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Description of the R functions associated to the scenario

Description of the functions of the API

runConditionalDistributionSampling Estimate the individual parameters using conditional distribution sampling algorithm.
runConditionalModeEstimation Estimate the individual parameters using the conditional mode estimation algorithm (EBEs).
runLogLikelihoodEstimation Run the log-Likelihood estimation algorithm.
runPopulationParameterEstimation Estimate the population parameters with the SAEM method.
runStandardErrorEstimation Estimate the Fisher Information Matrix and the standard errors of the population parameters.
computeChartsData Compute and export the charts data of scenario.
getLastRunStatus Return an execution report about the last run with a summary of the error which could have occurred.
getScenario For Monolix, get the list of tasks that will be run at the next call to runScenario, the associated method (linearization true or false), and the associated list of plots.
runScenario For Monolix, run the current scenario.
setScenario For Monolix, clear the current scenario and build a new one from a given list of tasks, the linearization option and the list of plots.

[Monolix] Sampling from the conditional distribution

Description

Estimate the individual parameters using conditional distribution sampling algorithm. The associated method keyword is "conditionalMean".

Usage

runConditionalDistributionSampling()

Click here to see examples

runConditionalDistributionSampling()

## End(Not run)


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[Monolix] Estimation of the conditional modes (EBEs)

Description

Estimate the individual parameters using the conditional mode estimation algorithm (EBEs). The associated method keyword is "conditionalMode".

Usage

runConditionalModeEstimation()

Click here to see examples

runConditionalModeEstimation()

## End(Not run)


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[Monolix] Log-Likelihood estimation

Description

Run the log-Likelihood estimation algorithm. By default, this task is not processed in the background of the R session.
Existing methods:

Method Identifier
Log-Likelihood estimation by linearization linearization = T
Log-Likelihood estimation by Importance Sampling (default) linearization = F

The Log-likelihood outputs(-2LL, AIC, BIC) are available using getEstimatedLogLikelihood function

Usage

runLogLikelihoodEstimation(linearization = FALSE)

Arguments

linearization option (boolean)[optional] method to be used. When no method is given, the importance sampling is used by default.

Click here to see examples

runLogLikelihoodEstimation(linearization = T)

## End(Not run)


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[Monolix] Population parameter estimation

Description

Estimate the population parameters with the SAEM method. The associated method keyword is "saem".
The initial values of the population parameters can be accessed by calling getPopulationParameterInformation and customized with setPopulationParameterInformation.
The estimated population parameters are available using getEstimatedPopulationParameters function.

Usage

runPopulationParameterEstimation()

Click here to see examples

runPopulationParameterEstimation()

## End(Not run)


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[Monolix] Standard error estimation

Description

Estimate the Fisher Information Matrix and the standard errors of the population parameters. By default, this task is not processed in the background of the R session.
Existing methods:

Method Identifier
Estimate the FIM by Stochastic Approximation linearization = F (default)
Estimate the FIM by Linearization linearization = T

The Fisher Information Matrix is available using getCorrelationOfEstimates function, while the standard errors are avalaible using getEstimatedStandardErrors function.

Usage

runStandardErrorEstimation(linearization = FALSE)

Arguments

linearization option (boolean)[optional] method to be used. When no method is given, the stochastic approximation is used by default.

Click here to see examples

runStandardErrorEstimation(linearization = T)

## End(Not run)


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[Monolix – PKanalix – Simulx] Compute the charts data

Description

Compute and export the charts data of scenario.
Notice that it does not impact the current scenario.

Usage

computeChartsData(exportVPCSimulations = FALSE)

Arguments

exportVPCSimulations (bool) [optional][Monolix] Should VPC simulations be exported if available. Equals FALSE by default.

Click here to see examples

computeChartsData()

## End(Not run)


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[Monolix – PKanalix] Get last run status

Description

Return an execution report about the last run with a summary of the error which could have occurred.

Usage

getLastRunStatus()

Value

A structure containing

  1. a boolean which equals TRUE if the last run has successfully completed,
  2. a summary of the errors which could have occurred.

Click here to see examples

lastRunInfo = getLastRunStatus()

lastRunInfo$status

-> TRUE

lastRunInfo$report

-> “”

## End(Not run)


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[Monolix – PKanalix] Get current scenario

Description

For Monolix, get the list of tasks that will be run at the next call to runScenario, the associated method (linearization true or false), and the associated list of plots.
The list of tasks consist of the following tasks: populationParameterEstimation, conditionalDistributionSampling, conditionalModeEstimation, standardErrorEstimation, logLikelihoodEstimation, and plots.
For PKanalix, get the list of the NCA tasks that will be run at the next call to runScenario.
The list of tasks consists of the following tasks: nca, bioequivalence.

Usage

getScenario()

Value

The list of tasks that corresponds to the current scenario, indexed by task names.

See Also

setScenario

Click here to see examples

[MONOLIX]

scenario = getScenario()

scenario

-> $tasks

populationParameterEstimation conditionalDistributionSampling conditionalModeEstimation standardErrorEstimation logLikelihoodEstimation plots

TRUE TRUE TRUE FALSE FALSE FALSE

$linearization = T

$plotList = “outputplot”, “vpc”

[PKANALIX]

scenario = getScenario()

scenario

nca be

TRUE FALSE

## End(Not run)


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[Monolix – PKanalix] Run scenario

Description

For Monolix, run the current scenario.
For PKanalix, run the NCA and Bioequivalence tasks.

Usage

runScenario()

See Also

setScenario getScenario

Click here to see examples

runScenario()

## End(Not run)


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[Monolix – PKanalix] Set scenario

Description

For Monolix, clear the current scenario and build a new one from a given list of tasks, the linearization option and the list of plots.
A task is the association of a task and a boolean.
NOTE: by default the boolean is false, thus, the user can only state what will run during the scenario.
NOTE: Within a MONOLIX scenario, the order according which the different algorithms are run is fixed:

Algorithm Algorithm Keyword
Population Parameter Estimation “populationParameterEstimation”
Conditional Mode Estimation (EBEs) “conditionalModeEstimation”
Sampling from the Conditional Distribution “conditionalDistributionSampling”
Standard Error and Fisher Information Matrix Estimation “standardErrorEstimation”
LogLikelihood Estimation “logLikelihoodEstimation”
Plots “plots”

Usage

setScenario(...)

Arguments

... A list of tasks as previously defined

Details

For PKanalix, clear the current scenario and build a new one from a given list of tasks.
A task is the association of a task and a boolean.
NOTE: By default the boolean is false, thus, the user can only state what will run during the scenario.
NOTE: Within a PKanalix scenario, the order according to which the different algorithms are run is fixed:

Algorithm Algorithm keyword
Non Compartmental Analysis “nca”
Bioequivalence estimation “be”

See Also

getScenario.

Click here to see examples

[MONOLIX]

scenario = getScenario()

scenario$tasks = c(populationParameterEstimation = T, conditionalModeEstimation = T, conditionalDistributionSampling = T)

setScenario(scenario)

[PKANALIX]

scenario = getScenario()

scenario = c(nca = T, be = F)

setScenario(scenario)

## End(Not run)


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