Represents parameters for a bootstrap run

Description

Class initializer for BootstrapParams that represents parameters for a bootstrap run.

Slots

numReplicates

Number of bootstrap replicates to run

initialEstimates

Get model final estimates to run bootstrap (T|F)

numRetries

Number of times to retry a failed replicated

randomNumSeed

Seed for random number generator

confidenceLevel

Confidence level

stratifyColumns

What columns to stratify on(comma separated)

Examples

# same object as BootstrapParams()
boot <- BootstrapParams(
  numReplicates = 100,
  initialEstimates = FALSE,
  numRetries = 2,
  randomNumSeed = 1234,
  confidenceLevel = 95
)

NLME covariate effects model object class

Description

Class represents an NLME covariate effects model

Slots

numCovariates

Number of covariate effects

covariateList

Comma separated list of covariate effects names

scenarioNames

Comma separated list of scenario names

isDefault

Comma separated list of flags

degreesOfFreedom

Comma separated list of degrees of freedom

Examples

CovariateEffectModel(numCovariates = 2,
                     covariateList = "V-Age,Cl-BW",
                     scenarioNames = "S,S",
                     isDefault = "1,1",
                     degreesOfFreedom = "1,1")

Creates an extra dose parameter

Description

Creates an extra dose parameter

Arguments

Defines an extra dose point

Description

Defines an extra dose point

Slots

name

Dose point name

type

Character; Options are "Bolus" or "Infusion"

amount

Optional; Character specifying corresponding column in data or numeric specifying dose amount

rate

Optional; Character specifying corresponding column in data or numeric specifying dose rate

deltaTime

Optional; Character specifying corresponding column in data or numeric specifying delta time

isSecondDose

Use second dose point on compartment?

data

Optional data frame. Must specify data if supplying column as character value to amount, rate, deltaTime arguments

Class represents map between a model variable and a dataset column

Description

Class represents map between a model variable and a dataset column

Arguments

Class represents mapping list between model variables and dataset columns

Description

Class represents mapping list between model variables and dataset columns

Class represents an NLME Covariate parameter

Description

Class represents an NLME Covariate parameter

Arguments

NlmeCovariateParameter

Description

Class represents an NLME Covariate parameter

Arguments

NLME dataset object

Description

Class represents an NLME dataset object

Arguments

Examples

dataset <- NlmeDataset(workingDir)

Class represents mapping list between model variables and dose columns

Description

Class represents mapping list between model variables and dose columns

Class represents an NLME/PML Emax model parameters

Description

Class represents an NLME/PML Emax model parameters

Arguments

Create a new NlmeEngineExtraParams object

Description

This function creates a new instance of the NlmeEngineExtraParams class

Usage

NlmeEngineExtraParams(...)

Arguments

Value

An object of class NlmeEngineExtraParams.

NlmeEngineExtraParams : Defines all engine parameters for NLME models Wrapped up by engineParams function.

Description

NlmeEngineExtraParams : Defines all engine parameters for NLME models Wrapped up by engineParams function.

Slots

isPopulation

logical; TRUE if the model is a population model, FALSE for an individual model.

sort

character; String to pass sorting options to the NLME engine. Typically " -sort " to enable sorting or "" to disable it.

csv

character; String to control CSV input options. Typically " -csv " to strict the input data to comma-separated-values. Use "" to disable it (NLME will try to guess the input format).

method

numeric; Integer code specifying the estimation method.

• 1: QRPEM

• 2: IT2S-EM

• 3: FOCE-LB

• 4: FO

• 5: FOCE-ELS/LAPLACIAN (see below for choice between FOCE-ELS and LAPLACIAN)

• 6: NAIVE-POOLED

The choice between FOCE-ELS and LAPLACIAN (when method is 5) depends on the xfocehess slot: xfocehess = 1 selects FOCE-ELS, and xfocehess = 0 selects LAPLACIAN.

numIterations

numeric; The maximum number of iterations allowed for the estimation algorithm. Values must be non-negative integers.

odeToUse

numeric; Integer code specifying the ODE solver to be used. Possible values are:

• 1: LSODE with numerical Jacobian

• 2: LSODE with analytical Jacobian

• 3: Runge-Kutta

• 4: LSODA with numerical Jacobian

• 5: LSODA with analytical Jacobian

• 6: Matrix Exponent

• 7: DOPRI5

rtol

numeric; Specifies the relative tolerance for the ODE solver.

atol

numeric; Specifies the absolute tolerance for the ODE solver.

nmxstep

numeric; Specifies the maximum number of steps allowed for the ODE solver.

anagrad

numeric; Flag controlling the differentiation method used during the optimization of random effects (etas). 0 uses a finite difference approach, and 1 uses automatic differentiation where possible.

xnp

numeric; Controls the use of non-parametric (NP) optimization.

• 0: No NP optimization.

• 1: NONMEM-style NP optimization using posthoc estimates as support points.

• >1: Evolutionary NP algorithm with xnp generations.

xnorderagq

numeric; Specifies the number of quadrature points per dimension for Adaptive Gaussian Quadrature (AGQ). Only applicable when method is FOCE-ELS or LAPLACIAN.

• 1: Standard FOCE-ELS/LAPLACIAN computation (no AGQ).

• >1: AGQ is performed. The total number of quadrature points used is (number of ETAs)^xnorderagq.

xfocehess

numeric; Determines the method for calculating the Hessian matrix when using FOCE methods.

• 0: Use numerical second derivatives.

• 1: Use the FOCE approximation.

Applicable only when method is FOCE-ELS or LAPLACIAN.

xstderr

numeric; Specifies the method for standard error estimation.

• 0: No standard error estimation.

• 1: Central difference method.

• 2: Forward difference method.

sand

character; String to request sandwich standard error calculation. Typically " -sand " or "".

fisher

character; String to request Fisher score standard error calculation. Typically " -fscore " or "".

autodetect

character; String to request auto-detection of standard error method. Typically " -AutoSE " or "".

xlandig

numeric; Specifies the optimization accuracy (NDIGIT) for the outer loop (thetas and sigmas) when using FOCE-ELS or LAPLACIAN methods.

xlatol

numeric; Specifies the relative step size used for numerical computation of the Hessian matrix (second derivatives) during standard error calculation.

xblndig

numeric; Specifies the optimization accuracy (NDIGIT) for the inner loop (optimization of etas). Also applies to the single optimization loop in the NAIVE-POOLED method.

xbltol

numeric; Specifies the relative step size for numerical differentiation during model linearization.

gradTolOuter

Numeric maximum gradient tolerance in the outer (Theta/Omega/Sigma) optimization loop. Applicable to FOCE-ELS and LAPLACIAN methods.

stepTolOuter

Numeric maximum step tolerance in the outer (Theta/Omega/Sigma) optimization loop. Applicable to FOCE-ELS and LAPLACIAN methods.

gradTolInner

Numeric maximum gradient tolerance in the inner (Eta) optimization loop. Applicable to FOCE-ELS and LAPLACIAN methods.

stepTolInner

Numeric maximum step tolerance in the inner (Eta) optimization loop. Applicable to FOCE-ELS and LAPLACIAN methods.

refDeltaLagl

Numeric LL Delta tolerance value used during Theta/Omega/Sigma optimization. Applicable to FOCE-ELS and LAPLACIAN methods.

isPCWRES

numeric; Flag indicating if Population Conditional Weighted Residuals (PCWRES) should be computed. A value of 1 indicates computation, while 0 indicates no computation. Only applicable to population models.

xpcwresnrep

numeric; Stores the number of simulation replicates used for PCWRES computation. Applicable only when isPCWRES is 1.

xisample

numeric; Specifies the number of sample points used in the QRPEM algorithm. Only applicable when method is QRPEM.

xmapassist

numeric; Controls the use of MAP assistance in the QRPEM algorithm.

• 0: No MAP assistance.

• >0: The inner ETAs optimization loop is used in the QRPEM outer optimization loop with a periodicity equal to the value of xmapassist.

Only applicable when method is QRPEM.

xmapnp

numeric; Specifies the number of iterations for a preliminary Naive-Pooled optimization run before the main estimation. Applicable when the method is not NAIVE-POOLED.

ximpsampdof

numeric; Controls the importance sampling distribution used in the QRPEM algorithm. Only applicable when method is QRPEM.

• 0: Multivariate Normal distribution.

• 1: Multivariate Double Exponential (Laplace) distribution.

• 2: Direct sampling from the prior.

• 3-30: Multivariate T distribution with degrees of freedom equal to the value of ximpsampdof.

• -2: Mixture-2 distribution.

• -3: Mixture-3 distribution.

xmcpem

numeric; Controls the sampling method used in the QRPEM algorithm.

• 0: Quasi-Random sampling.

• 1: Monte-Carlo sampling.

Only applicable when method is QRPEM.

xpemrunall

numeric; Set to 1 to execute all requested iterations specified in numIterations. Only applicable to population models with method = "QRPEM".

xsirsamp

numeric; Specifies the number of samples per eta per subject used in the Sampling Importance Resampling (SIR) algorithm within QRPEM. Only applicable when method is QRPEM.

xburnin

numeric; Specifies the number of burn-in iterations in the QRPEM algorithm. During burn-in, omegas can be frozen (see xnonomegaburn). Only applicable when method is QRPEM.

xnonomegaburn

numeric; Controls whether omegas are frozen during the burn-in phase of the QRPEM algorithm.

• 0: burn-in with frozen omegas is off.

• 1: burn-in with frozen omegas is on.

Only applicable when method is QRPEM. See also xburnin.

xaccratio

numeric; Specifies the acceptance ratio used in the QRPEM algorithm for scaling the covariance matrix. Only applicable when method is QRPEM. Only applicable to population models with method = "QRPEM".

xscramble

numeric; Specifies the scrambling method for quasi-random number generation in the QRPEM algorithm.

• 0: No scrambling.

• 1: Owen-type scrambling.

• 2: Faure-Tezuka scrambling.

Only applicable when method is QRPEM.

emTolType

Numeric specifying QRPEM convergence check type:

0

Default (no rollout, LL & Thetas)

1

LL & Params with rollout

2

LL with rollout

3

Params with rollout

Only applicable when method is QRPEM.

emConvLen

Numeric specifying the number of iterations to check for convergence. Only applicable when method is QRPEM.

emConvCritVal

Numeric specifying the convergence critical value. Only applicable when method is QRPEM.

pardern

numeric; Specifies the number of time steps used for outputting partial derivatives of observed variables with respect to parameters. Only applicable to individual models.

parderd

numeric; Specifies the step size for numerical calculation of partial derivatives of observed variables with respect to parameters. Only applicable to individual models.

logtran

numeric; Engine flag controlling log-transformation behavior for single LogAdditive error model.

See Also

[engineParams()]

Examples

param <- NlmeEngineExtraParams(
  method = 3,
  numIterations = 1000
)
param <- NlmeEngineExtraParams(
  method = 1,
  numIterations = 300
)
param <- NlmeEngineExtraParams(
  method = 1,
  numIterations = 300,
  isPopulation = TRUE,
  odeToUse = 2
)

Class represents an NLME/PML error model

Description

Class represents an NLME/PML error model

Arguments

Class represents an NLME/PML Indirect PD model parameters

Description

Class represents an NLME/PML Indirect PD model parameters

Arguments

Reads progress file and returns the status of a job

Description

Reads progress file and returns the status of a job

Usage

NlmeJobStatus(job)

Arguments

Value

Character. Job status messages.

Examples

  model <- pkmodel(
    parameterization = "Clearance",
    numCompartments = 2,
    data = pkData,
    ID = "Subject",
    Time = "Act_Time",
    A1 = "Amount",
    CObs = "Conc",
    workingDir = tempdir()
  )

  params <- NlmeEngineExtraParams(
    method = 3,
    numIterations = 1
  )

  host <- hostParams(
    sharedDirectory = tempdir(),
    parallelMethod = "LOCAL_MPI",
    hostName = "local_mpi",
    numCores = 4
  )

  profile1 <- ProfileVar(
    "tvV",
    9.548,
    "-2,0"
  )

  profile2 <- ProfileVar(
    "tvCl",
    0.919,
    "-0.5,0"
  )

  profiles <- ProfileParameters(
    "USE_DELTA",
    c(profile1, profile2)
  )

  job <- profilePertubate(
    hostPlatform = host,
    params = params,
    profiles = profiles,
    model = model
  )

  status <- NlmeJobStatus(job)

Class initializer for NlmeObservationVar-class

Description

Describes an observation(observe,multi,...)

Arguments

Examples

var <- NlmeObservationVar(
  name = "Cobs",
  xaxis = "t",
  binningMethod = "none",
  quantilesValues = c(5, 50, 95)
)

NlmeParallelHost Class

Description

This class defines an NLME parallel host, which can be either local or remote, for running jobs.

Slots

sharedDirectory

character. The directory where the run will take place. On Windows, UNC paths are mapped to a drive letter for local execution.

installationDirectory

character. The directory containing NLME libraries and scripts.

hostName

character. A user-friendly name for the host (e.g., "local_mpi").

machineName

character. The IP address or hostname of the machine. Defaults to the local system's node name.

hostType

character. The operating system of the host, either "windows" or "linux". For remote Linux systems, you can specify a supported distribution (e.g., "RHEL", "UBUNTU") to configure the PML_BIN_DIR environment variable. linux will be treated as "RHEL".

numCores

numeric. The number of compute cores to be used.

isLocal

logical. TRUE if the host is local, FALSE if remote.

rLocation

character. The path to the Rscript executable on a remote host. This is ignored for local runs.

scriptPath

character. The path to a script to execute on the remote host before Rscript is started. This is ignored for local runs.

userAuthentication

NlmeUserAuthentication. An object containing user credentials for remote host authentication. See NlmeUserAuthentication().

parallelMethod

NlmeParallelMethod. The parallel computing method to use (e.g., "LOCAL_MPI", "SGE", "TORQUE"). See NlmeParallelMethod().

Examples

host <- hostParams(
  parallelMethod = "LOCAL_MPI",
  hostName = "local_mpi",
  numCores = 4
)

Class initializer for NlmeParallelMethod

Description

Use to define NlmeParallelMethod as for parallelMethod argument in NlmeParallelHost().

Slots

method

Options are: None|Multicore|LOCAL_MPI|SGE|SGE_MPI|TORQUE|TORQUE_MPI|LSF|LSF_MPI|SLURM|SLURM_MPI.

Class represents mapping list between model variables and data columns

Description

Class represents mapping list between model variables and data columns

Class represents an NLME/PML PK model parameters

Description

Class represents an NLME/PML PK model parameters

Arguments

Class initializaer for NlmePmlModel object

Description

The following class represents an NLME/PML model object.

Slots

isPopulation

Is this a population model (TRUE) or individual (FALSE)?

modelType

Taken from NlmeModelType

isTimeBased

Is model time-based?

linearModelType

Type of linear model

isLinearFrozen

Is linear model frozen?

pkModelAttrs

Taken from NlmePkParameters

indirectModelAttrs

Taken from NlmeIndirectParameters

emaxModelAttrs

Taken from NlmeEmaxParameters

hasEffectsCompartment

Is there data available for an effects compartment?

errorModel

Taken from NlmeErrorModel

structuralParams

List of structural parameters

outputParams

List of output parameters

diffEquations

List of differential equations

statements

List of PML statements

dosePoints

List of dosepoints

covariateList

List of covariates

columnMapping

Taken from NlmeColumnMapping

doseMapping

Taken from NlmeDoseMapping

paramsMapping

Taken from NlmeParamsMapping

randParamsMapping

Taken from NlmeRandParamsMapping

inputData

Input data source

doseData

Dose data source

fixedParamData

Fixed effect parameter data source

randParamData

Random effect parameter data source

isTextual

Is model textual (TRUE) or graphical (FALSE)?

pmloutput

List of PML output to generate

modelInfo

Taken from NlmePmlModelInfo

objects

deprecated

objectsNeedRegenerating

deprecated

randomEffectsStatements

Custom random effects statements

randomOccasionalEffectsStatements

Custom random occasional effects statements

userDefinedExtraDefs

Custom definition for extra column and table generation

Examples

# helper class
setClass("NlmePmlModelInfo",
         slots = c(modelName = "character",
                   workingDir = "character"))
Model <-
  new("NlmePmlModel",
  modelInfo = new("NlmePmlModelInfo", modelName = "Model", workingDir = "."))

Class initializer for NlmeRemoteExecutor

Description

Creates Remote Executor object class

Slots

sharedDirectory

The directory in which the run executes

installationDirectory

Directory containing NLME libraries/scripts

hostName

IP or name of remote host

userAuthentication

Credential for user to log into remote system

Class represents an NLME/PML residual error model

Description

Class represents an NLME/PML residual error model

Arguments

Class initializer for NlmeScenario

Description

Creates NlmeScenario class object

Arguments

Examples

CovariateEffectNames <- c("dVdBodyWeight",  "dCldBodyWeight")
#   Do not use any covariate effects
scenario1 <- NlmeScenario("no_covariates", "")
#   Use covariate effect index 1
scenario2 <- NlmeScenario("dVdBodyWeight", "1")
#   Use second covariate effect in the model
scenario3 <- NlmeScenario("dCldBodyWeight", "2")
#   Use 1st and 2nd covariate effect
scenario4 <- NlmeScenario("dVdBodyWeight_dCldBodyWeight", "1,2")

Class initializer for NlmeSimTableDef

Description

Creates NlmeSimTableDef class object used to specify parameters for VPC/Simulation runs

Arguments

Class initializer for NlmeSimulationParams

Description

Use to create set of parameters for simulation runs. Parameters numPoints, maxXRange, yVariables, simAtObs are related to the model in individual mode. They will be outputted to the file specified in model@dataset@simoutFilename, simout.csv by default

Arguments

See Also

tableParams, NlmeSimTableDef

Examples

table1 <- tableParams(
  name = "simulate.csv",
  timesList = "0,2,4,12,24",
  variablesList = "V,Cl",
  timeAfterDose = TRUE,
  forSimulation = TRUE
)

simParam <- NlmeSimulationParams(
  numReplicates = 10,
  seed = 29423,
  simulationTables = c(table1)
)

simParam <- NlmeSimulationParams(
  numPoints = 100,
  maxXRange = 50,
  yVariables = "C,A1",
  simulationTables = table1
)

Class represents style of structural parameter

Description

Class represents style of structural parameter

Slots

style

Parameter style: 1=LogNormal, 2=Normal, 3=Combination, 4=Log, 5=Logit

Class initializer for NlmeTableDef

Description

Creates NlmeTableDef class object used to specify parameters for fitting runs

Arguments

Class initializer for NlmeUserAuthentication

Description

Use for authentication records

Slots

userName

How the user is identified to the remote system

privateKeyFile

path to private key file, see keyfile for details

userPassword

either a string or a callback function for password prompt, see passwd for details

Arguments for VPC runs

Description

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

Arguments

See Also

tableParams, NlmeSimTableDef, NlmeObservationVar

Examples

model <- pkmodel(
  parameterization = "Clearance",
  numCompartments = 2,
  data = pkData,
  ID = "Subject",
  Time = "Act_Time",
  A1 = "Amount",
  CObs = "Conc",
  workingDir = tempdir()
  )

# Define the host
host <- hostParams(parallelMethod = "NONE",
                   hostName = "local",
                   numCores = 1,
                   sharedDirectory = tempdir())
job <- fitmodel(model,
                hostPlatform = host)

# View estimation results
print(job)

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

# 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, )

Pharmacokinetic dataset containing 100 subjects with single dose given by infusion

Description

Pharmacokinetic dataset containing 16 subjects with single dose given by infusion.

Usage

OneCpt_IVInfusionData

Format

A data frame with 800 rows and 6 variables:

Subject

Subject ID

Time

Time point

Dose

Amount of dose

CObs

Observations of drug concentration in blood

Rate

Rate of infusion

Duration

Duration of infusion

Source

The data is simulated using a PK model described by a one-compartment model with IV infusion

Class initializer for ProfileParameters

Description

Class represents an NLME profile perturbation variable

Slots

howToPertubate

How to apply profile variables. Options are USE_DELTA or USE_PERCENTAGE

profileVars

List of profile variables

Examples

profile1 <- ProfileVar("tvV", 9.548, "-2,-1,0,1,2")
profile2 <- ProfileVar("tvCl", 3.219, "-1,0,1")
profiles <- ProfileParameters("USE_DELTA", c(profile1, profile2))

NLME Profile variable

Description

Class initializer for an NLME profile perturbation variable

Arguments

Examples

ProfileVar("tvV", 9.95, "-2,-1,0,1,2")

Class initializer for ResetColumnInfo

Description

Class initializer for ResetColumnInfo

Arguments

Execute an NLME profile perturbation

Description

Execute an NLME profile perturbation

Usage

RunProfilePertubation(
  hostPlatform,
  dataset,
  params,
  profiles,
  sortColumns,
  scenarios = list(),
  runInBackground = FALSE,
  workingDir = NULL
)

Arguments

Value

Object of class ProfileNlmeJob

Examples

  model <- pkmodel(
    parameterization = "Clearance",
    numCompartments = 2,
    data = pkData,
    ID = "Subject",
    Time = "Act_Time",
    A1 = "Amount",
    CObs = "Conc",
    workingDir = tempdir()
  )

  params <- NlmeEngineExtraParams(
    method = 3,
    numIterations = 1
  )

  host <- hostParams(
    sharedDirectory = tempdir(),
    parallelMethod = "None",
    hostName = "Local",
    numCores = 4
  )

  profile1 <- ProfileVar(
    "tvV",
    9.548,
    "-2,0,2"
  )

  profile2 <- ProfileVar(
    "tvCl",
    0.919,
    "-0.5,0,1.5"
  )

  profiles <- ProfileParameters(
    "USE_DELTA",
    c(profile1, profile2)
  )

  job <- profilePertubate(
    hostPlatform = host,
    params = params,
    profiles = profiles,
    model = model
  )

Class initializer for SortColumns

Description

Class represents an NLME sort columns object

Arguments

Examples

# The following two setups are equivalent
sortColumnSetUp <- SortColumns("Country,City")
sortColumnSetUp <- SortColumns(c("Country","City"))

# The following two setups are equivalent
sortColumnSetUp <- SortColumns("Sort1 Sort2", "Sort3")
sortColumnSetUp <- SortColumns(c("Sort1, Sort2 "), "Sort3 ")

Class initializer for NLME StepwiseParams

Description

Class represents an NLME Stepwise search parameters

Arguments

Examples

StepwiseParams(0.001, 0.001, "BIC")

Accepts all estimates for fixed effects, Sigma, and random effects

Description

Updates PML statements in model object with estimates returned from model execution. Use copyModel and set argument acceptAllEffects = TRUE to create new model object with final estimates from base model run.

Usage

acceptAllEffects(model)

Arguments

Value

NlmePmlModel object

See Also

copyModel

Examples

# Define the model
model <- pkmodel(numComp = 1,
                 absorption = "Intravenous",
                 ID = "Subject",
                 Time = "Act_Time",
                 CObs = "Conc",
                 A1 = "Amount",
                 data = pkData,
                 modelName = "PkModel",
                 workingDir = tempdir())

# Fit model
res <- fitmodel(model = model,
                hostPlatform = hostParams(sharedDirectory = tempdir()))
model <- acceptAllEffects(model)

Adds ADDL extra column definition to model object

Description

Specify ADDL column definition in model object instead of specifying ADDL through addDoseCycle

Usage

addADDL(.Object, ADDL, II)

Arguments

Value

Modified NlmePmlModel object

Examples

pkData1 <- pkData
pkData1$ii <- 0
pkData1$addl <- 0
 model <- pkmodel(numComp = 2,
                  absorption = "FirstOrder",
                  ID = "Subject",
                  Time = "Act_Time",
                  CObs = "Conc",
                  Aa = "Amount",
                  data = pkData1,
                  modelName = "PkModel",
                  workingDir = tempdir())
 model <- addADDL(model, ADDL = "addl", II = "ii")

Add covariate to model object

Description

Add a continuous, categorical, or occasion covariate to model object and set covariate effect on structural parameters.

Usage

addCovariate(
  .Object,
  covariate,
  effect = NULL,
  type = c("Continuous", "Categorical", "Occasion"),
  direction = c("Forward", "Interpolate", "Backward"),
  option = c("Yes", "PlusOne", "No"),
  center = NULL,
  centerValue = NULL,
  levels = NULL,
  labels = NULL,
  isDiagonal = TRUE,
  values = NULL,
  isPositive = TRUE
)

Arguments

Details

The following relationships are applicable for covariates:

• direction = "Forward" is equivalent to PML code 'fcovariate(CovName)';

• direction = "Backward" is equivalent to PML code 'covariate(CovName)';

• direction = "Interpolate" is equivalent to PML code 'interpolate(CovName)'.

  If the structural parameter has style = "LogNormal", the options are reflected in PML code as follows:

• option = "Yes" is equivalent to stparm(V = tvV * wt^dVdwt * exp(dVdsex1*(sex==1)) * exp(nV));

• ⁠option = "PlusOne⁠ is equivalent to stparm(V = tvV * (1+wt*dVdwt) * (1+dVdsex1*(sex==1)) * exp(nV)).

Value

Modified NlmePmlModel object

Examples

model <- pkmodel(
  numCompartments = 2,
  data = pkData,
  ID = "Subject",
  Time = "Act_Time",
  A1 = "Amount",
  CObs = "Conc",
  workingDir = tempdir()
)

# Add Gender covariate of type categorical
model <- addCovariate(model,
  covariate = "Gender",
  type = "Categorical",
  effect = c("V2", "Cl2"),
  levels = c(0, 1),
  labels = c("Female", "Male")
)

# Add BodyWeight covariate of type continuous
model <- addCovariate(model,
  covariate = "BodyWeight",
  type = "Continuous",
  direction = "Backward",
  center = "Mean",
  effect = c("V", "Cl")
)

Adds a dosing cycle to model

Description

Add Steady State or ADDL dosing cycle to model object.

Usage

addDoseCycle(
  .Object,
  type = "SteadyState",
  name,
  administration = "Bolus",
  amount = NULL,
  II = NULL,
  rate = NULL,
  duration = NULL,
  isSecondDose = FALSE,
  colName = NULL
)

Arguments

Value

Modified NlmePmlModel object

See Also

doseNames

Examples

model <- addDoseCycle(pkmodel(columnMap = FALSE,
                              isPopulation = FALSE,
                              workingDir = tempdir()),
                      type = "SteadyState",
                      name = "A1",
                      amount = "Amount",
                      II = "II")

Adds user defined extra column/table definitions to column definition file

Description

Adds user defined extra column/table definitions to column definition file

Usage

addExtraDef(.Object, value)

Arguments

Value

Modified NlmePmlModel object

Examples

model <- pkmodel(columnMap = FALSE,
                 workingDir = tempdir())
model <- addExtraDef(model, c("addlcol(ADDL)",
                              "table(file=\"res.csv\",time(0),Ka,V,Cl,Tlg)"))

Change existing dosing compartment to infusion

Description

Allows user to switch any dosing compartment to infusion

Usage

addInfusion(
  .Object,
  doseCptName,
  isDuration = FALSE,
  isSecondDose = FALSE,
  colName = NULL
)

Arguments

Value

Modified NlmePmlModel object

Examples

 pkData1 <- pkData
 pkData1$A1_1 <- 0
 model <- pkmodel(numComp = 2,
 absorption = "Intravenous",
 ID = "Subject",
 Time = "Act_Time",
 CObs = "Conc",
 A1 = "Amount",
 data = pkData1,
 modelName = "PkModel",
 workingDir = tempdir())
 newModel <- addInfusion(model, "A1", FALSE, FALSE, "A1_1")

Add levels and labels to categorical or occasion covariate

Description

Allows users to specify the name and the associated value for each category/occasion of a categorical/occasion covariate in a textual model object. Only applicable to the case where the corresponding input data column of a categorical/occasion covariate is of class character.

Usage

addLabel(.Object, covariate, levels, labels)

Arguments

Value

Modified NlmePmlModel object

Examples

model <- pkmodel(columnMap = FALSE,
                 isPopulation = FALSE,
                 workingDir = tempdir())

model <- suppressWarnings(addCovariate(model,
                      covariate = "Gender",
                      type = "Categorical",
                      effect = c("V"),
                      levels = c(0, 1)))
model@isTextual <- TRUE
model <- addLabel(model, "Gender", c(1, 2), c("male", "female"))

Adds MDV extra column definition to model object

Description

Use to add MDV statement to model@userDefinedExtraDefs

Usage

addMDV(.Object, MDV)

Arguments

Value

Modified NlmePmlModel object

Examples

pkData1 <- pkData
pkData1$MDV <- 0
model <- pkmodel(data = pkData1,
                 ID = "Subject",
                 Time = "Act_Time",
                 A1 = "Amount",
                 CObs = "Conc",
                 workingDir = tempdir()
                 )
model <- addMDV(model, MDV = "MDV")

Adds reset instructions to the model

Description

Adds reset instructions to the model

Usage

addReset(.Object, low, hi, Reset = NULL)

## S4 method for signature 'NlmePmlModel'
addReset(.Object, low, hi, Reset = NULL)

Arguments

Value

Depends on the specific methods

Returns the 'NlmePmlModel' object with updated reset information and definitions.

Functions

• addReset(NlmePmlModel): Method for the 'NlmePmlModel' class

  This method adds reset instructions to the NlmePmlModel object. It updates the reset information, checks column mappings if input data is not null, and adds a reset definition to user-defined extra definitions.

Adds a secondary parameter to model definition

Description

Adds a secondary parameter to model definition

Usage

addSecondary(.Object, name, definition, unit = "")

## S4 method for signature 'NlmePmlModel'
addSecondary(.Object, name, definition, unit = "")

Arguments

Value

Depends on the specific methods

Returns the 'NlmePmlModel' object with the added secondary parameter.

Functions

• addSecondary(NlmePmlModel): Method for the 'NlmePmlModel' class

  This method adds a secondary parameter to the NlmePmlModel object. It checks for duplicate parameter names, and if there is no duplicate, it adds the new secondary parameter to the object and updates the PML model.

Examples

model <- pkmodel(columnMap = FALSE,
                 absorption = "FirstOrder",
                 workingDir = tempdir())
model <- addSecondary(model, "Ke", "tvCl/tvV")
model <- addSecondary(
  model, "Tmax",
  "CalcTMax(tvA,tvCl/tvV)"
)

Adds Steady State extra column definition to model object

Description

Use to add Steady State column definition statement to model@userDefinedExtraDefs

Usage

addSteadyState(.Object, SS, II, SSOffset = NULL)

Arguments

Value

Modified NlmePmlModel object

Examples

pkData1 <- pkData
pkData1$SS <- 0
pkData1$II <- 0
model <- pkmodel(data = pkData1,
                 ID = "Subject",
                 Time = "Act_Time",
                 A1 = "Amount",
                 CObs = "Conc",
                 workingDir = tempdir()
                 )
model <- addSteadyState(model, SS = "SS", II = "II")

Add to the NLME Error model

Description

Add to the NLME Error model

Usage

addToErrorModel(model, effectsList)

Arguments

Executes an NLME Bootstrap

Description

Method to execute an NLME Bootstrap

Usage

bootstrap(
  model,
  hostPlatform = NULL,
  params,
  bootParams,
  runInBackground = FALSE,
  ...
)

Arguments

Value

if runInBackground = FALSE, a list is returned with bootstrap results, i.e. "BootOverall", "BootTheta", "BootOmega", "BootOmegaStderr", "BootVarCoVar" comma separated files. Otherwise the BootNlmeJob class object is returned.

See Also

hostParams, engineParams, BootstrapParams

Examples

input_data <- pkData

model <-
  pkmodel(
    numCompartments = 2,
    data = input_data,
    ID = "Subject",
    Time = "Act_Time",
    A1 = "Amount",
    CObs = "Conc",
    workingDir = tempdir()
  )

# multicore
multicoreHost <- hostParams(
  sharedDirectory = tempdir(),
  parallelMethod = "Multicore",
  hostName = "local_multicore",
  numCores = 4
)

bootstrapdf <- bootstrap(model,
  hostPlatform = multicoreHost,
  params = engineParams(model),
  numReplicates = 5,
  randomNumSeed = 1234,
  runInBackground = FALSE
)

Generic function for cancelling a job

Description

Generic function for cancelling a job

Usage

cancelJob(.Object)

## S4 method for signature 'SimpleNlmeJob'
cancelJob(.Object)

Arguments

Value

Depends on the specific methods

Prints the 'SimpleNlmeJob' object after attempting to cancel the job. No return value.

Functions

• cancelJob(SimpleNlmeJob): Method for cancelling a job of the 'SimpleNlmeJob' class

  This method attempts to cancel a job of the 'SimpleNlmeJob' class. If the job is running on a local host or is not running in the background, it throws an error and does nothing. Otherwise, it uploads a 'STOP' command to the host's remote executor.

Check Host Parameters

Description

Checks NLME Parallel Host object for correct settings for GCC, NLME Installation, MPI and Root directories.

Usage

checkHostParams(obj, verbose = FALSE)

Arguments

Value

TRUE if all checks are successful, otherwise FALSE

Examples

# multicore
multicoreHost <- hostParams(
  parallelMethod = "Multicore",
  hostName = "local_multicore",
  numCores = 4
)
checkHostParams(multicoreHost)

Add column mappings

Description

Piping compatible function for modelColumnMapping used to add column mappings from input data to model object

Usage

colMapping(.Object, mappings = NULL, ...)

Arguments

Value

modified NlmePmlModel object

See Also

dataMapping modelVariableNames

Examples

pkData$id2 <- pkData$Subject
model <- pkmodel(columnMap = FALSE,
                 data = pkData,
                 workingDir = tempdir())

modelvar <- unlist(modelVariableNames(model))

colnames <- c("Subject", "Act_Time", "Amount", "Conc")
names(colnames) <- modelvar
# will map subject directly
colnames <- colnames[-c(1)]

model <- colMapping(model, colnames, id = c(Subject, id2))
# also possible:
model <- colMapping(model, colnames, id = c("Subject", "id2"))
# not recommended since only not quoted names are identified
# if both types are provided:
model <- colMapping(model, colnames, id = c("Subject", id2))

Copy model object to iterate over base model

Description

Copies previously executed model into a new object and optionally accept all estimates returned from model execution. A new working directory is created and all files from base model are copied into it.

Usage

copyModel(model, acceptAllEffects = FALSE, modelName = "", workingDir = "")

Arguments

Value

Modified NlmePmlModel object

Examples

model <- pkmodel(
  parameterization = "Clearance",
  numCompartments = 2,
  data = pkData,
  ID = "Subject",
  Time = "Act_Time",
  A1 = "Amount",
  CObs = "Conc",
  workingDir = tempdir()
  )
 host <- hostParams(sharedDirectory = tempdir(),
                    parallelMethod = "None",
                    hostName = "local",
                    numCores = 1)
job <- fitmodel(model,
                numIterations = 3,
                hostPlatform = host)

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

Sets style for a covariate/variable

Description

Sets style for a covariate/variable

Usage

covariateEffect(.Object, covariateName, parameterName) <- value

Arguments

Creates set of covariate effects

Description

Use to create set of covariate effects to be checked during Stepwise or Shotgun covariate search

Usage

covariateModel(model)

Arguments

Value

CovariateEffectModel class object

Examples

# Define the model
model <- pkmodel(
  numCompartments = 2,
  data = pkData,
  ID = "Subject",
  Time = "Act_Time",
  A1 = "Amount",
  CObs = "Conc",
  workingDir = tempdir()
)

# Add Gender covariate of type categorical
model <- addCovariate(model,
  covariate = "Gender",
  type = "Categorical",
  effect = c("V2", "Cl2"),
  levels = c(0, 1),
  labels = c("Female", "Male")
)

# Add Bodyweight covariate of type continuous
model <- addCovariate(model,
  covariate = "BodyWeight",
  type = "Continuous",
  direction = "Backward",
  center = "Mean",
  effect = c("V", "Cl")
)
covariateModel(model)

Return covariate names

Description

Use to return character vector of covariate names available in model object.

Usage

covariateNames(model)

Arguments

Value

Character vector of covariate names defined in model

Examples

model <- pkmodel(columnMap = FALSE,
                 workingDir = tempdir())
model <- addCovariate(model, covariate = "BW", effect = "V")
model <- addCovariate(model, covariate = "Age", effect = "Cl")

covariateNames(model)

Creates string from each covariate attribute

Description

Creates string from each covariate attribute

Usage

covariatePartsString(obj)

Arguments

Parse the model and get the list of terms

Description

Calls TDL5 to parse the model and get the list of terms

Usage

createModelInfo(model, ForceRun = FALSE)

Arguments

Value

List of model information

Examples

  model <- pkmodel(columnMap = FALSE,
                   workingDir = tempdir())
  createModelInfo(model)

Use to create model object from parsed metamodel

Description

Define NlmePmlModel model class instance from metamodel blocks and creates NlmePmlModel model class from the given parsed metamodel. See Metamodel overview.

Usage

create_model_from_metamodel(mmdlfile, directoryToRun)

Arguments

Value

a list with the resulted model class instance and engine parameters. If multiple ESTARGS/SIMARGS blocks are used, a list of estimation argument classes (NlmeEngineExtraParams()) and simulation argument classes (simParamsBlock) are returned.

See Also

run_metamodel()

Examples

 
   ModelParamsList <-
     create_model_from_metamodel(
       mmdlfile = system.file("extdata/mmdlNoTime", "test.mmdl",
                              package = "Certara.RsNLME"),
       directoryToRun = tempdir())
 

Initialize input data for PK/PD model

Description

Used to initialize input data for PK/PD model

Usage

dataMapping(.Object, data)

Arguments

Value

Modified NlmePmlModel object

See Also

colMapping

Examples

model <- pkmodel(columnMap = FALSE,
                 workingDir = tempdir())

model <- dataMapping(model, pkData)

Deletes a secondary parameter from the model

Description

Deletes a secondary parameter from the model

Usage

deleteSecondary(.Object, name)

## S4 method for signature 'NlmePmlModel'
deleteSecondary(.Object, name)

Arguments

Value

Depends on the specific methods

Returns the 'NlmePmlModel' object with the secondary parameter removed.

Functions

• deleteSecondary(NlmePmlModel): Method for the 'NlmePmlModel' class

  This method deletes a secondary parameter from the NlmePmlModel object. It searches for the parameter by name, and if it is found, it removes it from the object and updates the PML model.

Return dose names

Description

Use to return character vector of dose point names in model object.

Usage

doseNames(model)

Arguments

Value

Character vector of dose names defined in model

Examples

model <- pkmodel(columnMap = FALSE,
                 workingDir = tempdir())

doses <- doseNames(model)

Directly edit PML text in model object

Description

Allows user to edit PML text in model object using internal text editor and return a new textual model containing the edited PML statements.

Usage

editModel(.Object)

Arguments

Value

Modified NlmePmlModel object

Examples

model <- pkmodel(columnMap = FALSE,
                 workingDir = tempdir())

if (FALSE) {
  # will open an additional window with the model text:
  newModel <- editModel(model)
}

Create an Emax or Imax model

Description

Use to create an Emax or Imax model

Usage

emaxmodel(
  isPopulation = TRUE,
  checkBaseline = FALSE,
  checkFractional = FALSE,
  checkInhibitory = FALSE,
  checkSigmoid = FALSE,
  data = NULL,
  columnMap = TRUE,
  modelName = "",
  workingDir = "",
  ...
)

Arguments

Value

NlmePmlModel object

Column mapping

Note that quoted and unquoted column names are supported. Please see colMapping.

Examples

model <- emaxmodel(data = pkpdData, ID = "ID", C = "CObs", EObs = "EObs")

model <- emaxmodel(
  checkBaseline = TRUE,
  checkFractional = TRUE,
  checkInhibitory = TRUE,
  data = pkpdData,
  ID = "ID",
  C = "CObs",
  EObs = "EObs"
)

# View PML Code
print(model)

Emax model mapping parameters

Description

Emax model mapping parameters

Usage

emaxmodel_MappingParameters(ID = NULL, C = NULL, EObs = NULL)

Arguments

Main function to specify engine parameters

Description

Use to define engine parameters for model execution.

Usage

engineParams(
  model,
  sort = NULL,
  ODE = "MatrixExponent",
  rtolODE = 1e-06,
  atolODE = 1e-06,
  maxStepsODE = 50000,
  numIterations = 1000,
  method = NULL,
  stdErr = NULL,
  isCentralDiffStdErr = TRUE,
  stepSizeStdErr = NULL,
  logTransform = NULL,
  numIntegratePtsAGQ = 1,
  numIterNonParametric = 0,
  allowSyntheticGradient = FALSE,
  fastOptimization = FALSE,
  numIterMAPNP = 0,
  numRepPCWRES = 0,
  stepSizeLinearize = 0.002,
  numDigitLaplacian = 7,
  numDigitBlup = 13,
  gradTolOuter = 2e-04,
  stepTolOuter = 1e-04,
  gradTolInner = 1.71e-05,
  stepTolInner = 7.07e-08,
  refDeltaLagl = 0.001,
  mapAssist = 0,
  iSample = 300,
  iAcceptRatio = 0.1,
  impDist = "Normal",
  tDOF = 4,
  numSampleSIR = 10,
  numBurnIn = 0,
  freezeOmega = FALSE,
  MCPEM = FALSE,
  runAllIterations = FALSE,
  scramble = "Owen",
  emTolType = 0,
  emConvLen = 10,
  emConvCritVal = 5,
  stepSizePartialDeriv = 1e-05,
  numTimeStepPartialDeriv = 20
)

Arguments

Details

Both "DVERK" and "DOPRI5" are non-stiff solvers. "AutoDetect" represents LSODA solver implemenation, which solves the initial value problem for stiff or nonstiff systems of first order ordinary differential equations. "Stiff" is a LSODE (Livermore solver). It is best suited for stiff problems. "MatrixExponent" is a matrix exponential solver.

For the QRPEM method, the impDist parameter controls the importance sampling distribution. The ximpsampdof slot in the internal NlmeEngineExtraParams object is set based on impDist as follows:

• "Normal": ximpsampdof = 0

• "DoubleExponential": ximpsampdof = 1

• "Direct": ximpsampdof = 2

• "T": ximpsampdof is set to the value of tDOF.

• "Mixture-2": ximpsampdof = -2

• "Mixture-3": ximpsampdof = -3

Value

List of engine parameters to be used during fitting or simulation

Return extra dose lines

Description

Use to return extra dose lines for model object

Usage

extraDoseLines(model)

Arguments

Value

List of extra dose information

Examples

data <- pkData
data$II <- 24
data$ADDL <- 1

model <-
pkmodel(
  parameterization = "Clearance",
  numCompartments = 2,
  data = data,
  ID = "Subject",
  Time = "Act_Time",
  A1 = "Amount",
  CObs = "Conc",
  workingDir = tempdir())
addDoseCycle(
  model,
  name = "A1",
  amount = 30000,
  II = 24,
  type = "ADDL",
  colName = "ADDL")

extraDoseLines(model)

Return extra dose names

Description

Use to return extra dose names for model object

Usage

extraDoseNames(model)

Arguments

Value

Character vector of extra dose names

Examples

data <- pkData
data$II <- 24
data$ADDL <- 1

model <-
pkmodel(
  parameterization = "Clearance",
  numCompartments = 2,
  data = data,
  ID = "Subject",
  Time = "Act_Time",
  A1 = "Amount",
  CObs = "Conc",
  workingDir = tempdir())
addDoseCycle(
  model,
  name = "A1",
  amount = 30000,
  II = 24,
  type = "ADDL",
  colName = "ADDL")

extraDoseNames(model)

extract files used for powershell script

Description

Use for extraction of the NLME files from mmdl file.

Usage

extract_mmdl(
  mmdlfile,
  directoryToExtract,
  dataFileName = "data1.txt",
  mdlOutput = "test.mdl",
  cols1Output = "cols1.txt",
  nlmeargsOutput = "nlmeargs.txt"
)

Arguments

Details

mdlOutput, dataFileName, cols1Output, nlmeargsOutput files are extracted into the folders, the names of the folders are built as ⁠{Number of estimation/simulation block in metamodel}⁠- ⁠{'est' for estimation block/'sim' for simulation block}⁠. All estimation blocks are going first irrespective of the simulation blocks presence, all simulation blocks are going next.

Value

The results of create_model_from_metamodel() run are returned.

See Also

create_model_from_metamodel()

Examples

# path to metamodel should be specified, all other arguments set to default
extract_mmdl(system.file("extdata/mmdlNoTime", "test.mmdl",
                              package = "Certara.RsNLME"),
             directoryToExtract = tempdir())

Executes an NLME simple estimation

Description

Executes an NLME simple estimation

Usage

fitmodel(
  model,
  hostPlatform = NULL,
  params,
  simpleTables,
  runInBackground = FALSE,
  filesToReturn = "*",
  ...
)

Arguments

Value

if runInBackground is FALSE, a list with main resulted dataframes is returned:

• Overall

• ConvergenceData

• residuals

• Secondary

• StrCovariate - if continuous covariates presented

• StrCovariateCat - if categorical covariates presented

• theta

• posthoc table

• posthocStacked table

• Requested tables

nlme7engine.log textual output is returned and loaded with the main information related to fitting. dmp.txt structure with the results of fitting (including LL by subject information) is returned and loaded. These 2 files are returned and loaded irrespective of filesToReturn argument value.

For individual models, additional dataframe with partial derivatives is returned:

• ParDer

For population models and the method specified is NOT Naive-Pooled, additional dataframes are returned:

• omega

• Eta

• EtaStacked

• EtaEta

• EtaCov

• EtaCovariate - if continuous covariates presented

• EtaCovariateCat - if categorical covariates presented

• bluptable.dat

If standard error computation was requested and it was successful, additional dataframes are returned:

• thetaCorrelation

• thetaCovariance

• Covariance

• omega_stderr

If nonparametric method was requested (numIterNonParametric > 0) and the method specified in engineParams is NOT Naive-Pooled, additional dataframes are returned:

• nonParSupportResult

• nonParStackedResult

• nonParEtaResult

• nonParOverallResult

if runInBackground is TRUE, only current status of job is returned.

filesToReturn with Certara.Xpose.NLME

If filesToReturn is used and "ConvergenceData.csv" and "residuals.csv" are not in the patterns, these files won't be returned and loaded. These files are essential for Certara.Xpose.NLME::xposeNlmeModel and Certara.Xpose.NLME::xposeNlme functions. This makes impossible to use the resulted object in Certara.Xpose.NLME functions.

Non-loaded but returned files

The non-loaded but returned files in the model working directory are:

• err1.txt - concatenated for all runs detailed logs for all steps of optimization,

• out.txt - general pivoted information about results,

• doses.csv - information about doses given for all subjects,

• iniest.csv - information about initial estimates

See Also

tableParams, hostParams, engineParams

Examples

 # Define the host
 host <- hostParams(sharedDirectory = tempdir(),
                    parallelMethod = "None",
                    hostName = "local",
                    numCores = 1)
 # Define the model
 model <- pkmodel(numComp = 2,
                  absorption = "FirstOrder",
                  ID = "Subject",
                  Time = "Act_Time",
                  CObs = "Conc",
                  Aa = "Amount",
                  data = pkData,
                  modelName = "PkModel",
                  workingDir = tempdir())

 Table01 <- tableParams(name = "SimTableObs.csv",
                        timesList = "0,1,2,4,4.9,55.1,56,57,59,60",
                        variablesList = "C, CObs",
                        timeAfterDose = FALSE,
                        forSimulation = FALSE)

 # Update fixed effects
 model <- fixedEffect(model,
                     effect = c("tvV", "tvCl", "tvV2", "tvCl2"),
                     value = c(16, 41, 7, 14))

 # Define the engine parameters
 params <- engineParams(model)
 # Fit model
 res <- fitmodel(model = model,
                 hostPlatform = host,
                 params = params,
                 simpleTables = Table01)

Specifies the initial values, lower bounds, upper bounds, and units for fixed effects in a model

Description

Specifies the initial values, lower bounds, upper bounds, and units for fixed effects in a model

Usage

fixedEffect(
  .Object,
  effect,
  value = NULL,
  lowerBound = NULL,
  upperBound = NULL,
  isFrozen = NULL,
  unit = NULL
)

Arguments

Value

Modified NlmePmlModel object

Examples

model <- pkmodel(
  numCompartments = 2,
  data = pkData,
  ID = "Subject",
  Time = "Act_Time",
  A1 = "Amount",
  CObs = "Conc",
  modelName = "TwCpt_IVBolus_FOCE_ELS",
  workingDir = tempdir()
  )

 # View initial/current fixed effect values
 initFixedEffects(model)

model <-
fixedEffect(
  model,
  effect = c("tvV", "tvCl", "tvV2", "tvCl2"),
  value = c(15, 5, 40, 15)
  )

Generates PML statements based on the current model

Description

Generates PML statements based on the current model

Usage

generatePML(.Object)

Arguments

Return random effect names in model

Description

Use to return character vector of random effect names (if available) in model object

Usage

getRandomEffectNames(model)

Arguments

Value

Characters vector of random effect names

Examples

model <- pkmodel(columnMap = FALSE,
                 workingDir = tempdir())
getRandomEffectNames(model)

Return theta names and values

Description

Returns named character vector of theta values by parsing PML fixed effect statements

Usage

getThetas(model)

Arguments

Value

Character vector of theta names defined in model

Examples

model <- emaxmodel(
  checkBaseline = TRUE,
  checkFractional = TRUE,
  checkInhibitory = TRUE,
  data = pkpdData,
  ID = "ID",
  C = "CObs",
  EObs = "EObs"
)
getThetas(model)

Create an NLME Parallel Host Configuration

Description

This helper function simplifies the creation and configuration of an NlmeParallelHost object, which defines the environment for running NLME jobs.

Usage

hostParams(
  sharedDirectory,
  installationDirectory = Sys.getenv("INSTALLDIR"),
  hostName = Sys.info()[["nodename"]],
  machineName = "127.0.0.1",
  hostType = Sys.info()[["sysname"]],
  numCores = 4,
  parallelMethod = "LOCAL_MPI",
  userName = "",
  privateKeyFile = NULL,
  userPassword = "",
  scriptPath = "",
  rLocation = "",
  isLocal = TRUE
)

Arguments

Value

An NlmeParallelHost object configured with the specified parameters.

Examples

host <- hostParams(sharedDirectory = tempdir(),
                   parallelMethod = "LOCAL_MPI",
                   hostName = "Local",
                   numCores = 4)

Display/Set initial estimates for fixed effects

Description

Display/Set initial estimates for fixed effects

Usage

initFixedEffects(.Object)

## S4 method for signature 'NlmePmlModel'
initFixedEffects(.Object)

initFixedEffects(.Object) <- value

## S4 replacement method for signature 'NlmePmlModel'
initFixedEffects(.Object) <- value

Arguments

Value

Named numeric vector of fixed effects estimates

See Also

fixedEffect

Examples

model <- pkmodel(
  numCompartments = 2,
  data = pkData,
  ID = "Subject",
  Time = "Act_Time",
  A1 = "Amount",
  CObs = "Conc",
  modelName = "TwCpt_IVBolus_FOCE_ELS",
  workingDir = tempdir()
  )

# View initial/current fixed effect values
 initFixedEffects(model)

# May also use as a 'replacement function' to set the values
initFixedEffects(model) <- c(tvV = 15, tvCl = 5, tvV2 = 40, tvCl2 = 15)

Create linear model

Description

Use to create a constant, linear, or quadratic PD model

Usage

linearmodel(
  isPopulation = TRUE,
  type = "Constant",
  data = NULL,
  columnMap = TRUE,
  modelName = "",
  workingDir = "",
  ...
)

Arguments

Value

NlmePmlModel object

Column mapping

Note that quoted and unquoted column names are supported. Please see colMapping.

Examples

model <- linearmodel(type = "Linear", data = pkpdData, ID = "ID", C = "CObs", EObs = "EObs")

# View PML Code
print(model)

Linear model mapping parameters

Description

Linear model mapping parameters

Usage

linearmodel_MappingParameters(ID = NULL, C = NULL, EObs = NULL)

Arguments

Lists covariate effect names in the model

Description

This function lists the names of covariate effects in a provided pharmacokinetic/pharmacodynamic (PK/PD) model.

Usage

listCovariateEffectNames(.Object)

## S4 method for signature 'NlmePmlModel'
listCovariateEffectNames(.Object)

Arguments

Value

A vector of character strings containing the names of the covariate effects in the model.

Examples

model <- pkmodel(
  numCompartments = 2,
  data = pkData,
  ID = "Subject",
  Time = "Act_Time",
  A1 = "Amount",
  CObs = "Conc",
  workingDir = tempdir()
)
model <- addCovariate(model,
  covariate = "Gender",
  type = "Categorical",
  effect = c("V2", "Cl2"),
  levels = c(0, 1),
  labels = c("Female", "Male")
)
listCovariateEffectNames(model)

Load model.rda file

Description

Loads a previously saved model from disk.

Usage

loadModel(directory)

Arguments

Details

Note, the names of model, engine, and host objects will be appended with name of model directory when reloading model.rda file to global environment.

Value

No value is returned. Model object is loaded in global environment.

Examples

TempDir <- tempdir()
model <- emaxmodel(
  checkBaseline = TRUE,
  checkFractional = TRUE,
  checkInhibitory = TRUE,
  data = pkpdData,
  ID = "ID",
  C = "CObs",
  EObs = "EObs",
  modelName = "model",
  workingDir = TempDir
)

saveModel(model)

loadModel(TempDir)

Return model variable names

Description

Return a vector of model variable names from model object

Usage

modelVariableNames(model)

Arguments

Value

Character vector of required model variable names

Examples

model <- pkmodel(columnMap = FALSE,
                 workingDir = tempdir())
modelVariableNames(model)

Get observation names

Description

Get observation model variables names.

Usage

observationNames(model)

Arguments

Value

Character vector of observation model variables names.

Examples

model <- pkemaxmodel(columnMap = FALSE)
obsnames <- observationNames(model)

Obtain NLME License

Description

This function attempts to authenticate and obtain an NLME license using the specified installation directory and licensing tool.

Usage

obtain_NLMELicense(
  InstallDir = Sys.getenv("INSTALLDIR"),
  ForceAuth = FALSE,
  ForceLicenseGet = FALSE,
  verbose = getOption("verbose")
)

Arguments

Details

This function checks for the presence of the necessary appsettings.json file as indicated by the CAD_CONFIG_FILE environment variable, runs the licensing tool to authenticate the user, and attempts to obtain an NLME license. It prints detailed messages if the verbose parameter is set to TRUE.

Value

A logical value indicating whether the license was successfully obtained.

Examples

INSTALLDIR <- Sys.getenv("INSTALLDIR")
if (INSTALLDIR == "") INSTALLDIR <- "C:/Program Files/Certara/NLME_Engine"
result <- obtain_NLMELicense(INSTALLDIR, verbose = TRUE)
if (result) {
  message("License obtained successfully!")
} else {
  message("Failed to obtain license.")
}

Embed column definition info into the model

Description

Add/update column definition information for the model object

Usage

parsePMLColMap(.Object, ForceRun = TRUE)

Arguments

Details

Intended to be used by other packages

Value

modified NLMEPmlModel object with column mapping definitions

Create NlmeParallelHost object from json file with host definition

Description

Create NlmeParallelHost object from json file with host definition

Usage

parse_NLMEHosts(nlme_hostPath)

Arguments

Value

the NlmeParallelHost class object is returned.

Examples

# path nlme_hostPath should be specified
 nlme_hostPath <- tempfile()
 host1 <- paste0('{"profile_name":"Multicore",',
  '"hostname":"127.0.0.1",',
  '"cores_number":4,',
  '"parallel_mode":"MULTICORE"}')
 host2 <- paste0('{"profile_name":"MPI",',
 '"hostname":"127.0.0.1",',
 '"cores_number":8,',
 '"parallel_mode":"LOCAL_MPI"}')

writeLines(paste0("[", host1, ",", host2, "]"), nlme_hostPath)
hosts <- parse_NLMEHosts(nlme_hostPath)

Pharmacokinetic dataset containing 16 subjects with single bolus dose

Description

Pharmacokinetic dataset containing 16 subjects with single bolus dose.

Usage

pkData

Format

A data frame with 112 rows and 8 variables:

Subject

Subject ID

Nom_Time

Nominal Time

Act_Time

Actual Time

Amount

Amount of dose

Conc

Observations of drug concentration in blood

Age

Age

BodyWeight

Body weight

Gender

Gender ("male", "female")

Source

Certara University

Pharmacokinetic pediatric dataset containing 80 subjects with single bolus dose.

Description

Pharmacokinetic pediatric dataset containing 80 subjects with single bolus dose. Dataset includes covariates and observations Below Quantification Limit (BQL).

Usage

pkcovbqlData

Format

A data frame with 880 rows and 8 variables:

ID

Subject ID

Time

Nominal Time

Dose

Amount of dose

CObs

Observations of drug concentration in blood

LLOQ

Lower Limit of Quantification

CObsBQL

Variable that indicates whether the observed drug concentration is below the limit of quantification

BW

Body weight

PMA

Postmenstrual age

Source

The data is simulated using a one-compartment model with IV bolus, where the central volume is allometric weight scaled, and the clearance is scaled by a combination of allometric weight scaling and a sigmoidal maturation function driven by PMA. Germovsek E., et al, Pharmacokinetic–Pharmacodynamic Modeling in Pediatric Drug Development, and the Importance of Standardized Scaling of Clearance, Clin Pharmacokinet (2019) 58:39–52.

Create a PK/Emax or PK/Imax model

Description

Use to create a PK/Emax or PK/Imax model

Usage

pkemaxmodel(
  isPopulation = TRUE,
  parameterization = "Clearance",
  absorption = "Intravenous",
  numCompartments = 1,
  isClosedForm = TRUE,
  isTlag = FALSE,
  hasEliminationComp = FALSE,
  isFractionExcreted = FALSE,
  isSaturating = FALSE,
  infusionAllowed = FALSE,
  isDuration = FALSE,
  isSequential = FALSE,
  isPkFrozen = FALSE,
  hasEffectsCompartment = FALSE,
  checkBaseline = FALSE,
  checkFractional = FALSE,
  checkInhibitory = FALSE,
  checkSigmoid = FALSE,
  isEmaxFrozen = FALSE,
  data = NULL,
  columnMap = TRUE,
  modelName = "",
  workingDir = "",
  ...
)

Arguments

Value

NlmePmlModel object

Column mapping

Note that quoted and unquoted column names are supported. Please see colMapping.

Examples

model <- pkemaxmodel(
  parameterization = "Macro",
  data = pkpdData,
  Time = "Time",
  ID = "ID",
  A1 = "Dose",
  C1Obs = "CObs",
  EObs = "EObs"
)

# View the model as well as its associated column mappings
print(model)

Create a PK/Indirect response model

Description

Use to create a PK/Indirect response model.

Usage

pkindirectmodel(
  isPopulation = TRUE,
  parameterization = "Clearance",
  absorption = "Intravenous",
  numCompartments = 1,
  isClosedForm = TRUE,
  isTlag = FALSE,
  hasEliminationComp = FALSE,
  isFractionExcreted = FALSE,
  isSaturating = FALSE,
  infusionAllowed = FALSE,
  isDuration = FALSE,
  isSequential = FALSE,
  isPkFrozen = FALSE,
  hasEffectsCompartment = FALSE,
  indirectType = "LimitedStimulation",
  isBuildup = TRUE,
  isExponent = FALSE,
  indirectFrozen = FALSE,
  data = NULL,
  columnMap = TRUE,
  modelName = "",
  workingDir = "",
  ...
)

Arguments

Value

NlmePmlModel object

Column mapping

Note that quoted and unquoted column names are supported. Please see colMapping.

Examples

model <- pkindirectmodel(
  parameterization = "Micro",
  data = pkpdData,
  ID = "ID",
  Time = "Time",
  A1 = "Dose",
  CObs = "CObs",
  EObs = "EObs"
)

# View PML Code
print(model)

PK Indirect model mapping parameters

Description

PK Indirect mapping parameters

Usage

pkindirectmodel_MappingParameters(
  ID = NULL,
  Time = NULL,
  A1 = NULL,
  Aa = NULL,
  A = NULL,
  A1_Rate = NULL,
  A1_Duration = NULL,
  Aa_Rate = NULL,
  Aa_Duration = NULL,
  A_Rate = NULL,
  A_Duration = NULL,
  A1Strip = NULL,
  CObs = NULL,
  C1Obs = NULL,
  A0Obs = NULL,
  EObs = NULL,
  nV = NULL,
  nV2 = NULL,
  nV3 = NULL,
  nCl = NULL,
  nCl2 = NULL,
  nCl3 = NULL,
  nKa = NULL,
  nA = NULL,
  nAlpha = NULL,
  nB = NULL,
  nBeta = NULL,
  nC = NULL,
  nGamma = NULL,
  nKe = NULL,
  nK12 = NULL,
  nK21 = NULL,
  nK13 = NULL,
  nK31 = NULL,
  nTlag = NULL,
  nKm = NULL,
  nVmax = NULL,
  nFe = NULL,
  nMeanDelayTime = NULL,
  nShapeParamMinusOne = NULL,
  nShapeParam = NULL
)

Arguments

Create PK linear model

Description

Use to create a PK/PD model with PD described by either constant, linear, or quadratic model

Usage

pklinearmodel(
  isPopulation = TRUE,
  parameterization = "Clearance",
  absorption = "Intravenous",
  numCompartments = 1,
  isClosedForm = TRUE,
  isTlag = FALSE,
  hasEliminationComp = FALSE,
  isFractionExcreted = FALSE,
  isSaturating = FALSE,
  infusionAllowed = FALSE,
  isDuration = FALSE,
  isSequential = FALSE,
  isPkFrozen = FALSE,
  hasEffectsCompartment = FALSE,
  linearType = "Constant",
  isLinearFrozen = FALSE,
  data = NULL,
  columnMap = TRUE,
  modelName = "",
  workingDir = "",
  ...
)

Arguments

Value

NlmePmlModel object

Column mapping

Note that quoted and unquoted column names are supported. Please see colMapping.

Examples

model <- pklinearmodel(
  parameterization = "Clearance",
  linearType = "Constant",
  data = pkpdData,
  ID = "ID",
  Time = "Time",
  A1 = "Dose",
  CObs = "CObs",
  EObs = "EObs"
)

# View the model as well as its associated column mappings
print(model)

Creates a PK model

Description

Use to create a PK model

Usage

pkmodel(
  isPopulation = TRUE,
  parameterization = "Clearance",
  absorption = "Intravenous",
  numCompartments = 1,
  isClosedForm = TRUE,
  isTlag = FALSE,
  hasEliminationComp = FALSE,
  isFractionExcreted = FALSE,
  isSaturating = FALSE,
  infusionAllowed = FALSE,
  isDuration = FALSE,
  isStdevFrozen = FALSE,
  data = NULL,
  columnMap = TRUE,
  modelName = "",
  workingDir = "",
  ...
)

Arguments

Value

NlmePmlModel object

Column mapping

Note that quoted and unquoted column names are supported. Please see colMapping.

Examples

model <- pkmodel(
  parameterization = "Clearance",
  numCompartments = 2,
  data = pkData,
  ID = "Subject",
  Time = "Act_Time",
  A1 = "Amount",
  CObs = "Conc",
  workingDir = tempdir()
)

# View the model as well as its associated column mappings
print(model)

PK model mapping parameters

Description

PK model mapping parameters

Usage

pkmodel_MappingParameters(
  ID = NULL,
  Time = NULL,
  A1 = NULL,
  Aa = NULL,
  A = NULL,
  A1_Rate = NULL,
  A1_Duration = NULL,
  Aa_Rate = NULL,
  Aa_Duration = NULL,
  A_Rate = NULL,
  A_Duration = NULL,
  A1Strip = NULL,
  CObs = NULL,
  C1Obs = NULL,
  A0Obs = NULL
)

Arguments

Column mapping

Note that quoted and unquoted column names are supported. Please see colMapping.

Pharmacokinetic/Pharmacodynamic dataset containing 200 subjects with single bolus dose

Description

Pharmacokinetic/Pharmacodynamic dataset containing 200 subjects with single bolus dose.

Usage

pkpdData

Format

A data frame with 2600 rows and 5 variables:

ID

Subject ID

Time

Nominal Time

Dose

Amount of dose

CObs

Observations of drug concentration in blood

EObs

Observations of drug effect

Source

The data is simulated using a PKPD model with PK described by a one-compartment model with IV bolus and PD described by an indirect response model with the loss inhibited.

Print generic for class NlmeEngineExtraParams

Description

Print generic for class NlmeEngineExtraParams

Usage

## S3 method for class 'NlmeEngineExtraParams'
print(x, ...)

Arguments

Value

NULL

Examples

print(NlmeEngineExtraParams())

Print an NlmeParallelHost Object

Description

Prints a summary of the configuration of an NlmeParallelHost object.

Usage

## S3 method for class 'NlmeParallelHost'
print(x, ...)

Arguments

Value

NULL. This function is called for its side effect of printing to the console.

Examples

host <- NlmeParallelHost(
  sharedDirectory = "~/shared/",
  installationDirectory = "~/nlme/",
  hostName = "my_host",
  machineName = "192.168.1.100",
  hostType = "RHEL",
  numCores = 8,
  isLocal = FALSE,
  rLocation = "/usr/bin/R",
  scriptPath = "/path/to/script.R",
  userAuthentication =
    NlmeUserAuthentication(userName = "myuser", userPassword = "mypassword"),
  parallelMethod = NlmeParallelMethod("SGE_MPI")
)

print(host)

Print generic for class NlmePmlModel

Description

Prints model information, including PML and column mappings.

Usage

## S3 method for class 'NlmePmlModel'
print(x, ...)

Arguments

Value

NULL

Examples

model <- pkmodel(columnMap = FALSE,
                 data = pkData,
                 workingDir = tempdir())
print(model)

Print generic for class Simple.NlmeJob

Description

Reads progress file and prints out its contents. Applicable to remote jobs or jobs running in backgroud.

Usage

## S3 method for class 'SimpleNlmeJob'
print(x, ...)

Arguments

Value

NULL

Examples

model <- pkmodel(
  parameterization = "Clearance",
  numCompartments = 2,
  data = pkData,
  ID = "Subject",
  Time = "Act_Time",
  A1 = "Amount",
  CObs = "Conc",
  workingDir = tempdir()
  )
 host <- hostParams(sharedDirectory = tempdir(),
                    parallelMethod = "None",
                    hostName = "local",
                    numCores = 1)
if (.Platform$OS.type == "unix") {
# background is not supported on Windows
  job <- fitmodel(model,
                  numIterations = 3,
                  hostPlatform = host,
                  runrunInBackground = TRUE)
  print(job)
}

Executes an NLME profile perturbation

Description

Executes an NLME profile perturbation

Usage

profilePertubate(
  hostPlatform,
  params = NULL,
  profiles,
  model = NULL,
  sortColumns = SortColumns(""),
  scenarios = list(),
  runInBackground = FALSE
)

Arguments

Value

List of results from NLME execution.

Examples

  model <- pkmodel(
    parameterization = "Clearance",
    numCompartments = 2,
    data = pkData,
    ID = "Subject",
    Time = "Act_Time",
    A1 = "Amount",
    CObs = "Conc",
    workingDir = tempdir()
  )

  params <- NlmeEngineExtraParams(
    method = 3,
    numIterations = 1
  )

  host <- hostParams(
    sharedDirectory = tempdir(),
    parallelMethod = "MULTICORE",
    hostName = "local",
    numCores = 4
  )

  profile1 <- ProfileVar(
    "tvV",
    9.548,
    "-2,2"
  )

  profile2 <- ProfileVar(
    "tvCl",
    0.919,
    "-0.5,1.5"
  )

  profiles <- ProfileParameters(
    "USE_DELTA",
    c(profile1, profile2)
  )

  job <- profilePertubate(
    hostPlatform = host,
    params = params,
    profiles = profiles,
    model = model
  )

Returns random block statement

Description

Returns random block statement

Usage

randomBlockStatement(.Object)

Arguments

Sets or updates the covariance matrix of random effects

Description

Use to set or update the covariance matrix of random effects in a model object.

Usage

randomEffect(
  .Object,
  effect,
  value = NULL,
  isDiagonal = TRUE,
  isFrozen = FALSE,
  ...
)

Arguments

Value

Modified NlmePmlModel object

Examples

model <- pkmodel(
  numCompartments = 2,
  data = pkData,
  ID = "Subject",
  Time = "Act_Time",
  A1 = "Amount",
  CObs = "Conc",
  modelName = "TwCpt_IVBolus_FOCE_ELS",
  workingDir = tempdir()
  )

model <-
  randomEffect(model,
               effect = c("nV", "nCl", "nCl2"), value = rep(0.1, 3))

Remove covariate from structural parameters in a model object.

Description

Remove one or more covariates from structural parameters in a model object.

Usage

removeCovariate(.Object, covariate = NULL, paramName = NULL)

Arguments

Value

Modified NlmePmlModel object

Examples

model <- pkmodel(
  numCompartments = 2,
  data = pkData,
  ID = "Subject",
  Time = "Act_Time",
  A1 = "Amount",
  CObs = "Conc",
  workingDir = tempdir()
)

# Add Gender covariate of type categorical
model <- addCovariate(model,
  covariate = "Gender",
  type = "Categorical",
  effect = c("V2", "Cl2"),
  levels = c(0, 1),
  labels = c("Female", "Male")
)

# Add BodyWeight covariate of type continuous
model <- addCovariate(model,
  covariate = "BodyWeight",
  type = "Continuous",
  direction = "Backward",
  center = "Mean",
  effect = c("V", "Cl")
)

# Remove all covariates from model
model <- removeCovariate(model)

Remove NLME License

Description

This function attempts to remove an NLME license using the specified installation directory and licensing tool.

Usage

remove_NLMELicense(InstallDir = Sys.getenv("INSTALLDIR"))

Arguments

Details

The function checks for the presence of the necessary 'appsettings.json' file in the specified directory or the CAD config file specified by the 'CAD_CONFIG_FILE' environment variable, runs the licensing tool to log out the user, and attempts to remove the NLME license.

Value

A logical value indicating whether the license information was successfully removed.

Examples

INSTALLDIR <- Sys.getenv("INSTALLDIR")
if (INSTALLDIR == "") INSTALLDIR <- "C:/Program Files/Certara/NLME_Engine"
if (FALSE) { # to prevent unintended logout
  result <- remove_NLMELicense(INSTALLDIR)
}

Sets residual effect attributes

Description

Sets residual effect attributes

Usage

residualEffect(.Object, effectName) <- value

Arguments

Return residual effect terms available in model

Description

Use to return character vector of residual effect names in model object

Usage

residualEffectNames(model)

Arguments

Value

Character vector of residual effect names

Examples

model <- pkemaxmodel(columnMap = FALSE)
residualEffectNames(model)

Assign residual error model to model object

Description

Use to change or update residual error model for model object

Usage

residualError(
  .Object,
  predName = "C",
  errorType = NULL,
  SD = NULL,
  isFrozen = FALSE,
  isBQL = FALSE,
  staticLLOQ = NULL,
  EObsBQL = NULL,
  CObsBQL = NULL,
  C1ObsBQL = NULL,
  A0ObsBQL = NULL,
  exponent = NULL
)

Arguments

Value

Modified NlmePmlModel object

Examples

model <- pkindirectmodel(indirectType = "LimitedInhibition", isBuildup = FALSE,
 data = pkpdData, ID = "ID", Time = "Time", A1 = "Dose", CObs = "CObs", EObs = "EObs")

residualEffectNames(model)

# Change error type to "Multiplicative" and value of SD to 0.1 for "E"
model <- residualError(model, predName = "E", errorType = "Multiplicative", SD = 0.1)

# Change error type to "Power", value of SD to 0.15, and set exponent = 2 for "C"
model <- residualError(model, predName = "C", errorType = "Power", SD = 0.15, exponent = 2)

Fit the NLME metamodel

Description

Use for simple model execution given information contained in mmdl file.

Usage

run_metamodel(mmdlfile, directoryToRun, nlme_hostPath, host)

Arguments

Details

See Metamodel documentation.

If both nlme_hostPath and host specified, the former is used. If nlme_hostPath is missing, host is used instead. If both are missing, MPI local host with 4 threads is used for simple estimation mode, multicore host is used for the others.

Multiple ESTARGS/SIMARGS are supported, they are applied for the model sequentially, the results of previous estimation are applied to the model before the next one. ESTARGS queue is executed first, SIMARGS queue is executed the second.

Value

the results of fitmodel() run are returned if singular ESTARGS block is provided; otherwise a list of fitmodel() and simmodel() results are returned.

See Also

NlmeParallelHost, fitmodel

Examples

mmdlfile <- system.file("extdata/mmdlNoTime/test.mmdl",
                       package = "Certara.RsNLME",
                       mustWork = TRUE)
directoryToRun <- file.path(tempdir(TRUE), "MmdlNoTimeTest")
# using default host
mmdlResults <- run_metamodel(mmdlfile = mmdlfile,
                             directoryToRun = directoryToRun)

Save model object to .rda file

Description

Saves the model, engine, and host objects to a single model.rda file in model's working directory. If no working directory exists it will be created by default.

Usage

saveModel(model, engine = NULL, host = NULL)

Arguments

Value

NULL

Examples

model <- emaxmodel(
  checkBaseline = TRUE,
  checkFractional = TRUE,
  checkInhibitory = TRUE,
  data = pkpdData,
  ID = "ID",
  C = "CObs",
  EObs = "EObs",
  workingDir = tempdir()
)

saveModel(model)

workhorse for change_ThetasMmdlin RsNLME.ModelBuilder

Description

workhorse for change_ThetasMmdlin RsNLME.ModelBuilder

Usage

saveUpdatedMetamodel(
  mmdl_withComments,
  mmdl_model,
  resmodel,
  metamodelFile,
  updateModel = TRUE,
  updatedBasedOn = "",
  updatedEstArgsBlock = NULL,
  updatedTablesBlock = NULL
)

Arguments

Value

text of new metamodel

Get secondary parameter names

Description

Returns character vector of secondary parameter names for model object.

Usage

secondaryParameterNames(model)

Arguments

Value

Character vector of secondary parameter names defined in model

Examples

model <- pkemaxmodel(columnMap = FALSE)
secondaryparms <- secondaryParameterNames(model)

Executes an NLME shotgun covariate search

Description

Executes an NLME shotgun covariate search

Usage

shotgunSearch(
  model,
  hostPlatform = NULL,
  params,
  covariateModel,
  runInBackground = FALSE,
  ...
)

Arguments

Value

if runInBackground = FALSE, a data frame is returned with shotgun (all combinations given the covariate model) search results, i.e. "Overall" comma separated file. Otherwise the ShotgunNlmeJob class object is returned.

See Also

hostParams, engineParams

Examples

# Define the model
model <- pkmodel(numCompartments = 2,
                 data = pkData,
                 ID = "Subject",
                 Time = "Act_Time",
                 A1 = "Amount",
                 CObs = "Conc",
                 workingDir = tempdir())

# Add Gender covariate of type categorical
model <- addCovariate(model,
                      covariate = "Gender",
                      type = "Categorical",
                      effect = c("V2", "Cl2"),
                      levels = c(0, 1),
                      labels = c("Female", "Male"))

# Add Bodyweight covariate of type continuous
model <- addCovariate(model,
             covariate = "BodyWeight",
             type = "Continuous",
             direction = "Backward",
             center = "Mean",
             effect = c("V", "Cl"))

# Define the host
host <- hostParams(parallelMethod = "MULTICORE",
                   hostName = "local",
                   numCores = 8,
                   sharedDirectory = tempdir())

# Define the engine parameters
params <- engineParams(model, fastOptimization = TRUE, numIterations = 7)

# Define covariate model
cp <- covariateModel(model)

# Perform shotgun search
OverallDF <-  shotgunSearch(model = model,
                            hostPlatform = host,
                            params = params,
                            covariateModel = cp,
                            runInBackground = FALSE)

Executes an NLME simulation

Description

Executes an NLME simulation

Usage

simmodel(
  model,
  simParams,
  params,
  hostPlatform = NULL,
  runInBackground = FALSE,
  ...
)

Arguments

Value

returns job properties if runInBackground is TRUE; if runInBackground is FALSE and the function is called in interactive mode, the resulted simulated tables wil be loaded and presented as a list; if runInBackground is FALSE and the function is called in non-interactive mode, the list returned will have just the full paths of the tables generated.

Examples

SimTableObs <- tableParams(
  name = "SimTableObs.csv",
  timesList = "0,1,2,4,4.9,55.1,56,57,59,60",
  variablesList = "C, CObs",
  timeAfterDose = FALSE,
  forSimulation = TRUE
)

simParams <- NlmeSimulationParams(
  numReplicates = 2,
  simulationTables = SimTableObs
)
# Define the model
model <- pkmodel(
  numComp = 2,
  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
 )

results <- simmodel(model, simParams, hostPlatform = host)
# with seed given additionally:
results <- simmodel(model, simParams, hostPlatform = host, seed = 3527)

Executes an NLME simple estimation with sort keys and given scenarios

Description

Executes an NLME simple estimation with sort keys and given scenarios

Usage

sortfit(
  model,
  hostPlatform = NULL,
  params,
  sortColumns,
  scenarios = list(),
  simpleTables,
  runInBackground = FALSE,
  filesToReturn = "*",
  ...
)

Arguments

Details

All the results in tabular format have scenario column and sorts columns appended. The resulted logs (nlme7engine.log, err1.txt, dmp.txt, out.txt) are appended with a row delimiter where the name of the Scenario and sort values are specified.

Value

if runInBackground is FALSE, a list with main resulted dataframes is returned:

• Overall

• ConvergenceData

• residuals

• Secondary

• StrCovariate - if continuous covariates presented

• StrCovariateCat - if categorical covariates presented

• theta

• posthoc table

• posthocStacked table

• Requested tables

nlme7engine.log textual output is returned and loaded with the main information related to fitting. dmp.txt structure with the results of fitting (including LL by subject information) is returned and loaded. These 2 files are returned and loaded irrespective of filesToReturn argument value.

For individual models, additional dataframe with partial derivatives is returned:

• ParDer

For population models and the method specified is NOT Naive-Pooled, additional dataframes are returned:

• omega

• Eta

• EtaStacked

• EtaEta

• EtaCov

• EtaCovariate - if continuous covariates presented

• EtaCovariateCat - if categorical covariates presented

• bluptable.dat

If standard error computation was requested and it was successful, additional dataframes are returned:

• thetaCorrelation

• thetaCovariance

• Covariance

• omega_stderr

If nonparametric method was requested (numIterNonParametric > 0) and the method specified in engineParams is NOT Naive-Pooled, additional dataframes are returned:

• nonParSupportResult

• nonParStackedResult

• nonParEtaResult

• nonParOverallResult

if runInBackground is TRUE, only current status of job is returned.

Non-loaded but returned files

The non-loaded but returned files in the model working directory are:

• err1.txt - concatenated for all runs detailed logs for all steps of optimization,

• out.txt - general pivoted information about results,

• doses.csv - information about doses given for all subjects,

• iniest.csv - information about initial estimates

See Also

hostParams, engineParams, SortColumns, NlmeScenario, tableParams

Examples

input_data <- pkData

model <-
  pkmodel(numCompartments = 2,
          data = input_data,
          ID = "Subject",
          Time = "Act_Time",
          A1 = "Amount",
          CObs = "Conc",
          workingDir = tempdir())

model <-
  addCovariate(model,
               covariate = "BodyWeight",
               direction = "Backward",
               center = "Mean",
               effect = c("V", "Cl"))

# multicore
multicoreHost <-
   hostParams(parallelMethod = "Multicore",
              hostName = "multicore",
              numCores = 4,
              sharedDirectory = tempdir())

# specify scenarios
CovariateEffectNames <- listCovariateEffectNames(model)
combinations <-
  combn(c("", CovariateEffectNames),
        length(CovariateEffectNames),
        simplify = FALSE)

scenarioNames <-
  lapply(combinations,
         function(x) {paste(x, collapse = " ")})

scenarios <-
  lapply(scenarioNames,
         function(x, CovariateEffectNames) {
           CovariateCombinations <- unlist(strsplit(x, " ", fixed = TRUE))
           scenarioIndex <-
             paste(which(CovariateEffectNames %in% CovariateCombinations,
                         arr.ind = TRUE),
                         collapse = ", ")
           NlmeScenario(trimws(x), scenarioIndex)
         },
         CovariateEffectNames)

res <-
  sortfit(model,
          hostPlatform = multicoreHost,
          params = engineParams(model, numIterations = 5, fastOptimization = TRUE),
          sortColumns = SortColumns("Gender"),
          scenarios = scenarios)

Executes an NLME stepwise covariate search

Description

Executes an NLME stepwise covariate search

Usage

stepwiseSearch(
  model,
  hostPlatform = NULL,
  params,
  covariateModel,
  stepwiseParams,
  runInBackground = FALSE,
  ...
)

Arguments

Value

if runInBackground = FALSE, a data frame is returned with stepwise search results, i.e. "Overall" comma separated file. Otherwise the StepwiseNlmeJob class object is returned.

See Also

hostParams, engineParams

Examples

# Define the model
model <- pkmodel(numCompartments = 1,
                 data = pkData,
                 ID = "Subject",
                 Time = "Act_Time",
                 A1 = "Amount",
                 CObs = "Conc",
                 workingDir = tempdir())

# Add Gender covariate of type categorical
model <- addCovariate(model,
                      covariate = "Gender",
                      type = "Categorical",
                      effect = c("V", "Cl"),
                      levels = c(0, 1),
                      labels = c("Female", "Male"))

# Add Bodyweight covariate of type continuous
model <- addCovariate(model,
             covariate = "BodyWeight",
             type = "Continuous",
             direction = "Backward",
             center = "Mean",
             effect = c("V", "Cl"))

# Define the host
defaultHost <- hostParams(parallelMethod = "MULTICORE",
                   hostName = "local",
                   numCores = 8,
                   sharedDirectory = tempdir())

# Define the engine parameters
params <- engineParams(model, numIterations = 6)

# Define covariate model
cp <- covariateModel(model)

# Define the stepwise parameters
sp <- StepwiseParams(0.01, 0.001, "-2LL")

# Perform stepwise search
OverallDF <-  stepwiseSearch(model = model,
                      hostPlatform = defaultHost,
                      params = params,
                      covariateModel = cp,
                      stepwiseParams = sp,
                      runInBackground = FALSE)

Set structural parameter in model object

Description

Use to specify the relationship of the structural parameter with corresponding fixed effect, random effect, and covariate.

Usage

structuralParameter(
  .Object,
  paramName,
  fixedEffName = NULL,
  randomEffName = NULL,
  style = "LogNormal",
  hasRandomEffect = NULL
)

Arguments

Value

Modified NlmePmlModel object

Examples

model <- pkindirectmodel(
  indirectType = "LimitedInhibition",
  isBuildup = FALSE,
  data = pkpdData,
  ID = "ID",
  Time = "Time",
  A1 = "Dose",
  CObs = "CObs",
  EObs = "EObs"
)

# Change style of structural parameter "Imax" to "LogitNormal"
# and rename fixed effect to "tvlogitImax"
model <- structuralParameter(model,
  paramName = "Imax",
  style = "LogitNormal", fixedEffName = "tvlogitImax"
)

# Remove random effect for structural parameter "IC50"
model <- structuralParameter(model,
  paramName = "IC50",
  hasRandomEffect = FALSE
)

Get structural parameter names

Description

Returns character vector of structural parameter names for model object.

Usage

structuralParameterNames(model, omitEmpties = TRUE)

Arguments

Value

Character vector of structural parameter names defined in model

Examples

model <- pkemaxmodel(columnMap = FALSE)
stparms <- structuralParameterNames(model)

Wrapper around NlmeTableDef/NlmeSimTableDef-classes initializers.

Description

Wrapper around NlmeTableDef/NlmeSimTableDef-classes initializers.

Usage

tableParams(
  name = "",
  timesList = numeric(0),
  covrSet = "",
  whenDose = "",
  whenObs = "",
  variablesList = "",
  keepSource = FALSE,
  timeAfterDose = FALSE,
  IRES = FALSE,
  Weight = FALSE,
  IWRES = FALSE,
  mode = "all",
  forSimulation = FALSE
)

Arguments

Value

NlmeTableDef object if forSimulation is FALSE, NlmeSimTableDef object otherwise.

Examples

Table1 <- tableParams(
  name = "Table1.csv",
  timesList = seq(0, 24, 2),
  whenObs = c("CObs"),
  variablesList = "C",
  IRES = TRUE,
  IWRES = TRUE,
  Weight = TRUE)

SimTable1 <- tableParams(
  name = "SimTable1.csv",
  variablesList = "CL, V",
  keepSource = TRUE,
  forSimulation = TRUE)

Create a textual model object

Description

Use to create an empty model object and optionally supply location of .mdl file to initialize model with PML statements.

Usage

textualmodel(modelName = "", workingDir = "", data, mdl = NULL)

Arguments

Value

NlmePmlModel object

Examples

model <- textualmodel(data = pkData)

Perform visual predictive check for NLME models

Description

Perform visual predictive check for NLME models

Usage

vpcmodel(
  model,
  vpcParams,
  params,
  hostPlatform = NULL,
  runInBackground = FALSE,
  ...
)

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

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)

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

# 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, hostPlatform = host)
# 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)

Writes out data/column mapping and engine parameter files for the engine

Description

Writes out data/column mapping and engine parameter files for the engine

Usage

writeDefaultFiles(model, dataset, simParams, Tables, sortColumns = NULL)

Arguments

Value

NULL