On the use of the R-functions
We now propose to use Monolix via R-functions. The package lixoftConnectors provides access to the project exactly in the same way as you would do with the interface. All the installation guidelines and initialization procedure can be found here. All the functions of lixoftConnectors are described below. To go beyond what the interface allows, the Rsmlx package provides additional functions for automatic PK model building, bootstrap simulation and likelihood profiling, among others.
Note: Due to possible conflicts, the package mlxR, whose function simulx
can be used to perform simulations with Monolix, should not be loaded at the same time as lixoftConnectors.
- Installation guidelines and initialization procedure
- Examples using R functions
- Load and run a project
- Convergence assessment
- Profile likelihood
- Bayesian individual dynamic predictions
- Covariate search
- Generate plots in R
- Handling of warning/error/info messages
- List of the R functions
- Description of the functions concerning the algorithm settings
- Description of the functions concerning the convergence assessment
- Description of the functions concerning the covariate model
- Description of the functions concerning the dataset
- Description of the functions concerning the individual model
- Description of the functions concerning the initial values and estimation method
- Description of the functions concerning the initialization and path to demo projects
- Description of the functions concerning the model building tasks
- Description of the functions concerning the observation model
- Description of the functions concerning the plots
- Description of the functions concerning the project management
- Description of the functions concerning the project settings and preferences
- Description of the functions concerning the reporting
- Description of the functions concerning the results
- Description of the functions concerning the scenario
List of the R functions
Description of the functions concerning the algorithm 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.
- 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.
- setStandardErrorEstimationSettings : Set the value of one or several of the standard error estimation settings.
Description of the functions concerning the convergence assessment
- getAssessmentResults : Get the results of the assessment.
- getAssessmentSettings : Get the settings that will be used during the run of assessment.
- runAssessment : Run assessment.
Description of the functions concerning the covariate model
- addCategoricalTransformedCovariate : Create a new categorical covariate by transforming an existing one.
- addContinuousTransformedCovariate : Create a new continuous covariate by transforming an existing one.
- addMixture : Add a new latent covariate to the current model giving its name and its modality number.
- removeCovariate : Remove some of the transformed covariates (discrete and continuous) and/or latent covariates.
Description of the functions concerning the dataset
- addAdditionalCovariate : Create an additional covariate for stratification purpose.
- applyFilter : Apply a filter on the current data.
- createFilter : Create a new filtered data set by applying a filter on an existing one and/or complementing it.
- deleteAdditionalCovariate : Delete a created additinal covariate.
- deleteFilter : Delete a data set.
- editFilter : Edit the definition of an existing filtered data set.
- formatData : Adapt and export a data file as a MonolixSuite formatted data set.
- getAvailableData : Get information about the data sets and filters defined in the project.
- getCovariateInformation : Get the name, the type and the values of the covariates present in the project.
- getFormatting : Get data formatting from a loaded project.
- getObservationInformation : Get the name, the type and the values of the observations present in the project.
- getTreatmentsInformation : Get information about doses present in the loaded dataset.
- removeFilter : Remove the last filter applied on the current data set.
- renameAdditionalCovariate : Rename an existing additional covariate.
- renameFilter : Rename an existing filtered data set.
- selectData : Select the new current data set within the previously defined ones (original and filters).
Description of the functions concerning the individual model
- getIndividualParameterModel : Get a summary of the information concerning the individual parameter model.
- getVariabilityLevels : Get a summary of the variability levels (inter-individual and/or intra-individual variability) present in the current project.
- setCorrelationBlocks : Define the correlation block structure associated to some of the variability levels of the current project.
- setCovariateModel : Set which are the covariates influencing individual parameters present in the project.
- setIndividualLogitLimits : Set the minimum and the maximum values between the individual parameter can be used.
- setIndividualParameterDistribution : Set the distribution of the estimated parameters.
- setIndividualParameterModel : Set the information concerning the individual parameter model.
- setIndividualParameterVariability : Add or remove inter-individual and/or intra-individual variability from some of the individual parameters present in the project.
Description of the functions concerning the initial values and estimation method
- getFixedEffectsByAutoInit : Compute optimized values for initial population parameters.
- getPopulationParameterInformation : Get the name, the initial value, the estimation method and, if relevant, MAP parameters value of the population parameters present in the project.
- setInitialEstimatesToLastEstimates : Set the initial value of all the population parameters present within the current project to the ones previously estimated.
- setPopulationParameterInformation : Set the initial value, the estimation method and, if relevant, the MAP parameters of one or several of the population parameters present within the current project (fixed effects + individual variances + error model parameters).
Description of the functions concerning the initialization and path to demo projects
- initializeLixoftConnectors : Initialize lixoftConnectors API for a given software.
- getDemoPath : Get the path to the demo projects.
Description of the functions concerning the model building tasks
- getModelBuildingResults : Get the results (detailed models) of the model building.
- getModelBuildingSettings : Get the settings that will be used during the run of model building.
- runModelBuilding : Run model building.
Description of the functions concerning the observation model
- getContinuousObservationModel : Get a summary of the information concerning the continuous observation models in the project.
- setAutocorrelation : Add or remove auto-correlation from the error model used on some of the observation models.
- setErrorModel : Set the error model type to be used with some of the observation models.
- setObservationDistribution : Set the distribution in the Gaussian space of some of the observation models.
- setObservationLimits : Set the minimum and the maximum values between which some of the observations can be found.
Description of the functions concerning the plots
- getChartsData : Compute Charts data with custom stratification options and custom computation settings.
- plotBivariateDataViewer : Plot the bivariate viewer.
- plotCovariates : Plot the covariates.
- plotObservedData : Plot the observed data.
- plotImportanceSampling : Plot iterations of the likelihood estimation by importance sampling.
- plotMCMC : Plot iterations and convergence for the conditional distribution task.
- plotSaem : Plot iterations and convergence for the SAEM algorithm (population parameters estimation).
- plotParametersDistribution : Plot the distribution of the individual parameters.
- plotParametersVsCovariates : Plot individual parameters vs covariates.
- plotRandomEffectsCorrelation : Plot correlations between random effects.
- plotStandardizedRandomEffectsDistribution : Plot the distribution of the standardized random effects.
- plotIndividualFits : Plot the individual fits.
- plotObservationsVsPredictions : Plot the observation vs the predictions.
- plotResidualsDistribution : Plot the distribution of the residuals.
- plotResidualsScatterPlot : Plot the scatter plots of the residuals.
- plotBlqPredictiveCheck : Plot the BLQ predictive checks.
- plotNpc : Plot the numerical predictive checks.
- plotPredictionDistribution : Plot the prediction distribution.
- plotVpc : Plot the visual predictive checks.
- getPlotPreferences : Define the preferences to customize plots.
Description of the functions concerning the project management
- exportProject : Export the current project to another application of the MonolixSuite, and load the exported project.
- getData : Get a description of the data used in the current project.
- getInterpretedData : Get data after interpretation done by the software, how it is displayed in the Data tab in the interface.
- getLibraryModelContent : Get the content of a library model.
- getLibraryModelName : Get the name of a library model given a list of library filters.
- getMapping : Get mapping between data and model.
- getStructuralModel : Get the model file for the structural model used in the current project.
- importProject : Import a Monolix or a PKanalix project into the currently running application initialized in the connectors.
- isProjectLoaded : Get a boolean saying if a project is currently loaded.
- loadProject : Load a project in the currently running application initialized in the connectors.
- newProject : Create a new project.
- saveProject : Save the current project as a file that can be reloaded in the connectors or in the GUI.
- setData : Set project data giving a data file and specifying headers and observations types.
- setMapping : Set mapping between data and model.
- setStructuralModel : Set the structural model.
Description of the functions concerning the project settings and preferences
- getConsoleMode : Get console mode, ie volume of output after running estimation tasks.
- getPreferences : Get a summary of the project preferences.
- getProjectSettings : Get a summary of the project settings.
- setConsoleMode : Set console mode, ie volume of output after running estimation tasks.
- 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.
Description of the functions concerning the reporting
- generateReport : Generate a project report with default options or from a custom Word template.
Description of the functions concerning the results
- exportChartDataSet : Export the data of a chart into Lixoft suite compatible data set format.
- getCorrelationOfEstimates : Get the inverse of the last estimated Fisher matrix computed either by all the Fisher methods used during the last scenario run or by the specific one passed in argument.
- getEstimatedIndividualParameters : Get the last estimated values for each subject of some of the individual parameters present within the current project.
- getEstimatedLogLikelihood : Get the values computed by using a log-likelihood algorithm during the last scenario run, with or without a method-based filter.
- getEstimatedPopulationParameters : Get the last estimated value of some of the population parameters present within the current project (fixed effects + individual variances + correlations + latent probabilities + error model parameters).
- getEstimatedRandomEffects : Get the random effects for each subject of some of the individual parameters present within the current project.
- getEstimatedStandardErrors : Get the last estimated standard errors of population parameters computed either by all the Fisher methods used during the last scenario run or by the specific one passed in argument.
- getLaunchedTasks : Get a list of the tasks which have results to provide.
- getSAEMiterations : Retrieve the successive values of some of the population parameters present within the current project (fixed effects + individual variances + correlations + latent probabilities + error model parameters) during the previous run of the SAEM algorithm.
- getSimulatedIndividualParameters : Get the simulated values for each replicate of each subject of some of the individual parameters present within the current project.
- getSimulatedRandomEffects : Get the simulated values for each replicate of each subject of some of the individual random effects present within the current project.
- getTests : Get the results of performed statistical tests.
Description of the functions concerning the scenario
- computeChartsData : Compute (if needed) and export the charts data of a given plot or, if not specified, all the available project plots.
- 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
. - runScenario : Run the scenario that has been set with
setScenario
. - setScenario : Clear the current scenario and build a new one from a given list of tasks.
- 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.
[Monolix] Get conditional distribution sampling settings
Description
Get the conditional distribution sampling settings. Associated settings are:
“ratio” | (0< double <1) | Width of the confidence interval. |
“enableMaxIterations” | (bool) | Enable maximum of iterations. |
“nbMinIterations” | (int >=1) | Minimum number of iterations. |
“nbMaxIterations” | (int >=1) | Maximum 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 all the conditional distribution sampling settings getConditionalDistributionSamplingSettings("ratio","nbMinIterations") # retrieve only the ratio and nbMinIterations settings values ## End(Not run)
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[Monolix] 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 only the nbOptimizationIterationsMode setting value ## End(Not run)
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[Monolix] 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
Click here to see examples
# ## Not run: getGeneralSettings() # retrieve a list of all the general settings getGeneralSettings("nbChains","autoChains") # retrieve only the nbChains and autoChains settings values. ## End(Not run)
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[Monolix] 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” | (vector<int(>0)>) | 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 only nbFixedIterations and samplingMethod settings values ## End(Not run)
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[Monolix] Get MCMC algorithm settings
Description
Get the MCMC algorithm settings of the current project. Associated settings are:
“strategy” | (vector<int>[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
Click here to see examples
# ## Not run: getMCMCSettings() # retrieve a list of all the MCMC settings getMCMCSettings("strategy") # retrieve only the strategy setting ## End(Not run)
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[Monolix] 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 only the nbBurningIterations and smoothingInterval settings values ## End(Not run)
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[Monolix] 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 only minIterations and maxIterations settings values ## End(Not run)
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[Monolix] 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. |
“enableMaxIterations” | (bool) | Enable maximum of iterations. |
“nbMinIterations” | (int >=1) | Minimum number of iterations. |
“nbMaxIterations” | (int >=1) | Maximum 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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[Monolix] 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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[Monolix] 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
Click here to see examples
# ## Not run: setGeneralSettings(autoChains = FALSE, nbchains = 10) ## End(Not run)
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[Monolix] 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” | (vector<int(>0)>) | 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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[Monolix] 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<int>[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
Click here to see examples
# ## Not run: setMCMCSettings(strategy = c(2,1,2)) ## End(Not run)
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[Monolix] 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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[Monolix] 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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[Monolix] Get the results of the assessment
Description
Get the results of the assessment.
Usage
getAssessmentResults()
Value
A vector of lists containing, for each assessment run:
- populationParameters: results of population parameter estimation using SAEM:
- nbexploratoryiterations (int) number of iterations during exploratory phase
- nbsmoothingiterations (int) number of iterations during smoothing phase
- convergence (data.frame) convergence history of estimated population parameters and convergence indicator (-2*log-likelihood)
- standardErrors: [optional] results of standard errors estimation:
- method (string) fisher method used (stochasticApproximation or linearization)
- values (vector) standard error associated to each population parameter
- loglikelihood: [optional] results of log-likelihood estimation
- method (string) fisher method used (importanceSampling or linearization)
- AIC (double)
- BIC (double)
- BICc (double) modified BIC
- LL (double)
- chosenDegree (int) [importanceSampling]
- standardError (double) [importanceSampling]
- convergence (data.frame) [importanceSampling]
See Also
Click here to see examples
# ## Not run: getAssessmentResults() ## End(Not run)
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[Monolix] Get assessment settings
Description
Get the settings that will be used during the run of assessment.
Usage
getAssessmentSettings()
Value
The list of settings
- nbRuns: (int) number of runs
- extendedEstimation: (boolean) if TRUE, standard errors and log-likelihood are estimated
- useLin: (boolean) if TRUE, use linearization to estimate standard errors and log-likelihood instead of stochastic approximation (sd) and importance sampling (ll)
- initialParameters: (list) a list giving, for each parameter, if its initial value is fixed (fixed = [FALSE]|TRUE) and, if is it not the case, the bounds within which the initial value is drawn (min = double, max = double)
See Also
Click here to see examples
# ## Not run: set = getAssessmentSettings() set$nbRuns = 5 set$extendedEstimation = TRUE set$useLin = FALSE set$initialParameters parameters fixed min max 1 ka FALSE 0.5 0.75 2 V TRUE NaN NaN runAssessment(settings = set) ## End(Not run)
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[Monolix] Run assessment
Description
Run assessment.
To change the initialization before a run, use getAssessmentSettings to receive all the settings. See example.
Usage
runAssessment(settings = NULL)
Arguments
settings |
(list<settings>) [optional] Settings to initialize the assessment algorithm. If not provided, current settings are used. See getAssessmentSettings. |
See Also
getAssessmentSettings getAssessmentResults
Click here to see examples
# ## Not run: runAssessment() set = getAssessmentSettings() runAssessment(set) ## End(Not run)
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[Monolix] Add categorical transformed covariate
Description
Create a new categorical covariate by transforming an existing one. Transformed covariates cannot be use to produce new covariates.
Call getCovariateInformation to know which covariates can be transformed.
Usage
addCategoricalTransformedCovariate(...)
Arguments
... |
A list of comma-separated pairs {transformedCovariateName = { from = (array<(string)>)["basicCovariateNames"], transformed = (array<array<string>>)"transformation"} } |
See Also
getCovariateInformation removeCovariate
Click here to see examples
# ## Not run: addCategoricalTransformedCovariate( Country2 = list(reference = "A1", from = "Country", transformed = list( A1 = c("A","B"), A2 = c("C"))) ) ## End(Not run)
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[Monolix] Add continuous transformed covariate
Description
Create a new continuous covariate by transforming an existing one. Transformed covariates cannot be use to produce new covariates.
Call getCovariateInformation to know which covariates can be transformed.
Usage
addContinuousTransformedCovariate(...)
Arguments
... |
A list of comma-separated pairs {transformedCovariateName = (string)"transformation"} |
See Also
getCovariateInformation removeCovariate
Click here to see examples
# ## Not run: addContinuousTransformedCovariate( tWt2 = "3*exp(Wt)" ) ## End(Not run)
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[Monolix] Add mixture to the covariate model
Description
Add a new latent covariate to the current model giving its name and its modality number.
Usage
addMixture(...)
Arguments
... |
A list of comma-separated pairs {latentCovariateName = (int)modalityNumber} |
See Also
getCovariateInformation removeCovariate
Click here to see examples
# ## Not run: addMixture(lcat = 2) ## End(Not run)
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[Monolix] Remove covariate
Description
Remove some of the transformed covariates (discrete and continuous) and/or latent covariates.
Call getCovariateInformation to know which covariates can be removed.
Usage
removeCovariate(...)
Arguments
... |
A list of covariate names. |
See Also
getCovariateInformation addContinuousTransformedCovariate addCategoricalTransformedCovariate
addMixture
Click here to see examples
# ## Not run: removeCovariate("tWt","lcat1") ## End(Not run)
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[Monolix – PKanalix] Add an additional covariate
Description
Create an additional covariate for stratification purpose. Notice that these covariates are available only if they are not
contant through the dataset.
Available column transformations are:
[continuous] | ‘firstDoseAmount’ | (first dose amount) |
[continuous] | ‘doseNumber’ | (dose number) |
[discrete] | ‘administrationType’ | (admninistration type) |
[discrete] | ‘administrationSequence’ | (administration sequence) |
[discrete] | ‘dosingDesign’ | (dose multiplicity) |
[continuous] | ‘observationNumber’ | (observation number per individual, for a given observation type) |
Usage
addAdditionalCovariate(transformation, base = "", name = "")
Arguments
transformation |
(string) applied transformation. |
base |
(string) [optional] base data on which the transformation is applied. |
name |
(string) [optional] name of the covariate. |
See Also
Click here to see examples
# ## Not run: addAdditionalCovariate("firstDoseAmount") addAdditionalCovariate(transformation = "observationNumberPerIndividual", headerName = "CONC") ## End(Not run)
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[Monolix – PKanalix] Apply filter
Description
Apply a filter on the current data.
Usage
applyFilter(filter, name = "")
Arguments
filter |
(list< list< action = "headerName-comparator-value" > > or "complement") filter definition. Existing actions are "selectLines", "selectIds", "removeLines" and "removeIds". First vector level is for set unions, the second one for set intersection. It is possible to give only a list of actions if there is only no high-level union. |
name |
(string) [optional] created data set name. If not defined, the default name is "currentDataSet_filtered". |
Details
The possible actions are line selection (selectLines), line removal (removeLines), Ids selection (selectIds) or removal (removeIds).
The selection is a string containing the header name, a comparison operator and a value
selection = <string> "headerName*-comparator**-value" (ex: "id==’100’", "WEIGHT<70", "SEX!=’M’")
Notice that :
– The headerName corresponds to the data set header or one of the header aliases defined in MONOLIX software preferences
– The comparator possibilities are "==", "!=" for all types of value and "<=", "<", ">=", ">" only for numerical types
Syntax:
* apply a simple filter:
applyFilter( filter = list(act = sel)), e.g. applyFilter( filter = list(removeIds = "WEIGHT<50"))
=> apply a filter with the action act on the selection sel. In this example, we apply a filter that removes all subjects with a weight less than 50.
* apply a filter with several concurrent conditions, i.e AND condition:
applyFilter( list(act1 = sel1, act2 = sel2)), e.g. applyFilter( filter = list(removeIds = "WEIGHT<50", removeIds = " AGE<20"))
=> apply a filter with both the action act1 on sel1 AND the action act2 on sel2. In this example, we apply a filter that removes all subjects with a weight less than 50 and an age less than 20.
It corresponds to the intersecton of the subjects with a weight less than 50 and the subjects with an age less than 20.
* apply a filter with several non-concurrent conditions, i.e OR condition:
applyFilter(filter = list(list(act1 = sel1), list(act2 = sel2)) ), e.g. applyFilter( filter = list(list(removeIds = "WEIGHT<50"),list(removeIds = " AGE<20")))
=> apply a filter with the action act1 on sel1 OR the action act2 on sel2. In this example, we apply a filter that removes all subjects with a weight less than 50 and an age less than 20.
It corresponds to the union of the subjects with a weight less than 50 and the subjects with an age less than 20.
* It is possible to have any combination:
applyFilter(filter = list(list(act1 = sel1), list(act2 = sel2, act3 = sel3)) ) <=> act1,sel1 OR ( act2,sel2 AND act3,sel3 )
* It is possible to apply the complement of an existing filter:
applyFilter(filter = "complement")
See Also
getAvailableData createFilter removeFilter
Click here to see examples
# ## Not run: ---------------------------------------------------------------------------------------- LINE [ int ] applyFilter( filter = list(removeLines = "line>10") ) # keep only the 10th first rows ---------------------------------------------------------------------------------------- ID [ string | int ] If there are only integer identifiers within the data set, ids will be considered as integers. On the contrary, they will be treated as strings. applyFilter( filter = list(selectIds = "id==100") ) # select the subject called '100' applyFilter( filter = list(list(removeIds = "id!='id_2'")) ) # select all the subjects excepted the one called 'id_2' ---------------------------------------------------------------------------------------- ID INDEX [int] applyFilter( filter = list(list(removeIds = "idIndex!=2"), list(selectIds = "id<5")) ) # select the 4 first subjects excepted the second one ---------------------------------------------------------------------------------------- OCC [ int ] applyFilter( filter = list(selectIds = "occ1==1", removeIds = "occ2!=3") ) # select the subjects whose first occasion level is '1' and whose second one is different from '3' ---------------------------------------------------------------------------------------- TIME [ double ] applyFilter( filter = list(removeIds='TIME>120') ) # remove the subjects who have time over 120 applyFilter( filter = list(selectLines='TIME>120') ) # remove the all the lines where the time is over 120 ---------------------------------------------------------------------------------------- OBSERVATION [ double ] applyFilter( filter = list(selectLines = "CONC>=5.5", removeLines = "CONC>10")) # select the lines where CONC value superior or equal to 5.5 or strictly higher than 10 applyFilter( filter = list(removeIds = "CONC<0") ) # remove subjects who have negative CONC values applyFilter( filter = list(removeIds = "E==0") ) # remove subjects for who E equals 0 ---------------------------------------------------------------------------------------- OBSID [ string ] applyFilter( filter = list(removeIds = "y1==1") ) # remove subject who have at least one observation for y1 applyFilter( filter = list(selectLines = "y1!=2") ) # select all lines corresponding to observations exepected those for y2 ---------------------------------------------------------------------------------------- AMOUNT [ double ] applyFilter( filter = list(selectIds = "AMOUT==10") ) # select subjects who have a dose equals to 10 ---------------------------------------------------------------------------------------- INFUSION RATE AND INFUSION DURATION [ double ] applyFilter( filter = list(selectIds = "RATE<10") ) # select subjects who have dose with a rate less than 10 ---------------------------------------------------------------------------------------- COVARIATE [ string (categorical) | double (continuous) ] applyFilter( filter = list(selectIds = "SEX==M", selectIds = "WEIGHT<80") ) # select subjects who are men and whose weight is lower than 80kg ---------------------------------------------------------------------------------------- REGERSSOR [ double ] applyFilter( filter = list(selectLines = "REG>10") ) # select the lines where the regressor value is over 10 ---------------------------------------------------------------------------------------- COMPLEMENT applyFilter(origin = "data_filtered", filter = "complement" ) ## End(Not run)
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[Monolix – PKanalix] Create filter
Description
Create a new filtered data set by applying a filter on an existing one and/or complementing it.
Usage
createFilter(filter, name = "", origin = "")
Arguments
filter |
(list< list< action = "headerName-comparator-value" > > or "complement") [optional] filter definition. Existing actions are "selectLines", "selectIds", "removeLines" and "removeIds". First vector level is for set unions, the second one for set intersection. It is possible to give only a list of actions if there is only no high-level union. |
name |
(string) [optional] created data set name. If not defined, the default name is "currentDataSet_filtered". |
origin |
(string) [optional] name of the data set to be filtered. The current one is used by default. |
Details
The possible actions are line selection (selectLines), line removal (removeLines), Ids selection (selectIds) or removal (removeIds).
The selection is a string containing the header name, a comparison operator and a value
selection = <string> "headerName*-comparator**-value" (ex: "id==’100’", "WEIGHT<70", "SEX!=’M’")
Notice that :
– The headerName corresponds to the data set header or one of the header aliases defined in MONOLIX software preferences
– The comparator possibilities are "==", "!=" for all types of value and "<=", "<", ">=", ">" only for numerical types
Syntax:
* create a simple filter:
createFilter( filter = list(act = sel)), e.g. createFilter( filter = list(removeIds = "WEIGHT<50"))
=> create a filter with the action act on the selection sel. In this example, we create a filter that removes all subjects with a weight less than 50.
* create a filter with several concurrent conditions, i.e AND condition:
createFilter( list(act1 = sel1, act2 = sel2)), e.g. createFilter( filter = list(removeIds = "WEIGHT<50", removeIds = " AGE<20"))
=> create a filter with both the action act1 on sel1 AND the action act2 on sel2. In this example, we create a filter that removes all subjects with a weight less than 50 and an age less than 20.
It corresponds to the intersecton of the subjects with a weight less than 50 and the subjects with an age less than 20.
* create a filter with several non-concurrent conditions, i.e OR condition:
createFilter(filter = list(list(act1 = sel1), list(act2 = sel2)) ), e.g. createFilter( filter = list(list(removeIds = "WEIGHT<50"),list(removeIds = " AGE<20")))
=> create a filter with the action act1 on sel1 OR the action act2 on sel2. In this example, we create a filter that removes all subjects with a weight less than 50 and an age less than 20.
It corresponds to the union of the subjects with a weight less than 50 and the subjects with an age less than 20.
* It is possible to have any combinaison:
createFilter(filter = list(list(act1 = sel1), list(act2 = sel2, act3 = sel3)) ) <=> act1,sel1 OR ( act2,sel2 AND act3,sel3 )
* It is possible to create the complement of an existing filter:
createFilter(filter = "complement")
See Also
Click here to see examples
# ## Not run: ---------------------------------------------------------------------------------------- LINE [ int ] createFilter( filter = list(removeLines = "line>10") ) # keep only the 10th first rows ---------------------------------------------------------------------------------------- ID [ string | int ] If there are only integer identifiers within the data set, ids will be considered as integers. On the contrary, they will be treated as strings. createFilter( filter = list(selectIds = "id==100") ) # select the subject called '100' createFilter( filter = list(list(removeIds = "id!='id_2'")) ) # select all the subjects excepted the one called 'id_2' ---------------------------------------------------------------------------------------- ID INDEX [int] createFilter( filter = list(list(removeIds = "idIndex!=2"), list(selectIds = "id<5")) ) # select the 4 first subjects excepted the second one ---------------------------------------------------------------------------------------- OCC [ int ] createFilter( filter = list(selectIds = "occ1==1", removeIds = "occ2!=3") ) # select the subjects whose first occasion level is '1' and whose second one is different from '3' ---------------------------------------------------------------------------------------- TIME [ double ] createFilter( filter = list(removeIds='TIME>120') ) # remove the subjects who have time over 120 createFilter( filter = list(selectLines='TIME>120') ) # remove the all the lines where the time is over 120 ---------------------------------------------------------------------------------------- OBSERVATION [ double ] createFilter( filter = list(selectLines = "CONC>=5.5", removeLines = "CONC>10")) # select the lines where CONC value superior or equal to 5.5 or strictly higher than 10 createFilter( filter = list(removeIds = "CONC<0") ) # remove subjects who have negative CONC values createFilter( filter = list(removeIds = "E==0") ) # remove subjects for who E equals 0 ---------------------------------------------------------------------------------------- OBSID [ string ] createFilter( filter = list(removeIds = "y1==1") ) # remove subject who have at least one observation for y1 createFilter( filter = list(selectLines = "y1!=2") ) # select all lines corresponding to observations exepected those for y2 ---------------------------------------------------------------------------------------- AMOUNT [ double ] createFilter( filter = list(selectIds = "AMOUT==10") ) # select subjects who have a dose equals to 10 ---------------------------------------------------------------------------------------- INFUSION RATE AND INFUSION DURATION [ double ] createFilter( filter = list(selectIds = "RATE<10") ) # select subjects who have dose with a rate less than 10 ---------------------------------------------------------------------------------------- COVARIATE [ string (categorical) | double (continuous) ] createFilter( filter = list(selectIds = "SEX==M", selectIds = "WEIGHT<80") ) # select subjects who are men and whose weight is lower than 80kg ---------------------------------------------------------------------------------------- REGERSSOR [ double ] createFilter( filter = list(selectLines = "REG>10") ) # select the lines where the regressor value is over 10 ---------------------------------------------------------------------------------------- COMPLEMENT createFilter(origin = "data_filtered", filter = "complement" ) ## End(Not run)
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[Monolix – PKanalix] Delete additional covariate
Description
Delete a created additinal covariate.
Usage
deleteAdditionalCovariate(name)
Arguments
name |
(string) name of the covariate. |
See Also
Click here to see examples
# ## Not run: deleteAdditionalCovariate("firstDoseAmount")\cr deleteAdditionalCovariate("observationNumberPerIndividual_y1") ## End(Not run)
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[Monolix – PKanalix] Delete filter
Description
Delete a data set. Only filtered data set which are not active and whose children are not active either can be deleted.
Usage
deleteFilter(name)
Arguments
name |
(string) data set name. |
See Also
Click here to see examples
# ## Not run: deleteFilter(name = "filter2") ## End(Not run)
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[Monolix – PKanalix] Edit filter
Description
Edit the definition of an existing filtered data set. Refere to createFilter for more details about syntax, allowed parameters and examples.
Notice that all the filtered data set which depend on the edited one will be deleted.
Usage
editFilter(filter, name = "")
Arguments
filter |
(list< list< action = "headerName-comparator-value" > >) filter definition. |
name |
(string) [optional] data set name to edit (current one by default) |
See Also
[Monolix – PKanalix] Adapt and export a data file as a MonolixSuite formatted data set.
Description
Adapt and export a data file as a MonolixSuite formatted data set.
Usage
formatData( dataFile, formattedFile, headerLines = 1, headers, linesToExclude = NULL, observationSettings = NULL, observations = NULL, treatmentSettings = NULL, treatments = NULL, additionalColumns = NULL )
Arguments
dataFile |
(string) Path to the original data file. |
formattedFile |
(string) Path to the data file that will be exported (must end with the .csv extension). |
headerLines |
(optional) (int or vector<int>) Line numbers containing headers (if multiple numbers are given, formatted headers will contain values from all header lines concatenated with the "_" character) – default: 1. |
headers |
(list) List of headers containing information about ID, time, volume (in case of urine data) and sort columns.
|
linesToExclude |
(optional) (int or vector<int>) Numbers of lines that should be removed from the data set. |
observationSettings |
(optional) (list) List containing settings applied when different observation columns are merged into a single column.
|
observations |
(optional) (list) List of lists containing information about different observation types:
|
treatmentSettings |
(optional) (list) List containing settings applied to all treatments.
|
treatments |
(optional) (list) List that can contain lists with information about different treatments or strings with paths to files that contain treatment information. Lists with information about different treatments need to have the following elements:
|
additionalColumns |
(optional) (string or vector<string>) Path(s) to the file(s) containing additional columns (needs to have the ID column). |
Details
Data formatting can be performed as in the Data Formatting Tab of Monolix and PKanalix interface. Look at the examples to see how each data formatting demo project could be created with the connectors.
Click here to see examples
# # example: create a new project with a dataset to format: initializeLixoftConnectors(software = "pkanalix") FormattedDataPath = tempfile("formatted_data", fileext = ".csv") formatData(paste0(getDemoPath(),"/0.data_formatting/data/units_BLQ_tags_data.csv"), formattedFile = FormattedDataPath, headerLines = c(1,2), headers = c(id="ID", time="TIME_h"), observations = list(header="CONC_mg_L", censoring = list(type="interval", tags = c("BLQ"), limits=list(0,"LLOQ_mg_L"))), treatments = list(times=0, amount=100)) colnames(read.csv(FormattedDataPath)) # to check column names of the generated file and tag them as desired newProject(data = list(dataFile = FormattedDataPath, headerTypes = c("id","time","observation","contcov","contcov","catcov","ignore","amount","cens","limit"))) plotObservedData() # demo merge_occ_ParentMetabolite.pkx formatData(paste0(getDemoPath(),"/0.data_formatting/data/parent_metabolite_data.csv"), formattedFile = FormattedDataPath, headers = c(id="ID", time="TIME"), observations = list(list(header="PARENT", censoring = list(type="interval", tags = c("BLQ"), limits=list(0,0.01))), list(header="METABOLITE")), observationSettings = list(distinguishWithObsId = FALSE), treatments = list(times=0, amount="DOSE")) # demo merge_obsID_ParentMetabolite.pkx formatData(paste0(getDemoPath(),"/0.data_formatting/data/parent_metabolite_data.csv"), formattedFile = FormattedDataPath, headers = c(id="ID", time="TIME"), observations = list(list(header="PARENT", censoring = list(type="interval", tags = c("BLQ"), limits=list(0,0.01))), list(header="METABOLITE")), treatments = list(times=0, amount="DOSE")) # demo DoseAndLOQ_byCategory.pkx formatData(paste0(getDemoPath(),"/0.data_formatting/data/units_BLQ_tags_data.csv"), formattedFile = FormattedDataPath, headerLines = c(1,2), headers = c(id="ID", time="TIME_h"), observations = list(header="CONC_mg_L", censoring = list(type="interval", tags = c("BLQ"), limits=list(0,list(category="STUDY", values=list("SD_400mg"=0.01, "SD_500mg"=0.1, "SD_600mg"=0.1))))), treatments = list(times=0, amount=list(category="STUDY", values=list("SD_400mg"=400, "SD_500mg"=500, "SD_600mg"=600)))) # demo DoseAndLOQ_fromData.pkx formatData(paste0(getDemoPath(),"/0.data_formatting/data/units_BLQ_tags_data.csv"), formattedFile = FormattedDataPath, headerLines = c(1,2), headers = c(id="ID", time="TIME_h"), observations = list(header="CONC_mg_L", censoring = list(type="interval", tags = c("BLQ"), limits=list(0,"LLOQ_mg_L"))), treatments = list(times=0, amount="STUDY")) # demo DoseAndLOQ_manual.pkx formatData(paste0(getDemoPath(),"/0.data_formatting/data/units_multiple_BLQ_tags_data.csv"), formattedFile = FormattedDataPath, headerLines = c(1,2), headers = c(id="ID", time="TIME_h"), observations = list(header="CONC_mg_L", censoring = list(list(type="interval", tags = c("BLQ1"), limits=list(0,0.06)), list(type="interval", tags = c("BLQ2"), limits=list(0,0.1)))), treatments = list(times=0, amount=600)) # demo Urine_LOQinObs.pkx formatData(paste0(getDemoPath(),"/0.data_formatting/data/urine_LOQinObs_data.csv"), formattedFile = FormattedDataPath, headers = c(id="ID", start="START_TIME", end="END_TIME", volume="VOLUME"), observations = list(header="CONC", censoring=list(type="LLOQ", tags="<LOQ=1>", limits="CONC")), treatments = list(paste0(getDemoPath(),"/0.data_formatting/data/urine_data_doses.csv"))) # demo CreateOcc_AdmIdbyCategory.pkx formatData(paste0(getDemoPath(),"/0.data_formatting/data/two_formulations_data.csv"), formattedFile = FormattedDataPath, linesToExclude = 1, headerLines = c(2,3), headers = c(id="ID", time="TIME_h", sort="FORM"), observations = list(header="CONC_mg_L", censoring=list(type="LLOQ", tags="BLQ", limits=0.06)), treatments = list(times=0, amount=600, admId=list(category="FORM", values=list("ref"=1,"test"=2)))) # MONOLIX EXAMPLES initializeLixoftConnectors(software = "monolix") FormattedDataPath = tempfile("formatted_data") # demo doseIntervals_as_Occ.mlxtran formatData(paste0(getDemoPath(),"/0.data_formatting/data/data_multidose.csv"), formattedFile = FormattedDataPath, headers = c(id="ID", time="TIME"), observations = list(header="CONC"), treatments = list(times=seq(0,by=12,length=7), amount=40), treatmentSettings = list(doseIntervalsAsOccasions = TRUE)) # demo warfarin_PKPDseq_project.mlxtran formatData(paste0(getDemoPath(),"/0.data_formatting/data/warfarin_data.csv"), formattedFile = FormattedDataPath, headers = c(id="id", time="time"), additionalColumns = paste0(getDemoPath(),"/0.data_formatting/data/warfarinPK_regressors.txt"))
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[Monolix – PKanalix] Get data sets descriptions
Description
Get information about the data sets and filters defined in the project.
Usage
getAvailableData()
Value
A list containing a list containing elements that describe the data set:
-
name
: a string, the name of the data set -
file
: a string, the path of the data set file -
current
: a boolean indicating if the data set is applied (currently in use) -
children
: a list containing lists with information about data sets created from this one using filters -
filter
(only if the dataset was created using filters): a list containing name of theparent
and details about filterdefinition
Click here to see examples
# ## Not run: getAvailableData() ## End(Not run)
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[Monolix – PKanalix] Get covariates information
Description
Get the name, the type and the values of the covariates present in the project.
Usage
getCovariateInformation()
Value
A list containing the following fields :
- name (vector<string>): covariate names
- type (vector<string>): covariate types. Existing types are "continuous", "continuoustransformed", "categorical", "categoricaltransformed"./
In Monolix mode, "latent" covariates are also allowed. - [Monolix] modalityNumber (vector<int>): number of modalities (for latent covariates only)
- covariate: a data frame giving the values of continuous and categorical covariates for each subject.
Latent covariate values exist only if they have been estimated, ie if the covariate is used and if the population parameters have been estimated.
Call getEstimatedIndividualParameters to retrieve them.
Click here to see examples
# ## Not run: info = getCovariateInformation() # Monolix mode with latent covariates info -> $name c("sex","wt","lcat") -> $type c(sex = "categorical", wt = "continuous", lcat = "latent") -> $modalityNumber c(lcat = 2) -> $covariate id sex wt 1 M 66.7 . . . N F 59.0 ## End(Not run)
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[Monolix – PKanalix] Get data formatting from a loaded project
Description
Get data formatting from a loaded project.
Usage
getFormatting()
[Monolix – PKanalix] Get observations information
Description
Get the name, the type and the values of the observations present in the project.
Usage
getObservationInformation()
Value
A list containing the following fields :
- name (vector<string>): observation names.
- type (vector<string>): observation generic types. Existing types are "continuous", "discrete", "event".
- [Monolix] detailedType (vector<string>): observation specialized types set in the structural model. Existing types are "continuous", "bsmm", "wsmm", "categorical", "count", "exactEvent", "intervalCensoredEvent".
- [Monolix] mapping (vector<string>): mapping between the observation names (defined in the mlxtran project) and the name of the corresponding entry in the data set.
- ["obsName"] (data.frame): observation values for each observation id.
In PKanalix mode, the observation type is not provided as only continuous observations are allowed. Neither do the mapping as dataset names are always used.
Click here to see examples
# ## Not run: info = getObservationInformation() info -> $name c("concentration") -> $type # [Monolix] c(concentration = "continuous") -> $detailedType # [Monolix] c(concentration = "continuous") -> $mapping # [Monolix] c(concentration = "CONC") -> $concentration id time concentration 1 0.5 0.0 . . . N 9.0 10.8 ## End(Not run)
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[Monolix – PKanalix] Get treatments information
Description
Get information about doses present in the loaded dataset.
Usage
getTreatmentsInformation()
Value
A dataframe whose columns are:
- id and occasion level names (string)
- time (double)
- amount (double)
- [optional] administrationType (int)
- [optional] infusionTime (double)
- [optional] isArtificial (bool): is created from SS or ADDL column
- [optional] isReset (bool): IOV case only
Click here to see examples
# { ## Not run: initializeLixoftConnectors("monolix") project_name <- file.path(getDemoPath(), "6.PK_models", "6.3.multiple_doses", "ss1_project.mlxtran") loadProject(project_name) getTreatmentsInformation() ## End(Not run) }
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[Monolix – PKanalix] Remove filter
Description
Remove the last filter applied on the current data set.
Usage
removeFilter()
See Also
Click here to see examples
# ## Not run: removeFilter() ## End(Not run)
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[Monolix – PKanalix] Rename additional covariate
Description
Rename an existing additional covariate.
Usage
renameAdditionalCovariate(oldName, newName)
Arguments
oldName |
(string) current name of the covariate to rename |
newName |
(string) new name. |
See Also
Click here to see examples
# ## Not run: renameAdditionalCovariate(oldName = "observationNumberPerIndividual_y1", newName = "nbObsForY1") ## End(Not run)
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[Monolix – PKanalix] Rename filter
Description
Rename an existing filtered data set.
Usage
renameFilter(newName, oldName = "")
Arguments
newName |
(string) new name. |
oldName |
(string) [optional] current name of the filtered data set to rename (current one by default) |
See Also
Click here to see examples
# ## Not run: renameFilter("newFilter")\cr renameFilter(oldName = "filter", newName = "newFilter") ## End(Not run)
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[Monolix – PKanalix] Select data set
Description
Select the new current data set within the previously defined ones (original and filters).
Usage
selectData(name)
Arguments
name |
(string) data set name. |
See Also
Click here to see examples
# ## Not run: selectData(name = "filter1") ## End(Not run)
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[Monolix] Get individual parameter model
Description
Get a summary of the information concerning the individual parameter model. The available informations are:
- name: (string) name of the individual parameter
- distribution: (string) distribution of the parameter values. The distribution type can be "normal", "logNormal", or "logitNormal".
- formula: (string) formula applied on individual parameters distribution
- variability: a list giving, for each variability level, if individual parameters have variability or not
- covariateModel: a list giving, for each individual parameter, if the related covariates are used or not.
If no covariate is used, this field is empty. - correlationBlocks : a list giving, for each variability level, the blocks of the correlation matrix of the random effects.
A block is represented by a vector of individual parameter names. If there is no block, this field is empty.
Usage
getIndividualParameterModel()
Value
A list of individual parameter model properties.
See Also
setIndividualParameterDistribution setIndividualParameterVariability setCovariateModel
Click here to see examples
# ## Not run: indivModel = getIndividualParameterModel() indivModel -> $name c("ka","V","Cl") $distribution c(ka = "logNormal", V = "normal", Cl = "logNormal") $formula "\\tlog(ka) = log(ka_pop) + eta_ka\\n\\n \\tlog(V) = V_pop + eta_V\\n\\n \\tlog(Cl) = log(Cl_pop) + eta_Cl\\n\\n" $variability list( id = c(ka = TRUE, V = FALSE, Cl = TRUE) ) $covariateModel list( ka = c(age = TRUE, sex = FALSE, wt = TRUE), V = c(age = FALSE, sex = FALSE, wt = FALSE), Cl = c(age = FALSE, sex = FALSE, wt = FALSE) ) $correlationBlocks list( id = c("ka","V","Tlag") ) ## End(Not run)
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[Monolix] Get variability levels
Description
Get a summary of the variability levels (inter-individual and/or intra-individual variability) present in the current project.
Usage
getVariabilityLevels()
Value
A collection of the variability levels present in the currently loaded project.
Click here to see examples
# ## Not run: getVariabilityLevels() ## End(Not run)
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[Monolix] Set correlation block structure
Description
Define the correlation block structure associated to some of the variability levels of the current project.
Call getVariabilityLevels to get a list of the variability levels and getIndividualParameterModel to get a list of the available individual parameters within the current project.
Usage
setCorrelationBlocks(...)
Arguments
... |
A list of comma-separated pairs {variabilityLevel = vector< (array<string>)parameterNames} > }. |
See Also
getVariabilityLevels getIndividualParameterModel
Click here to see examples
# ## Not run: setCorrelationBlocks(id = list( c("ka","V","Tlag") ), iov1 = list( c("ka","Cl"), c("Tlag","V") ) ) ## End(Not run)
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[Monolix] Set covariate model
Description
Set which are the covariates influencing individual parameters present in the project.
Call getIndividualParameterModel to get a list of the individual parameters present within the current project.
and getCovariateInformation to know which are the available covariates for a given level of variability and a given individual parameter.
Usage
setCovariateModel(...)
Arguments
... |
A list of comma-separated pairs {parameterName = { covariateName = (bool)isInfluent, …} } |
See Also
Click here to see examples
# ## Not run: setCovariateModel( ka = c( Wt = FALSE, tWt = TRUE, lcat2 = TRUE), Cl = c( SEX = TRUE ) ) ## End(Not run)
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[Monolix] Set individual parameter distribution limits
Description
Set the minimum and the maximum values between the individual parameter can be used.
Used only if the distribution of the parameter is "logitNormal", else wise it will not be taken into account
Usage
setIndividualLogitLimits(...)
Arguments
... |
A list of comma-separated pairs {individualParameter = [(double)min,(double)max] } |
See Also
Click here to see examples
# ## Not run: setIndividualLogitLimits( V = c(0, 1), ka = c(-1, 2) ) ## End(Not run)
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[Monolix] Set individual parameter distribution
Description
Set the distribution of the estimated parameters.
Available distributions are "normal", "logNormal" and "logitNormal".
Call getIndividualParameterModel to get a list of the available individual parameters within the current project.
Usage
setIndividualParameterDistribution(...)
Arguments
... |
A list of comma-separated pairs {parameterName = (string)"distribution"}. |
See Also
Click here to see examples
# ## Not run: setIndividualParameterDistribution(V = "logNormal") setIndividualParameterDistribution(Cl = "normal", V = "logNormal") ## End(Not run)
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[Monolix] Set individual parameter model
Description
Set the information concerning the individual parameter model. The editable informations are:
- distribution: (string) distribution of the parameter values. The distribution type can be "normal", "logNormal", or "logitNormal".
- limits: a list giving the distribution limits for each parameter following a "logitNormal" distribution
- variability: a list giving, for each variability level, if individual parameters have variability or not
- covariateModel: a list giving, for each individual parameter, if the related covariates are used or not.
- correlationBlocks : a list giving, for each variability level, the blocks of the correlation matrix of the random effects.
A block is represented by a vector of individual parameter names.
Usage
setIndividualParameterModel(...)
Arguments
... |
A list of comma-separated pairs {[info] = [value]}. |
[Monolix] Individual variability management
Description
Add or remove inter-individual and/or intra-individual variability from some of the individual parameters present in the project.
Call getIndividualParameterModel to get a list of the available parameters within the current project.
Usage
setIndividualParameterVariability(...)
Arguments
... |
A list of comma-separated pairs {variabilityLevel = {individualParameterName = (bool)hasVariability} }. |
See Also
Click here to see examples
# ## Not run: setIndividualParameterVariability(ka = TRUE, V = FALSE) setIndividualParameterVariability(id = list(ka = TRUE), iov1 = list(ka = FALSE)) ## End(Not run)
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[Monolix] Automatically estimate initial parameters value
Description
Compute optimized values for initial population parameters. The values are returned in the same format as getPopulationParameterInformation.
Usage
getFixedEffectsByAutoInit(parameters = NULL)
Click here to see examples
# ## Not run: getFixedEffectsByAutoInit() -> optimizedParameters setPopulationParameterInformation(optimizedParameters) ## End(Not run)
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[Monolix] Get population parameters information
Description
Get the name, the initial value, the estimation method and, if relevant, MAP parameters value of the population parameters present in the project.
It is available for fixed effects, random effects, error model parameters, and latent covariates probabilities.
Usage
getPopulationParameterInformation()
Value
A data frame giving, for each population parameter, the corresponding :
- initialValue : (double) initial value
- method : (string) estimation method
- priorValue : (double) [MAP] typical value
- priorSD : (double) [MAP] standard deviation
See Also
setPopulationParameterInformation
Click here to see examples
# ## Not run: info = getPopulationParameterInformation() info name initialValue method typicalValue stdDeviation ka_pop 1.0 MLE NA NA V_pop 10.0 MAP 10.0 0.5 omega_ka 1.0 FIXED NA NA ## End(Not run)
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[Monolix] Initialize population parameters with the last estimated ones
Description
Set the initial value of all the population parameters present within the current project to the ones previously estimated.
These the values will be used in the population parameter estimation algorithm during the next scenario run.
WARNING: If there is any set after a run, it will not be possible to set the initial values as the structure of the project has changed since last results.
Usage
setInitialEstimatesToLastEstimates(fixedEffectsOnly = FALSE)
Arguments
fixedEffectsOnly |
(bool) If this boolean is set to TRUE, only the fixed effects are initialized to their last estimated values. Otherwise, individual variances and error model parameters are re-initialized too. Equals FALSE by default. |
See Also
getEstimatedPopulationParameters getPopulationParameterInformation
Click here to see examples
# ## Not run: setInitialEstimatesToLastEstimates() # fixedEffectsOnly = FALSE by default setInitialEstimatesToLastEstimates(TRUE) ## End(Not run)
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[Monolix] Population parameters initialization and estimation method
Description
Set the initial value, the estimation method and, if relevant, the MAP parameters of one or several of the population parameters present within the current project (fixed effects + individual variances + error model parameters).
Available methods are:
- "FIXED": Fixed
- "MLE": Maximum Likelihood Estimation
- "MAP": Maximum A Posteriori
Call getPopulationParameterInformation to get a list of the initializable population parameters present within the current project.
Usage
setPopulationParameterInformation(...)
Arguments
... |
A list of comma-separated pairs {paramName = list( initialValue = (double), method = (string)"method"}. In case of "MAP" method, the user can specify the associated typical value and standard deviation values by using an additional list elements {paramName = list( priorValue = (double)1, priorSD = (double)2 )}. By default, the prior value corresponds to the the population parameter and the prior standard deviation is set to 1. |
See Also
getPopulationParameterInformation
Click here to see examples
# ## Not run: setPopulationParameterInformation(Cl_pop = list(initialValue = 0.5, method = "FIXED"), V_pop = list(initialValue = 1), ka_pop = list(method = "MAP", priorValue = 1, priorSD = 0.1)) ## End(Not run)
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[Monolix – PKanalix – Simulx] Initialize lixoftConnectors API
Description
Initialize lixoftConnectors API for a given software.
Usage
initializeLixoftConnectors(software = "monolix", path = "", force = FALSE)
Arguments
software |
(character) [optional] Name of the software to be loaded. By default, "monolix" software is used. |
path |
(character) [optional] Path to installation directory of the Lixoft suite. If lixoftConnectors library is not already loaded and no path is given, the directory written in the lixoft.ini file is used for initialization. |
force |
(bool) [optional] Should software switch security be overpassed or not. Equals FALSE by default. |
Value
A boolean equaling TRUE if the initialization has been successful and FALSE if not.
Click here to see examples
# ## Not run: initializeLixoftConnectors(software = "monolix", path = "/path/to/lixoftRuntime/") ## End(Not run)
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[Monolix – PKanalix – Simulx] Get Lixoft demos path
Description
Get the path to the demo projects. The path depends on the software used to initialize the connectors with initializeLixoftConnectors.
Usage
getDemoPath()
Value
A string corresponding to Lixoft demos path corresponding to the currently active software.
Click here to see examples
# ## Not run: getDemoPath() ## End(Not run)
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[Monolix] Get the results of the model building
Description
Get the results (detailed models) of the model building.
Usage
getModelBuildingResults()
Value
The results of model building
All the detailed tried models are returned
- LL: result of -2*Log-Likelihood
- BICc: modified BIC.
- individualModels: (data.frame) individual model for each individual parameter.
The columns are the covariates and the elements of the data.frame notes if a covariate is used or not for the current parameter.
COSSAC send 2 additional fields:
- tested: (vector<string>) first element is the individual parameter and the second one is the covariate.
This combination notes if the covariate is tested or not with respect to the previous model. - bestModel (boolean) best model amongst all the tried models according to the chosen criterion.
SAMBA send the error model and covariance model information if there are exist
- errorModels: chosen type for each error model
- covarianceModels: chosen correlations between individual parameters
See Also
Click here to see examples
# ## Not run: getModelBuildingResults() ## End(Not run)
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[Monolix] Get model building settings
Description
Get the settings that will be used during the run of model building.
Usage
getModelBuildingSettings()
Value
The list of settings
- covariates: (list<string>) covariate names
- parameters: (list<string>) parameters names
- strategy: (string) strategy to search best model ([cossac], samba, covsamba, scm)
- criterion: (string) crtierion to search best model ([BIC], LRT)
- relationships: (data.frame<parameters, covariates, locked>) Use to lock relationships between parameters and covariates.
By default, all the combinations are possible. This parameter forces the use or not of some combinations.
See example where ka must have SEX and V must not have WEIGHT - threshold$lrt: threshold used by criterion LRT to continue or not to improve the model (first element is for forward and the second one is for the backward method)
- threshold$correlation: threshold used by cossac to choose what combinations (parameter- covariate) must be tried as next candidate model (first element is for forward and the second one is for the backward method)
- useLin: (boolean) computes linearization ([TRUE]) or the Importance Sampling (FALSE)
See Also
Click here to see examples
# ## Not run: set = getModelBuildingSettings() set$relationships[1,] = c("ka", "SEX", TRUE) set$relationships[2,] = c("V", "WEIGHT", FALSE) -> set$relationships parameters covariates locked 1 ka SEX TRUE 2 V WEIGHT FALSE runModelBuilding(settings = set) ## End(Not run)
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[Monolix] Run model building
Description
Run model building.
To change the initialization before a run, use getModelBuildingSettings to receive all the settings. See example.
Usage
runModelBuilding(...)
Arguments
... |
(list<settings>) Settings to initialize the model buildign algorithm. See getModelBuildingSettings. |
See Also
getModelBuildingSettings getModelBuildingResults
Click here to see examples
# ## Not run: runModelBuilding() set = getModelBuildingSettings() runModelBuilding(settings = set) ## End(Not run)
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[Monolix] Get continuous observation models information
Description
Get a summary of the information concerning the continuous observation models in the project. The following informations are provided.
- prediction: (vector<string>) name of the associated prediction
- formula: (vector<string>) formula applied on the observation
- distribution: (vector<string>) distribution of the observation in the Gaussian space. The distribution type can be "normal", "logNormal", or "logitNormal".
- limits: (vector< pair<double,double> >) lower and upper limits imposed to the observation.
Used only if the distribution is logitNormal. If there is no logitNormal distribution, this field is empty. - errormodel: (vector<string>) type of the associated error model
- autocorrelation: (vector<bool>) defines if there is auto correlation
Call getObservationInformation to get a list of the continuous observations present in the current project.
Usage
getContinuousObservationModel()
Value
A list associating each continuous observation to its model properties.
See Also
getObservationInformation setObservationDistribution setObservationLimits
setErrorModel setAutocorrelation
Click here to see examples
# ## Not run: obsModels = getContinuousObservationModel() obsModels -> $prediction c(Conc = "Cc") $formula c(Conc = "Conc = Cc + (a+b*Cc)*e") $distribution c(Conc = "logitNormal") $limits list(Conc = c(0,11.5)) $errormodel c(Conc = "combined1") $autocorrelation c(Conc = TRUE) ## End(Not run)
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[Monolix] Set auto-correlation
Description
Add or remove auto-correlation from the error model used on some of the observation models.
Call getObservationInformation to get a list of the observation models present in the current project.
Usage
setAutocorrelation(...)
Arguments
... |
Sequence of comma-separated pairs {(string)"observationModel",(boolean)hasAutoCorrelation}. |
See Also
Click here to see examples
# ## Not run: setAutocorrelation(Conc = TRUE) setAutocorrelation(Conc = TRUE, Effect = FALSE) ## End(Not run)
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[Monolix] Set error model
Description
Set the error model type to be used with some of the observation models.
Call getObservationInformation to get a list of the observation models present in the current project.
Usage
setErrorModel(...)
Arguments
... |
A list of comma-separated pairs {observationModel = (string)errorModelType}. |
Details
Available error model types are :
“constant” | obs = pred + a*err |
“proportional” | obs = pred + (b*pred)*err |
“combined1” | obs = pred + (b*pred^c + a)*err |
“combined2” | obs = pred + sqrt(a^2 + (b^2)*pred^(2c))*err |
Error model parameters will be initialized to 1 by default.
Call setPopulationParameterInformation to modify their initial value.
The value of the exponent parameter is fixed by default when using the "combined1" and "combined2" models.
Use setPopulationParameterInformation to enable its estimation.
See Also
getContinuousObservationModel setPopulationParameterInformation
Click here to see examples
# ## Not run: setErrorModel(Conc = "constant", Effect = "combined1") ## End(Not run)
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[Monolix] Set observation model distribution
Description
Set the distribution in the Gaussian space of some of the observation models.
Available distribution types are "normal", "logNormal", or "logitNormal".
Call getObservationInformation to get a list of the available observation models within the current project.
Usage
setObservationDistribution(...)
Arguments
... |
A list of comma-separated pairs {observationModel = (string)"distribution"}. |
See Also
Click here to see examples
# ## Not run: setObservationDistribution(Conc = "normal") setObservationDistribution(Conc = "normal", Effect = "logNormal") ## End(Not run)
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[Monolix] Set observation model distribution limits
Description
Set the minimum and the maximum values between which some of the observations can be found.
Used only if the distribution of the error model is "logitNormal", else wise it will not be taken into account
Usage
setObservationLimits(...)
Arguments
... |
A list of comma-separated pairs {observationModel = [(double)min,(double)max] } |
See Also
getContinuousObservationModel getObservationInformation
Click here to see examples
# ## Not run: setObservationLimits( Conc = c(-Inf,Inf), Effect = c(0,Inf) ) ## End(Not run)
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[Monolix – PKanalix] Compute Charts data with custom stratification options and
custom computation settings
Description
Compute Charts data with custom stratification options and custom computation settings.
Usage
getChartsData( plotName, computeSettings = NULL, ids = NULL, splitGroup = NULL, colorGroup = NULL, filter = NULL )
Arguments
plotName |
(string) Name of the plot function. |
computeSettings |
(list) list with computational settings (it can include arguments from the settings argument of the plot, as well as obsName) |
ids |
list of ids to display (by default all ids are displayed). |
splitGroup |
data group criteria. a list, or a list of list with fields:
(by default no split is applied). |
colorGroup |
data group criteria. a list, or a list of list with fields:
(by default no color group is defined). |
filter |
data filtering criteria. a list, or a list of list with fields:
(by default no filtering is applied). |
Value
A dataframe object or a list of dataframe object to pass to "data" argument
of plot functions
Click here to see examples
# ## Not run: initializeLixoftConnectors(software = "pkanalix") project <- file.path(getDemoPath(), "2.case_studies/project_Theo_extravasc_SD.pkx") loadProject(project) data <- getChartsData(plotName = "plotObservedData", ids = c(1, 2, 3, 4)) data <- getChartsData(plotName = "plotNCAParametersCorrelation") initializeLixoftConnectors(software = "monolix") project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project) xBinsSettings <- list(is.fixedNbBins = TRUE, nbBins = 10) data <- getChartsData(plotName = "plotVpc", computeSettings = list(xBinsSettings = xBinsSettings)) data <- getChartsData(plotName = "plotVpc", computeSettings = list(level = 75)) splitGroup <- list(name = "WEIGHT", breaks = c(75)) filter <- list(name = "WEIGHT", interval = c(75, 100)) data <- getChartsData(plotName = "plotVpc", splitGroup = splitGroup) data <- getChartsData(plotName = "plotVpc", filter = filter) ## End(Not run)
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[Monolix – PKanalix] Generate Bivariate observations plots
Description
Plot the bivariate viewer.
Usage
plotBivariateDataViewer( obs1 = NULL, obs2 = NULL, data = NULL, settings = list(), stratify = list(), preferences = list() )
Arguments
obs1 |
(string) Name of the observation to display in x axis (in dataset header). By default the first observation is considered. |
obs2 |
(string) Name of the observation to display in y axis (in dataset header). By default the second observation is considered. |
data |
List of charts data as dataframe – Output of getChartsData (getChartsData("plotBivariateDataViewer", …)) If data not specified, charts data will be computed inside the function. |
settings |
List with the following settings
|
stratify |
List with the stratification arguments
|
preferences |
(optional) preferences for plot display, run getPlotPreferences("plotBivariateDataViewer") to check available displays. |
Value
A ggplot object
See Also
getChartsData getPlotPreferences
Click here to see examples
# initializeLixoftConnectors(software = "monolix") project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "warfarinPKPD_project.mlxtran") loadProject(project) plotBivariateDataViewer(obs1 = "y1", obs2 = "y2") plotBivariateDataViewer(settings = list(lines = FALSE)) # stratification plotBivariateDataViewer(obs1 = "y1", obs2 = "y2", stratify = list(ids = "10")) plotBivariateDataViewer(stratify = list(splitGroup = list(name = "age", breaks = 25), filter = list(name = "sex", cat = 1))) plotBivariateDataViewer(stratify = list(colorGroup = list(name = "wt", breaks = 75))) plotBivariateDataViewer(stratify = list(splitGroup = list(list(name = "age", breaks = 25), list(name = "sex")))) # update plot settings or preferences plotBivariateDataViewer(preferences = list(obs = list(color = "#32CD32")))
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[Monolix – PKanalix] Generate Covariate plots
Description
Plot the covariates.
Usage
plotCovariates( covariatesRows = NULL, covariatesColumns = NULL, data = NULL, settings = list(), preferences = list(), stratify = list() )
Arguments
covariatesRows |
vector with the name of covariates to display on rows (by default the first 4 covariates are displayed). |
covariatesColumns |
vector with the name of covariates to display on columns (by default the first 4 covariates are displayed). |
data |
List of charts data as dataframe – Output of getChartsData (getChartsData("plotCovariates", …)) If data not specified, charts data will be computed inside the function. |
settings |
List with the following settings
|
preferences |
(optional) preferences for plot display, run getPlotPreferences("plotCovariates") to check available displays. |
stratify |
List with the stratification arguments
|
Details
Generate scatterplots between two continuous covariates or bar plot between categorical covariates.
Value
- A ggplot object if one element in covariatesRows and covariatesColumns,
- A TableGrob object if multiple plots (output of grid.arrange)
See Also
getChartsData getPlotPreferences
Click here to see examples
# initializeLixoftConnectors(software = "pkanalix") project <- file.path(getDemoPath(), "2.case_studies/project_Theo_extravasc_SD.pkx") loadProject(project) # covariate distribution when only one covariate is specified plotCovariates(covariatesRows = "HT", settings = list(bins = 10)) # scatter plot when both covariates are continuous plotCovariates(covariatesRows = "HT", covariatesColumns = "AGE", settings = list(spline = TRUE)) plotCovariates(covariatesRows = "HT", covariatesColumns = c("AGE", "FORM")) # box plot when one covariate is categorical and the othe one is continuous preferences <- list(boxplot = list(fill = "#2075AE"), boxplotOutlier = list(shape = 3)) plotCovariates(covariatesRows = "FORM", covariatesColumns = "AGE", preferences = preferences) # histogram when covariate on column is categorical plotCovariates(covariatesRows = "FORM", covariatesColumns = "SEQ", settings = list(histogramColors = c("#5DC088", "#DBA92B"))) plotCovariates(covariatesRows = "AGE", covariatesColumns = "SEQ", settings = list(histogramColors = c("#5DC088", "#DBA92B"))) # stratification plotCovariates(covariatesRows = "HT", covariatesColumns = "WT", stratify = list( splitGroup = list(name = "AGE", breaks = 25), filter = list(name = "Period", cat = 1))) preferences <- list(regressionLine = list(color = "#E5551B")) plotCovariates(covariatesRows = "AGE", covariatesColumns = "WT", stratify = list( colorGroup = list(name = "HT", breaks = 181), colors = c("#2BB9DB", "#DD6BD2")), preferences = preferences) plotCovariates(covariatesRows = "HT", covariatesColumns = "WT", stratify = list(splitGroup = list(list(name = "AGE", breaks = 25), list(name = "SEQ")))) # Mulitple covariates plotCovariates() plotCovariates(covariatesRows = c("AGE", "SEQ", "HT"), covariatesColumns = c("AGE", "SEQ", "HT")) plotCovariates(stratify = list(filter = list(name = "AGE", interval = c(20, 30)))) plotCovariates(stratify = list(splitGroup = list(name = "AGE", breaks = c(25)))) plotCovariates(stratify = list(colorGroup = list(name = "AGE", breaks = c(25))))
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[Monolix – PKanalix] Generate Observation plots
Description
Plot the observed data.
Usage
plotObservedData( obsName = NULL, data = NULL, settings = list(), stratify = list(), preferences = list() )
Arguments
obsName |
(string) Name of the observation (in dataset header). By default the first observation is considered. |
data |
List of charts data as dataframe – Output of getChartsData (getChartsData("plotObservedData", …)) If data not specified, charts data will be computed inside the function. |
settings |
List with the following settings [CONTINUOUS – DISCRETE] Settings specific to continuous and discrete data
[DISCRETE] Settings specific to discrete data
[EVENT] Settings specific to event data
Other settings
|
stratify |
List with the stratification arguments
|
preferences |
(optional) preferences for plot display, run getPlotPreferences("plotObservedData") to check available displays. |
Value
A ggplot object
See Also
getChartsData getPlotPreferences
Click here to see examples
# initializeLixoftConnectors(software = "pkanalix") project <- file.path(getDemoPath(), "2.case_studies/project_Theo_extravasc_SD.pkx") loadProject(project) plotObservedData() plotObservedData(settings = list(binLimits = TRUE)) plotObservedData(settings = list(dosingTimes = TRUE)) plotObservedData(settings = list(meanMethod = "geometric", mean = TRUE)) plotObservedData(settings = list(mean = TRUE, error = TRUE, dots = FALSE, lines = TRUE)) # stratification plotObservedData(stratify = list(splitGroup = list(name = "AGE", breaks = 25), filter = list(name = "Period", cat = 1))) plotObservedData(stratify = list(colorGroup = list(name = "HT", breaks = 181))) plotObservedData(stratify = list(splitGroup = list(list(name = "AGE", breaks = 25), list(name = "Period")))) # update plot theme or preferences plotObservedData(settings = list(xlab = "Time", ylab = "Plasma Concentration")) plotObservedData(preferences = list(obs = list(color = "#32CD32"), observationStatistics = list(lineType = "dashed")))
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[Monolix] Plot Importance sampling convergence.
Description
Plot iterations of the likelihood estimation by importance sampling.
Usage
plotImportanceSampling(settings = list(), data = NULL)
Arguments
settings |
a list of optional settings:
|
data |
dataframe – Output of getChartsData (getChartsData("plotImportanceSampling", …)) If data not specified, charts data will be computed inside the function. |
Value
A ggplot object
See Also
Click here to see examples
# initializeLixoftConnectors(software = "monolix") project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project) runPopulationParameterEstimation() runLogLikelihoodEstimation() plotImportanceSampling()
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[Monolix] Plot MCMC convergence
Description
Plot iterations and convergence for the conditional distribution task.
Usage
plotMCMC(settings = list(), data = NULL)
Arguments
settings |
a list of optional settings:
|
data |
List of charts data as dataframe – Output of getChartsData (getChartsData("plotMCMC", …)) If data not specified, charts data will be computed inside the function. |
Value
A TableGrob object if multiple plots (output of grid.arrange)
See Also
Click here to see examples
# initializeLixoftConnectors(software = "monolix") project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project) runPopulationParameterEstimation() runConditionalDistributionSampling() plotMCMC()
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[Monolix] Plot SAEM convergence
Description
Plot iterations and convergence for the SAEM algorithm (population parameters estimation).
Usage
plotSaem(settings = list(), data = NULL)
Arguments
settings |
a list of optional settings:
|
data |
dataframe – Output of getChartsData (getChartsData("plotSaem", …)) If data not specified, charts data will be computed inside the function. |
Value
A TableGrob object if multiple plots (output of grid.arrange)
See Also
Click here to see examples
# initializeLixoftConnectors(software = "monolix") project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project) runPopulationParameterEstimation() plotSaem()
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[Monolix] Distribution of the individual parameters computed by Monolix
Description
Plot the distribution of the individual parameters.
Usage
plotParametersDistribution( parameters = NULL, plot = "pdf", settings = list(), preferences = NULL, stratify = list(), data = NULL )
Arguments
parameters |
vector of parameters to display. (by default the first 4 computed parameters are displayed). |
plot |
(string) Type of plot: probability density distribution ("pdf"), cumulative density distribution ("cdf") (default "pdf) |
settings |
a list of optional plot settings:
|
preferences |
(optional) preferences for plot display, run getPlotPreferences("plotParametersDistribution") to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of charts data as dataframe – Output of getChartsData (getChartsData("plotParametersDistribution", …)) If data not specified, charts data will be computed inside the function. |
Value
- A ggplot object if one parameter,
- A TableGrob object if multiple plots (output of grid.arrange)
See Also
getChartsData getPlotPreferences
Click here to see examples
# initializeLixoftConnectors(software="monolix") project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project) runPopulationParameterEstimation() runConditionalDistributionSampling() plotParametersDistribution(parameters="ka") plotParametersDistribution(parameters="Cl", plot="pdf") plotParametersDistribution(parameters="ka", plot="cdf") plotParametersDistribution(parameters="ka", plot="cdf", settings=list(indivEstimate="simulated")) plotParametersDistribution(parameters="Cl", plot="pdf", settings=list(theoretical=F)) # stratification plotParametersDistribution(stratify=list(filter=list(name="WEIGHT", interval=c(0, 75)))) plotParametersDistribution(parameters="Cl", stratify=list(splitGroup=list(name="SEX"))) colorGroup <- list(name="WEIGHT", breaks=c(75)) plotParametersDistribution(parameters= "Cl", plot="pdf", stratify=list(colorGroup=colorGroup, colors=c("#46B4AF", "#B4468A"))) plotParametersDistribution(parameters="Cl", plot="cdf", stratify=list(colorGroup=colorGroup, colors=c("#46B4AF", "#B4468A"))) # update preferences preferences = list(theoretical=list(color="#B4468A", lineType="solid", lineWidth=0.8)) plotParametersDistribution(parameters="ka", plot="cdf", preferences=preferences) # pre compute dataset data <- getChartsData(plotName="plotParametersDistribution", computeSettings=list(indivEstimate="simulated")) plotParametersDistribution(data=data) # multiple plots plotParametersDistribution(parameters=c("ka", "Cl")) plotParametersDistribution(plot="pdf") plotParametersDistribution(plot="cdf") plotParametersDistribution(plot="cdf", settings=list(indivEstimate="simulated")) plotParametersDistribution(plot = "pdf", settings=list(theoretical=F))
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[Monolix] Individual monolix parameter vs covariate plot.
Description
Plot individual parameters vs covariates.
Usage
plotParametersVsCovariates( parameters = NULL, covariates = NULL, settings = list(), preferences = list(), stratify = list(), data = NULL )
Arguments
parameters |
vector of parameters to display. (by default the first 4 computed parameters are displayed). |
covariates |
vector of covariates to display. (by default the first 4 computed covariates are displayed). |
settings |
List with the following settings
|
preferences |
(optional) preferences for plot display, run getPlotPreferences("plotParametersVsCovariates") to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of charts data as dataframe – Output of getChartsData (getChartsData("plotParametersVsCovariates", …)) If data not specified, charts data will be computed inside the function. |
Value
- A ggplot object if one covariate and one parameter in argument,
- A TableGrob object if multiple plots (output of grid.arrange)
See Also
getChartsData getPlotPreferences
Click here to see examples
# initializeLixoftConnectors(software="monolix") project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project) runPopulationParameterEstimation() runConditionalDistributionSampling() # Individual parameters plotParametersVsCovariates(covariates="SEX", parameters="Cl") plotParametersVsCovariates(covariates="WEIGHT", parameters="V", settings=list(spline=T)) plotParametersVsCovariates(covariates="WEIGHT", parameters="V", settings=list(indivEstimate="simulated")) # Random effects plotParametersVsCovariates(covariates="SEX", parameters="V", settings=list(parameterType="randomEffect")) plotParametersVsCovariates(covariates="WEIGHT", parameters="V", settings=list(indivEstimate="simulated", parameterType="randomEffect")) # Stratification plotParametersVsCovariates(covariates="SEX", parameters="ka", stratify=list(filter=list(name="WEIGHT", interval=c(0, 75)))) plotParametersVsCovariates(covariates="WEIGHT", parameters="ka", stratify=list(splitGroup=list(name="SEX"))) plotParametersVsCovariates(covariates="SEX", parameters="Cl", stratify=list(colorGroup=list(name="WEIGHT", breaks=75))) plotParametersVsCovariates(covariates="WEIGHT", parameters="V", stratify=list(colorGroup=list(name="SEX"))) plotParametersVsCovariates(covariates="WEIGHT", parameters="V", stratify = list(colorGroup = list(list(name = "SEX"), list(name="WEIGHT", breaks=70)))) # pre process dataset data <- getChartsData(plotName="plotParametersVsCovariates", computeSettings=list(indivEstimate="simulated")) plotParametersVsCovariates(data=data) # multiple plots plotParametersVsCovariates() plotParametersVsCovariates(covariates="WEIGHT") plotParametersVsCovariates(settings=list(indivEstimate="simulated")) plotParametersVsCovariates(settings=list(parameterType="randomEffect")) plotParametersVsCovariates(settings=list(parameterType="randomEffect", indivEstimate="simulated")) plotParametersVsCovariates(stratify=list(colorGroup=list(name="WEIGHT", breaks=75))) plotParametersVsCovariates(stratify=list(colorGroup=list(name="SEX")))
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[Monolix] Correlations between random effect.
Description
Plot correlations between random effects.
Usage
plotRandomEffectsCorrelation( parametersRows = NULL, parametersColumns = NULL, settings = list(), preferences = list(), stratify = list(), data = NULL )
Arguments
parametersRows |
vector with the name of parameters to display on rows (by default the first 4 computed parameters are displayed). |
parametersColumns |
vector with the name of parameters to display on columns (by default parametersColumns = parametersRows). |
settings |
List with the following settings
|
preferences |
(optional) preferences for plot display, run getPlotPreferences("plotRandomEffectsCorrelation") to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of charts data as dataframe – Output of getChartsData (getChartsData("plotRandomEffectsCorrelation", …)) If data not specified, charts data will be computed inside the function. |
Value
- A ggplot object if one element in parametersRows and parametersColumns,
- A TableGrob object if multiple plots (output of grid.arrange)
See Also
getChartsData getPlotPreferences
Click here to see examples
# initializeLixoftConnectors(software = "monolix") project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project) runPopulationParameterEstimation() runConditionalDistributionSampling() plotRandomEffectsCorrelation() plotRandomEffectsCorrelation(parametersRows = "ka", parametersColumns = "V", settings = list(indivEstimate = "simulated")) plotRandomEffectsCorrelation(parametersRows = "ka", parametersColumns = "V", settings = list(spline = TRUE)) plotRandomEffectsCorrelation(parametersRows = c("ka", "V")) # stratification plotRandomEffectsCorrelation(parametersRows = "ka", parametersColumns = "V", stratify = list(filter = list(name = "SEX", cat = "M"))) plotRandomEffectsCorrelation(parametersRows = "ka", parametersColumns = "V", stratify = list( colorGroup = list(name = "WEIGHT", breaks = 75), colors = c("#46B4AF", "#B4468A"))) plotRandomEffectsCorrelation(parametersRows = "ka", parametersColumns = "V", stratify = list(splitGroup = list(name = "SEX"))) plotRandomEffectsCorrelation(parametersRows = "ka", parametersColumns = "V", stratify = list(splitGroup = list(list(name = "SEX"), list(name="WEIGHT", breaks=70)))) # pre compute dataset data <- getChartsData(plotName = "plotRandomEffectsCorrelation", computeSettings = list(indivEstimate = "simulated")) plotRandomEffectsCorrelation(data = data) plotRandomEffectsCorrelation(settings = list(indivEstimate = "mean"))
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[Monolix] Distribution of the standardized random effects.
Description
Plot the distribution of the standardized random effects.
Usage
plotStandardizedRandomEffectsDistribution( parameters = NULL, plot = "boxplot", settings = list(), preferences = list(), stratify = list(), data = NULL )
Arguments
parameters |
vector of parameters to display. (by default the first 4 computed parameters are displayed). |
plot |
Type of plot: probability density distribution ("pdf"), cumulative density distribution ("cdf"), boxplot ("boxplot") (default "boxplot"). |
settings |
a list of optional plot settings:
|
preferences |
(optional) preferences for plot display, run getPlotPreferences("plotStandardizedRandomEffectsDistribution") to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of charts data as dataframe – Output of getChartsData (getChartsData("plotStandardizedRandomEffectsDistribution", …)) If data not specified, charts data will be computed inside the function. |
Value
- A ggplot object if one parameter,
- A TableGrob object if multiple plots (output of grid.arrange)
See Also
getChartsData getPlotPreferences
Click here to see examples
# initializeLixoftConnectors(software="monolix") project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project) runPopulationParameterEstimation() runConditionalDistributionSampling() runConditionalModeEstimation() # Random effect distribution as boxplot plotStandardizedRandomEffectsDistribution(parameters="ka", plot="boxplot") plotStandardizedRandomEffectsDistribution(parameters="ka", plot="boxplot", settings=list(indivEstimate="mode")) plotStandardizedRandomEffectsDistribution(parameters="ka", plot="boxplot", settings=list(quartile=F)) # Random effect distribution as pdf plotStandardizedRandomEffectsDistribution(parameters="ka", plot="pdf") plotStandardizedRandomEffectsDistribution(parameters="ka", plot="pdf", settings=list(empirical=F)) plotStandardizedRandomEffectsDistribution(parameters="ka", plot="pdf", settings=list(theoretical=F)) # Random effect distribution as cdf plotStandardizedRandomEffectsDistribution(parameters="ka", plot="cdf") plotStandardizedRandomEffectsDistribution(parameters="ka", plot="cdf", settings=list(indivEstimate="simulated")) plotStandardizedRandomEffectsDistribution(parameters="ka", plot="cdf", settings=list(theoretical=F)) # stratification plotStandardizedRandomEffectsDistribution( stratify=list(filter=list(name="WEIGHT", interval=c(0, 75))) ) plotStandardizedRandomEffectsDistribution(parameters="Cl", stratify=list(splitGroup=list(name="SEX"))) colorGroup <- list(name="WEIGHT", breaks=c(75)) plotStandardizedRandomEffectsDistribution( parameters="Cl", plot="pdf", stratify=list(colorGroup=colorGroup, colors=c("#46B4AF", "#B4468A")) ) plotStandardizedRandomEffectsDistribution( parameters="Cl", plot="cdf", stratify=list(colorGroup=colorGroup, colors=c("#46B4AF", "#B4468A")) ) plotStandardizedRandomEffectsDistribution( parameters="Cl", settings=list(plot="boxplot"), stratify=list(colorGroup=colorGroup, colors=c("#46B4AF", "#B4468A")) ) data <- getChartsData(plotName="plotStandardizedRandomEffectsDistribution", computeSettings=list(indivEstimate="simulated")) plotStandardizedRandomEffectsDistribution(data=data) plotStandardizedRandomEffectsDistribution(parameters=c("ka", "Cl")) plotStandardizedRandomEffectsDistribution(plot="boxplot") plotStandardizedRandomEffectsDistribution(plot="pdf") plotStandardizedRandomEffectsDistribution(plot="cdf") plotStandardizedRandomEffectsDistribution(plot="pdf", settings=list(theoretical=F))
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[Monolix] Plot Monolix Individual Fits
Description
Plot the individual fits.
Usage
plotIndividualFits( obsName = NULL, settings = list(), preferences = list(), stratify = list(), data = NULL )
Arguments
obsName |
(string) Name of the observation (in dataset header). By default the first observation is considered. |
settings |
List with the following settings
|
preferences |
(optional) preferences for plot display, run getPlotPreferences("plotIndividualFits") to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of charts data as dataframe – Output of getChartsData (getChartsData("plotIndividualFits", …)) If data not specified, charts data will be computed inside the function. |
Details
Only available for Continuous data.
Value
A ggplot object
See Also
getChartsData getPlotPreferences
Click here to see examples
# initializeLixoftConnectors(software = "monolix") project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project) runPopulationParameterEstimation() runConditionalDistributionSampling() runConditionalModeEstimation() plotIndividualFits() plotIndividualFits(settings=list(popFits=T)) plotIndividualFits(settings=list(obsLines=T, obsDots=F, predInterval=T)) plotIndividualFits(settings=list(dosingTimes=T)) # stratification options plotIndividualFits(stratify=list(ids=c(1, 2, 3, 4))) plotIndividualFits(stratify=list(filter=list(name="WEIGHT", interval=c(75, 100)))) plotIndividualFits(stratify=list(filter=list(name="SEX", cat ="F"))) plotIndividualFits(stratify=list(colorGroup=list(name="SEX"), colors=c("#5DC088", "#DBA92B"))) plotIndividualFits( settings=list(legend=T), stratify = list(colorGroup=list(list(name = "SEX"), list(name = "WEIGHT", breaks = 70))) ) # settings and preferences options plotIndividualFits(settings=list(ylog=T, ylim=c(0.8, 11))) preferences <- list(popFits=list(lineType="solid", legend="Population fits")) plotIndividualFits(settings=list(popFits=T), preferences=preferences) data <- getChartsData(plotName="plotIndividualFits", computeSettings=list(indivEstimate="mean"), ids=c(1, 2, 3, 4)) plotIndividualFits(data=data)
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[Monolix] Plot Observation VS Prediction
Description
Plot the observation vs the predictions.
Usage
plotObservationsVsPredictions( obsName = NULL, predictions = c("indiv"), settings = list(), preferences = list(), stratify = list(), data = NULL )
Arguments
obsName |
(string) Name of the observation (in dataset header). By default the first observation is considered. |
predictions |
(string) LIst of predictions to display: population prediction ("pop"), individual prediction ("indiv") (default c("indiv")). |
settings |
List with the following settings
|
preferences |
(optional) preferences for plot display, run getPlotPreferences("plotObservationsVsPredictions") to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of cahrts data as dataframe – Output of getChartsData (getChartsData("plotObservationsVsPredictions", …)) If data not specified, charts data will be computed inside the function. |
Value
- A ggplot object if one prediction type,
- A TableGrob object if multiple plots (output of grid.arrange)
See Also
getChartsData getPlotPreferences
Click here to see examples
# initializeLixoftConnectors(software = "monolix") project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project) runPopulationParameterEstimation() runConditionalDistributionSampling() runConditionalModeEstimation() plotObservationsVsPredictions() plotObservationsVsPredictions(predictions = "pop") plotObservationsVsPredictions(prediction = "indiv", settings = list(indivEstimate = "simulated")) plotObservationsVsPredictions(settings = list(indivEstimate = "mean", spline = TRUE)) plotObservationsVsPredictions(settings = list(indivEstimate = "mode", predInterval = TRUE)) # stratification plotObservationsVsPredictions(stratify = list(filter = list(name = "SEX", cat = "F"))) plotObservationsVsPredictions(settings = list(ylog = TRUE, xlog = TRUE)) plotObservationsVsPredictions(stratify = list(splitGroup = list(name = "WEIGHT", breaks = c(75)))) plotObservationsVsPredictions(stratify = list(colorGroup = list(name = "WEIGHT", breaks = c(75)))) plotObservationsVsPredictions( settings=list(legend=T), stratify = list(colorGroup=list(list(name = "SEX"), list(name = "WEIGHT", breaks = 70))) ) data <- getChartsData(plotName = "plotObservationsVsPredictions", computeSettings = list(indivEstimate = "simulated"), colorGroup = list(name = "WEIGHT", breaks = c(75))) plotObservationsVsPredictions(data = data) # display multiple predictions plotObservationsVsPredictions(predictions = c("pop", "indiv"))
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[Monolix] Generate Distribution of the residuals
Description
Plot the distribution of the residuals.
Usage
plotResidualsDistribution( obsName = NULL, residuals = c("indiv", "npde"), plots = c("pdf", "cdf"), settings = list(), preferences = list(), stratify = list(), data = NULL )
Arguments
obsName |
(string) Name of the observation (in dataset header). By default the first observation is considered. |
residuals |
(string) List of residuals to display: population residuals ("pop"), individual residuals ("indiv"), normalized prediction distribution error ("npde") (default c("indiv", "npde)). |
plots |
Type of plots: probability density distribution ("pdf"), cumulative density distribution ("cdf") (default c("pdf", "cdf")). |
settings |
List with the following settings
|
preferences |
(optional) preferences for plot display, run getPlotPreferences("plotResidualsDistribution") to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of charts data as dataframe – Output of getChartsData (getChartsData("plotResidualsDistribution", …)) If data not specified, charts data will be computed inside the function. |
Value
- A ggplot object if one prediction type,
- A TableGrob object if multiple plots (output of grid.arrange)
See Also
getChartsData getPlotPreferences
Click here to see examples
# initializeLixoftConnectors(software="monolix") project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project) runPopulationParameterEstimation() runConditionalDistributionSampling() plotResidualsDistribution() plotResidualsDistribution(residuals="indiv", settings=list(indivEstimate="simulated")) plotResidualsDistribution(residuals="indiv", settings=list(indivEstimate="mode")) plotResidualsDistribution(residuals="pop", plots="pdf") plotResidualsDistribution(residuals="npde", plots="cdf") plotResidualsDistribution(stratify=list(filter=list(name="SEX", cat="F"))) plotResidualsDistribution(stratify=list(splitGroup=list(name="WEIGHT", breaks=c(75)))) plotResidualsDistribution( residuals="indiv", settings=list(legend=T), stratify = list(splitGroup=list(list(name = "SEX"), list(name = "WEIGHT", breaks = 70))) ) data <- getChartsData(plotName="plotResidualsDistribution", computeSettings=list(indivEstimate="simulated")) plotResidualsDistribution(data=data) plotResidualsDistribution() plotResidualsDistribution(residuals=c("indiv", "npde"), settings=list(indivEstimate="simulated")) plotResidualsDistribution(residuals=c("pop", "indiv"), settings=list(indivEstimate="mode")) plotResidualsDistribution(plots=c("pdf", "cdf")) plotResidualsDistribution(plots=c("cdf")) plotResidualsDistribution(residuals="npde")
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[Monolix] Generate Scatter plots of the residuals
Description
Plot the scatter plots of the residuals.
Usage
plotResidualsScatterPlot( obsName = NULL, residuals = c("indiv"), xaxis = c("time", "prediction"), settings = list(), preferences = list(), stratify = list(), data = NULL )
Arguments
obsName |
(string) Name of the observation (in dataset header). By default the first observation is considered. |
residuals |
(string) List of residuals to display: population residuals ("pop"), individual residuals ("indiv"), normalized prediction distribution error ("npde") (default c("indiv")). |
xaxis |
(string) List of x-axis to display: time ("time"), prediction ("prediction") (default c("time", "prediction") for continuous data, c("time") for discrete data). |
settings |
List with the following settings
|
preferences |
(optional) preferences for plot display, run getPlotPreferences("plotResidualsScatterPlot") to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of cahrts data as dataframe – Output of getChartsData (getChartsData("plotResidualsScatterPlot", …)) If data not specified, charts data will be computed inside the function. |
Details
Note that ‘prediction interval’ setting is not available in 2021 version for this connector.
Value
- A ggplot object if one prediction type,
- A TableGrob object if multiple plots (output of grid.arrange)
See Also
getChartsData getPlotPreferences
Click here to see examples
# initializeLixoftConnectors(software="monolix") project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project) runPopulationParameterEstimation() runConditionalDistributionSampling() runConditionalModeEstimation() plotResidualsScatterPlot() plotResidualsScatterPlot(residuals="indiv", settings=list(indivEstimate="simulated")) plotResidualsScatterPlot(residuals="indiv", settings=list(indivEstimate="mode")) plotResidualsScatterPlot(xaxis="prediction", residuals="pop") plotResidualsScatterPlot(xaxis="time", residuals="pop") plotResidualsScatterPlot(residuals="npde") plotResidualsScatterPlot(settings=list(spline=T)) plotResidualsScatterPlot(settings=list(empPercentiles=T, level=90, binsSettings=list(is.fixedNbBins=T, nbBins=5), binLimits=T)) # Stratification plotResidualsScatterPlot(stratify=list(filter=list(name="SEX", cat="F"))) plotResidualsScatterPlot(stratify=list(splitGroup=list(name="WEIGHT", breaks=c(75)))) plotResidualsScatterPlot(stratify=list(colorGroup=list(name="WEIGHT", breaks=c(75)))) data <- getChartsData(plotName="plotResidualsScatterPlot", computeSettings=list(indivEstimate="simulated")) plotResidualsScatterPlot(data=data) plotResidualsScatterPlot(residuals=c("indiv", "pop"), settings=list(indivEstimate="simulated")) plotResidualsScatterPlot(residuals="indiv", xaxis=c("prediction"), settings=list(indivEstimate="mode")) plotResidualsScatterPlot(xaxis=c("prediction"), residuals=c("indiv", "pop")) plotResidualsScatterPlot(residuals="npde")
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[Monolix] Plot BLQ predictive checks
Description
Plot the BLQ predictive checks.
Usage
plotBlqPredictiveCheck( obsName = NULL, settings = list(), preferences = list(), data = NULL )
Arguments
obsName |
(string) Name of the observation (in dataset header). By default the first observation is considered. |
settings |
a list of optional plot settings:
|
preferences |
(optional) preferences for plot display, run getPlotPreferences("plotBlqPredictiveCheck") to check available displays. |
data |
List of charts data as dataframe – Output of getChartsData (getChartsData("plotBlqPredictiveCheck", …)) If data not specified, charts data will be computed inside the function. |
Value
a ggplot2 object
See Also
getChartsData getPlotPreferences
Click here to see examples
# initializeLixoftConnectors(software = "monolix") # continuous data project <- file.path(getDemoPath(), "2.models_for_continuous_outcomes", "2.2.censored_data", "censoring1_project.mlxtran") loadProject(project) runScenario() plotBlqPredictiveCheck(obsName = "Y")
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[Monolix] Plot Numerical predictive checks
Description
Plot the numerical predictive checks.
Usage
plotNpc( obsName = NULL, settings = list(), preferences = list(), stratify = list(), data = NULL )
Arguments
obsName |
(string) Name of the observation (in dataset header). By default the first observation is considered. |
settings |
a list of optional settings:
|
preferences |
(optional) preferences for plot display, run getPlotPreferences("plotNpc") to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of charts data as dataframe – Output of getChartsData (getChartsData("plotNpc", …)) If data not specified, charts data will be computed inside the function. |
Value
a ggplot2 object
See Also
getChartsData getPlotPreferences
Click here to see examples
# initializeLixoftConnectors(software = "monolix") # continuous data project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project) runPopulationParameterEstimation() plotNpc(obsName = "CONC")
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[Monolix] Plot distribution of the predictions
Description
Plot the prediction distribution.
Usage
plotPredictionDistribution( obsName = NULL, settings = list(), preferences = list(), data = NULL )
Arguments
obsName |
(string) Name of the observation (in dataset header). By default the first observation is considered. |
settings |
a list of optional settings
|
preferences |
(optional) preferences for plot display, run getPlotPreferences("plotPredictionDistribution") to check available displays. |
data |
List of charts data as dataframe – Output of getChartsData (getChartsData("plotPredictionDistribution", …)) If data not specified, charts data will be computed inside the function. |
Details
Note that computation settings are not available for this connector in 2021 version:
Number of bands is set to 9 and Level is set to 90
Note that stratification options are not available for this connector in 2021 version:
Value
a ggplot2 object
See Also
getChartsData getPlotPreferences
Click here to see examples
# initializeLixoftConnectors(software = "monolix") # continuous data project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project) runPopulationParameterEstimation() plotPredictionDistribution()
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[Monolix] Plot Visual predictive checks
Description
Plot the visual predictive checks.
Usage
plotVpc( obsName = NULL, eventPlot = "survivalFunction", settings = list(), preferences = list(), stratify = list(), data = NULL )
Arguments
obsName |
(string) Name of the observation (in dataset header). By default the first observation is considered. |
eventPlot |
(string) Display Survival function ("survivalFunction") or average number of event ("averageEventNumber) (default "survivalFunction"). For event data only. |
settings |
a list of optional settings:
|
preferences |
(optional) preferences for plot display, run getPlotPreferences("plotVpc") to check available displays. |
stratify |
List with the stratification arguments
|
data |
List of charts data as dataframe – Output of getChartsData (getChartsData("plotVpc", …)) If data not specified, charts data will be computed inside the function. |
Value
a ggplot2 object
See Also
getChartsData getPlotPreferences
Click here to see examples
# initializeLixoftConnectors(software = "monolix") # continuous data project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project) runPopulationParameterEstimation() data <- getChartsData("plotVpc") p <- plotVpc(data = data, obsName = "CONC", settings = list(outlierDots = FALSE, grid = FALSE, ylab = "Concentration", xlab = "time (in hour)")) # categorical data project <- file.path(getDemoPath(), "3.models_for_noncontinuous_outcomes", "3.1.categorical_data_model", "categorical1_project.mlxtran") loadProject(project) runPopulationParameterEstimation() data <- getChartsData(plotName = "plotVpc") p <- plotVpc(data = data, obsName = "level", settings = list(theoretical = TRUE, outlierDots = FALSE)) # countable data project <- file.path(getDemoPath(), "3.models_for_noncontinuous_outcomes", "3.2.count_data_model", "count1a_project.mlxtran") loadProject(project) runPopulationParameterEstimation() data <- getChartsData(plotName = "plotVpc") p <- plotVpc(data = data, obsName = "Y") # time to event data project <- file.path(getDemoPath(), "3.models_for_noncontinuous_outcomes", "3.3.time_to_event_data_model", "tte1_project.mlxtran") loadProject(project) runPopulationParameterEstimation() data <- getChartsData(plotName = "plotVpc") plotVpc(data = data, obsName = "Event", eventPlot = "survivalFunction")
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[Monolix – PKanalix] Define Preferences to customize plots
Description
Define the preferences to customize plots.
Usage
getPlotPreferences(plotName = NULL, update = NULL, ...)
Arguments
plotName |
(string) Name of the plot function. if plotName is NULL, all preferences are returned |
update |
list containing the plot elements to be updated. |
... |
additional arguments – dataType for some plots |
Details
This function creates a theme that customizes how a plot looks, i.e. legend, colors
fills, transparencies, linetypes an sizes, etc.
For each curve, list of available customizations:
- color: color (when lines or points)
- fill: color (when surfaces)
- opacity: color transparency
- radius: size of points
- shape: shape of points
- lineType: linetype
- lineWidth: line size
- legend: name of the legend (if NULL, no legend is displayed for the element)
Value
A list with theme specifiers
See Also
setPlotPreferences resetPlotPreferences
Click here to see examples
# ## Not run: preferences <- getPlotPreferences(update = list( obs = list(color = "red", legend = "Observations"), obsCens = list(color = rgb(70, 130, 180, maxColorValue = 255)) )) # preferences that are used by default in the plots preferences <- getPlotPreferences() # preferences that are used by default in plotObservedData preferences <- getPlotPreferences(plotName = "plotObservedData") ## End(Not run)
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[Monolix – PKanalix – Simulx] Export current project to Monolix, PKanalix or Simulx
Description
Export the current project to another application of the MonolixSuite, and load the exported project.
NOTE: This action switches the current session to the target software. Current unsaved modifications will be lost.
The extensions are .mlxtran for Monolix, .pkx for PKanalix, .smlx for Simulx and .dxp for Datxplore.
WARNING: R is sensitive between ‘\’ and ‘/’, only ‘/’ can be used.
Usage
exportProject(settings, force = F)
Arguments
settings |
(character) Export settings:
|
force |
(bool) [optional] Should software switch security be overpassed or not. Equals FALSE by default. |
Details
At export, a new project is created in a temporary folder. By default, the file is created with a project setting filesNextToProject = TRUE, which means that file dependencies such as data and model files are copied and kept next to the new project (or in the result folder for Simulx). This new project can be saved to the desired location withsaveProject.
Exporting a Monolix or a PKanalix project to Simulx automatically creates elements that can be used for simulation, exactly as in the GUI.
To see which elements of some type have been created in the new project, you can use the get..Element functions: getOccasionElements, getPopulationElements,
.getPopulationElements
, getIndividualElements, getCovariateElements, getTreatmentElements, getOutputElements, getRegressorElements
See Also
newProject, loadProject, importProject
Click here to see examples
# ## Not run: [PKanalix only] exportProject(settings = list(targetSoftware = "monolix", filesNextToProject = F)) [Monolix only] exportProject(settings = list(targetSoftware = "simulx", filesNextToProject = T, dataFilePath = "data.txt", dataFileType = "vpc")) exportProject(settings = list(targetSoftware = "simulx", filesNextToProject = F, dataFilePath = "/path/to/data/data.txt")) [Simulx only] exportProject(settings = list(targetSoftware = "pkanalix", dataFilePath = "data.txt", modelFileName = "model.txt")) exportProject(settings = list(targetSoftware = "pkanalix", dataFilePath = "/path/to/data/data.txt")) ## End(Not run) # Working example to export a Monolix project to Simulx. The resulting .smlx file can be opened from Simulx GUI. initializeLixoftConnectors(software = "monolix", force = TRUE) loadProject(file.path(getDemoPath(),"1.creating_and_using_models","1.1.libraries_of_models","warfarinPK_project.mlxtran")) runScenario() exportProject(settings = list(targetSoftware = "simulx"), force = TRUE) saveProject("importFromMonolix.smlx")
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[Monolix – PKanalix] Get project data
Description
Get a description of the data used in the current project. Available informations are:
- dataFile (string): path to the data file
- header (array<character>): vector of header names
- headerTypes (array<character>): vector of header types
- observationNames (vector<string>): vector of observation names
- observationTypes (vector<string>): vector of observation types
- nbSSDoses (int): number of doses (if there is a SS column)
Usage
getData()
Value
A list describing project data.
See Also
Click here to see examples
# ## Not run: data = getData() data -> $dataFile "/path/to/data/file.txt" $header c("ID","TIME","CONC","SEX","OCC") $headerTypes c("ID","TIME","OBSERVATION","CATEGORICAL COVARIATE","IGNORE") $observationNames c("concentration") $observationTypes c(concentration = "continuous") ## End(Not run)
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[Monolix – PKanalix] Get interpreted project data
Description
Get data after interpretation done by the software, how it is displayed in the Data tab in the interface.
Interpretation of data includes, but is not limited to, data formatting, addition of doses through the ADDL column and steady state settings, addition of additional covariates, interpolation of regressors.
Usage
getInterpretedData()
[Monolix – PKanalix – Simulx] Get a library model’s content.
Description
Get the content of a library model.
Usage
getLibraryModelContent(filename, print = TRUE)
Arguments
filename |
(string) The filename of the requested model. Can start with "lib:", end with ".txt", but neither are mandatory. |
print |
(logical) If TRUE (default), model’s content is printed with human-readable line breaks (alongside regular output with "\n"). |
Value
The model’s content as a raw string.
Click here to see examples
# ## Not run: getLibraryModelContent("oral1_1cpt_kaVCl") model <- getLibraryModelContent(filename = "lib:oral1_1cpt_kaVCl.txt", print = FALSE) ## End(Not run)
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[Monolix – PKanalix – Simulx] Get the name of a library model given a list of library filters.
Description
Get the name of a library model given a list of library filters.
Usage
getLibraryModelName(library, filters = list())
Arguments
library |
(string) One of the MonolixSuite library of models. Possible values are "pk", "pd", "pkpd", "pkdoubleabs", "pm", "tmdd", "tte", "count" and "tgi". |
filters |
(list(name = string)) Named list of filters (optional), format: list(filterKey = "filterValue", …). Default empty list. Since available filters are not in any particular order, filterKey should always be stated. |
Details
Models can be loaded from a library based on a selection of filters as in PKanalix, Monolix and Simulx GUI. For a complete description of each model library, and guidelines on how to select models, please visit https://mlxtran.lixoft.com/model-libraries/.
getLibraryModelName enables to get the name of the model to be loaded. You can then use it in setStructuralModel or newProject to load the model in an existing or in a new project.
All possible keys and values for each of the libraries are listed below.
PK library
key | values | |
administration | bolus, infusion, oral, oralBolus | |
delay | noDelay, lagTime, transitCompartments | |
absorption | zeroOrder, firstOrder | |
distribution | 1compartment, 2compartments, 3compartments | |
elimination | linear, MichaelisMenten | |
parametrization | rate, clearance, hybridConstants | |
bioavailability | true, false | |
PD library
key | values | |
response | immediate, turnover | |
drugAction | linear, logarithmic, quadratic, Emax, Imax, productionInhibition, | |
degradationInhibition, degradationStimulation, productionStimulation | ||
baseline | const, 1-exp, exp, linear, null | |
inhibition | partialInhibition, fullInhibition | |
sigmoidicity | true, false | |
PKPD library
key | values | |
administration | bolus, infusion, oral, oralBolus | |
delay | noDelay, lagTime, transitCompartments | |
absorption | zeroOrder, firstOrder | |
distribution | 1compartment, 2compartments, 3compartments | |
elimination | linear, MichaelisMenten | |
parametrization | rate, clearance | |
bioavailability | true, false | |
response | direct, effectCompartment, turnover | |
drugAction | Emax, Imax, productionInhibition, degradationInhibition, | |
degradationStimulation, productionStimulation | ||
baseline | const, null | |
inhibition | partialInhibition, fullInhibition | |
sigmoidicity | true, false | |
PK double absorption library
key | values | |
firstAbsorption | zeroOrder, firstOrder | |
firstDelay | noDelay, lagTime, transitCompartments | |
secondAbsorption | zeroOrder, firstOrder | |
secondDelay | noDelay, lagTime, transitCompartments | |
absorptionOrder | simultaneous, sequential | |
forceLongerDelay | true, false | |
distribution | 1compartment, 2compartments, 3compartments | |
elimination | linear, MichaelisMenten | |
parametrization | rate, clearance | |
Parent-metabolite library
key | values | |
administration | bolus, infusion, oral, oralBolus | |
firstPassEffect | noFirstPassEffect, withDoseApportionment, | |
withoutDoseApportionment | ||
delay | noDelay, lagTime, transitCompartments | |
absorption | zeroOrder, firstOrder | |
transformation | unidirectional, bidirectional | |
parametrization | rate, clearance | |
parentDistribution | 1compartment, 2compartments, 3compartments | |
parentElimination | linear, MichaelisMenten | |
metaboliteDistribution | 1compartment, 2compartments, 3compartments | |
metaboliteElimination | linear, MichaelisMenten | |
TMDD library
key | values | |
administration | bolus, infusion, oral, oralBolus | |
delay | noDelay, lagTime, transitCompartments | |
absorption | zeroOrder, firstOrder | |
distribution | 1compartment, 2compartments, 3compartments | |
tmddApproximation | MichaelisMenten, QE, QSS, full, Wagner, | |
constantRtot, constantRtotIB, irreversibleBinding | ||
output | totalLigandLtot, freeLigandL | |
parametrization | rate, clearance | |
TTE library
key | values | |
tteModel | exponential, Weibull, Gompertz, loglogistic, | |
uniform, gamma, generalizedGamma | ||
delay | true, false | |
numberOfEvents | singleEvent, repeatedEvents | |
typeOfEvent | intervalCensored, exact | |
dummyParameter | true, false | |
Count library
key | values | |
countDistribution | Poisson, binomial, negativeBinomial, betaBinomial, | |
generalizedPoisson, geometric, hypergeometric, | ||
logarithmic, Bernoulli | ||
zeroInflation | true, false | |
timeEvolution | constant, linear, exponential, Emax, Hill | |
parametrization | probabilityOfSuccess, averageNumberOfCounts | |
TGI library
key | values | |
shortcut | ClaretExponential, Simeoni, Stein, Wang, | |
Bonate, Ribba, twoPopulation | ||
initialTumorSize | asParameter, asRegressor | |
kinetics | true, false | |
model | linear, quadratic, exponential, generalizedExponential, | |
exponentialLinear, Simeoni, Koch, logistic, | ||
generalizedLogistic, SimeoniLogisticHybrid, Gompertz, | ||
exponentialGompertz, vonBertalanffy, generalizedVonBertalanffy | ||
additionalFeature | none, angiogenesis, immuneDynamics | |
treatment | none, pkModel, exposureAsRegressor, startAtZero, | |
startTimeAsRegressor, armAsRegressor | ||
killingHypothesis | logKill, NortonSimon | |
dynamics | firstOrder, MichaelisMenten, MichaelisMentenHill, | |
exponentialKill, constant | ||
resistance | ClaretExponential, resistantCells, none | |
delay | signalDistribution, cellDistribution, none | |
additionalTreatmentEffect | none, angiogenesisInhibition, immuneEffectorDecay | |
Value
Name of the filtered model, or vector of names of the available models if not all filters were selected. Names start with "lib:".
Click here to see examples
# ## Not run: getLibraryModelName(library = "pk", filters = list(administration = "oral", delay = "lagTime", absorption = "firstOrder", distribution = "1compartment", elimination = "linear", parametrization = "clearance")) # returns "lib:oral1_1cpt_TlagkaVCl.txt" getLibraryModelName("pd", list(response = "turnover", drugAction = "productionStimulation")) # returns c("lib:turn_input_Emax.txt", "lib:turn_input_gammaEmax.txt") ## End(Not run)
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[Monolix – PKanalix] Get mapping
Description
Get mapping between data and model.
Usage
getMapping()
Value
A list of mapping information:
- mapping (list<list>) A list of lists representing a link between data and model. Each list contains:
- data (string) Data name
- prediction (string) Prediction name
- model [Monolix] (string) Model observation name (for continuous observations only)
- type (string) Type of linked data ("continuous" | "discrete" | "event")
- freeData (list<list>) A list of lists describing not mapped data:
- data (string) Data name
- type (string) Data type
- freePredictions (list<list>) A list of lists describing not mapped predictions:
- prediction (string) Prediction name
- type (string) Prediction type
See Also
Click here to see examples
# ## Not run: f = getMapping() f$mapping -> list( list(data = "1", prediction = "Cc", model = "concentration", type = "continuous"), list(data = "2", prediction = "Level", type = "discrete") ) f$freeData -> list( list(data = "3", type = "event") ) f$freePredictions -> list( list(prediction = "Effect", type = "continuous") ) ## End(Not run)
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[Monolix – PKanalix – Simulx] Get structural model file
Description
Get the model file for the structural model used in the current project.
Usage
getStructuralModel()
Details
For Simulx, this function will return the path to the structural model only if the project was imported from Monolix, and the path to the full custom model otherwise.
Note that a custom model in Simulx may include also a statistical part.
For Simulx, there is no associated function getStructuralModel() because setting a new model is equivalent to creating a new project. Use newProject instead.
If a model was loaded from the libraries, the returned character is not a path,
but the name of the library model, such as "lib:model_name.txt". To see the content of a library model, use getLibraryModelContent.
Value
A string corresponding to the path to the structural model file.
See Also
For Monolix and PKanalix only: setStructuralModel
Click here to see examples
# ## Not run: getStructuralModel() => "/path/to/model/inclusion/modelFile.txt" ## End(Not run) # Get the name and see the content of the model used in warfarin demo project initializeLixoftConnectors("monolix", force = TRUE) loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "warfarinPK_project.mlxtran")) libModelName <- getStructuralModel() getLibraryModelContent(libModelName) # Get the name of the model file used in Simulx initializeLixoftConnectors("simulx", force = TRUE) project_name <- file.path(getDemoPath(), "1.overview", "newProject_TMDDmodel.smlx") loadProject(project_name) getStructuralModel() # Get the name of the model file imported to Simulx initializeLixoftConnectors("monolix", force = TRUE) project_name <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "warfarinPK_project.mlxtran") loadProject(project_name) getStructuralModel() initializeLixoftConnectors("simulx", force = TRUE) importProject(project_name) getStructuralModel()
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[Monolix – PKanalix – Simulx] Import project from Datxplore, Monolix or PKanalix
Description
Import a Monolix or a PKanalix project into the currently running application initialized in the connectors.
The extensions are .mlxtran for Monolix, .pkx for PKanalix, .smlx for Simulx and .dxp for Datxplore.
WARNING: R is sensitive between ‘\’ and ‘/’, only ‘/’ can be used.
Allowed import sources are:
CURRENT SOFTWARE | ALLOWED IMPORTS |
Monolix | PKanalix |
PKanalix | Monolix, Datxplore |
Simulx | Monolix, PKanalix. |
Usage
importProject(projectFile)
Arguments
projectFile |
(character) Path to the project file. Can be absolute or relative to the current working directory. |
Details
At import, a new project is created in a temporary folder with a project setting filesNextToProject = TRUE,
which means that file dependencies such as data and model files are copied and kept next to the new project
(or in the result folder for Simulx). This new project can be saved to the desired location withsaveProject.
Simulx projects can only be exported, not imported. To export a Simulx project to another application,
please load the Simulx project with the Simulx connectors and use exportProject.
Importing a Monolix or a PKanalix project into Simulx automatically creates elements that can be used for
simulation, exactly as in the GUI.
To see which elements of some type have been created in the new project, you can use the get..Element functions:
getOccasionElements, getPopulationElements,
,getPopulationElements
, getIndividualElements
getCovariateElements, getTreatmentElements, getOutputElements, getRegressorElements
.
See Also
Click here to see examples
# ## Not run: initializeLixoftConnectors(software = "simulx", force = TRUE) importProject("/path/to/project/file.mlxtran") importProject("/path/to/project/file.pkx") initializeLixoftConnectors(software = "monolix", force = TRUE) importProject("/path/to/project/file.pkx") initializeLixoftConnectors(software = "pkanalix", force = TRUE) importProject("/path/to/project/file.mlxtran") ## End(Not run) # working example to import a Monolix demo project into Simulx. The resulting .smlx file can be opened from Simulx GUI. initializeLixoftConnectors(software = "monolix", force = TRUE) MonolixDemoPath = file.path(getDemoPath(),"1.creating_and_using_models","1.1.libraries_of_models","warfarinPK_project.mlxtran") initializeLixoftConnectors(software = "simulx", force = TRUE) importProject(MonolixDemoPath) saveProject("importFromMonolix.smlx")
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[Monolix – PKanalix – Simulx] Get current project load status.
Description
Get a boolean saying if a project is currently loaded.
Usage
isProjectLoaded()
Value
TRUE if a project is currently loaded, FALSE otherwise
Click here to see examples
# initializeLixoftConnectors("monolix") project_name <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "warfarinPK_project.mlxtran") loadProject(project_name) isProjectLoaded() initializeLixoftConnectors("pkanalix") isProjectLoaded()
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[Monolix – PKanalix – Simulx] Load project from file
Description
Load a project in the currently running application initialized in the connectors.
The extensions are .mlxtran for Monolix, .pkx for PKanalix, and .smlx for Simulx.
WARNING: R is sensitive between ‘\’ and ‘/’, only ‘/’ can be used.
Usage
loadProject(projectFile)
Arguments
projectFile |
(character) Path to the project file. Can be absolute or relative to the current working directory. |
See Also
saveProject, importProject, exportProject, newProject
Click here to see examples
# ## Not run: loadProject("/path/to/project/file.mlxtran") for Linux platform loadProject("C:/Users/path/to/project/file.mlxtran") for Windows platform ## End(Not run) # Load a Monolix project initializeLixoftConnectors("monolix") project_name <- file.path(getDemoPath(), "8.case_studies", "hiv_project.mlxtran") loadProject(project_name) # Load a PKanalix project initializeLixoftConnectors("pkanalix") project_name <- file.path(getDemoPath(), "1.basic_examples", "project_censoring.pkx") loadProject(project_name) # Load a Simulx project initializeLixoftConnectors("simulx") project_name <- file.path(getDemoPath(), "2.models", "longitudinal.smlx") loadProject(project_name)
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[Monolix – PKanalix – Simulx] Create a new project
Description
Create a new project. New projects can be created in the connectors as in PKanalix, Monolix or Simulx GUI. The creation of a new project requires a dataset in PKanalix, a dataset and a model in Monolix, and a model in Simulx.
Usage
newProject(modelFile = NULL, data = NULL)
Arguments
modelFile |
(character) Path to the model file. Mandatory for Monolix and Simulx, optional for PKanalix (used only for the CA part). Can be absolute or relative to the current working directory. To use a model from the libraries, you can find the model name with getLibraryModelName and set modelFile = "lib:modelName.txt" with the name obtained. To simulate inter-individual variability in Simulx with a new project, the model file has to include the statistical model, contrary to Monolix and PKanalix for which the model file only contains the structural model. Check here in detail how to write such a model from scratch. |
data |
(list) Structure describing the data. Mandatory for Monolix and PKanalix.
|
Details
Note: instead of creating a project from scratch, it is also possible in Monolix and PKanalix to load an existing project with loadProject or importProject and change the dataset or the model with setData or setStructuralModel
.
See Also
Click here to see examples
# # Create a new Monolix project initializeLixoftConnectors("monolix") data_file <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "data", "warfarin_data.csv") newProject(data = list(dataFile = data_file, headerTypes = c("id", "time", "amount", "observation", "obsid", "contcov", "catcov", "contcov"), observationTypes = list("1" = "continuous", "2" = "continuous"), mapping = list(list(data = "1", prediction = "Cc", model = "y1"), list(data = "2", prediction = "R", model = "y2"))), modelFile = "lib:oral1_1cpt_IndirectModelInhibitionKin_TlagkaVClR0koutImaxIC50.txt") # Create a new PKanalix project initializeLixoftConnectors("pkanalix") data_file <- file.path(getDemoPath(), "1.basic_examples", "data", "data_BLQ.csv") newProject(data = list(dataFile = data_file, headerTypes = c("id", "time", "amount", "observation", "cens", "catcov"))) # Create a new Simulx project initializeLixoftConnectors("simulx") newProject(modelFile = "lib:oral1_1cpt_IndirectModelInhibitionKin_TlagkaVClR0koutImaxIC50.txt")
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[Monolix – PKanalix – Simulx] Save current project
Description
Save the current project as a file that can be reloaded in the connectors or in the GUI.
Usage
saveProject(projectFile = "")
Arguments
projectFile |
[optional](character) Path where to save a copy of the current mlxtran model. Can be absolute or relative to the current working directory. If no path is given, the file used to build the current configuration is updated. |
Details
The extensions are .mlxtran for Monolix, .pkx for PKanalix, and .smlx for Simulx.
WARNING: R is sensitive between ‘\’ and ‘/’, only ‘/’ can be used.
If the project setting "userfilesnexttoproject" is set to TRUE with setProjectSettings, all file dependencies such as model, data or external files are saved next to the project for Monolix and PKanalix, and in the result folder for Simulx.
See Also
Click here to see examples
# ## Not run: [PKanalix only] saveProject("/path/to/project/file.pkx") # save a copy of the model [Monolix only] saveProject("/path/to/project/file.mlxtran") # save a copy of the model [Simulx only] saveProject("/path/to/project/file.smlx") # save a copy of the model [Monolix - PKanalix - Simulx] saveProject() # update current model ## End(Not run) # Load, change and save a PKanalix project under a new name initializeLixoftConnectors("pkanalix") project_name <- file.path(getDemoPath(), "1.basic_examples", "project_censoring.pkx") loadProject(project_name) setNCASettings(blqMethodAfterTmax = "missing") saveProject("~/changed_project.pkx") # Load, change and save a Monolix project under a new name initializeLixoftConnectors("monolix") project_name <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "warfarinPK_project.mlxtran") loadProject(project_name) addContinuousTransformedCovariate(tWt = "3*exp(wt)") saveProject("~/changed_project.mlxtran") # Load, change and save a Simulx project under a new name initializeLixoftConnectors("simulx") project_name <- file.path(getDemoPath(), "2.models", "longitudinal.smlx") loadProject(project_name) defineTreatmentElement(name = "trt", element = list(data = data.frame(time = 0, amount = 100))) saveProject("~/changed_project.smlx")
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[Monolix – PKanalix] Set project data
Description
Set project data giving a data file and specifying headers and observations types.
Usage
setData(dataFile, headerTypes, observationTypes, nbSSDoses = NULL)
Arguments
dataFile |
(character): Path to the data file. Can be absolute or relative to the current working directory. |
headerTypes |
(array<character>): A collection of header types. The possible header types are: "ignore", "id", "time", "observation", "amount", "contcov", "catcov", "occ", "evid", "mdv", "obsid", "cens", "limit", "regressor","admid", "rate", "tinf", "ss", "ii", "addl", "date". Notice that these are not the types displayed in the interface, these one are shortcuts. |
observationTypes |
[optional] (list): A list giving the type of each observation present in the data file. If there is only one y-type, the corresponding observation name can be omitted. The possible observation types are "continuous", "discrete", and "event". |
nbSSDoses |
[optional](int): Number of doses (if there is a SS column). |
See Also
Click here to see examples
# ## Not run: setData(dataFile = "/path/to/data/file.txt", headerTypes = c("IGNORE", "OBSERVATION"), observationTypes = "continuous") setData(dataFile = "/path/to/data/file.txt", headerTypes = c("IGNORE", "OBSERVATION", "YTYPE"), observationTypes = list(Concentration = "continuous", Level = "discrete")) ## End(Not run)
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[Monolix – PKanalix] Set mapping
Description
Set mapping between data and model.
Usage
setMapping(mapping)
Arguments
mapping |
(list<list>) A list of lists representing a link between the data and the model. Each list contains:
|
See Also
Click here to see examples
# ## Not run: [Monolix] setMapping(list(list(data = "1", prediction = "Cc", model = "concentration"), list(data = "2", prediction = "Level"))) [PKanalix] setMapping(list(list(data = "1", prediction = "Cc"), list(data = "2", prediction = "Level"))) ## End(Not run)
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[Monolix – PKanalix] Set structural model file
Description
Set the structural model.
Usage
setStructuralModel(modelFile)
Arguments
modelFile |
(character) Path to the model file. Can be absolute or relative to the current working directory. |
Details
To use a model from the libraries, you can find the model name with getLibraryModelName
and set modelFile = "lib:modelName.txt" with the name obtained.
See Also
Click here to see examples
# ## Not run: setStructuralModel("/path/to/model/file.txt") setStructuralModel("'lib:oral1_2cpt_kaClV1QV2.txt'") # working example to set a model from the library: initializeLixoftConnectors("monolix",force = TRUE) loadProject(file.path(getDemoPath(),"1.creating_and_using_models","1.1.libraries_of_models","warfarinPK_project.mlxtran")) #check model currently loaded: getStructuralModel() #get the name for a model from the library with 2 compartments: LibModel2cpt = getLibraryModelName(library = "pk", filters = list(administration = "oral", delay = "lagTime", absorption = "firstOrder", distribution = "2compartments", elimination = "linear", parametrization = "clearance")) #check model content: getLibraryModelContent(LibModel2cpt) #set this new model in the project: setStructuralModel(LibModel2cpt) # check that the project has now the new model instead of the previous one: getStructuralModel() ## End(Not run)
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[Monolix – Pkanalix – Simulx] Get console mode
Description
Get console mode, ie volume of output after running estimation tasks. Possible verbosity levels are:
“none” | no output |
“basic” | at the end of each algorithm, associated results are displayed |
“complete” | each algorithm iteration and/or status is displayed |
Usage
getConsoleMode()
Value
A string corresponding to current console mode
See Also
[Monolix – PKanalix – Simulx] 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. |
“temporarydirectory” | (string) | Path to the directory used to save temporary files. |
“timestamping” | (bool) | Create an archive containing result files after each run. |
“delimiter” | (string) | Character use as delimiter in exported result files. |
“exportchartsdata” | (bool) | Should charts data be exported. |
“exportchartsdatasets” | (bool) | [Monolix] Should charts datasets be exported if possible. |
“exportvpcsimulations” | (bool) | [Monoliw] Should vpc simulations be exported if possible. |
“exportsimulationfiles” | (bool) | [Simulx] Should simulation results files be exported. |
“headeraliases” | (list("header" = vector<string>)) | For each header, the list of the recognized aliases. |
“ncaparameters” | (vector<string>) | [PKanalix] Defaulty computed NCA parameters. |
“units” | (list("type" = string) | [PKanalix] Time, amount and/or volume units. |
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.
Click here to see examples
# ## Not run: getPreferences() # retrieve a list of all the general settings getPreferences("imageFormat","exportCharts") # retrieve only the imageFormat and exportCharts settings values ## End(Not run)
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[Monolix – PKanalix – Simulx] Get project settings
Description
Get a summary of the project settings.
Associated settings for Monolix projects 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 simulations. |
“dataandmodelnexttoproject” | (bool) | Should data and model files be saved next to project. |
“project” | (string) | Path to the Monolix project. |
Associated settings for PKanalix projects are:
“directory” | (string) | Path to the folder where simulation results will be saved. It should be a writable directory. |
“seed” | (0< int <2147483647) | Seed used by random generators. |
“datanexttoproject” | (bool) | Should data and model (in case of CA) files be saved next to project. |
Associated settings for Simulx projects are:
“directory” | (string) | Path to the folder where simulation results will be saved. It should be a writable directory. |
“seed” | (0< int <2147483647) | Seed used by random generators. |
“userfilesnexttoproject” | (bool) | Should user 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
Click here to see examples
# ## Not run: getProjectSettings() # retrieve a list of all the project settings ## End(Not run)
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[Monolix – Pkanalix – Simulx] Set console mode
Description
Set console mode, ie volume of output after running estimation tasks. Possible verbosity levels are:
“none” | no output |
“basic” | for each algorithm, display current iteration then associated results at algorithm end |
“complete” | display all iterations then associated results at algorithm end |
Usage
setConsoleMode(mode)
Arguments
mode |
(string) Accepted values are: "none" [default], "basic", "complete" |
See Also
[Monolix – PKanalix – Simulx] 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. |
“temporarydirectory” | (string) | Path to the directory used to save temporary files. |
“timestamping” | (bool) | Create an archive containing result files after each run. |
“delimiter” | (string) | Character use as delimiter in exported result files. |
“exportchartsdata” | (bool) | Should charts data be exported. |
“exportchartsdatasets” | (bool) | [Monolix] Should charts datasets be exported if possible. |
“exportvpcsimulations” | (bool) | [Monoliw] Should vpc simulations be exported if possible. |
“exportsimulationfiles” | (bool) | [Simulx] Should simulation results files be exported. |
“headeraliases” | (list("header" = vector<string>)) | For each header, the list of the recognized aliases. |
“ncaparameters” | (vector<string>) | [PKanalix] Defaulty computed NCA parameters. |
“units” | (list("type" = string) | [PKanalix] Time, amount and/or volume units. |
Usage
setPreferences(...)
Arguments
... |
A collection of comma-separated pairs {preferenceName = settingValue}. |
See Also
Click here to see examples
# ## Not run: setPreferences(exportCharts = FALSE, delimiter = ",") ## End(Not run)
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[Monolix – PKanalix – Simulx] Set project settings
Description
Set the value of one or several of the settings of the project.
Associated settings for Monolix projects 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 simulations. |
“dataandmodelnexttoproject” | (bool) | Should data and model files be saved next to project. |
Associated settings for PKanalix projects are:
“directory” | (string) | Path to the folder where simulation results will be saved. It should be a writable directory. |
“dataNextToProject” | (bool) | Should data and model (in case of CA) files be saved next to project. |
“seed” | (0< int <2147483647) | Seed used by random generators. |
Associated settings for Simulx projects are:
“directory” | (string) | Path to the folder where simulation results will be saved. It should be a writable directory. |
“seed” | (0< int <2147483647) | Seed used by random generators. |
“userfilesnexttoproject” | (bool) | Should user files be saved next to project. |
Usage
setProjectSettings(...)
Arguments
... |
A collection of comma-separated pairs {settingName = settingValue}. |
See Also
Click here to see examples
# ## Not run: setProjectSettings(directory = "/path/to/export/directory", seed = 12345) ## End(Not run)
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[Monolix – PKanalix] Generate report
Description
Generate a project report with default options or from a custom Word template.
Usage
generateReport( templateFile = NULL, tablesStyle = NULL, watermark = NULL, reportFile = NULL )
Arguments
templateFile |
[optional] (character) Path to the .docx template file used as reporting base. If not provided, a default report file is generated (as default option in the GUI). |
tablesStyle |
[optional] (character) |
watermark |
[optional] (list)
|
reportFile |
[optional] (list) If not provided, the report will be saved next to the project file with the name <projectname>_report.docx.
|
Details
Reports can be generated as in the GUI, either by using the default reporting or by using a custom template. Placeholders for tables can be used in the template, and they are replaced by result tables. It is not possible to replace plots placeholders with the connector, because this requires an interface to be open. If plots placeholders are present in the template, they will be replaced by nothing in the generated report.
Click here to see examples
# ## Not run: generateReport() generateReport(templateFile = "/path/to/template.docx") generateReport(templateFile = "/path/to/template.docx", tablesStyle = "Plain Table 1", watermark = list(text = "watermark", fontSize = 15)) ## End(Not run) # Working example to generate a default report ### initializeLixoftConnectors("monolix") loadProject(file.path(getDemoPath(),"1.creating_and_using_models","1.1.libraries_of_models","warfarinPK_project.mlxtran")) runScenario() reportPath = tempfile("report", fileext = ".docx") generateReport(reportFile = list(nextToProject = FALSE, path = reportPath)) file.show(reportPath) # Working example to generate a report with a custom template### # Note that only tables get replaced. It is not possible to add plots to a report via connectors, but it can be done in the GUI. initializeLixoftConnectors("pkanalix") loadProject(file.path(getDemoPath(),"2.case_studies","project_aPCSK9_SAD.pkx")) runScenario() reportPath = tempfile("report", fileext = ".docx") generateReport(templateFile = file.path(getDemoPath(),"2.case_studies","report_templates","PK_report_template_aPCSK9.docx"), reportFile = list(nextToProject = FALSE, path = reportPath)) file.show(reportPath)
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[Monolix] Export chart dataset
Description
Export the data of a chart into Lixoft suite compatible data set format.
It can be generated only if the concerned chart has been built.
The file is written in the results folder of the current project.
Usage
exportChartDataSet(type, filePath = "")
Arguments
type |
(string) Chart type whose data must be exported. Available types are: "vpc", "indfits". |
filePath |
[optional](string) Custom path for the exported file. By default, it is written in the DataFile folder of the current project. |
See Also
Click here to see examples
# ## Not run: exportChartDataSet(type = "vpc") exportChartDataSet(type = "indfits", filePath = "/path/to/exported/file.txt") ## End(Not run)
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[Monolix] Get the inverse of the Fisher Matrix
Description
Get the inverse of the last estimated Fisher matrix computed either by all the Fisher methods used during the last scenario run or by the specific one passed in argument.
WARNING: The Fisher matrix cannot be accessible until the Fisher algorithm has been launched once.
The user can choose to display only the Fisher matrix estimated with a specific method.
Existing Fisher methods :
Fisher by Linearization | “linearization” |
Fisher by Stochastic Approximation | “stochasticApproximation” |
WARNING: Only the methods which have been used during the last scenario run can provide results.
Usage
getCorrelationOfEstimates(method = "")
Arguments
method |
[optional](string) Fisher method whose results should be displayed. If this field is not specified, the results provided by all the methods used during the last scenario run are displayed. |
Value
A list whose each field contains the Fisher matrix computed by one of the available Fisher methods used during the ast scenario run.
A matrix is defined as a structure containing the following fields :
rownames | list of row names |
columnnames | list of column names |
rownumber | number of rows |
data | vector<…> containing matrix raw values (column major) |
Click here to see examples
# ## Not run: getCorrelationOfEstimates("linearization") -> list( linearization = list(data = c(1,0,0,0,1,-0.06,0,-0.06,1), rownumber = 3, rownames = c("Cl_pop","omega_Cl","a"), columnnames = c("Cl_pop","omega_Cl","a"))) getCorrelationOfEstimates() -> list(linearization = list(...), stochasticApproximation = list(...) ) ## End(Not run)
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[Monolix] Get last estimated individual parameter values
Description
Get the last estimated values for each subject of some of the individual parameters present within the current project.
WARNING: Estimated individual parameters values cannot be accessible until the individual estimation algorithm has been launched once.
NOTE: The user can choose to display only the individual parameter values estimated with a specific method.
Existing individual estimation methods :
Conditional Mean SAEM | “saem” |
Conditional Mean | “conditionalMean” |
Conditional Mode | “conditionalMode” |
WARNING: Only the methods which have been used during the last scenario run can provide estimation results.
Usage
getEstimatedIndividualParameters(..., method = "")
Arguments
... |
(string) Name of the individual parameters whose values must be displayed. Call getIndividualParameterModel to get a list of the individual parameters present within the current project. |
method |
[optional](string) Individual parameter estimation method whose results should be displayed. If there are latent covariate used in the model, the estimated modality is displayed too If this field is not specified, the results provided by all the methods used during the last scenario run are displayed. |
Value
A data frame giving, for each wanted method, the last estimated values of the individual parameters of interest for each subject with the corresponding standard deviation values.
See Also
Click here to see examples
# ## Not run: indivParams = getEstimatedIndividualParameters() # retrieve the values of all the available individual parameters for all methods -> $saem id Cl V ka 1 0.28 7.71 0.29 . ... ... ... N 0.1047.62 1.51 indivParams = getEstimatedIndividualParameters("Cl", "V", method = "conditionalMean") # retrieve the values of the individual parameters "Cl" and "V" # estimated by the conditional mode method ## End(Not run)
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[Monolix] Get Log-Likelihood values
Description
Get the values computed by using a log-likelihood algorithm during the last scenario run, with or without a method-based filter.
WARNING: The log-likelihood values cannot be accessible until the log-likelihood algorithm has been launched once.
The user can choose to display only the log-likelihood values computed with a specific method.
Existing log-likelihood methods :
Log-likelihood by Linearization | “linearization” |
Log-likelihood by Important Sampling | “importanceSampling” |
WARNING: Only the methods which have been used during the last scenario run can provide results.
Usage
getEstimatedLogLikelihood(method = "")
Arguments
method |
[optional](string) Log-likelihood method whose results should be displayed. If this field is not specified, the results provided by all the methods used during the last scenario run are retrieved. |
Value
A list associating the name of each method passed in argument to the corresponding log-likelihood values computed by during the last scenario run.
Click here to see examples
# ## Not run: getEstimatedLogLikelihood() -> list( linearization = [LL = -170.505, AIC = 350.280, BIC = 365.335] , importanceSampling = [...] ) getEstimatedLogLikelihood("linearization") -> list( linearization = [LL = -170.505, AIC = 350.280, BIC = 365.335] ) ## End(Not run)
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[Monolix] Get last estimated population parameter value
Description
Get the last estimated value of some of the population parameters present within the current project (fixed effects + individual variances + correlations + latent probabilities + error model parameters).
WARNING: Estimated population parameters values cannot be accessible until the SAEM algorithm has been launched once.
Usage
getEstimatedPopulationParameters(..., coefficientsOfVariation = FALSE)
Arguments
... |
[optional] (array<string>) Names of the population parameters whose value must be displayed. Call getPopulationParameterInformation to get a list of the population parameters present within the current project. If this field is not specified, the function will retrieve the values of all the available population parameters. |
coefficientsOfVariation |
[optional](boolean) if this option is TRUE, the standard deviations of random effects are also given as coefficients of variation (relative standard deviations as percentages) with _CV suffix. |
Value
A named vector containing the last estimated value of each one of the population parameters passed in argument.
Click here to see examples
# ## Not run: getEstimatedPopulationParameters("V_pop") -> [V_pop = 0.5] getEstimatedPopulationParameters("V_pop","Cl_pop") -> [V_pop = 0.5, Cl_pop = 0.25] getEstimatedPopulationParameters() -> [V_pop = 0.5, Cl_pop = 0.25, ka_pop = 0.05] ## End(Not run)
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[Monolix] Get estimated the random effects
Description
Get the random effects for each subject of some of the individual parameters present within the current project.
WARNING: Estimated random effects cannot be accessible until the individual estimation algorithm has been launched once.
The user can choose to display only the random effects estimated with a specific method.
NOTE: The random effects are defined in the gaussian referential, e.g. if ka is lognormally distributed around ka_pop, eta_i = log(ka_i)-log(ka_pop)
Existing individual estimation methods :
Conditional Mean SAEM | “saem” |
Conditional Mean | “conditionalMean” |
Conditional Mode | “conditionalMode” |
WARNING: Only the methods which have been used during the last scenario run can provide estimation results. Please call getLaunchedTasks to get a list of the methods whose results are available.
Usage
getEstimatedRandomEffects(..., method = "")
Arguments
... |
(string) Name of the individual parameters whose random effects must be displayed. Call getIndividualParameterModel to get a list of the individual parameters present within the current project. |
method |
[optional](string) Individual parameter estimation method whose results should be displayed. If this field is not specified, the results provided by all the methods used during the last scenario run are displayed. |
Value
A data frame giving, for each wanted method, the last estimated eta values of the individual parameters of interest for each subject with the corresponding standard deviation values.
See Also
getEstimatedIndividualParameters
Click here to see examples
# ## Not run: etaParams = getEstimatedRandomEffects() # retrieve the values of all the available random effects for all methods # without the associated standard deviations -> $saem id Cl V ka 1 0.28 7.71 0.29 . ... ... ... N 0.1047.62 1.51 etaParams = getEstimatedRandomEffects("Cl", "V", method = "conditionalMode") # retrieve the values of the individual parameters "Cl" and "V" # estimated by the conditional mean from SAEM algorithm ## End(Not run)
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[Monolix] Get standard errors of population parameters
Description
Get the last estimated standard errors of population parameters computed either by all the Fisher methods used during the last scenario run or by the specific one passed in argument.
WARNING: The standard errors cannot be accessible until the Fisher algorithm has been launched once.
Existing Fisher methods :
Fisher by Linearization | “linearization” |
Fisher by Stochastic Approximation | “stochasticApproximation” |
WARNING: Only the methods which have been used during the last scenario run can provide results.
Usage
getEstimatedStandardErrors(method = "")
Arguments
method |
[optional](string) Fisher method whose results should be displayed. If this field is not specified, the results provided by all the methods used during the last scenario run are retrieved |
Value
A list associating each retrieved Fisher algorithm method to a data frame containing the standard errors and relative standard errors (
Click here to see examples
# ## Not run: getEstimatedStandardErrors() -> $linearization parameter se rse ka_pop 0.313449586 20.451556 V_pop 0.020422507 4.483500 omega_V 0.037975960 30.072470 omega_Cl 0.062976601 23.270939 $stochasticApproximation parameter se rse ka_pop 0.311284296 20.310278 V_pop 0.020424882 4.484022 omega_V 0.161053665 24.000077 omega_Cl 0.035113095 27.805419 ## End(Not run)
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[Monolix] Get tasks with results
Description
Get a list of the tasks which have results to provide. A task is the association of:
- an algorithm (string)
- a vector of methods (string) relative to this algorithm for the standardErrorEstimation and the loglikelihoodEstimation, TRUE or FALSE for the other one.
Usage
getLaunchedTasks()
Value
The list of tasks with results, indexed by algorithm names.
Click here to see examples
# ## Not run: tasks = getLaunchedTasks() tasks -> $populationParameterEstimation = TRUE $conditionalModeEstimation = TRUE $standardErrorEstimation = "linearization" ## End(Not run)
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[Monolix] Get SAEM algorithm iterations
Description
Retrieve the successive values of some of the population parameters present within the current project (fixed effects + individual variances + correlations + latent probabilities + error model parameters) during the previous run of the SAEM algorithm.
WARNING: Convergence history of population parameters values cannot be accessible until the SAEM algorithm has been launched once.
Usage
getSAEMiterations(...)
Arguments
... |
[optional] (array<string>) Names of the population parameters whose convergence history must be displayed. Call getPopulationParameterInformation to get a list of the population parameters present within the current project. If this field is not specified, the function will retrieve the values of all the available population parameters. |
Value
A list containing a pair composed by the number of exploratory and smoothing iterations and a data frame which associates each wanted population parameter to its successive values over SAEM algorithm iterations.
Click here to see examples
# ## Not run: report = getSAEMiterations() report -> $iterationNumbers c(50,25) $estimates V Cl 0.25 0 0.3 0.5 . . 0.35 0.25 ## End(Not run)
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[Monolix] Get simulated individual parameters
Description
Get the simulated values for each replicate of each subject of some of the individual parameters present within the current project.
WARNING: Simulated individual parameters values cannot be accessible until the individual estimation with conditional mean algorithm has been launched once.
Usage
getSimulatedIndividualParameters(...)
Arguments
... |
(string) Name of the individual parameters whose values must be displayed. Call getIndividualParameterModel to get a list of the individual parameters present within the current project. |
Value
A list giving the last simulated values of the individual parameters of interest for each replicate of each subject.
See Also
Click here to see examples
# ## Not run: simParams = getSimulatedIndividualParameters() # retrieve the values of all the available individual parameters simParams rep id Cl V ka 1 1 0.022 0.37 1.79 1 2 0.033 0.42 -0.92 . . ... ... ... 2 1 0.021 0.33 1.47 . . ... ... ... ## End(Not run)
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[Monolix] Get simulated random effects
Description
Get the simulated values for each replicate of each subject of some of the individual random effects present within the current project.
WARNING: Simulated individual random effects values cannot be accessible until the individual estimation algorithm with conditional mean has been launched once.
Usage
getSimulatedRandomEffects(...)
Arguments
... |
(string) Name of the individual parameters whose values must be displayed. Call getIndividualParameterModel to get a list of the individual parameters present within the current project. |
Value
A list giving the last simulated values of the individual random effects of interest for each replicate of each subject.
See Also
Click here to see examples
# ## Not run: simEtas = getSimulatedRandomEffects() # retrieve the values of all the available individual random effects simEtas rep id Cl V ka 1 1 0.022 0.37 1.79 1 2 0.033 0.42 -0.92 . . ... ... ... 2 1 0.021 0.33 1.47 . . ... ... ... ## End(Not run)
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[Monolix] Get statistical tests results
Description
Get the results of performed statistical tests.
Existing tests: Wald, Individual parameters normality, individual parameters marginal distribution, random effects normality,
random effects correlation, individual parameters vs covariates correlation, random effects vs covariates correlation,
residual normality and residual symmetry.
WARNING: Only the tests performed during the last scenario run can provide results.
Usage
getTests()
Value
A list associating the name of the test to the corresponding results values computed during the last scenario run.
Click here to see examples
# ## Not run: getTests() -> list( wald = [...] , individualParametersNormality = [...] , ... ) ## End(Not run)
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[Monolix – PKanalix – Simulx] Compute the charts data
Description
Compute (if needed) and export the charts data of a given plot or, if not specified, all the available project plots.
Usage
computeChartsData(plot = NULL, output = NULL, exportVPCSimulations = NULL)
Arguments
plot |
(character) [optional][Monolix] Plot type. If not specified, all the available project plots will be considered. Available plots: bivariatedataviewer, covariateviewer, outputplot, indfits, obspred, residualsscatter, residualsdistribution, vpc, npc, predictiondistribution, parameterdistribution, randomeffects, covariancemodeldiagnosis, covariatemodeldiagnosis, likelihoodcontribution, fisher, saemresults, condmeanresults, likelihoodresults. |
output |
(character) [optional][Monolix] Plotted output (depending on the software, it can represent an observation, a simulation output, …). By default, all available outputs are considered. |
exportVPCSimulations |
(bool) [optional][Monolix] Should VPC simulations be exported if available. Equals FALSE by default. NOTE: If ‘plot" argument is not provided, ‘output’ and "task’ arguments are ignored. |
Details
computeChartsData can be used to compute and export the charts data for plots available in the graphical user interface as in Monolix, PKanalix or Simulx, when you export > export charts data.
The exported charts data is saved as txt files in the result folder, in the ChartsData subfolder.
Notice that it does not impact the current scenario.
To get a ggplot equivalent to the plot in the GUI, but customizable in R with the ggplot2 library, better use one of the plot… functions available in the connectors for Monolix and PKanalix (not available for Simulx). To get the charts data for one of these plot functions as a dataframe, you can use getChartsData.
See Also
Click here to see examples
# ## Not run: computeChartsData() # Monolix - PKanalix - Simulx computeChartsData(plot = "vpc", output = "y1") # Monolix ## 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
# ## Not run: lastRunInfo = getLastRunStatus() lastRunInfo$status -> TRUE lastRunInfo$report -> "" ## End(Not run)
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[Monolix – PKanalix – Simulx] Get current scenario
Description
Get the list of tasks that will be run at the next call to runScenario. For Monolix, get in addition the associated method (linearization true or false), and the associated list of plots.
Usage
getScenario()
Details
For Monolix, getScenario returns a given list of tasks, the linearization option and the list of plots.
Every task in the list is associated to a boolean.
NOTE: Within a MONOLIX scenario, the order according to 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” |
For PKanalix, getScenario returns a given list of tasks.
Every task in the list is associated to a boolean.
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” |
For Simulx, setScenario returns a given list of tasks.
Every task in the list is associated to a boolean.
NOTE: Within a Simulx scenario, the order according to which the different algorithms are run is fixed:
Algorithm | Algorithm keyword |
Simulation | “simulation” |
Outcomes and endpoints | “endpoints” |
Note: every task can also be run separately with a specific function, such as runSimulation in Simulx, runEstimation in Monolix. The CA task in PKanalix cannot be part of a scenario, it must be run with runCAEstimation
.
Value
The list of tasks that corresponds to the current scenario, indexed by task names.
See Also
Click here to see examples
# ## Not run: [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 [SIMULX] scenario = getScenario() scenario simulation endpoints TRUE FALSE ## End(Not run)
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[Monolix – PKanalix – Simulx] Run scenario
Description
Run the scenario that has been set with setScenario.
Usage
runScenario()
Details
A scenario is a list of tasks to be run. Setting the scenario is equivalent to selecting tasks in Monolix, PKanalix or Simulx GUI that will be performed when clicking on RUN.
Note: every task can also be run separately with a specific function, such as runSimulation in Simulx, runEstimation in Monolix. The CA task in PKanalix cannot be part of a scenario, it must be run with runCAEstimation
.
See Also
Click here to see examples
# ## Not run: runScenario() ## End(Not run)
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[Monolix – PKanalix – Simulx] Set scenario
Description
Clear the current scenario and build a new one from a given list of tasks.
Usage
setScenario(...)
Arguments
... |
A list of tasks as previously defined |
Details
A scenario is a list of tasks to be run by runScenario. Setting the scenario is equivalent to selecting tasks in Monolix, PKanalix or Simulx
GUI that will be performed when clicking on RUN.
For Monolix, setScenario requires a given list of tasks, the linearization option and the list of plots.
Every task in the list should be associated to 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 to 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” |
For PKanalix, setScenario requires a given list of tasks.
Every task in the list should be associated to 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” |
For Simulx, setScenario requires a given list of tasks.
Every task in the list should be associated to a boolean.
NOTE: By default the boolean is false, thus, the user can only state what will run during the scenario.
NOTE: Within a Simulx scenario, the order according to which the different algorithms are run is fixed:
Algorithm | Algorithm keyword |
Simulation | “simulation” |
Outcomes and endpoints | “endpoints” |
Note: every task can also be run separately with a specific function, such as runSimulation in Simulx, runEstimation in Monolix. The CA task in PKanalix cannot be part of a scenario, it must be run with runCAEstimation
.
See Also
Click here to see examples
# ## Not run: [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) [SIMULX] scenario = getScenario() scenario = c(simulation = T, endpoints = F) setScenario(scenario) ## End(Not run)
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[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
# ## Not run: 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
# ## Not run: 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
# ## Not run: 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
# ## Not run: 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
# ## Not run: runStandardErrorEstimation(linearization = T) ## End(Not run)
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