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
- a boolean which equals TRUE if the last run has successfully completed,
- 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
Click here to see examples
[MONOLIX]
scenario = getScenario()
scenario
-> $tasks
populationParameterEstimation conditionalDistributionSampling conditionalModeEstimation standardErrorEstimation logLikelihoodEstimation plots
TRUE TRUE TRUE FALSE FALSE FALSE
$linearization = T
$plotList = “outputplot”, “vpc”
[PKANALIX]
scenario = getScenario()
scenario
nca be
TRUE FALSE
## End(Not run)
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[Monolix – PKanalix] Run scenario
Description
For Monolix, run the current scenario.
For PKanalix, run the NCA and Bioequivalence tasks.
Usage
runScenario()
See Also
Click here to see examples
runScenario()
## End(Not run)
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[Monolix – PKanalix] Set scenario
Description
For Monolix, clear the current scenario and build a new one from a given list of tasks, the linearization option and the list of plots.
A task is the association of a task and a boolean.
NOTE: by default the boolean is false, thus, the user can only state what will run during the scenario.
NOTE: Within a MONOLIX scenario, the order according which the different algorithms are run is fixed:
| Algorithm | Algorithm Keyword |
| Population Parameter Estimation | “populationParameterEstimation” |
| Conditional Mode Estimation (EBEs) | “conditionalModeEstimation” |
| Sampling from the Conditional Distribution | “conditionalDistributionSampling” |
| Standard Error and Fisher Information Matrix Estimation | “standardErrorEstimation” |
| LogLikelihood Estimation | “logLikelihoodEstimation” |
| Plots | “plots” |
Usage
setScenario(...)
Arguments
... |
A list of tasks as previously defined |
Details
For PKanalix, clear the current scenario and build a new one from a given list of tasks.
A task is the association of a task and a boolean.
NOTE: By default the boolean is false, thus, the user can only state what will run during the scenario.
NOTE: Within a PKanalix scenario, the order according to which the different algorithms are run is fixed:
| Algorithm | Algorithm keyword |
| Non Compartmental Analysis | “nca” |
| Bioequivalence estimation | “be” |
See Also
Click here to see examples
[MONOLIX]
scenario = getScenario()
scenario$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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