| abort | Stop the current task run. |
| getLastRunStatus | Return an execution report about the last run with a summary of the error which could have occurred. |
| getScenario | 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. |
| isRunning | Check if a scenario is currently running. |
| 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. |
| runScenario | Run the current scenario. |
| runStandardErrorEstimation | Estimate the Fisher Information Matrix and the standard errors of the population parameters. |
| setScenario | Clear the current scenario and build a new one from a given list of tasks, the linearization option and the list of plots. |
| computeChartsData | Compute and export the charts data. |
Stop the current task run
Description
Stop the current task run.
Usage
abort()
See Also
Click here to see examples
## Not run:
abort()
## End(Not run)
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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.
See Also
Click here to see examples
## Not run:
lastRunInfo = getLastRunStatus()
lastRunInfo$status
-> TRUE
lastRunInfo$report
-> “”
## End(Not run)
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Get current scenario
Description
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.
Usage
getScenario()
Value
The list of tasks that corresponds to the current scenario, indexed by algorithm names.
See Also
Click here to see examples
## Not run:
scenario = getScenario()
scenario
-> $tasks
populationParameterEstimation conditionalDistributionSampling conditionalModeEstimation standardErrorEstimation logLikelihoodEstimation plots
TRUE TRUE TRUE FALSE FALSE FALSE
$linearization = T
$plotList = “outputplot”, “vpc”
## End(Not run)
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Get current scenario state
Description
Check if a scenario is currently running. If yes, information about the current running task are displayed. This function has been removed in 2020 version because of issues with some R libraries.
Usage
isRunning(verbose = FALSE)
Arguments
verbose
(bool) Should information about the current running task be displayed in the console or not. Equals FALSE by default.
Value
A boolean which equals TRUE if a scenario is currently running.
See Also
Click here to see examples
## Not run:
isRunning()
## End(Not run)
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Sampling from the conditional distribution
Description
Estimate the individual parameters using conditional distribution sampling algorithm. The associated method keyword is “conditionalMean”.
By default, this task is not processed in the background of the R session.
Notice that it does not impact the current scenario. Call
isRunningto check if the scenario is still running and get information about the current task,abortto stop the execution.
Usage
runConditionalDistributionSampling()
See Also
Click here to see examples
## Not run:
runConditionalDistributionSampling()
## End(Not run)
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Estimation of the conditional modes (EBEs)
Description
Estimate the individual parameters using the conditional mode estimation algorithm (EBEs). The associated method keyword is “conditionalMode”.
By default, this task is not processed in the background of the R session.
Notice that it does not impact the current scenario. Call
isRunningto check if the scenario is still running and get information about the current task,abortto stop the execution.
Usage
runConditionalModeEstimation()
See Also
Click here to see examples
## Not run:
runConditionalModeEstimation()
## End(Not run)
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Log-Likelihood estimation
Description
Run the log-Likelihood estimation algorithm. By default, this task is not processed in the background of the R session.
Notice that it does not impact the current scenario. Call
isRunningto check if the scenario is still running and get information about the current task,abortto stop the execution.
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.
See Also
Click here to see examples
## Not run:
runLogLikelihoodEstimation(linearization = T)
## End(Not run)
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Population parameter estimation
Description
Estimate the population parameters with the SAEM method. The associated method keyword is “saem”.
By default, this task is not processed in the background of the R session.
Notice that it does not impact the current scenario. Call
isRunningto check if the scenario is still running and get information about the current task,abortto stop the execution.
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()
See Also
Click here to see examples
## Not run:
runPopulationParameterEstimation()
## End(Not run)
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Run current scenario
Description
Run the current scenario. By default, this task is processed sequentially.
Call
isRunningto check if the scenario is still running and get information about the current task,abortto stop the execution.
Note: if the plots task is selected in the scenario, and if “Export charts data” is selected in Monolix’s preferences, the charts data are saved in the result folder. Generating the interactive plots requires to open the project in the GUI.
Usage
runScenario()
See Also
setScenario getScenario abort isRunning
Click here to see examples
## Not run:
runScenario()
## End(Not run)
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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.
Notice that it does not impact the current scenario. Call
isRunningto check if the scenario is still running and get information about the current task,abortto stop the execution.
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.
Details
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.
See Also
Click here to see examples
## Not run:
runStandardErrorEstimation(linearization = T)
## End(Not run)
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Set scenario
Description
Clear the current scenario and build a new one from a given list of tasks, the linearization option and the list of plots.
The scenario is a list of 3 objects:
- tasks: named vector of boolean, defining for each task if it should run or not
- linearization: boolean, defining if linearization method should be used or not for standard errors and log-likelihood estimation
- plotList: vector of strings, defining the list of graphics to generate
NOTE
by default the boolean is false.
Usage
setScenario(...)
Details
NOTE
Within a MONOLIX scenario, the order in which the different algorithms are run is fixed.
Options for the “task” object of the list:
| Algorithm in GUI | Keyword in connector |
| 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” |
Options for the “linearization” object of the list: TRUE or FALSE
Options for the “plotList” object of the list:
| Name in GUI | Keyword for connector |
| Observed data | “outputplot” |
| Individual fits | “indfits” |
| Observations vs predictions | “obspred” |
| Scatter plot of the residuals | “residualsscatter” |
| Distribution of the residuals | “residualsdistribution” |
| Distribution of the individual parameters | “parameterdistribution” |
| Distribution of the random effects | “randomeffects” |
| Correlation between random effects | “covariancemodeldiagnosis” |
| Individual parameters vs covariates | “covariatemodeldiagnosis” |
| Visual predictive check | “vpc” |
| Visual predictive check (discrete data) | “categorizedoutput” |
| Numerical predictive check | “npc” |
| BLQ predictive check | “blq” |
| Prediction distribution | “predictiondistribution” |
| Likelihood contribution | “likelihoodcontribution” |
| Standard errors of the estimates | “fisher” |
| SAEM | “saemresults” |
| MCMC | “condmeanresults” |
| Importance sampling | “likelihoodresults” |
See Also
Click here to see examples
## Not run:
scenario = getScenario()
scenario$tasks = c(populationParameterEstimation = T, conditionalModeEstimation = T, conditionalDistributionSampling = T)
scenario$linearization = TRUE
scenario$plotList = c(“outputplot”,”fisher”)
setScenario(scenario)
## End(Not run)
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Compute the charts data
Description
Compute and export the charts data o scenario. Notice that it does not impact the current scenario. Call isRunning to check if the scenario is still running and get information about the current task, abort to stop the execution.
Usage
computeChartsData()
See Also
Click here to see examples
## Not run:
computeChartsData()
##End(Not run)
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