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 <- "UseCase1"
datafile <- "warfarin_conc.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)
## Warning in dir.create(wd):
## 'C:\SEE\MDL_IDE\workspace\UseCasesDemo\UseCase1' already exists
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] "Bootstrap" "GOF_MLX.pdf"
## [3] "GOF_NM.pdf" "GOF_NM_FOCEI.pdf"
## [5] "Monolix" "NONMEM"
## [7] "NONMEM_FOCEI" "UseCase1.mdl"
## [9] "UseCase1.xml" "UseCase1_Bootstrap.pdf"
## [11] "UseCase1_EGA.pdf" "UseCase1_FOCEI.mdl"
## [13] "UseCase1_tr.txt" "UseCase1_VPC.mdl"
## [15] "UseCase1_VPC.pdf" "VPC"
## [17] "warfarin_conc.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_ODE_dat" "warfarin_PK_ODE_par" "warfarin_PK_ODE_mdl"
## [4] "warfarin_PK_ODE_task"
str(myMDLObj)
## List of 4
## $ warfarin_PK_ODE_dat :Formal class 'dataObj' [package "ddmore"] with 5 slots
## .. ..@ SOURCE :List of 1
## .. .. ..$ srcfile:List of 2
## .. .. .. ..$ file : chr "warfarin_conc.csv"
## .. .. .. ..$ inputFormat: chr "nonmemFormat"
## .. ..@ DECLARED_VARIABLES :List of 2
## .. .. ..$ :List of 3
## .. .. .. ..$ .subtype: chr "EquationDef"
## .. .. .. ..$ name : chr "GUT"
## .. .. .. ..$ typeSpec: chr "dosingTarget"
## .. .. ..$ :List of 3
## .. .. .. ..$ .subtype: chr "EquationDef"
## .. .. .. ..$ name : chr "Y"
## .. .. .. ..$ typeSpec: chr "observation"
## .. ..@ DATA_INPUT_VARIABLES :List of 8
## .. .. ..$ ID :List of 1
## .. .. .. ..$ use: chr "id"
## .. .. ..$ TIME :List of 1
## .. .. .. ..$ use: chr "idv"
## .. .. ..$ WT :List of 1
## .. .. .. ..$ use: chr "covariate"
## .. .. ..$ AMT :List of 2
## .. .. .. ..$ use : chr "amt"
## .. .. .. ..$ variable: chr "GUT"
## .. .. ..$ DVID :List of 1
## .. .. .. ..$ use: chr "dvid"
## .. .. ..$ DV :List of 2
## .. .. .. ..$ use : chr "dv"
## .. .. .. ..$ variable: chr "Y"
## .. .. ..$ MDV :List of 1
## .. .. .. ..$ use: chr "mdv"
## .. .. ..$ logtWT:List of 1
## .. .. .. ..$ use: chr "covariate"
## .. ..@ DATA_DERIVED_VARIABLES: list()
## .. ..@ name : chr "warfarin_PK_ODE_dat"
## $ warfarin_PK_ODE_par :Formal class 'parObj' [package "ddmore"] with 4 slots
## .. ..@ DECLARED_VARIABLES: list()
## .. ..@ STRUCTURAL :List of 6
## .. .. ..$ POP_CL :List of 2
## .. .. .. ..$ value: chr "0.1"
## .. .. .. ..$ lo : chr "0.001"
## .. .. ..$ POP_V :List of 2
## .. .. .. ..$ value: chr "8"
## .. .. .. ..$ lo : chr "0.001"
## .. .. ..$ POP_KA :List of 2
## .. .. .. ..$ value: chr "0.362"
## .. .. .. ..$ lo : chr "0.001"
## .. .. ..$ POP_TLAG :List of 2
## .. .. .. ..$ value: chr "1"
## .. .. .. ..$ lo : chr "0.001"
## .. .. ..$ BETA_CL_WT:List of 2
## .. .. .. ..$ value: chr "0.75"
## .. .. .. ..$ fix : chr "true"
## .. .. ..$ BETA_V_WT :List of 2
## .. .. .. ..$ value: chr "1"
## .. .. .. ..$ fix : chr "true"
## .. ..@ VARIABILITY :List of 7
## .. .. ..$ PPV_CL :List of 1
## .. .. .. ..$ value: chr "0.1"
## .. .. ..$ PPV_V :List of 1
## .. .. .. ..$ value: chr "0.1"
## .. .. ..$ PPV_KA :List of 1
## .. .. .. ..$ value: chr "0.1"
## .. .. ..$ PPV_TLAG :List of 2
## .. .. .. ..$ value: chr "0.1"
## .. .. .. ..$ fix : chr "true"
## .. .. ..$ CORR_CL_V:List of 1
## .. .. .. ..$ value: chr "0.01"
## .. .. ..$ RUV_PROP :List of 2
## .. .. .. ..$ value: chr "0.1"
## .. .. .. ..$ lo : chr "0"
## .. .. ..$ RUV_ADD :List of 2
## .. .. .. ..$ value: chr "0.1"
## .. .. .. ..$ lo : chr "1.0E-4"
## .. ..@ name : chr "warfarin_PK_ODE_par"
## $ warfarin_PK_ODE_mdl :Formal class 'mdlObj' [package "ddmore"] with 11 slots
## .. ..@ IDV : chr "T"
## .. ..@ COVARIATES :List of 1
## .. .. ..$ :List of 2
## .. .. .. ..$ .subtype: chr "EquationDef"
## .. .. .. ..$ name : chr "logtWT"
## .. ..@ VARIABILITY_LEVELS :List of 2
## .. .. ..$ :List of 1
## .. .. .. ..$ ID:List of 2
## .. .. .. .. ..$ level: chr "2"
## .. .. .. .. ..$ type : chr "parameter"
## .. .. ..$ :List of 1
## .. .. .. ..$ DV:List of 2
## .. .. .. .. ..$ level: chr "1"
## .. .. .. .. ..$ type : chr "observation"
## .. ..@ STRUCTURAL_PARAMETERS : chr [1:6] "POP_CL" "POP_V" "POP_KA" "POP_TLAG" ...
## .. ..@ VARIABILITY_PARAMETERS : chr [1:7] "PPV_CL" "PPV_V" "CORR_CL_V" "PPV_KA" ...
## .. ..@ RANDOM_VARIABLE_DEFINITION:List of 6
## .. .. ..$ :List of 4
## .. .. .. ..$ .subtype: chr "RandVarDefn"
## .. .. .. ..$ blkAttrs:List of 1
## .. .. .. .. ..$ level: chr "DV"
## .. .. .. ..$ name : chr "EPS_Y"
## .. .. .. ..$ distn : chr "Normal(mean=0, var=1)"
## .. .. ..$ :List of 4
## .. .. .. ..$ .subtype: chr "RandVarDefn"
## .. .. .. ..$ blkAttrs:List of 1
## .. .. .. .. ..$ level: chr "ID"
## .. .. .. ..$ name : chr "ETA_CL"
## .. .. .. ..$ distn : chr "Normal(mean=0, sd=PPV_CL)"
## .. .. ..$ :List of 4
## .. .. .. ..$ .subtype: chr "RandVarDefn"
## .. .. .. ..$ blkAttrs:List of 1
## .. .. .. .. ..$ level: chr "ID"
## .. .. .. ..$ name : chr "ETA_V"
## .. .. .. ..$ distn : chr "Normal(mean=0, sd=PPV_V)"
## .. .. ..$ :List of 4
## .. .. .. ..$ .subtype: chr "RandVarDefn"
## .. .. .. ..$ blkAttrs:List of 1
## .. .. .. .. ..$ level: chr "ID"
## .. .. .. ..$ name : chr "ETA_KA"
## .. .. .. ..$ distn : chr "Normal(mean=0, sd=PPV_KA)"
## .. .. ..$ :List of 2
## .. .. .. ..$ :List of 4
## .. .. .. .. ..$ type : chr "correlation"
## .. .. .. .. ..$ rv1 : chr "ETA_CL"
## .. .. .. .. ..$ rv2 : chr "ETA_V"
## .. .. .. .. ..$ value: chr "CORR_CL_V"
## .. .. .. ..$ blkAttrs:List of 1
## .. .. .. .. ..$ level: chr "ID"
## .. .. ..$ :List of 4
## .. .. .. ..$ .subtype: chr "RandVarDefn"
## .. .. .. ..$ blkAttrs:List of 1
## .. .. .. .. ..$ level: chr "ID"
## .. .. .. ..$ name : chr "ETA_TLAG"
## .. .. .. ..$ distn : chr "Normal(mean=0, sd=PPV_TLAG)"
## .. ..@ INDIVIDUAL_VARIABLES :List of 4
## .. .. ..$ :List of 1
## .. .. .. ..$ CL:List of 5
## .. .. .. .. ..$ type : chr "linear"
## .. .. .. .. ..$ trans : chr "ln"
## .. .. .. .. ..$ pop : chr "POP_CL"
## .. .. .. .. ..$ fixEff:List of 1
## .. .. .. .. .. ..$ :List of 2
## .. .. .. .. .. .. ..$ coeff: chr "BETA_CL_WT"
## .. .. .. .. .. .. ..$ cov : chr "logtWT"
## .. .. .. .. ..$ ranEff: chr "ETA_CL"
## .. .. ..$ :List of 1
## .. .. .. ..$ V:List of 5
## .. .. .. .. ..$ type : chr "linear"
## .. .. .. .. ..$ trans : chr "ln"
## .. .. .. .. ..$ pop : chr "POP_V"
## .. .. .. .. ..$ fixEff:List of 1
## .. .. .. .. .. ..$ :List of 2
## .. .. .. .. .. .. ..$ coeff: chr "BETA_V_WT"
## .. .. .. .. .. .. ..$ cov : chr "logtWT"
## .. .. .. .. ..$ ranEff: chr "ETA_V"
## .. .. ..$ :List of 1
## .. .. .. ..$ KA:List of 4
## .. .. .. .. ..$ type : chr "linear"
## .. .. .. .. ..$ trans : chr "ln"
## .. .. .. .. ..$ pop : chr "POP_KA"
## .. .. .. .. ..$ ranEff: chr "ETA_KA"
## .. .. ..$ :List of 1
## .. .. .. ..$ TLAG:List of 4
## .. .. .. .. ..$ type : chr "linear"
## .. .. .. .. ..$ trans : chr "ln"
## .. .. .. .. ..$ pop : chr "POP_TLAG"
## .. .. .. .. ..$ ranEff: chr "ETA_TLAG"
## .. ..@ MODEL_PREDICTION :List of 2
## .. .. ..$ :List of 2
## .. .. .. ..$ DEQ :List of 3
## .. .. .. .. ..$ :List of 3
## .. .. .. .. .. ..$ .subtype: chr "EquationDef"
## .. .. .. .. .. ..$ name : chr "RATEIN"
## .. .. .. .. .. ..$ expr : chr "if (T>=TLAG) then GUT*KA else 0"
## .. .. .. .. ..$ :List of 1
## .. .. .. .. .. ..$ GUT:List of 3
## .. .. .. .. .. .. ..$ deriv: chr "(-RATEIN)"
## .. .. .. .. .. .. ..$ init : chr "0"
## .. .. .. .. .. .. ..$ x0 : chr "0"
## .. .. .. .. ..$ :List of 1
## .. .. .. .. .. ..$ CENTRAL:List of 3
## .. .. .. .. .. .. ..$ deriv: chr "(RATEIN-CL*CENTRAL/V)"
## .. .. .. .. .. .. ..$ init : chr "0"
## .. .. .. .. .. .. ..$ x0 : chr "0"
## .. .. .. ..$ .subtype: chr "BlockStmt"
## .. .. ..$ :List of 3
## .. .. .. ..$ .subtype: chr "EquationDef"
## .. .. .. ..$ name : chr "CC"
## .. .. .. ..$ expr : chr "CENTRAL/V"
## .. ..@ OBSERVATION :List of 1
## .. .. ..$ :List of 1
## .. .. .. ..$ Y:List of 5
## .. .. .. .. ..$ type : chr "combinedError1"
## .. .. .. .. ..$ additive : chr "RUV_ADD"
## .. .. .. .. ..$ proportional: chr "RUV_PROP"
## .. .. .. .. ..$ eps : chr "EPS_Y"
## .. .. .. .. ..$ prediction : chr "CC"
## .. ..@ GROUP_VARIABLES : list()
## .. ..@ name : chr "warfarin_PK_ODE_mdl"
## $ warfarin_PK_ODE_task:Formal class 'taskObj' [package "ddmore"] with 5 slots
## .. ..@ ESTIMATE:List of 2
## .. .. ..$ algo : chr "saem"
## .. .. ..$ blocks: list()
## .. ..@ SIMULATE: NULL
## .. ..@ EVALUATE: NULL
## .. ..@ OPTIMISE: NULL
## .. ..@ name : chr "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 Hover 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 model 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 AMT DVID DV MDV logtWT
## 1 1 0.0 66.7 100 0 NA 1 -0.04829029
## 2 1 0.5 66.7 NA 1 0.0 0 -0.04829029
## 3 1 1.0 66.7 NA 1 1.9 0 -0.04829029
## 4 1 2.0 66.7 NA 1 3.3 0 -0.04829029
## 5 1 3.0 66.7 NA 1 6.6 0 -0.04829029
## 6 1 6.0 66.7 NA 1 9.1 0 -0.04829029
Extract only observation records
myEDAData<-myData[myData$MDV==0,]
Open an R window to record and access all your plots
windows(record=TRUE)
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 the results in 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")
## -- Mon Aug 15 14:26:38 2016
## New
## Submitted
## Job 6c624368-0348-4904-ad5c-c4f1e6a90bf8 progress:
## Running [ ...... ]
## Importing Results
## Copying the result data back to the local machine for job ID 6c624368-0348-4904-ad5c-c4f1e6a90bf8...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job35642e07595c to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase1/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
## -- Mon Aug 15 14:28:40 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/UseCase1.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
parameters_mlx
## POP_V BETA_V_WT POP_KA POP_CL BETA_CL_WT POP_TLAG
## 8.08213 1.00000 1.91540 0.13404 0.75000 0.97620
## PPV_V PPV_KA PPV_CL PPV_TLAG CORR_CL_V RUV_ADD
## 0.13068 1.17050 0.26539 0.10000 0.20615 0.22301
## RUV_PROP
## 0.06579
getPopulationParameters(mlx, what="precisions")
## $MLE
## Parameter MLE SE RSE
## 1 BETA_CL_WT 0.75000 0.00000 0.00
## 2 BETA_V_WT 1.00000 0.00000 0.00
## 3 CORR_CL_V 0.20615 0.20897 101.37
## 4 POP_CL 0.13404 0.00652 4.86
## 5 POP_KA 1.91540 0.64466 33.66
## 6 POP_TLAG 0.97620 0.02119 2.17
## 7 POP_V 8.08213 0.22096 2.73
## 8 PPV_CL 0.26539 0.03555 13.39
## 9 PPV_KA 1.17050 0.25543 21.82
## 10 PPV_TLAG 0.10000 0.00000 0.00
## 11 PPV_V 0.13068 0.02251 17.23
## 12 RUV_ADD 0.22301 0.04731 21.21
## 13 RUV_PROP 0.06579 0.00947 14.40
getEstimationInfo(mlx)
## $OFMeasures
## $OFMeasures$LogLikelihood
## $OFMeasures$LogLikelihood[[1]]
## [1] -369.305
##
##
## $OFMeasures$IndividualContribToLL
## Subject ICtoLL
## 1 1 -24.74
## 2 2 -5.28
## 3 3 -15.02
## 4 4 -14.06
## 5 5 -14.62
## 6 6 -7.56
## 7 7 -19.51
## 8 8 -22.20
## 9 9 -31.28
## 10 10 -5.77
## 11 12 -19.88
## 12 13 -19.23
## 13 14 -19.13
## 14 15 -11.29
## 15 16 -16.60
## 16 17 -7.29
## 17 18 -5.43
## 18 19 -8.10
## 19 20 -6.69
## 20 21 -7.69
## 21 22 -7.93
## 22 23 -8.07
## 23 24 -6.98
## 24 25 -7.27
## 25 26 -9.76
## 26 27 -5.44
## 27 28 -9.14
## 28 29 -5.68
## 29 30 -8.34
## 30 31 -7.73
## 31 32 -4.76
## 32 33 -6.82
##
## $OFMeasures$InformationCriteria
## $OFMeasures$InformationCriteria$AIC
## [1] 758.61
##
## $OFMeasures$InformationCriteria$BIC
## [1] 773.27
##
##
##
## $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
print(basic.gof(mlx.xpdb))
print(ind.plots(mlx.xpdb))
Export graphs to 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")
## -- Mon Aug 15 15:51:33 2016
## New
## Submitted
## Job a2d41579-6e9f-4160-b1a2-d96f5d4e12e2 progress:
## Running [ ............................. ]
## Importing Results
## Copying the result data back to the local machine for job ID a2d41579-6e9f-4160-b1a2-d96f5d4e12e2...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job35646d455ffe to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase1/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
## -- Mon Aug 15 16:01:18 2016
Load previous results NM <- LoadSOObject(“NONMEM/UseCase1.SO.xml”)
Results from NONMEM should be comparable with results from MONOLIX
parameters_nm <- getPopulationParameters(NM, what="estimates")
print(parameters_nm)
## $MLE
## POP_CL POP_V POP_KA POP_TLAG RUV_PROP RUV_ADD
## 0.132862000 8.163970000 1.755850000 0.950650000 0.107732000 0.000100001
## BETA_CL_WT BETA_V_WT PPV_CL CORR_CL_V PPV_V PPV_KA
## 0.750000000 1.000000000 0.262838000 0.275564000 0.138113000 0.943956000
## PPV_TLAG
## 0.100000000
print(parameters_mlx)
## POP_V BETA_V_WT POP_KA POP_CL BETA_CL_WT POP_TLAG
## 8.08213 1.00000 1.91540 0.13404 0.75000 0.97620
## PPV_V PPV_KA PPV_CL PPV_TLAG CORR_CL_V RUV_ADD
## 0.13068 1.17050 0.26539 0.10000 0.20615 0.22301
## RUV_PROP
## 0.06579
print(getEstimationInfo(NM))
## $OFMeasures
## $OFMeasures$Deviance
## $OFMeasures$Deviance[[1]]
## [1] -310.8532
##
##
##
## $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$nmoutput2so_version
## [1] "This SOBlock was created with nmoutput2so version 4.5.27"
nm.xpdb<-as.xpdb(NM,datafile)
##
## Removed dose rows in rawData+Predictions slot of SO to enable merge with Residuals data.
##
## Residuals data does not currently contain dose rows in output from Nonmem executions.
Perform some basic goofness of fit
print(basic.gof(nm.xpdb))
print(ind.plots(nm.xpdb))
print(parm.hist(nm.xpdb))
Export plots to a pdf file
pdf("GOF_NM.pdf")
print(basic.gof(nm.xpdb))
print(ind.plots(nm.xpdb))
print(parm.hist(nm.xpdb))
dev.off()
## png
## 2
MDL Objects can be manipulated from R to change for example the estimation algorithm
myTaskProperties <- getTaskPropertiesObjects(mdlfile)[[1]]
myNewTaskProperties <- myTaskProperties
myNewTaskProperties@ESTIMATE$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 in NONMEM via PsN
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")
## -- Mon Aug 15 16:01:33 2016
## New
## Submitted
## Job 1b928cc4-f793-4b5b-820c-3abf378e6f94 progress:
## Running [ ...... ]
## Importing Results
## Copying the result data back to the local machine for job ID 1b928cc4-f793-4b5b-820c-3abf378e6f94...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job356437e2464b to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase1/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
## -- Mon Aug 15 16:03:36 2016
Load previous results NM.FOCEI <- LoadSOObject(“NONMEM_FOCEI/UseCase1_FOCEI.SO.xml”) Results from NONMEM should be comparable to previous results
getPopulationParameters(NM.FOCEI, what="estimates")
## $MLE
## POP_CL POP_V POP_KA POP_TLAG RUV_PROP RUV_ADD
## 0.1346600 8.0468500 1.2944300 0.8629310 0.0628024 0.3147830
## BETA_CL_WT BETA_V_WT PPV_CL CORR_CL_V PPV_V PPV_KA
## 0.7500000 1.0000000 0.2635890 0.2128860 0.1366690 0.9175980
## PPV_TLAG
## 0.1000000
parameters_nm
## $MLE
## POP_CL POP_V POP_KA POP_TLAG RUV_PROP RUV_ADD
## 0.132862000 8.163970000 1.755850000 0.950650000 0.107732000 0.000100001
## BETA_CL_WT BETA_V_WT PPV_CL CORR_CL_V PPV_V PPV_KA
## 0.750000000 1.000000000 0.262838000 0.275564000 0.138113000 0.943956000
## PPV_TLAG
## 0.100000000
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 graph to a pdf
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)
## -- Mon Aug 15 16:03:39 2016
## New
## Submitted
## Job 6b50dc81-b512-4384-9614-3b678761952e progress:
## Running [ ......................................................................... ]
## Importing Results
## Copying the result data back to the local machine for job ID 6b50dc81-b512-4384-9614-3b678761952e...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job3564439f3a39 to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase1/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
## -- Mon Aug 15 16:28:04 2016
## Warning: NAs introduced by coercion
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Load results from a bootstrap previously performed bootstrapResults <- LoadSOObject(“Bootstrap/UseCase1_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]]
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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
## 0.1346600 8.0468500 1.2944300 0.8629310 0.0628024 0.3147830
## BETA_CL_WT BETA_V_WT PPV_CL CORR_CL_V PPV_V PPV_KA
## 0.7500000 1.0000000 0.2635890 0.2128860 0.1366690 0.9175980
## PPV_TLAG
## 0.1000000
##
## $Bootstrap
## Parameter Mean Median
## BETA_CL_WT BETA_CL_WT 0.750000000 0.750000000
## BETA_V_WT BETA_V_WT 1.000000000 1.000000000
## CORR_CL_V CORR_CL_V 0.006601379 0.005672145
## POP_CL POP_CL 0.135766800 0.136824000
## POP_KA POP_KA 1.357139000 1.242695000
## POP_TLAG POP_TLAG 0.849418600 0.869470500
## POP_V POP_V 8.034198000 7.973425000
## PPV_CL PPV_CL 0.071233230 0.070891400
## PPV_KA PPV_KA 0.715497300 0.716611500
## PPV_TLAG PPV_TLAG 0.010000000 0.010000000
## PPV_V PPV_V 0.015006800 0.015667400
## RUV_ADD RUV_ADD 0.208932400 0.249919500
## RUV_PROP RUV_PROP 0.088466900 0.079072550
Extract the information regarding the precision intervals
print(getPopulationParameters(bootstrapResults, what="intervals")$Bootstrap)
## Parameter Mean Median Perc_5 Perc_95
## 1 BETA_CL_WT 0.750000000 0.750000000 0.750000000 0.75000000
## 2 BETA_V_WT 1.000000000 1.000000000 1.000000000 1.00000000
## 3 CORR_CL_V 0.006601379 0.005672145 -0.004049103 0.01591210
## 4 POP_CL 0.135766800 0.136824000 0.123975500 0.14836720
## 5 POP_KA 1.357139000 1.242695000 0.706269800 2.38384400
## 6 POP_TLAG 0.849418600 0.869470500 0.551942100 0.97224690
## 7 POP_V 8.034198000 7.973425000 7.618959000 8.55206800
## 8 PPV_CL 0.071233230 0.070891400 0.033271180 0.12462570
## 9 PPV_KA 0.715497300 0.716611500 0.185977000 1.35855100
## 10 PPV_TLAG 0.010000000 0.010000000 0.010000000 0.01000000
## 11 PPV_V 0.015006800 0.015667400 0.005174977 0.02571412
## 12 RUV_ADD 0.208932400 0.249919500 0.001099000 0.42160650
## 13 RUV_PROP 0.088466900 0.079072550 0.037310650 0.16461970
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] "0.13466"
##
## $POP_CL$lo
## [1] "0.001"
##
##
## $POP_V
## $POP_V$value
## [1] "8.04685"
##
## $POP_V$lo
## [1] "0.001"
##
##
## $POP_KA
## $POP_KA$value
## [1] "1.29443"
##
## $POP_KA$lo
## [1] "0.001"
##
##
## $POP_TLAG
## $POP_TLAG$value
## [1] "0.862931"
##
## $POP_TLAG$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"
print(myParObjUpdated@VARIABILITY)
## $PPV_CL
## $PPV_CL$value
## [1] "0.263589"
##
##
## $PPV_V
## $PPV_V$value
## [1] "0.136669"
##
##
## $PPV_KA
## $PPV_KA$value
## [1] "0.917598"
##
##
## $PPV_TLAG
## $PPV_TLAG$value
## [1] "0.1"
##
## $PPV_TLAG$fix
## [1] "true"
##
##
## $CORR_CL_V
## $CORR_CL_V$value
## [1] "0.212886"
##
##
## $RUV_PROP
## $RUV_PROP$value
## [1] "0.0628024"
##
## $RUV_PROP$lo
## [1] "0"
##
##
## $RUV_ADD
## $RUV_ADD$value
## [1] "0.314783"
##
## $RUV_ADD$lo
## [1] "1.0E-4"
Assembling the new MOG. Note that we reuse the data, model and tasks 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)
## -- Mon Aug 15 16:28:16 2016
## New
## Submitted
## Job 634d58ef-0b3b-43d7-a591-814e9a1d138e progress:
## Running [ ... ]
## Importing Results
## Copying the result data back to the local machine for job ID 634d58ef-0b3b-43d7-a591-814e9a1d138e...
## From C:\Users\smith_mk\AppData\Local\Temp\4\RtmpYF4SFO\DDMORE.job356467e95dfc to C:/SEE/MDL_IDE/workspace/UseCasesDemo/UseCase1/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
## -- Mon Aug 15 16:29:18 2016
To replay the visualisation using information from the VPC SO file
pdf(paste0(uc,"_VPC.pdf"))
plot(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
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.
myPharmML <- as.PharmML(mdlfile)
Use parameter values from the FOCEI estimation
parValues <- getPopulationParameters(NM.FOCEI, what="estimates")$MLE
Simulate for the typical weight of 70. Recall that logtWT = log(WT/70).
p <- c(parValues,logtWT=0)
Parameter values used in simulation
print(p)
## POP_CL POP_V POP_KA POP_TLAG RUV_PROP RUV_ADD
## 0.1346600 8.0468500 1.2944300 0.8629310 0.0628024 0.3147830
## BETA_CL_WT BETA_V_WT PPV_CL CORR_CL_V PPV_V PPV_KA
## 0.7500000 1.0000000 0.2635890 0.2128860 0.1366690 0.9175980
## PPV_TLAG logtWT
## 0.1000000 0.0000000
Simulate PK parameters for individuals
ind <- list(name = c('TLAG','KA','CL','V'))
Simulate predicted (CC) and observed concentration values (Y)
f <- list( name = c('CC'), time = seq(0,to=50,by=1))
y <- list( name = c('Y'), time = c(0, 0.5, 1, 2, 3, 4, 6, 8, 12, 24, 36, 48))
Simulate for a dose of 100mg given at time 0 into the GUT (oral administration)
adm <- list(target='GUT', time = 0, amount = 100)
Simulate 12 subjects
g <- list( size = 12, level = 'individual', treatment=adm)
Call simulx
res <- simulx(model =myPharmML,
parameter = p,
group = g,
output = list(ind,y,f))
Simulated parameter values for each individual
print(res$parameter)
## id TLAG KA CL V
## 1 1 0.7913309 1.5383068 0.14977929 7.694128
## 2 2 1.0028087 0.8058306 0.12444606 6.488144
## 3 3 0.8986409 24.7808753 0.11482284 8.042080
## 4 4 0.8513698 0.4824796 0.11496917 8.546734
## 5 5 0.9004174 0.2229009 0.10748701 9.209824
## 6 6 0.8876357 1.3460423 0.12275335 10.165668
## 7 7 0.8109790 0.1928436 0.07716102 10.188859
## 8 8 0.9601427 1.9267014 0.11136785 10.973770
## 9 9 0.7487865 0.9263937 0.14117459 6.909233
## 10 10 0.8856971 2.2336167 0.13579359 7.471812
## 11 11 0.8826824 0.8462787 0.19315549 8.113318
## 12 12 0.8500256 0.4582425 0.15521015 8.560211
Plot simulated results
plot(ggplot() +
geom_line(data=res$CC, aes(x=time, y=CC, colour=id)) +
geom_point(data=res$Y, aes(x=time, y=Y, colour=id)) +
xlab("time (h)") + ylab("concentration") )
Simulate 1000 subjects - with simulx this is a QUICK process!
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:
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=FALSE)$y)
## time 0% 10% 20% 30% 40%
## 1 0.0 -0.6650112 -0.3878409 -0.2759908 -0.20125958 -0.084938410
## 2 0.5 -0.7144047 -0.3428163 -0.1961387 -0.09181983 -0.001034849
## 3 1.0 -0.4826743 0.3915174 0.7587353 1.23592441 1.713589255
## 4 2.0 3.2602299 7.5555810 11.4614198 13.69685676 15.503492007
## 5 3.0 6.1786625 12.3965200 16.2984887 18.27327380 19.846901466
## 6 4.0 7.7257979 15.5581680 18.5855787 19.99237765 21.515872370
## 7 6.0 11.0799901 17.5110975 19.2374108 20.23897449 20.991582528
## 8 8.0 11.7838145 16.9717234 18.7109402 19.68703190 20.653312683
## 9 12.0 15.2066912 17.3789136 18.5041136 19.04081139 19.715532772
## 10 24.0 10.8601329 14.0142020 15.0071817 15.54981228 16.057784898
## 11 36.0 7.7504395 10.7736954 12.2161534 12.72192003 13.391264032
## 12 48.0 4.8144638 8.6474204 9.7166439 10.48169193 10.925676476
## 50% 50% 60% 70% 80% 90%
## 1 -0.04139662 -0.04139662 0.02499712 0.09674689 0.1827609 0.3925643
## 2 0.04825664 0.04825664 0.09176159 0.17588343 0.2874130 0.3937877
## 3 2.40821987 2.40821987 3.44754941 4.98239016 6.3339425 10.6149923
## 4 17.48697864 17.48697864 18.66748103 20.22748358 22.5611373 25.8287706
## 5 21.04985374 21.04985374 22.44715043 23.93668815 24.9920746 26.2614674
## 6 22.47737119 22.47737119 23.45932426 23.90304699 25.2002925 27.4722474
## 7 21.82855279 21.82855279 23.13181901 24.37069119 25.7633099 27.8790810
## 8 21.58836014 21.58836014 22.72372931 23.55748618 24.8136814 25.8782012
## 9 20.31357567 20.31357567 21.93642073 22.76119255 23.3512499 24.4950710
## 10 16.92530863 16.92530863 17.56496214 18.40505437 19.0603044 20.1363024
## 11 13.78817353 13.78817353 14.65923716 15.48769747 16.5055051 17.9387153
## 12 11.50488542 11.50488542 12.31591407 12.97902404 13.5337158 14.3218896
## 100%
## 1 0.8552684
## 2 0.7803884
## 3 25.1728326
## 4 34.2603146
## 5 39.1122770
## 6 35.7701219
## 7 31.1222474
## 8 31.1155623
## 9 30.5263834
## 10 24.4986102
## 11 20.1233098
## 12 16.0807855