API concerning the scenario

Description of the functions of the API

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.

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Stop the current task run

Description

Stop the current task run.

Usage

abort()

See Also

runScenario

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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

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

See Also

runScenario abort isRunning

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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

setScenario

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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.

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

runScenario abort

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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

1. isRunning to check if the scenario is still running and get information about the current task,
2. abort to stop the execution.

To launch the function in the background, so that functions which do not modify the project (“get” functions for example) remains available, set the input argument “wait” to FALSE.

Usage

runConditionalDistributionSampling(wait = TRUE)

Arguments

wait
(bool) Should R wait for run completion before giving back the hand to the user. Equals TRUE by default.

See Also

isRunning abort

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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

1. isRunning to check if the scenario is still running and get information about the current task,
2. abort to stop the execution.

To launch the function in the background, so that functions which do not modify the project (“get” functions for example) remains available, set the input argument “wait” to FALSE.

Usage

runConditionalModeEstimation(wait = TRUE)

Arguments

wait
(bool) Should R wait for run completion before giving back the hand to the user. Equals TRUE by default.

See Also

isRunning abort

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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

1. isRunning to check if the scenario is still running and get information about the current task,
2. abort to stop the execution.

To launch the function in the background, so that functions which do not modify the project (“get” functions for example) remains available, set the input argument “wait” to FALSE.
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, wait = TRUE)

Arguments

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

wait
(bool) Should R wait for run completion before giving back the hand to the user. Equals TRUE by default.

See Also

isRunning abort

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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

1. isRunning to check if the scenario is still running and get information about the current task,
2. abort to stop the execution.

To launch the function in the background, so that functions which do not modify the project (“get” functions for example) remains available, set the input argument “wait” to FALSE.
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(wait = TRUE)

Arguments

wait
(bool) Should R wait for run completion before giving back the hand to the user. Equals TRUE by default.

See Also

isRunning abort

)
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Run current scenario

Description

Run the current scenario. By default, this task is processed sequentially.
Call

1. isRunning to check if the scenario is still running and get information about the current task,
2. abort to stop the execution.

To launch the function in the background, so that functions which do not modify the project (“get” functions for example) remains available, set the input argument “wait” to FALSE.

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(wait = TRUE)

Arguments

wait
(bool) Should R wait for run completion before giving back the hand to the user. Equals TRUE by default.

See Also

setScenario getScenario abort isRunning

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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

1. isRunning to check if the scenario is still running and get information about the current task,
2. abort to stop the execution.

To launch the function in the background, so that functions which do not modify the project (“get” functions for example) remains available, set the input argument “wait” to FALSE.

Usage

runStandardErrorEstimation(linearization = FALSE, wait = TRUE)

Arguments

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

wait
(bool) Should R wait for run completion before giving back the hand to the user. Equals TRUE 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

isRunning abort

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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

getScenario

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