# Description of the R functions associated to the settings

 getConditionalDistributionSamplingSettings Get the conditional distribution sampling settings. getConditionalModeEstimationSettings Get the conditional mode estimation settings. getGeneralSettings Get a summary of the common settings for Monolix algorithms. getLogLikelihoodEstimationSettings Get the loglikelihood estimation settings. getMCMCSettings Get the MCMC algorithm settings of the current project. getPopulationParameterEstimationSettings Get the population parameter estimation settings. getPreferences Get a summary of the project preferences. getProjectSettings Get a summary of the project settings. getStandardErrorEstimationSettings Get the standard error estimation settings. setConditionalDistributionSamplingSettings Set the value of one or several of the conditional distribution sampling settings. setConditionalModeEstimationSettings Set the value of one or several of the conditional mode estimation settings. setGeneralSettings Set the value of one or several of the common settings for Monolix algorithms. setLogLikelihoodEstimationSettings Set the value of the loglikelihood estimation settings. setMCMCSettings Set the value of one or several of the MCMC algorithm specific settings of the current project. setPopulationParameterEstimationSettings Set the value of one or several of the population parameter estimation settings. setPreferences Set the value of one or several of the project preferences. setProjectSettings Set the value of one or several of the settings of the project. setStandardErrorEstimationSettings Set the value of one or several of the standard error estimation settings.

## Get conditional distribution sampling settings

### Description

Get the conditional distribution sampling settings. Associated settings are:

 “ratio” (0< double <1) Width of the confidence interval. “nbMinIterations” (int >=1) Minimum number of iterations. “nbSimulatedParameters” (int >=1) Number of replicates.

### Usage

getConditionalDistributionSamplingSettings(...)


### Arguments

[optional] (string) Name of the settings whose value should be displayed. If no argument is provided, all the settings are returned.

### Value

An array which associates each setting name to its current value.

### See Also

 setConditionalDistributionSamplingSettings

### Click here to see examples

## Not run:

getConditionalDistributionSamplingSettings() # retrieve a list of all the conditional distribution sampling settings

getConditionalDistributionSamplingSettings(“ratio”,”nbMinIterations”) # retrieve a list containing only the value of the settings whose name has been passed in argument

## End(Not run)

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## Get conditional mode estimation settings

### Description

Get the conditional mode estimation settings. Associated settings are:

 “nbOptimizationIterationsMode” (int >=1) Maximum number of iterations. “optimizationToleranceMode” (double >0) Optimization tolerance.

### Usage

getConditionalModeEstimationSettings(...)


### Arguments

[optional] (string) Name of the settings whose value should be displayed. If no argument is provided, all the settings are returned.

### Value

An array which associates each setting name to its current value.

### See Also

 setConditionalModeEstimationSettings

### Click here to see examples

## Not run:

getConditionalModeEstimationSettings() # retrieve a list of all the conditional mode estimation settings

getConditionalModeEstimationSettings(“nbOptimizationIterationsMode”) # retrieve a list containing only the value of the settings whose name has been passed in argument

## End(Not run)

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## Get project general settings

### Description

Get a summary of the common settings for Monolix algorithms. Associated settings are:

 “autoChains” (bool) Automatically adjusted the number of chains to have at least a minimum number of subjects. “nbChains” (int >0) Number of chains. Used only if “autoChains” is set to FALSE. “minIndivForChains” (int >0) Minimum number of individuals by chain.

### Usage

getGeneralSettings(...)


### Arguments

[optional] (string) Name of the settings whose value should be displayed. If no argument is provided, all the settings are returned.

### Value

An array which associates each setting name to its current value.

### See Also

 setGeneralSettings

### Click here to see examples

## Not run:

getGeneralSettings() # retrieve a list of all the general settings

getGeneralSettings(“nbChains”,”autoChains”) # retrieve a list containing only the value of the settings whose name has been passed in argument

## End(Not run)

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## Get LogLikelihood algorithm settings

### Description

Get the loglikelihood estimation settings. Associated settings are:

 “nbFixedIterations” (int >0) Monte Carlo size for the loglikelihood evaluation. “samplingMethod” (string) Should the loglikelihood estimation use a given number of freedom degrees (“fixed”) or test a sequence of degrees of freedom numbers before choosing the best one (“optimized”). “nbFreedomDegrees” (int >0) Degree of freedom of the Student t-distribution. Used only if “samplingMethod” is “fixed”. “freedomDegreesSampling” (vector0)>) Sequence of freedom degrees to be tested. Used only if “samplingMethod” is “optimized”.

### Usage

getLogLikelihoodEstimationSettings(...)


### Arguments

[optional] (string) Name of the settings whose value should be displayed. If no argument is provided, all the settings are returned.

### Value

An array which associates each setting name to its current value.

### See Also

 setLogLikelihoodEstimationSettings

### Click here to see examples

## Not run:

getLogLikelihoodEstimationSettings() # retrieve a list of all the loglikelihood estimation settings

getLogLikelihoodEstimationSettings(“nbFixedIterations”,”samplingMethod”) # retrieve a list containing only the value of the settings whose name has been passed in argument (here, the number of fixed iterations and the method)

## End(Not run)

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## Get MCMC algorithm settings

### Description

Get the MCMC algorithm settings of the current project. Associated settings are:

 “strategy” (vector[3]) Number of calls for each one of the three MCMC kernels. “acceptanceRatio” (double) Target acceptance ratio.

### Usage

getMCMCSettings(...)


### Arguments

[optional] (string) Names of the settings whose value should be displayed. If no argument is provided, all the settings are returned.

### Value

An array which associates each setting name to its current value.

### See Also

 setMCMCSettings

### Click here to see examples

## Not run:

getMCMCSettings() # retrieve a list of all the MCMC settings

getMCMCSettings(“strategy”) # retrieve a list containing only the value of the settings whose name has been passed in argument (here, the strategy)

## End(Not run)

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## Get population parameter estimation settings

### Description

Get the population parameter estimation settings. Associated settings are:

 “nbBurningIterations” (int >=0) Number of iterations in the burn-in phase. “nbExploratoryIterations” (int >=0) If “exploratoryAutoStop” is set to FALSE, it is the number of iterations in the exploratory phase. Else wise, if “exploratoryAutoStop” is set to TRUE, it is the maximum of iterations in the exploratory phase. “exploratoryAutoStop” (bool) Should the exploratory step automatically stop. “exploratoryInterval” (int >0) Minimum number of interation in the exploratory phase. Used only if “exploratoryAutoStop” is TRUE “exploratoryAlpha” (0<= double <=1) Convergence memory in the exploratory phase. Used only if “exploratoryAutoStop” is TRUE “nbSmoothingIterations” (int >=0) If “smoothingAutoStop” is set to FALSE, it is the number of iterations in the smoothing phase. Else wise, if “smoothingAutoStop” is set to TRUE, it is the maximum of iterations in the smoothing phase. “smoothingAutoStop” (bool) Should the smoothing step automatically stop. “smoothingInterval” (int >0) inimum number of interation in the smoothing phase. Used only if “smoothingAutoStop” is TRUE. “smoothingAlpha” (0.5< double <=1) Convergence memory in the smoothing phase. Used only if “smoothingAutoStop” is TRUE. “smoothingRatio” (0< double <1) Width of the confidence interval. Used only if “smoothingAutoStop” is TRUE. “simulatedAnnealing” (bool) Should annealing be simulated. “tauOmega” (double >0) Proportional rate on variance. Used only if “simulatedAnnealing” is TRUE. “tauErrorModel” (double >0) Proportional rate on error model. Used only if “simulatedAnnealing” is TRUE. “variability” (string) Estimation method for parameters without variability: “firstStage” | “decreasing” | “none”. Used only if arameters without variability are used in the project. “nbOptimizationIterations” (int >=1) Number of optimization iterations. “optimizationTolerance” (double >0) Tolerance for optimization.

### Usage

getPopulationParameterEstimationSettings(...)


### Arguments

[optional] (string) Name of the settings whose value should be displayed. If no argument is provided, all the settings are returned.

### Value

An array which associates each setting name to its current value.

### See Also

 setPopulationParameterEstimationSettings

### Click here to see examples

## Not run:

getPopulationParameterEstimationSettings() # retrieve a list of all the population parameter estimation settings

getPopulationParameterEstimationSettings(“nbBurningIterations”,”smoothingInterval”) # retrieve a list containing only the value of the settings whose name has been passed in argument (here, the number of burning iterations and the smoothing interval)

## End(Not run)

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## Get project preferences

### Description

Get a summary of the project preferences. Preferences are:

 “relativePath” (bool) Use relative path for save/load operations. “threads” (int >0) Number of threads. “timeStamping” (bool) Create an archive containing result files after each run. “dpi” (bool) Apply high density pixel correction. “imageFormat” (string) Image format used to save Monolix plots. “delimiter” (string) Character used as delimiter in exported result files (“comma”, “,”, “semicolon”, “;”, “space”, ” “, “tab”, “\t”). “exportGraphics” (bool) Should plots images be exported. “exportGraphicsData” (bool) Should charts data be exported.

### Usage

getPreferences(...)


### Arguments

[optional] (string) Name of the preference whose value should be displayed. If no argument is provided, all the preferences are returned.

### Value

An array which associates each preference name to its current value.

### See Also

 setGeneralSettings

### Click here to see examples

## Not run:

getPreferences() # retrieve a list of all the general settings

getPreferences(“imageFormat”,”exportGraphics”) # retrieve a list containing only the value of the preferences whose name has been passed in argument

## End(Not run)

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## Get project settings

### Description

Get a summary of the project settings. Associated settings are:

 “directory” (string) Path to the folder where simulation results will be saved. It should be a writable directory. “exportResults” (bool) Should results be exported. “seed” (0< int <2147483647) Seed used by random generators. “grid” (int) Number of points for the continuous simulation grid. “nbSimulations” (int) Number of simulation for the plots (in VPC, NPC, …). “dataAndModelNextToProject” (bool) Should data and model files be saved next to project.

### Usage

getProjectSettings(...)


### Arguments

[optional] (string) Name of the settings whose value should be displayed. If no argument is provided, all the settings are returned.

### Value

An array which associates each setting name to its current value.

### See Also

 getProjectSettings

### Click here to see examples

## Not run:

getProjectSettings() # retrieve a list of all the project settings

getProjectSettings(“directory”,”seed”) # retrieve a list containing only the value of the settings whose name has been passed in argument

## End(Not run)

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## Get standard error estimation settings

### Description

Get the standard error estimation settings. Associated settings are:

 “minIterations” (int >=1) Minimum number of iterations. “maxIterations” (int >=1) Maximum number of iterations.

### Usage

getStandardErrorEstimationSettings(...)


### Arguments

[optional] (string) Name of the settings whose value should be displayed. If no argument is provided, all the settings are returned.

### Value

An array which associates each setting name to its current value.

### See Also

 setStandardErrorEstimationSettings

### Click here to see examples

## Not run:

getStandardErrorEstimationSettings() # retrieve a list of all the standard error estimation settings

getStandardErrorEstimationSettings(“minIterations”,”maxIterations”) # retrieve a list containing only the value of the settings whose name has been passed in argument

## End(Not run)

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## Set conditional distribution sampling settings

### Description

Set the value of one or several of the conditional distribution sampling settings. Associated settings are:

 “ratio” (0< double <1) Width of the confidence interval. “nbMinIterations” (int >=1) Minimum number of iterations. “nbSimulatedParameters” (int >=1) Number of replicates.

### Usage

setConditionalDistributionSamplingSettings(...)


### Arguments

A collection of comma-separated pairs {settingName = settingValue}.

### See Also

 getConditionalDistributionSamplingSettings

### Click here to see examples

## Not run:

setConditionalDistributionSamplingSettings(ratio = 0.05, nbMinIterations = 50)

## End(Not run)

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## Set conditional mode estimation settings

### Description

Set the value of one or several of the conditional mode estimation settings. Associated settings are:

 “nbOptimizationIterationsMode” (int >=1) Maximum number of iterations. “optimizationToleranceMode” (double >0) Optimization tolerance.

### Usage

setConditionalModeEstimationSettings(...)


### Arguments

A collection of comma-separated pairs {settingName = settingValue}.

### See Also

 getConditionalModeEstimationSettings

### Click here to see examples

## Not run:

setConditionalModeEstimationSettings(nbOptimizationIterationsMode = 20, optimizationToleranceMode = 0.1)

## End(Not run)

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## Set common settings for algorithms

### Description

Set the value of one or several of the common settings for Monolix algorithms. Associated settings are:

 “autoChains” (bool) Automatically adjusted the number of chains to have at least a minimum number of subjects. “nbChains” (int >0) Number of chains to be used if “autoChains” is set to FALSE. “minIndivForChains” (int >0) Minimum number of individuals by chain.

### Usage

setGeneralSettings(...)


### Arguments

A collection of comma-separated pairs {settingName = settingValue}.

### See Also

 getGeneralSettings

### Click here to see examples

## Not run:

setGeneralSettings(autoChains = FALSE, nbchains = 10)

## End(Not run)

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## Set loglikelihood estimation settings

### Description

Set the value of the loglikelihood estimation settings. Associated settings are:

 “nbFixedIterations” (int >0) Monte Carlo size for the loglikelihood evaluation. “samplingMethod” (string) Should the loglikelihood estimation use a given number of freedom degrees (“fixed”) or test a sequence of degrees of freedom numbers before choosing the best one (“optimized”). “nbFreedomDegrees” (int >0) Degree of freedom of the Student t-distribution. Used only if “samplingMethod” is “fixed”. “freedomDegreesSampling” (vector0)>) Sequence of freedom degrees to be tested. Used only if “samplingMethod” is “optimized”.

### Usage

setLogLikelihoodEstimationSettings(...)


### Arguments

A collection of comma-separated pairs {settingName = settingValue}.

### See Also

 getLogLikelihoodEstimationSettings

### Click here to see examples

## Not run:

setLogLikelihoodEstimationSettings(nbFixedIterations = 20000)

## End(Not run)

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## Set settings associated to the MCMC algorithm

### Description

Set the value of one or several of the MCMC algorithm specific settings of the current project. Associated settings are:

 “strategy” (vector[3]) Number of calls for each one of the three MCMC kernels. “acceptanceRatio” (double) Target acceptance ratio.

### Usage

setMCMCSettings(...)


### Arguments

A collection of comma-separated pairs {settingName = settingValue}.

### See Also

 getMCMCSettings

### Click here to see examples

## Not run:

setMCMCSettings(strategy = c(2,1,2))

## End(Not run)

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## Set population parameter estimation settings

### Description

Set the value of one or several of the population parameter estimation settings. Associated settings are:

 “nbBurningIterations” (int >=0) Number of iterations in the burn-in phase. “nbExploratoryIterations” (int >=0) If “exploratoryAutoStop” is set to FALSE, it is the number of iterations in the exploratory phase. Else wise, if “exploratoryAutoStop” is set to TRUE, it is the maximum of iterations in the exploratory phase. “exploratoryAutoStop” (bool) Should the exploratory step automatically stop. “exploratoryInterval” (int >0) Minimum number of interation in the exploratory phase. Used only if “exploratoryAutoStop” is TRUE “exploratoryAlpha” (0<= double <=1) Convergence memory in the exploratory phase. Used only if “exploratoryAutoStop” is TRUE “nbSmoothingIterations” (int >=0) If “smoothingAutoStop” is set to FALSE, it is the number of iterations in the smoothing phase. Else wise, if “smoothingAutoStop” is set to TRUE, it is the maximum of iterations in the smoothing phase. “smoothingAutoStop” (bool) Should the smoothing step automatically stop. “smoothingInterval” (int >0) Minimum number of interation in the smoothing phase. Used only if “smoothingAutoStop” is TRUE. “smoothingAlpha” (0.5< double <=1) Convergence memory in the smoothing phase. Used only if “smoothingAutoStop” is TRUE. “smoothingRatio” (0< double <1) Width of the confidence interval. Used only if “smoothingAutoStop” is TRUE. “simulatedAnnealing” (bool) Should annealing be simulated. “tauOmega” (double >0) Proportional rate on variance. Used only if “simulatedAnnealing” is TRUE. “tauErrorModel” (double >0) Proportional rate on error model. Used only if “simulatedAnnealing” is TRUE. “variability” (string) Estimation method for parameters without variability: “firstStage” | “decreasing” | “none”. Used only if arameters without variability are used in the project. “nbOptimizationIterations” (int >=1) Number of optimization iterations. “optimizationTolerance” (double >0) Tolerance for optimization.

### Usage

setPopulationParameterEstimationSettings(...)


### Arguments

A collection of comma-separated pairs {settingName = SettingValue}.

### See Also

 getPopulationParameterEstimationSettings

### Click here to see examples

## Not run:

setPopulationParameterEstimationSettings(exploratoryAutoStop = TRUE, tauOmega = 0.95)

## End(Not run)

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## Set preferences

### Description

Set the value of one or several of the project preferences. Prefenreces are:

 “relativePath” (bool) Use relative path for save/load operations. “threads” (int >0) Number of threads. “timeStamping” (bool) Create an archive containing result files after each run. “dpi” (bool) Apply high density pixel correction. “imageFormat” (string) Image format used to save Mnolix plots. “delimiter” (string) Character used as delimiter in exported result files (“comma”, “,”, “semicolon”, “;”, “space”, ” “, “tab”, “\t”). “exportGraphics” (bool) Should plots images be exported. “exportGraphicsData” (bool) Should charts data be exported.

### Usage

setPreferences(...)


### Arguments

A collection of comma-separated pairs {preferenceName = settingValue}.

### See Also

 getPreferences

### Click here to see examples

## Not run:

setPreferences(“exportGraphics” = FALSE, “delimiter” = “,”)

## End(Not run)

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## Set project settings

### Description

Set the value of one or several of the settings of the project. Associated settings are:

 “directory” (string) Path to the folder where simulation results will be saved. It should be a writable directory. “exportResults” (bool) Should results be exported. “seed” (0< int <2147483647) Seed used by random generators. “grid” (int) Number of points for the continuous simulation grid. “nbSimulations” (int) Simulation number. “dataAndModelNextToProject” (bool) Should data and model files be saved next to project.

### Usage

setProjectSettings(...)


### Arguments

A collection of comma-separated pairs {settingName = settingValue}.

### See Also

 getProjectSettings

### Click here to see examples

## Not run:

setProjectSettings(directory = “/path/to/export/directory”, seed = 12345)

## End(Not run)

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## Set standard error estimation settings

### Description

Set the value of one or several of the standard error estimation settings. Associated settings are:

 “minIterations” (int >=1) Minimum number of iterations. “maxIterations” (int >=1) Maximum number of iterations.

### Usage

setStandardErrorEstimationSettings(...)


### Arguments

A collection of comma-separated pairs {settingName = settingValue}.

### See Also

 getStandardErrorEstimationSettings

### Click here to see examples

## Not run:

setStandardErrorEstimationSettings(minIterations = 20, maxIterations = 250)

## End(Not run)

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