrap
## Done.
##
##
## The following main elements were parsed successfully:
## RawResults
## Estimation::PopulationEstimates::MLE
## Estimation::PopulationEstimates::OtherMethodBootstrap
## Estimation::PrecisionPopulationEstimates::OtherMethodBootstrap
## Estimation::IndividualEstimates::Estimates
## Estimation::IndividualEstimates::RandomEffects
## Estimation::Residuals::ResidualTable
## Estimation::Predictions
## Estimation::OFMeasures::Deviance
##
## The following WARNINGs were raised during the job execution:
## bootstrap_parameter_scale: The parameters PPV_CL, CORR_CL_V, PPV_V, PPV_KA and PPV_TLAG were requested on the sd/corr scale but are given on the var/cov scale in all bootstrap results.
##
## The following MESSAGEs were raised during the job execution:
## estimation_successful: 1
## covariance_step_run: 0
## rounding_errors: 0
## hessian_reset: 0
## zero_gradients: 0
## final_zero_gradients: 0
## estimate_near_boundary: 0
## s_matrix_singular: 0
## significant_digits: 3.2
## nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.27
##
## Completed
## -- Tue Aug 16 12:47:04 2016
## Warning: NAs introduced by coercion
## [[1]]
##
## [[2]]
## NULL
##
## [[3]]
##
## [[4]]
## NULL
##
## [[5]]
## NULL
Load results from a bootstrap previously performed
bootstrapResults <- LoadSOObject(“Bootstrap/UseCase5_FOCEI.SO.xml”)
Export bootstrap histograms to a pdf
pdf(paste0(uc,"_Bootstrap.pdf"))
print(boot.hist(results.file = file.path("Bootstrap",paste0("raw_results_",uc,"_FOCEI.csv")),
incl.ids.file = file.path("Bootstrap","included_individuals1.csv")))
## [[1]]
##
## [[2]]
## NULL
##
## [[3]]
##
## [[4]]
## NULL
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## [[5]]
## NULL
dev.off()
## png
## 2
Extract parameter estimates and precision from bootstrap results.
print(getPopulationParameters(bootstrapResults, what="estimates"))
## $MLE
## POP_CL POP_V POP_KA POP_TLAG RUV_PROP RUV_ADD
## 0.1416380 8.1059900 1.5625700 0.9676060 0.0708693 0.1971440
## BETA_CL_AGE POP_FCL_FEM BETA_CL_WT BETA_V_WT PPV_CL CORR_CL_V
## 0.0084406 0.1384690 0.7500000 1.0000000 0.2391590 0.2197880
## PPV_V PPV_KA PPV_TLAG
## 0.1347520 0.9333550 0.1000000
##
## $Bootstrap
## Parameter Mean Median
## BETA_CL_AGE BETA_CL_AGE 0.007395582 0.00610113
## BETA_CL_WT BETA_CL_WT 0.750000000 0.75000000
## BETA_V_WT BETA_V_WT 1.000000000 1.00000000
## CORR_CL_V CORR_CL_V 0.005746716 0.00520061
## POP_CL POP_CL 0.140022800 0.13875250
## POP_FCL_FEM POP_FCL_FEM 0.202825200 0.16671250
## POP_KA POP_KA 1.377451000 1.25151000
## POP_TLAG POP_TLAG 0.924424300 0.92490900
## POP_V POP_V 8.128746000 8.10086500
## PPV_CL PPV_CL 0.052005950 0.05808665
## PPV_KA PPV_KA 0.603459100 0.54871450
## PPV_TLAG PPV_TLAG 0.010000000 0.01000000
## PPV_V PPV_V 0.015249290 0.01537295
## RUV_ADD RUV_ADD 0.100929000 0.00109900
## RUV_PROP RUV_PROP 0.096397020 0.09835285
Extract the information regarding the precision intervals
print(getPopulationParameters(bootstrapResults, what="intervals")$Bootstrap)
## Parameter Mean Median Perc_5 Perc_95
## 1 BETA_CL_AGE 0.007395582 0.00610113 -0.0003933975 0.02380566
## 2 BETA_CL_WT 0.750000000 0.75000000 0.7500000000 0.75000000
## 3 BETA_V_WT 1.000000000 1.00000000 1.0000000000 1.00000000
## 4 CORR_CL_V 0.005746716 0.00520061 -0.0020135530 0.01327641
## 5 POP_CL 0.140022800 0.13875250 0.1222508000 0.16438250
## 6 POP_FCL_FEM 0.202825200 0.16671250 0.0100000000 0.63829940
## 7 POP_KA 1.377451000 1.25151000 0.8084377000 2.08947600
## 8 POP_TLAG 0.924424300 0.92490900 0.7721754000 1.03391600
## 9 POP_V 8.128746000 8.10086500 7.6829890000 8.56017400
## 10 PPV_CL 0.052005950 0.05808665 0.0146228900 0.08469345
## 11 PPV_KA 0.603459100 0.54871450 0.0064315750 1.68081400
## 12 PPV_TLAG 0.010000000 0.01000000 0.0100000000 0.01000000
## 13 PPV_V 0.015249290 0.01537295 0.0073927160 0.02853726
## 14 RUV_ADD 0.100929000 0.00109900 0.0010990000 0.32564240
## 15 RUV_PROP 0.096397020 0.09835285 0.0475589900 0.14464240
Before running the VPC with PsN we must update the (initial) values in the MDL Parameter Object. MLE estimates from previous step can be used
structuralPar <- getPopulationParameters(NM.FOCEI, what="estimates",block='structural')$MLE
variabilityPar <- getPopulationParameters(NM.FOCEI, what="estimates",block='variability')$MLE
Update the parameter object using the ddmore “updateParObj” function. This function updates an R object of (S4) class “parObj”. The user chooses which block to update, what items within that block, and what to replace those items with. NOTE: that updateParObj can only update attributes which ALREADY EXIST in the MDL Parameter Object for that item. This ensures that valid MDL is preserved.
myParObj <- getParameterObjects(mdlfile)[[1]]
myParObjUpdated <- updateParObj(myParObj,block="STRUCTURAL",
item=names(structuralPar),
with=list(value=structuralPar))
myParObjUpdated <- updateParObj(myParObjUpdated,block="VARIABILITY",
item=names(variabilityPar),
with=list(value=variabilityPar))
Check that the appropriate initial values have been updated to the MLE values from the previous fit.
print(myParObjUpdated@STRUCTURAL)
## $POP_CL
## $POP_CL$value
## [1] "0.141638"
##
## $POP_CL$lo
## [1] "0.001"
##
##
## $POP_V
## $POP_V$value
## [1] "8.10599"
##
## $POP_V$lo
## [1] "0.001"
##
##
## $POP_KA
## $POP_KA$value
## [1] "1.56257"
##
## $POP_KA$lo
## [1] "0.001"
##
##
## $POP_TLAG
## $POP_TLAG$value
## [1] "0.967606"
##
## $POP_TLAG$lo
## [1] "0.001"
##
##
## $BETA_CL_WT
## $BETA_CL_WT$value
## [1] "0.75"
##
## $BETA_CL_WT$fix
## [1] "true"
##
##
## $BETA_V_WT
## $BETA_V_WT$value
## [1] "1"
##
## $BETA_V_WT$fix
## [1] "true"
##
##
## $BETA_CL_AGE
## $BETA_CL_AGE$value
## [1] "0.0084406"
##
##
## $POP_FCL_FEM
## $POP_FCL_FEM$value
## [1] "0.138469"
##
## $POP_FCL_FEM$lo
## [1] "0"
print(myParObjUpdated@VARIABILITY)
## $PPV_CL
## $PPV_CL$value
## [1] "0.239159"
##
##
## $PPV_V
## $PPV_V$value
## [1] "0.134752"
##
##
## $PPV_KA
## $PPV_KA$value
## [1] "0.933355"
##
##
## $PPV_TLAG
## $PPV_TLAG$value
## [1] "0.1"
##
## $PPV_TLAG$fix
## [1] "true"
##
##
## $PPV_FORAL
## $PPV_FORAL$value
## [1] "0.1"
##
##
## $CORR_CL_V
## $CORR_CL_V$value
## [1] "0.219788"
##
##
## $RUV_PROP
## $RUV_PROP$value
## [1] "0.0708693"
##
## $RUV_PROP$lo
## [1] "0"
##
##
## $RUV_ADD
## $RUV_ADD$value
## [1] "0.197144"
##
## $RUV_ADD$lo
## [1] "1.0E-4"
Assembling the new MOG. Note that we reuse the data and model from the previous run.
myVPCMOG <- createMogObj(dataObj = getDataObjects(mdlfile)[[1]],
parObj = myParObjUpdated,
mdlObj = getModelObjects(mdlfile)[[1]],
taskObj = getTaskPropertiesObjects(mdlfile)[[1]])
We can then write the MOG back out to an .mdl file.
mdlfile.VPC <- paste0(uc,"_VPC.mdl")
writeMogObj(myVPCMOG,mdlfile.VPC)
Similarly as above, ddmore “VPC.PsN” function can be used to run a VPC using PsN as target tool
vpcFiles <- VPC.PsN(mdlfile.VPC,samples=20, seed=12345,
vpcOptions ="-n_simulation=10 -auto_bin=10",
subfolder="VPC", plot=TRUE)
## -- Tue Aug 16 12:47:17 2016
## New
## Submitted
## Job b8714b82-244a-480d-ba5e-606b266970d7 progress:
## Running [ ... ]
## Importing Results
## Copying the result data back to the local machine for job ID b8714b82-244a-480d-ba5e-606b266970d7...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job35642ad36ccd to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase5/VPC
## Done.
##
##
## The following main elements were parsed successfully:
## RawResults
## SimulationSimulationBlock
## SimulationSimulationBlock
##
## The following MESSAGEs were raised during the job execution:
## nmoutput2so_version: This SOBlock was created with nmoutput2so version 4.5.27
##
## Completed
## -- Tue Aug 16 12:48:20 2016
To replay the visualisation using information from the VPC SO file
pdf(paste0(uc,"_VPC.pdf"))
print(xpose.VPC(vpc.info= file.path("./VPC",vpcFiles@RawResults@DataFiles$PsN_VPC_results$path),
vpctab= file.path("./VPC",vpcFiles@RawResults@DataFiles$PsN_VPC_vpctab$path),
main="VPC warfarin"))
dev.off()
## png
## 2
Simulation with simulx is not yet possible in models with categorical covariates.
# #' The mlxR package has been developed to visualize and explore models that are encoded in
# #' MLXTRAN or PharmML
# #' The ddmore function as.PharmML translates an MDL file (extension .mdl) to its PharmML
# #' representation. The output file (extension .xml) is saved in the working directory.
# #+ Simulation via simulx
# myPharmML <- as.PharmML(mdlfile)
#
# #' Use parameter values from the FOCEI estimation
# parValues <- getPopulationParameters(NM.FOCEI, what="estimates")$MLE
#
# #' Simulate for the typical weight of 70, typical age of 40, and female.
# p <- c(parValues,WT=70,AGE=40,SEX=1)
#
# #' Parameter values used in simulation
# print(p)
#
# #' Simulate PK parameters for individuals
# ind <- list(name = c('TLAG','KA','CL','V'))
#
# #' Simulate predicted (CC) and observed concentration values (Y)
# f <- list( name = c('CC'), time = seq(0,to=50,by=1))
# y <- list( name = c('Y'), time = c(0, 0.5, 1, 2, 3, 4, 6, 8, 12, 24, 36, 48))
#
# #' Simulate for a dose of 100mg given at time 0 into the GUT (oral administration)
# adm <- list(time = 0, amount = 100, target="GUT")
#
# #' Simulate 12 subjects
# g <- list( size = 12, level = 'individual', treatment=adm)
#
# #' Call simulx
# res <- simulx(model =myPharmML,
# parameter = p,
# group = g,
# output = list(ind,f,y))
# #' Simulated parameter values for each individual
# print(res$parameter)
#
# #' Plot simulated results
# plot(ggplot() +
# geom_line(data=res$CC, aes(x=time, y=CC, colour=id)) +
# geom_line(data=res$Y, aes(x=time, y=Y, colour=id)) +
# xlab("time (h)") + ylab("concentration") )
#
# #' Simulate 1000 subjects - with simulx this is a QUICK process!
# #+ VPC with simulx
# g <- list( size = 1000, level = 'individual', treatment = adm)
#
# #' Call simulx
# res.1000 <- simulx(model =myPharmML,
# parameter = p,
# group = g,
# output = list(ind,y))
#
# #' Plot prediction intervals with `prctilemlx`. `band` defines the percentile bands displayed:
# #' * level = range of values to examine (in %; 100 = full range of values)
# #' * number = number of bins within the level range.
# plot(prctilemlx(res.1000$Y,band=list(number=9, level=90)))
#
# #' Table of the same information
# print(prctilemlx(res.1000$Y,band=list(number=10, level=100), plot=F)$y)