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 RsSimulx package provides additional functions for automatic PK model building, bootstrap simulation and likelihood profiling, among others.
- 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 bootstrap
- 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 for the current project.
- getConditionalModeEstimationSettings: Get the conditional mode (EBEs) estimation settings for the current project.
- getGeneralSettings: Get a summary of the settings related to chains for Monolix algorithms for the current project.
- getLogLikelihoodEstimationSettings: Get the log-likelihood estimation settings of the current project.
- getMCMCSettings: Get the MCMC algorithm settings of the current project.
- getPopulationParameterEstimationSettings: Get the population parameter estimation settings for the current project.
- getStandardErrorEstimationSettings: Get the standard error estimation settings for the current project.
- setConditionalDistributionSamplingSettings: Set the value of one or more of the conditional distribution sampling settings for the current project.
- setConditionalModeEstimationSettings: Set the value of one or more of the conditional mode (EBEs) estimation settings for the current project.
- setGeneralSettings: Set the value of one or more of the settings related to chains for Monolix algorithms for the current project.
- setLogLikelihoodEstimationSettings: Set the value of the log-likelihood estimation settings for the current project.
- setMCMCSettings: Set the value of one or more of the MCMC algorithm settings related to transition kernels of the current project.
- setPopulationParameterEstimationSettings: Set the value of one or more of the population parameter estimation settings for the current project.
- setStandardErrorEstimationSettings: Set the value of one or more of the standard error estimation settings for the current project.
Description of the functions concerning the bootstrap
- getBootstrapResults: Get the results of boostrap.
- getBootstrapSettings: Get the settings that will be used during the run of bootstrap.
- runBootstrap: Run boostrap.
Description of the functions concerning the convergence assessment
- getAssessmentResults: Get the results of the convergence assessment.
- getAssessmentSettings: Get the current settings for running the convergence 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 (how many subpopulations).
- removeCovariate: Remove any 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 settings 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 individual parameter model.
- getVariabilityLevels: Get a summary of the variability levels (inter-individual and/or intra-individual variability, i.
- 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 for an individual parameter.
- setIndividualParameterDistribution: Set the distribution of the estimated parameters.
- setIndividualParameterModel: Update the individual parameter model.
- setIndividualParameterVariability: Add or remove inter-individual and/or intra-individual variability (i.
Description of the functions concerning the initial values and estimation method
- getFixedEffectsByAutoInit: Compute initial values for fixed-effect population parameters.
- getPopulationParameterInformation: Get population parameters information.
- setInitialEstimatesToLastEstimates: Set the initial value of all the population parameters in the current project to the ones previously estimated.
- setPopulationParameterInformation: Set population parameters initialization and estimation method.
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 of automatic covariate model building or automatic statistical model building.
- getModelBuildingSettings: Get the current settings for running model building.
- runModelBuilding: Run model building for automatic covariate model building or automatic statistical model building.
Description of the functions concerning the observation model
- getContinuousObservationModel: Get a summary of the information concerning the continuous observation statistical model(s) 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 for the observation model(s).
- setObservationDistribution: Set observation model distribution.
- setObservationLimits: Set observation model distribution limits for logitNormal observations.
Description of the functions concerning the plots
- 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.
- resetPlotPreferences: Reset plot preferences to go back to default preferences.
- setPlotPreferences: Set 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, as 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 logical 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.
- shareProject: Create a zip archive file from current project and its results.
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.
- getEstimatedConfidenceIntervals: Get the confidence interval of population parameters 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 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 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.
- getEtaShrinkage: Get the shrinkage values for each individual parameter.
- getLaunchedTasks: Get a list of the tasks which have available results.
- 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 conditional distribution which can be sampled from (i.
- 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 algorithm.
- runStandardErrorEstimation: Estimate the Fisher Information Matrix (FIM) and the standard errors of the population parameters.
[Monolix] Get conditional distribution sampling settings
Description
Get the conditional distribution sampling settings for the current project.
Associated settings are:
ratio |
(0 < double < 1) | Width of the relative interval for stopping criteria (i.e. 0.05 for 5%). |
enableMaxIterations |
(logical) | Enable maximum number of iterations if stopping criteria not met. |
nbMinIterations |
(integer >= 1) | Number of iterations to use for evaluating stopping criteria. |
nbMaxIterations |
(integer >= 1) | Maximum number of iterations if enableMaxIterations is TRUE . |
nbSimulatedParameters |
(integer >= 1) | Number of samples from the conditional distribution to retain per individual for plots. |
Usage
getConditionalDistributionSamplingSettings(...)
Arguments
... |
[optional] (character) Name of the settings whose value should be returned. If no argument is provided, all the settings are returned. |
Value
A list with each setting name mapped to its current value.
See Also
setConditionalDistributionSamplingSettings
Click here to see examples
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) # retrieve all the conditional distribution sampling settings getConditionalDistributionSamplingSettings() # retrieve only certain settings getConditionalDistributionSamplingSettings("ratio", "nbMinIterations")
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[Monolix] Get conditional mode estimation settings
Description
Get the conditional mode (EBEs) estimation settings for the current project.
Associated settings are:
nbOptimizationIterationsMode |
(integer >= 1) | Maximum number of iterations. |
optimizationToleranceMode |
(double > 0) | Optimization tolerance. |
Usage
getConditionalModeEstimationSettings(...)
Arguments
... |
[optional] (character) Name of the settings whose value should be returned. If no argument is provided, all the settings are returned. |
Value
A list with each setting name mapped to its current value.
See Also
setConditionalModeEstimationSettings
Click here to see examples
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) # retrieve a list of all the conditional mode estimation settings getConditionalModeEstimationSettings() # retrieve only the one setting value getConditionalModeEstimationSettings("nbOptimizationIterationsMode")
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[Monolix] Get project general settings for chains
Description
Get a summary of the settings related to chains for Monolix algorithms for the current project.
Associated settings are:
autoChains |
(logical) | Automatically adjust the number of chains to have at least a minimum number of subjects. |
nbChains |
(integer > 0) | Number of chains to be used if autoChains is set to FALSE . |
minIndivForChains |
(integer > 0) | Minimum number of individuals. |
Usage
getGeneralSettings(...)
Arguments
... |
[optional] (character) Name of the settings whose value should be returned. If no argument is provided, all the settings are returned. |
Value
A list with each setting name mapped to its current value.
See Also
Click here to see examples
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) # retrieve a list of all chain settings getGeneralSettings() # retrieve only specified settings getGeneralSettings("nbChains", "autoChains")
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[Monolix] Get Log-likelihood algorithm settings
Description
Get the log-likelihood estimation settings of the current project. Associated settings are:
nbFixedIterations |
(integer > 0) | Monte Carlo size for importance sampling. |
samplingMethod |
(character) | Should the log-likelihood estimation use a given number of degrees of freedom ( "fixed" ) or test a sequence of degrees of freedom numbers before choosing thebest one ( "optimized" ). |
nbFreedomDegrees |
(integer > 0) | Degree of freedom of the Student’s t-distribution. Used only if "samplingMethod" is "fixed" . |
freedomDegreesSampling |
(vector<integer > 0>) | Sequence of degrees of freedom of the Student’s t-distribution to be tested. Used only if "samplingMethod" is "optimized" . |
Usage
getLogLikelihoodEstimationSettings(...)
Arguments
... |
[optional] (character) Name of the settings whose value should be returned. If no argument is provided, all the settings are returned. |
Value
A list with each setting name mapped to its current value.
See Also
setLogLikelihoodEstimationSettings
Click here to see examples
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) # retrieve a list of all the loglikelihood estimation settings getLogLikelihoodEstimationSettings() # retrieve only certain settings values getLogLikelihoodEstimationSettings("nbFixedIterations", "samplingMethod")
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[Monolix] Get settings for transition kernels of the MCMC algorithm
Description
Get the MCMC algorithm settings of the current project.
Associated settings are:
strategy |
(vector<integer> of length 3) | Number of calls for each one of the three MCMC kernels. |
acceptanceRatio |
(double) | Target acceptance ratio. |
Usage
getMCMCSettings(...)
Arguments
... |
[optional] (character) Names of the settings whose value should be returned. If no argument is provided, all the settings are returned. |
Value
A list with each setting name mapped to its current value.
See Also
Click here to see examples
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) getMCMCSettings() # retrieve a list of all the MCMC settings getMCMCSettings("strategy") # retrieve only the strategy setting
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[Monolix] Get population parameter estimation settings
Description
Get the population parameter estimation settings for the current project.
Associated settings are:
nbBurningIterations |
(integer >= 0) | Number of iterations for the burn-in phase. |
nbExploratoryIterations |
(integer >= 0) | If exploratoryAutoStop is set to FALSE ,the number of iterations in the exploratory phase. Otherwise, if exploratoryAutoStop is set to TRUE ,the maximum number of iterations in the exploratory phase. |
exploratoryAutoStop |
(logical) | Should the exploratory phase stop automatically |
exploratoryInterval |
(integer > 0) | Minimum number of iterations 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 |
(integer >= 0) | If smoothingAutoStop is set to FALSE ,the number of iterations in the smoothing phase. Otherwise, if smoothingAutoStop is set to TRUE ,the maximum number of iterations in the smoothing phase. |
smoothingAutoStop |
(logical) | Should the smoothing phase stop automatically. |
smoothingInterval |
(integer > 0) | Minimum number of iteration 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 for smoothing. Used only if smoothingAutoStop is TRUE . |
simulatedAnnealing |
(logical) | Should simulated annealing be used. |
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 |
(character) | Estimation method for parameters without variability:"firstStage" , "decreasing" , or "none" .Used only if there are parameters without variability in the project. |
nbOptimizationIterations |
(integer >= 1) | Number of optimization iterations. |
optimizationTolerance |
(double > 0) | Tolerance for optimization. |
Usage
getPopulationParameterEstimationSettings(...)
Arguments
... |
[optional] (character) Name of the settings whose value should be returned. If no argument is provided, all the settings are returned. |
Value
A list with each setting name mapped to its current value.
See Also
setPopulationParameterEstimationSettings
Click here to see examples
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) # retrieve a list of all the population parameter estimation settings getPopulationParameterEstimationSettings() # retrieve only the setting related to the smoothing phase getPopulationParameterEstimationSettings("nbSmoothingIterations", "smoothingAutoStop", "smoothingAlpha", "smoothingRatio")
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[Monolix] Get standard error estimation settings
Description
Get the standard error estimation settings for the current project.
Associated settings are:
minIterations |
(integer >= 1) | Minimum number of iterations for stochastic approximation. |
maxIterations |
(integer >= 1) | Maximum number of iterations for stochastic approximation. |
intervalLevel |
(0 < double < 100) | Confidence interval level (percent). |
Usage
getStandardErrorEstimationSettings(...)
Arguments
... |
[optional] (character) Name of the settings whose value should be returned. If no argument is provided, all the settings are returned. |
Value
A list with each setting name mapped to its current value.
See Also
setStandardErrorEstimationSettings
Click here to see examples
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) # retrieve a list of all the standard error estimation settings getStandardErrorEstimationSettings() # retrieve only certain settings getStandardErrorEstimationSettings("minIterations","maxIterations")
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[Monolix] Set conditional distribution sampling settings
Description
Set the value of one or more of the conditional distribution sampling settings for the current project.
Associated settings are:
ratio |
(0 < double < 1) | Width of the relative interval for stopping criteria (i.e. 0.05 for 5%). |
enableMaxIterations |
(logical) | Enable maximum number of iterations if stopping criteria not met. |
nbMinIterations |
(integer >= 1) | Number of iterations to use for evaluating stopping criteria. |
nbMaxIterations |
(integer >= 1) | Maximum number of iterations if enableMaxIterations is TRUE . |
nbSimulatedParameters |
(integer >= 1) | Number of samples from the conditional distribution to retain per individual for plots. |
Usage
setConditionalDistributionSamplingSettings(...)
Arguments
... |
A collection of comma-separated pairs (settingName = settingValue). |
See Also
getConditionalDistributionSamplingSettings
Click here to see examples
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) setConditionalDistributionSamplingSettings(ratio = 0.1, nbSimulatedParameters = 20) getConditionalDistributionSamplingSettings()
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[Monolix] Set conditional mode estimation settings
Description
Set the value of one or more of the conditional mode (EBEs) estimation settings for the current project.
Associated settings are:
nbOptimizationIterationsMode |
(integer >= 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
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) setConditionalModeEstimationSettings(nbOptimizationIterationsMode = 100, optimizationToleranceMode = 0.001) getConditionalModeEstimationSettings()
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[Monolix] Set project general settings for chains
Description
Set the value of one or more of the settings related to chains for Monolix algorithms for the current project.
Associated settings are:
autoChains |
(logical) | Automatically adjust the number of chains to have at least a minimum number of subjects. |
nbChains |
(integer > 0) | Number of chains to be used if autoChains is set to FALSE . |
minIndivForChains |
(integer > 0) | Minimum number of individuals. |
Usage
setGeneralSettings(...)
Arguments
... |
Comma-separated pairs (settingName = settingValue). |
See Also
Click here to see examples
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) setGeneralSettings(autoChains = FALSE, nbchains = 10) getGeneralSettings()
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[Monolix] Set log-likelihood estimation settings
Description
Set the value of the log-likelihood estimation settings for the current project.
Associated settings are:
nbFixedIterations |
(integer > 0) | Monte Carlo size for importance sampling. |
samplingMethod |
(character) | Should the log-likelihood estimation use a given number of degrees of freedom ( "fixed" ) or test a sequence of degrees of freedom numbers before choosing thebest one ( "optimized" ). |
nbFreedomDegrees |
(integer > 0) | Degree of freedom of the Student’s t-distribution. Used only if "samplingMethod" is "fixed" . |
freedomDegreesSampling |
(vector<integer > 0>) | Sequence of degrees of freedom of the Student’s t-distribution 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
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) setLogLikelihoodEstimationSettings(nbFixedIterations = 20000) getLogLikelihoodEstimationSettings()
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[Monolix] Set settings for transition kernels of the MCMC algorithm
Description
Set the value of one or more of the MCMC algorithm settings related to transition kernels of the current project.
Associated settings are:
strategy |
(vector<integer> of length 3) | Number of calls for each 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
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) setMCMCSettings(strategy = c(2,1,2)) getMCMCSettings()
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[Monolix] Set population parameter estimation settings
Description
Set the value of one or more of the population parameter estimation settings for the current project.
Associated settings are:
nbBurningIterations |
(integer >= 0) | Number of iterations for the burn-in phase. |
nbExploratoryIterations |
(integer >= 0) | If exploratoryAutoStop is set to FALSE ,the number of iterations in the exploratory phase. Otherwise, if exploratoryAutoStop is set to TRUE ,the maximum number of iterations in the exploratory phase. |
exploratoryAutoStop |
(logical) | Should the exploratory phase stop automatically |
exploratoryInterval |
(integer > 0) | Minimum number of iterations 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 |
(integer >= 0) | If smoothingAutoStop is set to FALSE ,the number of iterations in the smoothing phase. Otherwise, if smoothingAutoStop is set to TRUE ,the maximum number of iterations in the smoothing phase. |
smoothingAutoStop |
(logical) | Should the smoothing phase stop automatically. |
smoothingInterval |
(integer > 0) | Minimum number of iteration 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 for smoothing. Used only if smoothingAutoStop is TRUE . |
simulatedAnnealing |
(logical) | Should simulated annealing be used. |
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 |
(character) | Estimation method for parameters without variability:"firstStage" , "decreasing" , or "none" .Used only if there are parameters without variability in the project. |
nbOptimizationIterations |
(integer >= 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
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) setPopulationParameterEstimationSettings(exploratoryAutoStop = TRUE, nbexploratoryiterations = 200) getPopulationParameterEstimationSettings()
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[Monolix] Set standard error estimation settings
Description
Set the value of one or more of the standard error estimation settings for the current project.
Associated settings are:
minIterations |
(integer >= 1) | Minimum number of iterations for stochastic approximation. |
maxIterations |
(integer >= 1) | Maximum number of iterations for stochastic approximation. |
intervalLevel |
(0 < double < 100) | Confidence interval level (percent). |
Usage
setStandardErrorEstimationSettings(...)
Arguments
... |
A collection of comma-separated pairs (settingName = settingValue). |
See Also
getStandardErrorEstimationSettings
Click here to see examples
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) setStandardErrorEstimationSettings(minIterations = 20, maxIterations = 500, intervalLevel = 99) getStandardErrorEstimationSettings()
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[Monolix] Get the results of boostrap
Description
Get the results of boostrap.
Usage
getBootstrapResults(removeFailedRuns = FALSE)
Arguments
removeFailedRuns |
[logical] if TRUE, bootstrap runs with failed convergence (maximum number of iterations reached before triggering of the autostop criterion) are removed from the results (default=FALSE). If all bootstrap runs have a failed convergence, the result is NULL. |
Value
The results of boostrap as a list of dataframes:
- populationEstimates: the population parameters estimated in all bootstrap runs
- populationSummary: the summary table of population parameter estimates
- logLikelihoodEstimates: if logLikelihood=TRUE in the bootstrap settings, the log-likelihood (OFV) and information criteria estimated in all bootstrap runs
- logLikelihoodSummary: if logLikelihood=TRUE in the bootstrap settings, the summary table of the OFV and information criteria
- standardErrorsEstimates: if standardErrors=TRUE in the bootstrap settings, the relative standard errors of population parameters estimated in all bootstrap runs
- standardErrorsSummary: if standardErrors=TRUE in the bootstrap settings, the summary table of relative standard errors
See Also
Click here to see examples
# # get bootstrap results using all runs getBootstrapResults() # get bootstrap results using only runs that converged (during the population parameter estimation, the autostop criterion was triggered before reaching the maximum number of iterations) getBootstrapResults(removeFailedRuns=TRUE) ## End(Not run)
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[Monolix] Get bootstrap settings
Description
Get the settings that will be used during the run of bootstrap.
Usage
getBootstrapSettings()
Value
The list of settings
- nbRuns [optional] (integer) number of bootstrap replicates (default=200)
- method [optional] (character) sampling method: “parametric” or “nonparametric” (default: nonparametric)
- initialValues [optional] (character) initial values used in the boostrap runs for the estimation of the population parameters: “initial” or “final (default: “initial”)
- cens [optional] (list) if method=”nonparametric” and there are censored observations in the dataset. A list, or a list of lists with elements obsid, type (“left,”right” or “interval”) and limit (single value or vector of two values). Ex: list(list(obsid=”y1″,type=”left”, limit=0.1), list(obsid=”y2″, type=”interval”, limit=c(0.2,10))
- tasks [optional] (list of character) tasks to perform in bootstrap runs in addition to population parameter estimation. Available tasks: “standardErrorEstimation”, “logLikelihoodEstimation” (default=list())
- useLin [optional] (logical) calculation method to estimate standard errors and log-likelihood (default = FALSE). If TRUE, they are estimated via linearization. If FALSE, standard errors are estimated via stochastic approximation and log-likelihood via importance sampling.
- sampleSize [optional] (integer) the number of individuals in each bootstrap data set (default value is the number of individuals in the original data set).
- covStrat [optional] (list of character) one or several categorical covariates of the project. The original distribution of this covariate is maintained in each resampled data set if covStrat is defined (default=list()). Notice that if the categorical covariate is varying within the subject (in case of occasions), it will not be taken into account.
- level [optional] (numeric) level of the bootstrap confidence intervals (default = 0.95)
- saveResultsFolders [optional] (logical) to choose if bootstrap projects results folders should be saved or deleted (default = FALSE)
- saveDatasets [optional] (logical) to choose if bootstrap datasets and mlxtran files (Monolix project) should be saved or deleted (default = FALSE)
- replaceFailedRuns [optional] (logical) to choose if bootstrap runs with failed convergence (maximum number of iterations reached before the autostop criterion) should be replaced by new runs (default=FALSE)
- maxNbFailedRuns [optional] (integer) if replaceFailedRuns=TRUE, maximum number of runs with failed convergence that can be replaced before bootstrap is stopped (default=20)
See Also
Click here to see examples
# # run parametric bootstrap with 100 runs set = getBootstrapSettings() set$nbRuns = 100 set$method = "parametric" runBootstrap(set) # run nonparametric bootstrap with 500 runs, stratified resampling by STUDY and DOSEGROUP categorical covariates, standard errors and log-likelihood estimated via linearization, and bootstrap datasets and mlxtran files saved in the results. runBootstrap(nbRuns=500, method="nonparametric", covStrat=list("STUDY", "DOSEGROUP"), tasks=list("standardErrorEstimation", "logLikelihoodEstimation"), useLin=FALSE, saveDatasets=TRUE) ## End(Not run)
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[Monolix] Run boostrap
Description
Run boostrap.
If no argument is given, it uses the previously used settings if bootstrap has already run in the project, or the default settings otherwise.
In both cases, use getBootstrapSettings to receive all the settings.
Usage
runBootstrap(...)
Arguments
... |
(list<settings>) Settings to initialize the bootstrap algorithm, given either as a list of settings, or as direct arguments. See getBootstrapSettings. |
See Also
getBootstrapSettings getBootstrapResults
Click here to see examples
# # run bootstrap with default settings runBootstrap() # run non-parametric bootstrap with 1000 runs that include the estimation of standard errors and likelihood via linearization, with resampled datasets containing 20 individuals stratified by "group" covariate. Each bootstrap run is saved with its dataset and results folder. runBootstrap(nbRuns = 1000, tasks= list("standardErrorEstimation", "logLikelihoodEstimation"), useLin = TRUE, sampleSize = 20, covStrat = "group", level = 90, saveResultsFolders = T, saveDatasets = T) # run parametric bootstrap with 100 runs set = getBootstrapSettings() set$nbRuns = 100 set$method = "parametric" runbootstrap(settings = set) ## End(Not run)
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[Monolix] Get the results of the convergence assessment
Description
Get the results of the convergence assessment. The populationParameters are always included and standardErrors and logLikelihood
are included when extendedEstimation
is TRUE
in the assessment settings.
Usage
getAssessmentResults()
Value
A vector of lists containing, for each assessment run:
populationParameters
: results of population parameter estimation using SAEM:nbexploratoryiterations
(integer) number of iterations during exploratory phasenbsmoothingiterations
(integer) number of iterations during smoothing phaseconvergence
(data.frame) convergence history of estimated population parameters and convergence indicator (-2*log-likelihood)
standardErrors
: [optional] results of standard errors estimation:method
(character) fisher method used (stochasticApproximation or linearization)values
(vector) standard error associated to each population parameter
loglikelihood
: [optional] results of log-likelihood estimationmethod
(character) fisher method used (importanceSampling or linearization)AIC
(double) Akaike Information CriterionBIC
(double) Bayesian Information CriterionBICc
(double) modified BICLL
(double) log likelihoodchosenDegree
(integer) [importanceSampling]standardError
(double) [importanceSampling]convergence
(data.frame) [importanceSampling]
See Also
runAssessment to run the assessment
Click here to see examples
# initializeLixoftConnectors("monolix") project_file <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project_file) assesSettings <- getAssessmentSettings() assesSettings$initialParameters$fixed <- rep(FALSE, 3) assesSettings$initialParameters$min <- rep(0, 3) assesSettings$initialParameters$max <- c(1.5, 1, 0.5) assesSettings$extendedEstimation <- TRUE runAssessment(settings = assesSettings) res = getAssessmentResults() ## End(Not run)
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[Monolix] Get convergence assessment settings
Description
Get the current settings for running the convergence assessment. These are the settings that will be used if runAssessment
is called without argument, or they can be used as a template to update and pass to runAssessment in order to change the settings.
Note that ‘fixed’ in the initialParameters
data.frame refers to whether the the initial value of the parameter is fixed for the assessment or
whether it should be sampled for each run, not whether the parameter is fixed for estimation purposes.
Usage
getAssessmentSettings()
Value
The list of settings
nbRuns
: (integer) number of runsextendedEstimation
: (logical) ifTRUE
, standard errors and log-likelihood are estimateduseLin
: (logical) ifTRUE
, use linearization to estimate standard errors and log-likelihood instead of stochastic approximation (sd) and importance sampling (ll)initialParameters
: (data.frame) a data.frame with columns parameters (name of each parameter), fixed (logical TRUE if its initial value is fixed or else FALSE),
min, and max (the bounds within which the initial value is drawn for non-fixed parameters)
See Also
runAssessment to run the assesment
Click here to see examples
# project_file <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project_file) getAssessmentSettings()
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[Monolix] Run convergence 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 to get the settings
getAssessmentResults to get the results of the run
Click here to see examples
# initializeLixoftConnectors("monolix") project_file <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project_file) assesSettings <- getAssessmentSettings() assesSettings$initialParameters$fixed <- rep(FALSE, 3) assesSettings$initialParameters$min <- rep(0, 3) assesSettings$initialParameters$max <- c(1.5, 1, 0.5) runAssessment(settings = assesSettings) res = getAssessmentResults() ## End(Not run)
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[Monolix] Add transformed categorical covariate
Description
Create a new categorical covariate by transforming an existing one. Transformed covariates cannot be use to produce new covariates.
Call getCovariateInformation to find out which covariates can be transformed.Those of type "categorical"
can be used.
Usage
addCategoricalTransformedCovariate(...)
Arguments
... |
Comma-separated pairs of the format (see example) transformedCovariateName = list(from = "existingCovariateName", |
See Also
getCovariateInformation get current covariates in the model
addContinuousTransformedCovariate to add a transformation of a continuous covariate
addMixture to add a latent covariate
removeCovariate to remove added covariates
Click here to see examples
# project_file <- file.path(getDemoPath(), "0.data_formatting", "DoseAndLOQ_byCategory.mlxtran") loadProject(project_file) addCategoricalTransformedCovariate( AGG_STUDY = list(from = "STUDY", reference = "SD_low", transformed = list( SD_low = c("SD_400mg", "SD_500mg"), SD_high = c("SD_600mg"))) ) getCovariateInformation()
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[Monolix] Add transformed continuous covariate
Description
Create a new continuous covariate by transforming an existing one. Transformed covariates cannot be use to produce new covariates.
Call getCovariateInformation to find out which covariates can be transformed. Those of type "continuous"
can be used.
Usage
addContinuousTransformedCovariate(...)
Arguments
... |
Comma-separated pairs {transformedCovariateName = (character)”formula”} |
See Also
getCovariateInformation get current covariates in the model
addCategoricalTransformedCovariate to add a transformation of a categorical covariate
addMixture to add a latent covariate
removeCovariate remove added covariates
Click here to see examples
# project_file <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project_file) addContinuousTransformedCovariate( logtWEIGHT = "log(WEIGHT/70)" ) getCovariateInformation()
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[Monolix] Add latent covariate to the model for a finite mixture model
Description
Add a new latent covariate to the current model giving its name and its modality number (how many subpopulations).
Usage
addMixture(...)
Arguments
... |
A list of comma-separated pairs latentCovariateName = modalityNumber , where modalityNumber is an integer |
See Also
getCovariateInformation get current covariates in the model
addContinuousTransformedCovariate to add a transformation of a continuous covariate
addCategoricalTransformedCovariate to add a transformation of a categorical covariate
removeCovariate to remove added covariates
Click here to see examples
# project_file <- file.path(getDemoPath(), "5.models_for_individual_parameters", "5.3.mixture_of_distributions", "PKgroup_project.mlxtran") loadProject(project_file) addMixture(lcat = 2) getCovariateInformation()
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[Monolix] Remove covariate
Description
Remove any of the transformed covariates (discrete and continuous) and/or latent covariates.
Call getCovariateInformation to know which covariates can be removed (only those with type “categoricaltransformed”,
“continuoustransformed” or “latent”).
Usage
removeCovariate(...)
Arguments
... |
Covariate names (comma separated). |
See Also
getCovariateInformation get current covariates in the model
addContinuousTransformedCovariate to add a transformation of a continuous covariate
addCategoricalTransformedCovariate to add a transformation of a categorical covariate
addMixture to add a latent covariate
Click here to see examples
# project_file <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project_file) addContinuousTransformedCovariate( logtWEIGHT = "log(WEIGHT/70)" ) getCovariateInformation() removeCovariate("logtWEIGHT") getCovariateInformation()
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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 |
(character) applied transformation. |
base |
(character) [optional] base data on which the transformation is applied. |
name |
(character) [optional] name of the covariate. |
See Also
Click here to see examples
# 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 |
(character) [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 = <character> “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
# ---------------------------------------------------------------------------------------- LINE [ integer ] applyFilter( filter = list(removeLines = "line>10") ) # keep only the 10th first rows ---------------------------------------------------------------------------------------- ID [ character | integer ] If there are only integer identifiers within the data set, ids will be considered as integers. On the contrary, they will be treated as character data. 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 [integer] applyFilter( filter = list(list(removeIds = "idIndex!=2"), list(selectIds = "id<5")) ) # select the 4 first subjects excepted the second one ---------------------------------------------------------------------------------------- OCC [ integer ] 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 [ character ] 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 [ character (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 |
(character) [optional] created data set name. If not defined, the default name is “currentDataSet_filtered”. |
origin |
(character) [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 = <character> “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
# ---------------------------------------------------------------------------------------- LINE [ integer ] createFilter( filter = list(removeLines = "line>10") ) # keep only the 10th first rows ---------------------------------------------------------------------------------------- ID [ character | integer ] If there are only integer identifiers within the data set, ids will be considered as integers. On the contrary, they will be treated as character data. 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 [integer] createFilter( filter = list(list(removeIds = "idIndex!=2"), list(selectIds = "id<5")) ) # select the 4 first subjects excepted the second one ---------------------------------------------------------------------------------------- OCC [ integer ] 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 [ character ] 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 [ character (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 |
(character) name of the covariate. |
See Also
Click here to see examples
# 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 |
(character) data set name. |
See Also
Click here to see examples
# 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 |
(character) [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,
sheet = NULL
)
Arguments
dataFile |
(character) Path to the original data file (csv, xlsx, xlsx, sas7bdat, xpt or txt). Can be absolute or relative to the current working directory. |
formattedFile |
(character) Path to the data file that will be exported (must end with the .csv, .txt, .tsv or .xpt extension). |
headerLines |
(optional) (integer or vector<integer>) 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 or indexes for columns containing information about ID, time, volume (in case of urine data) and sort columns. If the headers are changed by Data Formatting, the original headers should be given.
|
linesToExclude |
(optional) (integer or vector<integer>) 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 or character) 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:
Path to files that contain treatment information can be just one path (csv, xlsx, xlsx, sas7bdat, xpt or txt, absolute or relative to the current working directory),
or a list of lists with 2 elements to specify for each treatment an xls/xlsx file and sheet in the excel file:
|
additionalColumns |
(optional) (character or vector<character>) Path(s) to the file(s) containing additional columns (needs to have the ID column). Accepted formats are csv, xlsx, xlsx, sas7bdat, xpt or txt. It can be just one path, or a list of paths (to use columns from several external files):
or a list of lists with 2 elements to specify an xls/xlsx file and sheet in the excel file:
|
sheet |
[optional] (character): Name of the sheet in xlsx/xls file. If not provided, the first sheet is used. |
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.
See Also
Click here to see examples
# 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"), observations = list(header="CONC", censoring = list(type="interval", tags = c("BLQ"), limits=list(0,"LLOQ"))), 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"), observations = list(header="CONC", 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"), observations = list(header="CONC", censoring = list(type="interval", tags = c("BLQ"), limits=list(0,"LLOQ"))), 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"), observations = list(header="CONC", 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", sort="FORM"), observations = list(header="CONC", 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
: (character) the name of the data setfile
: (character) the path of the data set filecurrent
: a logical 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 filtersfilter
(only if the dataset was created using filters): a list containing name of theparent
and details about filterdefinition
Click here to see examples
# 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<character>): covariate names
- type (vector<character>): covariate types. Existing types are “continuous”, “continuoustransformed”, “categorical”, “categoricaltransformed”./
In Monolix mode, “latent” covariates are also allowed. - range (vector<pair<double>>): continuous covariate ranges
- categories (vector<vector<character>>): discrete covariates modalities
- [Monolix] modalityNumber (vector<integer>): 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
# info = getCovariateInformation() # Monolix mode with latent covariates info -> $name c("sex","wt","lcat") -> $type c(sex = "categorical", wt = "continuous", lcat = "latent") -> $range list(wt = c(55, 73.5)) -> $categories c(sex = c("F", "M")) -> $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 settings from a loaded project.
It returns a list with the same items as the arguments of formatData, where the header
items correspond to formatted headers if they have been changed by Data Formatting, and in addition:
originalHeaders
(character) – list of original names of the columns used for data formatting.
Usage
getFormatting()
See Also
Click here to see examples
# initializeLixoftConnectors(software = "pkanalix") loadProject(paste0(getDemoPath(),"/0.data_formatting/DoseAndLOQ_manual.pkx")) getFormatting() }
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[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<character>): observation names.
- type (vector<character>): observation generic types. Existing types are “continuous”, “discrete”, “event”.
- [Monolix] detailedType (vector<character>): observation specialized types set in the structural model. Existing types are “continuous”, “bsmm”, “wsmm”, “categorical”, “count”, “exactEvent”, “intervalCensoredEvent”.
- [Monolix] mapping (vector<character>): 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
# 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 (character)
- time (double)
- amount (double)
- [optional] administrationType (integer)
- [optional] infusionTime (logical)
- [optional] isArtificial (logical): is created from SS or ADDL column
- [optional] isReset (logical): 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
# 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 |
(character) current name of the covariate to rename |
newName |
(character) new name. |
See Also
Click here to see examples
# 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 |
(character) new name. |
oldName |
(character) [optional] current name of the filtered data set to rename (current one by default) |
See Also
Click here to see examples
# 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 |
(character) data set name. |
See Also
Click here to see examples
# selectData(name = "filter1") ## End(Not run)
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[Monolix] Get individual parameter model
Description
Get a summary of the individual parameter model. The available information is the following:
name
: (character) vector of names of the individual parametersdistribution
: (character) vector giving the probability distribution of each parameter. The distribution can be one of
"normal"
,"logNormal"
, or"logitNormal"
.limits
: (double) a list giving the distribution limits for each parameter with a"logitNormal"
distributionformula
: (character) the formula used for each parametervariability
: (logical) a list giving, for each variability level, a vector withTRUE
for each individual parameter that has variability orFALSE
if not.covariateModel
: (logical) a list giving, for each individual parameter, a vector withTRUE
for each covariate that is included in the model for that parameter orFALSE
if not.
If there are no covariates in the model, this is an empty list.correlationBlocks
: (character) a list with, for each variability level, a list of correlations, where each correlation block is a vector of the parameter names included in that correlation.
If there are no correlations in the model, this is omitted.
Usage
getIndividualParameterModel()
Value
A list containing the individual parameter model elements
See Also
setIndividualParameterModel to change the individual parameter model
The components of the individual parameter model can be updated individually:
setIndividualParameterDistribution to update just the individual parameter distributions
setIndividualLogitLimits to update just the limits for parameters with a logit distribution
setIndividualParameterVariability to update just the individual parameter variability
setCovariateModel to update just the covariate model
setCorrelationBlocks to update just the correlation structure
Click here to see examples
# project_file <- file.path(getDemoPath(), "5.models_for_individual_parameters", "5.1.probability_distribution", "warfarin_distribution3_project.mlxtran") loadProject(project_file) indivModel <- getIndividualParameterModel()
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[Monolix] Get variability levels
Description
Get a summary of the variability levels (inter-individual and/or intra-individual variability, i.e. random effects)
present in the current project.
Usage
getVariabilityLevels()
Value
A vector of the variability levels present in the currently loaded project.
See Also
getIndividualParameterModel to see the current individual parameter model settings
Click here to see examples
# project_file <- file.path(getDemoPath(), "5.models_for_individual_parameters", "5.4.inter_occasion_variability", "iov1_project.mlxtran") loadProject(project_file) getVariabilityLevels()
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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 = list(vector<character>)parameterNames}) } (see example). |
See Also
getVariabilityLevels to see the variability levels
getIndividualParameterModel to see the current individual parameter model settings
setIndividualParameterModel to change the individual parameter model
The components of the individual parameter model can be updated individually:
setIndividualParameterDistribution to update just the individual parameter distributions
setIndividualLogitLimits to update just the limits for parameters with a logit distribution
setIndividualParameterVariability to update just the individual parameter variability
setCovariateModel to update just the covariate model
Click here to see examples
# # creating multiple correlation blocks loadProject( file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "warfarinPK_project.mlxtran") ) setCorrelationBlocks(id = list( c("ka","Cl"), c("Tlag","V") ) ) getIndividualParameterModel()$correlationBlocks # creating blocks at multiple variability levels loadProject( file.path(getDemoPath(), "5.models_for_individual_parameters", "5.4.inter_occasion_variability", "iov1_project.mlxtran") ) setIndividualParameterVariability(list(OCC = c(Cl = TRUE))) # parameter must have variability to be included in correlation setCorrelationBlocks(id = list( c("ka","V", "Cl") ), OCC = list( c("Cl","V") ) ) getIndividualParameterModel()$correlationBlocks
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[Monolix] Set covariate model
Description
Set which are the covariates influencing individual parameters present in the project.
Call getIndividualParameterModel to get a list of the individual parameters present within the current project.
and getCovariateInformation to know which are the available covariates for a given level of variability and a given individual parameter.
Usage
setCovariateModel(...)
Arguments
... |
A list of comma-separated pairs {parameterName = { covariateName = (logical)isIncluded, …} } (see example) |
See Also
getCovariateInformation to see available covariates
getIndividualParameterModel to see the current individual parameter model settings
setIndividualParameterModel to change the individual parameter model
The components of the individual parameter model can be updated individually:
setIndividualParameterDistribution to update just the individual parameter distributions
setIndividualLogitLimits to update just the limits for parameters with a logit distribution
setIndividualParameterVariability to update just the individual parameter variability
setCorrelationBlocks to update just the correlation structure
Click here to see examples
# loadProject( file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") ) setCovariateModel( ka = c( SEX = TRUE, WEIGHT = TRUE), V = c( WEIGHT = TRUE ) ) getIndividualParameterModel()$covariateModel
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[Monolix] Set individual parameter distribution limits
Description
Set the minimum and the maximum values for an individual parameter.
Limits only apply to parameters with a "logitNormal"
distribution.
Call getIndividualParameterModel to get a list of the available
parameters within the current project. The initial estimate of the
parameter must be inside the limits, which can be set with setPopulationParameterInformation.
Usage
setIndividualLogitLimits(...)
Arguments
... |
Comma-separated pairs {parameterName = c((double)min,(double)max) } (see example) |
See Also
getIndividualParameterModel to see the current individual parameter model settings
setIndividualParameterModel to change the individual parameter model
getPopulationParameterInformation to see the parameter initial values
setPopulationParameterInformation to change the parameter initial values
The components of the individual parameter model can be updated individually:
setIndividualParameterDistribution to update just the individual parameter distributions
setIndividualParameterVariability to update just the individual parameter variability
setCovariateModel to update just the covariate model
setCorrelationBlocks to update just the correlation structure
Click here to see examples
# project_file <- file.path(getDemoPath(), "5.models_for_individual_parameters", "5.1.probability_distribution", "warfarin_distribution3_project.mlxtran") loadProject(project_file) setIndividualParameterDistribution(V = "logitNormal", ka = "logitNormal") setPopulationParameterInformation(V_pop = list(initialValue = 0.5)) setIndividualLogitLimits( V = c(0, 1), ka = c(-1, 2) ) getIndividualParameterModel()$limits
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[Monolix] Set individual parameter distribution
Description
Set the distribution of the estimated parameters.
Available distributions are “normal”, “logNormal” and “logitNormal”.
Call getIndividualParameterModel to get a list of the available individual parameters within the current project.
Usage
setIndividualParameterDistribution(...)
Arguments
... |
A list of comma-separated pairs (character) {parameterName = “distribution”} (see example). |
See Also
getIndividualParameterModel to see the current individual parameter model settings
setIndividualParameterModel to change the individual parameter model
The components of the individual parameter model can be updated individually:
setIndividualLogitLimits to update just the limits for parameters with a logit distribution
setIndividualParameterVariability to update just the individual parameter variability
setCovariateModel to update just the covariate model
setCorrelationBlocks to update just the correlation structure
Click here to see examples
# project_file <- file.path(getDemoPath(), "5.models_for_individual_parameters", "5.1.probability_distribution", "warfarin_distribution3_project.mlxtran") loadProject(project_file) setIndividualParameterDistribution(V = "normal") setIndividualParameterDistribution(Cl = "normal", V = "logNormal") getIndividualParameterModel()$distribution
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[Monolix] Set individual parameter model
Description
Update the individual parameter model. The following information is editable:
distribution
: (character) vector giving the probability distribution of each parameter.
The distribution can be one of"normal"
,"logNormal"
, or"logitNormal"
.limits
: (double) a list giving the distribution limits for each parameter with a
"logitNormal"
distributionvariability
: (logical) a list giving, for each variability level, a vector
withTRUE
for each individual parameter that has variability orFALSE
if not.covariateModel
: (logical) a list giving, for each individual parameter,
a vector withTRUE
for each covariate that is included in the model for that parameter orFALSE
if not.correlationBlocks
: a list with, for each variability level, a list of correlations,
where each correlation block is a vector of the parameter names included in that correlation.
Parameters must have random effects to be included in correlations.
Usage
setIndividualParameterModel(...)
Arguments
... |
A list of comma-separated pairs {[info] = [value]} (See example). |
See Also
getIndividualParameterModel to see the current individual parameter model settings
The components of the individual parameter model can be updated individually:
setIndividualParameterDistribution to update just the individual parameter distributions
setIndividualLogitLimits to update just the limits for parameters with a logit distribution
setIndividualParameterVariability to update just the individual parameter variability
setCovariateModel to update just the covariate model
setCorrelationBlocks to update just the correlation structure
Click here to see examples
# project_file <- file.path(getDemoPath(), "5.models_for_individual_parameters", "5.1.probability_distribution", "warfarin_distribution3_project.mlxtran") loadProject(project_file) # change the distribution of parameter V to be a logitNormal between 0 and 30 setIndividualParameterModel(list(distribution = c(V = "logitNormal"), limits = list(V = c(0, 30)))) getIndividualParameterModel() # remove correlation and add covariate wt on Cl setIndividualParameterModel(list( correlationBlocks = list(id = list()), covariateModel = list(Cl = c(sex = FALSE, age = FALSE, wt = TRUE))) ) getIndividualParameterModel() # remove variability (random effects) on parameter ka setIndividualParameterModel(list(variability = list(id = c(ka = FALSE)))) getIndividualParameterModel()
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[Monolix] Individual variability management
Description
Add or remove inter-individual and/or intra-individual variability (i.e. random effects) 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 = (logical)hasVariability} } (see example). |
See Also
getIndividualParameterModel to see the current individual parameter model settings
getVariabilityLevels to get a list of the variability levels
setIndividualParameterModel to change the individual parameter model
The components of the individual parameter model can be updated individually:
setIndividualParameterDistribution to update just the individual parameter distributions
setIndividualLogitLimits to update just the limits for parameters with a logit distribution
setCovariateModel to update just the covariate model
setCorrelationBlocks to update just the correlation structure
Click here to see examples
# loadProject( file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") ) setIndividualParameterVariability(ka = TRUE, V = FALSE) getIndividualParameterModel()$variability # multiple variability levels loadProject( file.path(getDemoPath(), "5.models_for_individual_parameters", "5.4.inter_occasion_variability", "iov1_project.mlxtran") ) setIndividualParameterVariability(id = list(ka = FALSE, V = FALSE), OCC = list(ka = TRUE)) getIndividualParameterModel()$variability
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[Monolix] Automatically estimate initial parameter values
Description
Compute initial values for fixed-effect population parameters. The values are returned in the same format
as getPopulationParameterInformation and can be passed to setPopulationParameterInformation to
set the inital values.
Usage
getFixedEffectsByAutoInit(ids = NULL)
Arguments
ids |
(integer) [optional] if included, a vector of indices of individuals to use to estimate initial values, otherwise all individuals are included (can be used to speed up estimation in the case of many individuals) |
See Also
getPopulationParameterInformation to get the current population parameter information, including initial values
setPopulationParameterInformation to set the population parameter information (e.g. with the results of this method)
setInitialEstimatesToLastEstimates to set the population parameter initial values to the last estimated values
Click here to see examples
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) initValues <- getFixedEffectsByAutoInit() setPopulationParameterInformation(initValues) # restrict initial value estimation to only certain individuals # (most useful when there are many similar individuals and estimation takes a long time) initValues <- getFixedEffectsByAutoInit(ids = 1:10)
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[Monolix] Get population parameters information
Description
Get population parameters information.
Get the name, the initial value, the estimation method and, if relevant, MAP (Maximum A Posteriori) parameter values
of the population parameters in the project.
Information is available for fixed effects (with suffix “pop”), random effects (with prefix “omega“),
error model parameters (i.e. a, b, c), covariates (with prefix “beta_”) including latent covariate
probabilities (with prefix “p” and numeric suffix), and correlations (with prefix “corr_”).
Usage
getPopulationParameterInformation()
Details
Available estimation methods are:
"FIXED" |
Fixed parameter | No estimation |
"MLE" |
Maximum Likelihood Estimation | SAEM algorithm |
"MAP" |
Maximum A Posteriori Estimation | Bayesian estimation |
Value
A data frame giving, for each population parameter, the following information:
name
: (character) parameter nameinitialValue
: (double) initial valuemethod
: (character) estimation method (see Details)priorValue
: (double) [MAP only] typical value for priorpriorSD
: (double) [MAP only] standard deviation for prior
See Also
setPopulationParameterInformation to set the population parameter information
getEstimatedPopulationParameters to get the population parameters estimated values
setInitialEstimatesToLastEstimates to set the population parameter initial values to the last estimated values
getFixedEffectsByAutoInit to estimate initial values for the population parameters
Click here to see examples
# # simple model (MLE/FIXED) loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) getPopulationParameterInformation() # model with MAP loadProject(file.path(getDemoPath(), "7.miscellaneous", "7.2.bayesian_estimation", "theobayes1_project.mlxtran")) getPopulationParameterInformation()
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[Monolix] Initialize population parameters with the last estimated ones
Description
Set the initial value of all the population parameters in the current project to the ones previously estimated.
These the values will be used in the population parameter estimation algorithm the next time the scenario is run.
WARNING: If there are changes to the model after the last run, it will not be possible to set the
initial values, as the structure of the project has changed since the last results. Call runPopulationParameterEstimation
to rerun the estimates before calling this method.
Usage
setInitialEstimatesToLastEstimates(fixedEffectsOnly = FALSE)
Arguments
fixedEffectsOnly |
(logical) If set to TRUE , only the fixed effects (with suffix “_pop”) are initialized to their last estimated values.Otherwise, if FALSE , all population parameters, including fixed effect, error model, covariate, and correlation parameters,are re-initialized too. FALSE by default. |
See Also
getEstimatedPopulationParameters to get the population parameters estimated values
(that values that will be used by this method_)
getPopulationParameterInformation to get the current population parameter information, including initial values
runPopulationParameterEstimation to estimate the population parameters
setPopulationParameterInformation to set the population parameter information
getFixedEffectsByAutoInit to estimate initial values for the population parameters
Click here to see examples
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) getPopulationParameterInformation() runPopulationParameterEstimation() # set only initial values for fixed effects to the last estimated values setInitialEstimatesToLastEstimates(fixedEffectsOnly = TRUE) getPopulationParameterInformation() # set all parameter initial values to last estimated values setInitialEstimatesToLastEstimates() getPopulationParameterInformation()
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[Monolix] Set population parameters initialization and estimation method
Description
Set population parameters initialization and estimation method.
Set the initial value, the estimation method and, if relevant, the MAP parameters of one or more
of the population parameters present within the current project. This includes fixed effects,
random effects, error model, covariate, and correlation parameters.
Usage
setPopulationParameterInformation(...)
Arguments
... |
A set of comma-separated lists, where any omitted list entry will remain unchanged paramName = list( initialValue = (double), method = (character) “method” ). (See Details for additional list entries for the MAP method) |
Details
Available estimation methods are:
"FIXED" |
Fixed parameter | No estimation |
"MLE" |
Maximum Likelihood Estimation | SAEM algorithm |
"MAP" |
Maximum A Posteriori Estimation | Bayesian estimation |
Call getPopulationParameterInformation to get a list of the initializable population parameters present within the current project.
For the "MAP"
estimation method, the user can specify the associated typical value and standard deviation values by using additional list elements:
paramName = list( initialValue = (double), method = “MAP”, priorValue = (double), priorSD = (double) )
By default, the prior value corresponds to the the population parameter and the prior standard deviation is set to 1. See example.
See Also
getPopulationParameterInformation to get the population parameter information
setInitialEstimatesToLastEstimates to set the population parameter initial values to the last estimated values
getFixedEffectsByAutoInit to estimate initial values for the population parameters which can be passed to this method
Click here to see examples
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) setPopulationParameterInformation(Cl_pop = list(initialValue = 0.5, method = "FIXED"), V_pop = list(initialValue = 1), ka_pop = list(method = "MAP", priorValue = 1, priorSD = 0.1)) getPopulationParameterInformation()
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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 |
(logical) [optional] Should software switch security be overpassed or not. Equals FALSE by default. |
Value
A logical equaling TRUE if the initialization has been successful and FALSE if not.
Click here to see examples
# 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
The Lixoft demos path corresponding to the currently active software.
Click here to see examples
# getDemoPath() ## End(Not run)
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[Monolix] Get the results of the model building
Description
Get the results of automatic covariate model building or automatic statistical model building. The exact details
of what is returned depend on the strategy used when running the model building.
Usage
getModelBuildingResults()
Value
A list containing the results of model building with one element for each model run.
For all strategies, each list item contains the following:
LL
: result of -2*Log-LikelihoodBICc
: modified BICindividualModels
: (data.frame of logical values) where the rows are the parameters
in the model and the columns are the covariates, and theTRUE/FALSE
value indicates if a covariate is used for that parameter
COSSAC returns two additional fields:
tested
: (vector<character>) which parameter-covariate pair was testing in this run with respect to the
previous model, where the first element is the individual model parameter and the second one is the covariatebestModel
: (logical) whether this model is the best model amongst all the tested models according to the chosen criterion
SAMBA returns the error model and covariance model information if they exist:
errorModels
: (data.frame) onservation model where each row specifies the observation id and the error model chosencovarianceModels
: list with one element for each variability level consisting of two elements: level, specifying the
variability level, and correlations, a list of the chosen correlations between individual model parameters in the groups element
See Also
getModelBuildingSettings for a description of settings
runModelBuilding to run model building
Click here to see examples
# project_file <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project_file) runModelBuilding(strategy = "samba") res <- getModelBuildingResults()
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[Monolix] Get model building settings
Description
Get the current settings for running model building. These are the settings that will be used if runModelBuilding
is called without argument, or they can be used as a template to update and pass to runModelBuilding in order to change the settings.
Usage
getModelBuildingSettings()
Value
The list of settings (default values indicated by square brackets)
covariates
: (vector<character>) covariate names to be considered in the model buildingparameters
: (vector<character>) parameters names to be considered in the model buildingstrategy
: (character) strategy to use for model building
(["cossac"
],"samba"
,"covsamba"
,"scm"
), where cossac, covsamba, and scm are algorithms for
automatic covariate model building and samba is an algorithm for automatic statistical model building,
which includes the residual error model and correlations between random effects in addition to the covariate effectscriterion
: (character) criterion to determine best model (["BIC"
],"LRT"
)relationships
: (data.frame with columns: parameters, covariates, locked)
Use to force specific parameter-covariate relationships to be included (locked = TRUE
) or excluded (locked = FALSE
),
See runModelBuilding for an example. By default, all the combinations are possible (i.e. this data.frame is empty).threshold$lrt
: threshold used by criterion LRT whether or not to continue to improve the model
(first element is for forward and the second one is for the backward method)threshold$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
: (logical) computation done using linearization ([TRUE
]) or importance sampling (FALSE
)
See Also
runModelBuilding to run model building
Click here to see examples
# project_file <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project_file) getModelBuildingSettings()
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[Monolix] Run model building
Description
Run model building for automatic covariate model building or automatic statistical model building.
The current settings for running model building can been seen by calling getModelBuildingSettings
and the returned settings can be modified and used for the settings
argument. See example.
Usage
runModelBuilding(...)
Arguments
... |
[optional] Settings to initialize the model building algorithm. See getModelBuildingSettings for a description of settings. Settings can be passed individually as name-value pairs or together in a single list. |
See Also
getModelBuildingSettings for a description of settings
getModelBuildingResults to get results
Click here to see examples
# project_file <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project_file) ## Not run: # these two lines are equivalent runModelBuilding() runModelBuilding(settings = getModelBuildingSettings()) ## End(Not run) # settings can be set individually as name-value pairs runModelBuilding(useLin = FALSE, covariates = "SEX", strategy = "cossac") # or settings can be modified in the list returned from getModelBuildingSettings, then passed to runModelBuilding mbSettings <- getModelBuildingSettings() # for example, to force including the covariate SEX on parameter ka, # and to exclude the covaraite WEIGHT from parameter V mbSettings$covariates <- c("SEX", "WEIGHT") mbSettings$relationships[1,] = c("ka", "SEX", TRUE) mbSettings$relationships[2,] = c("V", "WEIGHT", FALSE) mbSettings$relationships runModelBuilding(settings = mbSettings) getModelBuildingResults()
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[Monolix] Get continuous observation statistical model information
Description
Get a summary of the information concerning the continuous observation statistical model(s) in the project.
The following information is returned for each continuous observation:
prediction
: (vector<character>) name of the associated prediction (i.e. variable in the structural model).formula
: (vector<character>) formula applied to the observations, which depends on the mapping and error model chosen.distribution
: (vector<character>) distribution of the observations in the Gaussian space.
The distribution type can be"normal"
,"logNormal"
, or"logitNormal"
.limits
: (vector< pair<double,double> >) lower and upper limits imposed on the observation.
Used only if the distribution is"logitNormal"
, otherwise this field is not included.errormodel
: (vector<character>) type of the associated error model.
The error model type can be"constant"
,"proportional"
,"combined1"
, or"combined2"
.parameters
: (vector<character>) a vector of parameters for the residual error model.autocorrelation
: (vector<logical>)"TRUE"
to estimate autocorrelation, or"FALSE"
otherwise
(legacy only and not recommended to enable for new projects).
Usage
getContinuousObservationModel()
Value
A list specifying the statistical model properties for each continuous observation model.
See Also
getObservationInformation to get the continuous observations present in the current project
Set components of the continuous observation model(s):
setObservationDistribution
setObservationLimits
setErrorModel
Click here to see examples
# # single observation loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) getContinuousObservationModel() # multiple observations loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "warfarinPKPD_project.mlxtran")) getContinuousObservationModel()
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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. This is a legacy feature
and not recommended for new projects.
Usage
setAutocorrelation(...)
Arguments
... |
Sequence of comma-separated pairs {(character)”observationModel”,(logical)hasAutoCorrelation}. |
See Also
getContinuousObservationModel get the current observation model for the current project
getObservationInformation to get the continuous observations present in the current project
setPopulationParameterInformation to update error model parameters to be estimated
Set components of the continuous observation model:
setObservationDistribution
setObservationLimits
setErrorModel
[Monolix] Set error model
Description
Set the error model type to be used for the observation model(s).
Call getObservationInformation to get a list of
the available observation names within the current project.
Usage
setErrorModel(...)
Arguments
... |
A list of comma-separated pairs {observationModel = (character)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 |
where a, b, and c are parameters, obs is the observed data, pred is the prediction from the structural model,
and err is normally distributed with mean 0 and variance 1.
Error model parameters will be initialized to 1 by default.
Call setPopulationParameterInformation to modify their initial value.
The value of the exponent parameter c is fixed by default when using the "combined1"
and "combined2"
models.
Use setPopulationParameterInformation to enable its estimation.
See Also
getContinuousObservationModel get the current observation model for the current project
getObservationInformation to get the continuous observations present in the current project
setPopulationParameterInformation to update error model parameters to be estimated
Set components of the continuous observation model(s):
setObservationDistribution
setObservationLimits
Click here to see examples
# project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "warfarinPKPD_project.mlxtran") loadProject(project) # get observation model names available in the current project, how they are mapped to the data, # and how they are mapped to the predictions getObservationInformation()$name getObservationInformation()$mapping getContinuousObservationModel()$prediction # update the error model setErrorModel(y1 = "proportional", y2 = "combined1") getContinuousObservationModel()$errorModel getContinuousObservationModel()$formula
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[Monolix] Set observation model distribution
Description
Set observation model distribution.
Set the distribution in the Gaussian space for the observation
model(s). Available distribution types are "normal"
, "logNormal"
, or
"logitNormal"
. Call getObservationInformation to get a list of
the available observation model names within the current project. Only specified
observation models will be changed. Call setObservationLimits to set
limits for any logitNormal distributions.
Usage
setObservationDistribution(...)
Arguments
... |
A list of comma-separated pairs {observationModel = (character) “distribution”}. |
See Also
getContinuousObservationModel get the current observation model for the current project
getObservationInformation to get the continuous observations present in the current project
Set components of the continuous observation model(s):
setObservationDistribution
setErrorModel
Click here to see examples
# project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "warfarinPKPD_project.mlxtran") loadProject(project) # get observation model names available in the current project getObservationInformation()$name # set the distributions and the limits for the logitNormal distribution setObservationDistribution(y1 = "logNormal", y2 = "logitNormal") setObservationLimits(y2 = c(0, 100)) getContinuousObservationModel()$distribution
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[Monolix] Set observation model distribution limits for logitNormal observations
Description
Set observation model distribution limits for logitNormal observations.
Set the minimum and the maximum values between which observations
must fall. Used only if the distribution of the error model is
“logitNormal”. To set the observation distribution to “logitNormal”, use
setObservationDistribution. Call getObservationInformation to get the
observation model names present in the current project. Only specified observations will be changed.
Usage
setObservationLimits(...)
Arguments
... |
A list of comma-separated pairs {observationModel = c((double) min, (double) max) } |
See Also
getContinuousObservationModel get the current observation model for the current project
getObservationInformation to get the continuous observations present in the current project
Set components of the continuous observation model:
setObservationDistribution
setErrorModel
Click here to see examples
# project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "warfarinPKPD_project.mlxtran") loadProject(project) # get observation model names available in the current project getObservationInformation()$name # set the distribution to logitNormaland then set the limits setObservationDistribution(y2 = "logitNormal") setObservationLimits(y2 = c(0, 100)) getContinuousObservationModel()$limits
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[Monolix – PKanalix] Generate Bivariate observations plots
Description
Plot the bivariate viewer.
Usage
plotBivariateDataViewer(
obs1 = NULL,
obs2 = NULL,
settings = list(),
stratify = list(),
preferences = list()
)
Arguments
obs1 |
(character) Name of the observation to display in x axis (in dataset header). By default the first observation is considered. |
|||||||
obs2 |
(character) Name of the observation to display in y axis (in dataset header). By default the second observation is considered. |
|||||||
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
Click here to see examples
# 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(individualSelection = list(indices = 10))) plotBivariateDataViewer(stratify = list(split = "age", filter = list("sex", "1"), groups = list(name = "age", definition = c(25)))) plotBivariateDataViewer(stratify = list(color = "wt", groups = list(name = "wt", definition = 75))) plotBivariateDataViewer(stratify = list(split = c("age", "sex"), groups = list(name = "age", definition = 25))) # 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,
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). |
|||||||
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
Click here to see examples
# 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(split = "AGE", filter = list("Period", 1), groups = list(name = "AGE", definition = 25))) preferences <- list(regressionLine = list(color = "#E5551B")) plotCovariates(covariatesRows = "AGE", covariatesColumns = "WT", stratify = list(color = "HT", groups = list(name = "HT", definition = 181), colors = c("#2BB9DB", "#DD6BD2")), preferences = preferences) plotCovariates(covariatesRows = "HT", covariatesColumns = "WT", stratify = list(split = c("AGE", "SEQ"), groups = list(name = "AGE", definition = 25))) # Mulitple covariates plotCovariates() plotCovariates(covariatesRows = c("AGE", "SEQ", "HT"), covariatesColumns = c("AGE", "SEQ", "HT")) plotCovariates(stratify = list(filter = list("AGE", 2), groups = list(name = "AGE", definition = c(25, 30))))
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[Monolix – PKanalix] Generate Observed data plot
Description
Plot the observed data.
Usage
plotObservedData(
obsName = NULL,
settings = list(),
stratify = list(),
preferences = list()
)
Arguments
obsName |
(character) Name of the observation (if several OBS ID). By default the first observation is considered. |
|||||||
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 options. |
Value
A ggplot object
See Also
Click here to see examples
# project <- paste0(getDemoPath(), "/2.case_studies/project_aPCSK9_SAD.pkx") loadProject(project) # by default, individual profiles and mean curve are displayed plotObservedData() # displaying dots and mean curve by dose group, merged on a single plot plotObservedData(settings=list(lines=F, mean=T, meanMethod="geometric", ylog=T, ylab="mAb concentration (ug/mL)"), stratify=list(split="DOSE_mg", mergedSplits=T), preferences=list(observationStatistics=list(lineWidth=0.8), obs=list(radius=2))) # coloring by ID, without mean curve plotObservedData(settings=list(mean=F,error=F), stratify = list(color=c("id"))) # changing the settings to display only the mean curve with SE, with bin limits and dosing times plotObservedData(settings=list(dots=F, lines=F, mean=T, error=T, meanMethod="geometric", errorMethod="standardError", useCensored=T, binLimits=T, binsSettings=list(criteria="leastsquare", is.fixedNbBins=T, nbBins=20), cens=F, dosingTimes=T, legend=T, grid=F, xlog=F,ylog=T, xlab="Time since first dose (days)", ylab="mAb concentration (ug/mL)", xlim=c(0,96), ylim=c(0.1,70), fontsize=12, units=F)) # changing preferences for observations, censored observations and bin limits plotObservedData(settings=list(dots=T, lines=T, legend=T, dosingTimes=T, mean=F, error=F, ylog=T, cens=T), preferences=list(obs=list(color="#161617", radius=2, shape=18, lineWidth=0.2, lineType="dashed", legend="Observations"), censObs=list(color="#cdced1", radius=2, shape=16, legend="Censored observations"), dosingTimes=list(color="#fcba03", lineWidth=0.5, lineType="solid", legend="Time of doses"))) # changing preferences for mean and bin limits plotObservedData(settings=list(dots=F, lines=F, legend=T, binLimits=T, grid=F), preferences=list(observationStatistics=list(color="#161617", whiskersWidth=3, lineWidth=0.7, lineType="solid", legend="mean and standard deviation over bins"), binsValues=list(color="#cdced1", lineWidth=0.5, lineType="dashed", legend="bins"))) # color and split by DOSE_mg but grouping two doses levels together plotObservedData(settings=list(mean=F,error=F,ylim=c(0,120)), stratify = list(groups=list(name="DOSE_mg",definition=list(c("150mg"), c("300mg","800mg"))), color="DOSE_mg", split="DOSE_mg")) # selecting only one individual plotObservedData(settings=list(mean=F,error=F), stratify = list(individualSelection=list(indices=1))) plotObservedData(settings=list(mean=F,error=F), stratify = list(individualSelection=list(ids="1"))) #============= projects with several covariates to stratify initializeLixoftConnectors(software = "pkanalix", force=T) project <- file.path(getDemoPath(), "/2.case_studies/project_Theo_extravasc_SD.pkx") loadProject(project) # defining groups for AGE and HT, coloring by HT and filtering by AGE plotObservedData(settings=list(mean=F,error=F), stratify = list(groups=list(list(name="AGE", definition=c(24, 34)), list(name="HT", definition=c(184.5))), color="HT", colors=c("#cdced1","#161617"), filter=list("AGE",c(1,3)))) # filter to keep only second sequence (TR) and FORM=test plotObservedData(settings=list(mean=F,error=F), stratify = list(filter=list(list("SEQ",2),list("FORM","test")))) #============= project with time-to-event data initializeLixoftConnectors(software = "monolix", force=T) project <- file.path(getDemoPath(), "/4.joint_models/4.2.continuous_noncontinuous/PKrtte_project.mlxtran") loadProject(project) # survival Kaplan-Meier curve plotObservedData(obsName="Hemorrhaging") # mean number of events plotObservedData(obsName="Hemorrhaging", settings=list(eventPlot="averageEventNumber")) #============= project with categorical data initializeLixoftConnectors(software = "monolix", force=T) project <- file.path(getDemoPath(), "/4.joint_models/4.2.continuous_noncontinuous/warfarin_cat_project.mlxtran") loadProject(project) # display is the same as for continuous data by default plotObservedData(obsName="Level") # display as stacked or as grouped plotObservedData(obsName="Level", settings=list(plot="stacked")) plotObservedData(obsName="Level", settings=list(plot="grouped")) # splitting by sex plotObservedData(obsName="Level", settings=list(plot="stacked", ylim=c(0,30), legend=T), stratify=list(split="sex")) #============== project with multiple-dose to show timeAfterLastDose initializeLixoftConnectors(software = "monolix", force=T) project <- file.path(getDemoPath(), "/6.PK_models/6.3.multiple_doses/addl_project.mlxtran") loadProject(project) # with "time after first dose" by default plotObservedData() # with "time since previous dose" plotObservedData(settings=list(timeAfterLastDose=T)) #============= project with regressor to show regressor on x-axis initializeLixoftConnectors(software = "monolix", force=T) project <- file.path(getDemoPath(), "/7.miscellaneous/7.1.regression_variables/reg3_warfarinPD_linearInterp_project.mlxtran") loadProject(project) # with "time" on x-axis by default plotObservedData() # with regressor "Cc" (concentration) on x-axis plotObservedData(settings=list(xvariable="Cc")) #============= project with nominal time on x-axis initializeLixoftConnectors(software = "pkanalix", force=T) project <- file.path(getDemoPath(), "/1.basic_examples/project_nominal_time.pkx") loadProject(project) # with "actual time" by default plotObservedData() # with nominal time on x-axis plotObservedData(settings=list(xvariable="nominalTime"))
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[Monolix] Plot Importance sampling convergence.
Description
Plot iterations of the likelihood estimation by importance sampling.
Usage
plotImportanceSampling(settings = list())
Arguments
settings |
a list of optional settings:
|
Value
A ggplot object
Click here to see examples
# 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())
Arguments
settings |
a list of optional settings:
|
Value
A TableGrob object if multiple plots (output of grid.arrange)
Click here to see examples
# 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())
Arguments
settings |
a list of optional settings:
|
Value
A TableGrob object if multiple plots (output of grid.arrange)
Click here to see examples
# 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()
)
Arguments
parameters |
vector of parameters to display. (by default the first 4 computed parameters are displayed). |
|||||||
plot |
(character) 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:
|
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
# 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("WEIGHT", 1), groups = list(name = "WEIGHT", definition = 75))) plotParametersDistribution(parameters = "Cl", stratify = list(split = "SEX")) # update preferences preferences = list(theoretical = list(color = "#B4468A", lineType = "solid", lineWidth = 0.8)) plotParametersDistribution(parameters = "ka", plot = "cdf", preferences = preferences) # 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()
)
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:
|
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
# 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("WEIGHT", 1), groups = list(name = "WEIGHT", definition = 75))) plotParametersVsCovariates(covariates = "WEIGHT", parameters = "ka", stratify = list(split = "SEX")) plotParametersVsCovariates(covariates = "SEX", parameters = "Cl", stratify = list(color = "WEIGHT", groups = list(name = "WEIGHT", definition = 75))) plotParametersVsCovariates(covariates = "WEIGHT", parameters = "V", stratify = list(color = "SEX")) plotParametersVsCovariates(covariates = "WEIGHT", parameters = "V", stratify = list(color = c("SEX", "WEIGHT"), groups = list(name = "WEIGHT", definition = 70))) # 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(color = "WEIGHT", groups = list(name="WEIGHT", definition = 75))) plotParametersVsCovariates(stratify = list(color = "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()
)
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:
|
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
# 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(settings = list(indivEstimate = "mean")) plotRandomEffectsCorrelation(parametersRows = c("ka", "V")) # stratification plotRandomEffectsCorrelation(parametersRows = "ka", parametersColumns = "V", stratify = list(filter = list("SEX", "M"))) plotRandomEffectsCorrelation(parametersRows = "ka", parametersColumns = "V", stratify = list(color = "WEIGHT", groups = list(name = "WEIGHT", definition = 75), colors = c("#46B4AF", "#B4468A"))) plotRandomEffectsCorrelation(parametersRows = "ka", parametersColumns = "V", stratify = list(split = "SEX")) plotRandomEffectsCorrelation(parametersRows = "ka", parametersColumns = "V", stratify = list(split = c("SEX", "WEIGHT"), groups = list(name = "WEIGHT", definition = 70)))
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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()
)
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:
|
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
# 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("WEIGHT", 1), groups = list(name = "WEIGHT", definition = 75))) plotStandardizedRandomEffectsDistribution(parameters = "Cl", stratify = list(split = "SEX")) 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()
)
Arguments
obsName |
(character) 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:
|
Details
Only available for Continuous data.
Value
A ggplot object
See Also
getChartsData getPlotPreferences
Click here to see examples
# 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(individualSelection = list(ids = c(1, 2, 3, 4)))) plotIndividualFits(stratify = list(individualSelection = list(indices = c(1, 4), isRange = TRUE))) plotIndividualFits(stratify = list(filter = list("WEIGHT", 2), groups = list(name = "WEIGHT", definition = c(60, 75)))) plotIndividualFits(stratify = list(filter = list("SEX", "F"))) plotIndividualFits(stratify = list(color = "SEX", colors=c("#5DC088", "#DBA92B"))) plotIndividualFits(settings = list(legend = T), stratify = list(color = c("SEX", "WEIGHT"), groups = list(name = "WEIGHT", definition = 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)
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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()
)
Arguments
obsName |
(character) Name of the observation (in dataset header). By default the first observation is considered. |
|||||||
predictions |
(character) 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:
|
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
# 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)) plotObservationsVsPredictions(settings = list(ylog = TRUE, xlog = TRUE)) # stratification plotObservationsVsPredictions(stratify = list(filter = list("SEX", "F"))) plotObservationsVsPredictions(stratify = list(split = "WEIGHT", groups = list(name = "WEIGHT", definition = 75))) plotObservationsVsPredictions(stratify = list(color = "WEIGHT", groups = list(name = "WEIGHT", definition = 75))) plotObservationsVsPredictions(settings = list(legend = T), stratify = list(color = c("SEX", "WEIGHT"), groups = list(name = "WEIGHT", definition = 70))) # 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()
)
Arguments
obsName |
(character) Name of the observation (in dataset header). By default the first observation is considered. |
|||||||
residuals |
(character) 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:
|
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
# 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("SEX", "F"))) plotResidualsDistribution(stratify = list(split = "WEIGHT", groups = list(name = "WEIGHT", definition = c(75)))) plotResidualsDistribution(residuals = "indiv", settings = list(legend = T), stratify = list(split = c("SEX", "WEIGHT"), groups = list(name = "WEIGHT", definition = 70))) 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()
)
Arguments
obsName |
(character) Name of the observation (in dataset header). By default the first observation is considered. |
|||||||
residuals |
(character) List of residuals to display: population residuals (“pop”), individual residuals (“indiv”), normalized prediction distribution error (“npde”) (default c(“indiv”)). |
|||||||
xaxis |
(character) 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:
|
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
# 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)) 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") # Stratification plotResidualsScatterPlot(stratify = list(filter = list("SEX", "F"))) plotResidualsScatterPlot(stratify = list(split = "WEIGHT", groups = list(name = "WEIGHT", definition = c(75)))) plotResidualsScatterPlot(stratify = list(color = "WEIGHT", groups = list(name = "WEIGHT", definition = c(75))))
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[Monolix] Plot BLQ predictive checks
Description
Plot the BLQ predictive checks.
Usage
plotBlqPredictiveCheck(
obsName = NULL,
settings = list(),
preferences = list(),
stratify = list()
)
Arguments
obsName |
(character) 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. |
|||||||
stratify |
List with the stratification arguments:
|
Value
a ggplot2 object
See Also
Click here to see examples
# # 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()
)
Arguments
obsName |
(character) 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:
|
Value
a ggplot2 object
See Also
getChartsData getPlotPreferences
Click here to see examples
# # 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(),
stratify = list()
)
Arguments
obsName |
(character) 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. |
|||||||
stratify |
List with the stratification arguments:
|
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
# # continuous data project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project) runPopulationParameterEstimation() plotPredictionDistribution() plotPredictionDistribution(stratify = list(color = "SEX"), settings = list(obs = TRUE)) plotPredictionDistribution(stratify = list(split = "SEX"))
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[Monolix] Plot Visual predictive checks
Description
Plot the visual predictive checks.
Usage
plotVpc(
obsName = NULL,
eventPlot = NULL,
settings = list(),
preferences = list(),
stratify = list()
)
Arguments
obsName |
(character) Name of the observation (in dataset header). By default the first observation is considered. |
|||||||
eventPlot |
(character) 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:
|
Value
a ggplot2 object
See Also
Click here to see examples
# # continuous data project <- file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran") loadProject(project) runPopulationParameterEstimation() plotVpc(obsName = "CONC", settings = list(outlierDots = FALSE, grid = FALSE, ylab = "Concentration", xlab = "time (in hour)")) plotVpc(obsName = "CONC", settings = list(outlierDots = FALSE, grid = FALSE, ylab = "Concentration", xlab = "time (in hour)"), stratify = list(split = "SEX")) plotVpc(obsName = "CONC", settings = list(outlierDots = FALSE, grid = FALSE, obs = TRUE, ylab = "Concentration", xlab = "time (in hour)"), stratify = list(color = "SEX")) # categorical data project <- file.path(getDemoPath(), "3.models_for_noncontinuous_outcomes", "3.1.categorical_data_model", "categorical1_project.mlxtran") loadProject(project) runPopulationParameterEstimation() plotVpc(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() plotVpc(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() plotVpc(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 |
(character) 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
# 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] Reset plot preferences to go back to default preferences
Description
Reset plot preferences to go back to default preferences.
Usage
resetPlotPreferences()
See Also
getPlotPreferences setPlotPreferences
Click here to see examples
# getPlotPreferences()$obs[c("color", "legend")] update = list(obs = list(color = "green", legend = "Observation")) setPlotPreferences(update = update) getPlotPreferences()$obs[c("color", "legend")] resetPlotPreferences() getPlotPreferences()$obs[c("color", "legend")] ## End(Not run)
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[Monolix – PKanalix] Set preferences to customize plots
Description
Set preferences to customize plots.
When preferences are set, the updated preferences will used in all the plots.
Usage
setPlotPreferences(update = NULL)
Arguments
update |
list containing the plot elements to be updated. |
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)
See Also
getPlotPreferences resetPlotPreferences
Click here to see examples
# getPlotPreferences()$obs[c("color", "legend")] update = list(obs = list(color = "green", legend = "Observation")) setPlotPreferences(update = update) getPlotPreferences()$obs[c("color", "legend")] ## 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 |
(logical) [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:
getPopulationElementsgetOccasionElements,
getIndividualElements,
getPopulationElements
,
getCovariateElements,
getTreatmentElements,
getOutputElements,
getRegressorElements.,
See Also
newProject, loadProject, importProject
Click here to see examples
# [PKanalix only] exportProject(settings = list(targetSoftware = "monolix", filesNextToProject = F)) exportProject(settings = list(targetSoftware = "monolix", filesNextToProject = F, exportedUnusedCovariates = list(all = FALSE, name = c("sex", "weight")))) [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 (character): Path to the data file (csv, xlsx, xlsx, sas7bdat, xpt or txt). Can be absolute or relative to the current working directory.
- header (vector<character>): vector of header names
- headerTypes (vector<character>): vector of header types
- observationNames (vector<character>): vector of observation names
- observationTypes (vector<character>): vector of observation types
- nbSSDoses (integer): number of doses (if there is a SS column)
- regressorsSettings (character): regressors interpolation method (either last carried forward or linear)
- sheet (character): Name of the sheet in xlsx/xls file. If not provided, the first sheet is used.
Usage
getData()
Value
A list describing project data.
See Also
Click here to see examples
# 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") $sheet "sheet1" ## End(Not run)
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[Monolix – PKanalix] Get interpreted project data
Description
Get data after interpretation done by the software, as it is displayed in the Data tab in the interface.
Interpretation of data includes, but is not limited to, data formatting, filters, addition of doses through the ADDL column and steady state settings, addition of additional covariates, interpolation of regressors.
It returns as dataframe with all columns of type “character”.
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 |
(character) 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 single string.
Click here to see examples
# 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 |
(character) One of the MonolixSuite library of models. Possible values are “pk”, “pd”, “pkpd”, “pkdoubleabs”, “pm”, “tmdd”, “tte”, “count” and “tgi”. |
filters |
(list(name = character)) 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
# 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:
- obsId (character) Name of observation id present in the dataset. It corresponds to the content of column tagged as “obsid” in case of several obs ids, or to the header of the column tagged as “observation” otherwise
- modelOutput (character) Name of the model prediction listed in the output= line of the structural model
- observationName [Monolix] (character) Model observation name (for continuous observations only)
- type (character) Type of linked data (“continuous” | “discrete” | “event”)
- freeData (list<list>) A list of lists describing not mapped data:
- obsId (character) Name of observation id present in the dataset
- type (character) Data type
- freePredictions (list<list>) A list of lists describing not mapped predictions:
- modelOutput (character) Name of the model prediction listed in the output= line of the structural model
- type (character) Prediction type
See Also
Click here to see examples
# f = getMapping() f$mapping -> list( list(obsId = "1", modelOutput = "Cc", observationName = "concentration", type = "continuous"), list(obsId = "2", modelOutput = "Level", type = "discrete") ) f$freeData -> list( list(obsId = "3", type = "event") ) f$freePredictions -> list( list(modelOutput = "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
The path to the structural model file.
See Also
setStructuralModelFor Monolix and PKanalix only:
Click here to see examples
# 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:
getPopulationElementsgetOccasionElements,
getIndividualElements,,
getPopulationElements
,
getTreatmentElementsgetCovariateElements,
getOutputElements,
getRegressorElements.,
See Also
Click here to see examples
# 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 logical saying if a project is currently loaded.
Usage
isProjectLoaded()
Value
TRUE if a project is currently loaded, FALSE otherwise
Click here to see examples
# 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
# 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, mapping = 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.
|
mapping |
[optional](list): A list of lists representing a link between observation types and model outputs. Each list contains:
|
Details
Note: instead of creating a project from scratch, it is also possible in Monolix and PKanalix to load an existing project with
importProjectloadProject or
setData and change the dataset or the model with
setStructuralModel. or
See Also
Click here to see examples
# 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")), modelFile = "lib:oral1_1cpt_IndirectModelInhibitionKin_TlagkaVClR0koutImaxIC50.txt", mapping = list(list(obsId = "1", modelOutput = "Cc", observationName = "y1"), list(obsId = "2", modelOutput = "R", observationName = "y2"))) # 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
# [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 = NULL,
nbSSDoses = NULL,
regressorsSettings = NULL,
sheet = NULL
)
Arguments
dataFile |
(character): Path to the data file (csv, xlsx, xlsx, sas7bdat, xpt or txt). Can be absolute or relative to the current working directory. |
headerTypes |
(vector<character>): A vector of header types. The possible header types are: “ignore”, “id”, “time”, “observation”, “amount”, “contcov”, “catcov”, “occ”, “evid”, “mdv”, “obsid”, “cens”, “limit”, “regressor”, “nominaltime”, “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](integer): Number of doses (if there is a SS column). |
regressorsSettings |
[optional](character): Regressors interpolation method. Either ‘lastCarriedForward’ (default) or ‘linearInterpolation’. |
sheet |
[optional] (character): Name of the sheet in xlsx/xls file. If not provided, the first sheet is used. |
See Also
Click here to see examples
# 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")) setData(dataFile = "/path/to/data/file.xlsx", sheet = "sheet2", 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
# [Monolix] setMapping(list(list(obsId = "1", modelOutput = "Cc", observationName = "concentration"), list(obsId = "2", modelOutput = "Level"))) [PKanalix] setMapping(list(list(obsId = "1", modelOutput = "Cc"), list(obsId = "2", modelOutput = "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
# 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] Share project.
Description
Create a zip archive file from current project and its results.
Usage
shareProject(archiveFile)
Arguments
archiveFile |
(character) Path to the .zip archive file to create. |
Click here to see examples
# shareProject("/path/to/archive.zip") ## 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” | (logical) | Use relative path for save/load operations. |
“threads” | (integer > 0) | Number of threads. |
“temporarydirectory” | (character) | Path to the directory used to save temporary files. |
“usebinarydataset” | (logical) | Save dataset as binary file to speed project reload up. |
“timestamping” | (logical) | Create an archive containing result files after each run. |
“delimiter” | (character) | Character use as delimiter in exported result files. |
“reportingrenamings” | (list(“label” = “alias”>)) | For each label, an alias to be use at report generation (using generateReport). |
“exportchartsdata” | (logical) | Should charts data be exported. |
“savebinarychartsdata” | (logical) | [Monolix] Save charts simulations as binray file to speed charts creation up. |
“exportchartsdatasets” | (logical) | [Monolix] Should charts datasets be exported if possible. |
“exportvpcsimulations” | (logical) | [Monolix] Should vpc simulations be exported if possible. |
“exportsimulationfiles” | (logical) | [Simulx] Should simulation results files be exported. |
“headeraliases” | (list(“header” = vector<character>)) | For each header, the list of the recognized aliases. |
“ncaparameters” | (vector<character>) | [PKanalix] Defaulty computed NCA parameters. |
“units” | (list(“type” = character) | [PKanalix] Time, amount and/or volume units. |
Usage
getPreferences(...)
Arguments
... |
[optional] (character) Name of the preference whose value should be displayed. If no argument is provided, all the preferences are returned. |
Value
A list with each preference name mapped to its current value.
Click here to see examples
# 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” | (character) | Path to the folder where simulation results will be saved. It should be a writable directory. |
“exportResults” | (logical) | Should results be exported. |
“seed” | (0 < integer < 2147483647) | Seed used by random generators. |
“grid” | (integer) | Number of points for the continuous simulation grid. |
“nbSimulations” | (integer) | Number of simulations. |
“dataandmodelnexttoproject” | (logical) | Should data and model files be saved next to project. |
“project” | (character) | Path to the Monolix project. |
Associated settings for PKanalix projects are:
“directory” | (character) | Path to the folder where simulation results will be saved. It should be a writable directory. |
“seed” | (0 < integer < 2147483647) | Seed used by random generators. |
“datanexttoproject” | (logical) | Should data and model (in case of CA) files be saved next to project. |
Associated settings for Simulx projects are:
“directory” | (character) | Path to the folder where simulation results will be saved. It should be a writable directory. |
“seed” | (0 < integer < 2147483647) | Seed used by random generators. |
“userfilesnexttoproject” | (logical) | Should user files be saved next to project. |
Usage
getProjectSettings(...)
Arguments
... |
[optional] (character) Name of the settings whose value should be displayed. If no argument is provided, all the settings are returned. |
Value
A list with each setting name mapped to its current value.
See Also
Click here to see examples
# 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 |
(character) 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” | (logical) | Use relative path for save/load operations. |
“threads” | (integer > 0) | Number of threads. |
“temporarydirectory” | (character) | Path to the directory used to save temporary files. |
“usebinarydataset” | (logical) | Save dataset as binary file to speed project reload up. |
“timestamping” | (logical) | Create an archive containing result files after each run. |
“delimiter” | (character) | Character use as delimiter in exported result files. |
“reportingrenamings” | (list(“label” = “alias”)) | For each label, an alias to be use at report generation (using generateReport). |
“exportchartsdata” | (logical) | Should charts data be exported. |
“savebinarychartsdata” | (logical) | [Monolix] Save charts simulations as binray file to speed charts creation up. |
“exportchartsdatasets” | (logical) | [Monolix] Should charts datasets be exported if possible. |
“exportvpcsimulations” | (logical) | [Monolix] Should vpc simulations be exported if possible. |
“exportsimulationfiles” | (logical) | [Simulx] Should simulation results files be exported. |
“headeraliases” | (list(“header” = vector<character>)) | For each header, the list of the recognized aliases. |
“ncaparameters” | (vector<character>) | [PKanalix] Defaulty computed NCA parameters. |
“units” | (list(“type” = character) | [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
# 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” | (character) | Path to the folder where simulation results will be saved. It should be a writable directory. |
“exportResults” | (logical) | Should results be exported. |
“seed” | (0 < integer < 2147483647) | Seed used by random generators. |
“grid” | (integer) | Number of points for the continuous simulation grid. |
“nbSimulations” | (integer) | Number of simulations. |
“dataandmodelnexttoproject” | (logical) | Should data and model files be saved next to project. |
Associated settings for PKanalix projects are:
“directory” | (character) | Path to the folder where simulation results will be saved. It should be a writable directory. |
“dataNextToProject” | (logical) | Should data and model (in case of CA) files be saved next to project. |
“seed” | (0 < integer < 2147483647) | Seed used by random generators. |
Associated settings for Simulx projects are:
“directory” | (character) | Path to the folder where simulation results will be saved. It should be a writable directory. |
“seed” | (0 < integer < 2147483647) | Seed used by random generators. |
“userfilesnexttoproject” | (logical) | 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
# 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
# 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 |
(character) Chart type whose data must be exported. Available types are: “vpc”, “indfits”. |
filePath |
[optional](character) 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
# 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](character) 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
# 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 confidence interval of population parameters
Description
Get the confidence interval 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
getEstimatedConfidenceIntervals(method = "", withValuesByGroups = FALSE)
Arguments
method |
[optional](character) 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 |
withValuesByGroups |
[optional](logical) if this option is TRUE, confidence intervals of typical parameters having categorical covariate effects are given for each category. |
Value
A list associating each retrieved Fisher algorithm method to a data frame containing the confidence interval with 2.5 and 97.5 as bounds.
Click here to see examples
# getEstimatedConfidenceIntervals() -> $linearization parameter P2.5 P97.5 ka_pop 0.313449586 0.451556 V_pop 0.420422507 0.483500 omega_V 0.045675960 0.057247 omega_Cl 0.023297601 0.027093 $stochasticApproximation parameter P2.5 P97.5 ka_pop 0.315639586 0.384512 V_pop 0.410235533 0.501205 omega_V 0.046675960 0.058247 omega_Cl 0.024297601 0.028093 ## End(Not run)
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[Monolix] Get last estimated individual parameter values
Description
Get the last estimated values for each subject of the individual parameters present within the current project.
WARNING: Estimated individual parameters values are not available until at least SAEM has been run.
NOTE: The user can choose to display only the individual parameter values estimated with a specific method.
Existing individual estimation methods :
“saem” | Approximation of the conditional mean estimated by SAEM |
“conditionalMean” | Mean of the conditional distribution estimated by the Conditional Distribution task |
“conditionalMode” | Mean of the conditional distribution estimated by the EBEs task |
Usage
getEstimatedIndividualParameters(..., method = "")
Arguments
... |
(character) 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](character) A value among “saem”, “conditionalMean” or “conditionalMode”: the 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 list of dataframes with the same names as the methods, giving for each requested method the last estimated values of the individual parameters of interest for each subject. If the Conditional Distribution task has been run,
the list also includes a dataframe named conditionalSD with the standard deviations of the conditional distributions in addition to the dataframe named conditionalMean.
See Also
getEstimatedRandomEffects, runPopulationParameterEstimation, runConditionalDistributionSampling , runConditionalModeEstimation
Click here to see examples
# 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) initializeLixoftConnectors("monolix") loadProject(paste0(getDemoPath(),"/1.creating_and_using_models/1.1.libraries_of_models/theophylline_project.mlxtran")) runScenario() getEstimatedIndividualParameters()
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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](character) 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
# 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 are not available until the SAEM algorithm has been launched with runPopulationParameterEstimation .
Usage
getEstimatedPopulationParameters(
...,
coefficientsOfVariation = FALSE,
withValuesByGroups = FALSE
)
Arguments
... |
[optional] (character) 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 is not specified, the function will retrieve the values of all the available population parameters. |
coefficientsOfVariation |
[optional](logical) 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. |
withValuesByGroups |
[optional](logical) if this option is TRUE, typical values of parameters having categorical covariate effects are given for each category. |
Value
A named vector containing the last estimated value of each one of the population parameters passed as argument or all population parameters if unspecified.
See Also
runPopulationParameterEstimation
Click here to see examples
# 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, beta_ka_SEX_F=-0.12, omega_V=0.15, omega_ka=0.2] getEstimatedPopulationParameters(withValuesByGroups=TRUE) -> [V_pop = 0.5, Cl_pop = 0.25, ka_pop = 0.05, beta_ka_SEX_F=-0.12, omega_V=0.15, omega_ka=0.2, ka_SEX_F=0.04434602] getEstimatedPopulationParameters("omega_V",coefficientsOfVariation=TRUE) -> [omega_V=0.15, omega_V_CV = 15.08477] ## End(Not run) initializeLixoftConnectors("monolix") loadProject(paste0(getDemoPath(),"/5.models_for_individual_parameters/5.2.covariate_model/warfarin_covariate3_project.mlxtran")) runPopulationParameterEstimation() getEstimatedPopulationParameters() getEstimatedPopulationParameters(withValuesByGroups = T) getEstimatedPopulationParameters("omega_V", coefficientsOfVariation = T)
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[Monolix] Get the estimated random effects
Description
Get the random effects for each subject of the individual parameters present within the current project.
WARNING: Estimated random effects are not available until the individual estimation algorithm has been run. Please call getLaunchedTasks to get a list of the methods whose results are available.
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 :
“saem” | Approximation of the conditional mean estimated by SAEM |
“conditionalMean” | Mean of the conditional distribution estimated by the Conditional Distribution task |
“conditionalMode” | Mean of the conditional distribution estimated by the EBEs task |
Usage
getEstimatedRandomEffects(..., method = "")
Arguments
... |
(character) 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](character) A value among “saem”, “conditionalMean” or “conditionalMode”: the 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 list of dataframes giving, for each requested method, the last estimated random effects values of the individual parameters of interest for each subject, with the corresponding standard deviation values for the “conditionalMean” method.
See Also
getEstimatedIndividualParameters
Click here to see examples
# 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 = "saem") # 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 = "", withValuesByGroups = FALSE)
Arguments
method |
[optional](character) 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 |
withValuesByGroups |
[optional](logical) if this option is TRUE, standard errors of typical parameters having categorical covariate effects are given for each category. |
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
# 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 individual parameter shrinkage values
Description
Get the shrinkage values for each individual parameter.
WARNING: Shrinkage values are not available until the corresponding individual estimation algorithm has been run.
NOTE: The user can choose to display only the shrinkage based on individual parameter values estimated with a specific method.
Existing individual estimation methods :
“conditionalDist” | All samples from the conditional distribution, sampled by the Conditional Distribution task |
“conditionalMean” | Mean of the conditional distribution estimated by the Conditional Distribution task |
“conditionalMode” | Mean of the conditional distribution estimated by the EBEs task |
Usage
getEtaShrinkage(..., method = "")
Arguments
... |
(character) 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](character) A value among “saem”, “conditionalMean” or “conditionalMode”: the individual parameter estimation method whose results should be displayed. If this field is not specified, the results provided by all the methods that have been run are displayed. |
Value
A list of dataframes giving, for each requested method, the shrinkage values for each requested individual parameter.
See Also
getEstimatedIndividualParameters
Click here to see examples
# indivParams = getEtaShrinkage() # retrieve the values of all the available individual parameters for all methods -> $conditionalMean parameters shrinkage ka 28.5 V ... Cl 10.472 $conditionalMode parameters shrinkage ka 29.4 V ... Cl -9.2 indivParams = getEtaShrinkage("Cl", "V", method = "conditionalMean") # retrieve the values of the shrinkage for "Cl" and "V" from conditional mean method ## End(Not run)
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[Monolix] Get tasks with results
Description
Get a list of the tasks which have available results.
Usage
getLaunchedTasks()
Value
A list with items:
- populationParameterEstimation: TRUE if the “Population Parameters” task has been run and has results, FALSE otherwise. To run this task, use runPopulationParameterEstimation.
- conditionalDistributionSampling: TRUE if the “Conditional Distribution” task has been run and has results, FALSE otherwise. To run this task, use runConditionalDistributionSampling.
- conditionalModeEstimation: TRUE if the “EBEs” task has been run and has results, FALSE otherwise. To run this task, use runConditionalModeEstimation.
- standardErrorEstimation: character vector containing “linearization” and/or “stochasticApproximation” if the “Standard Errors” task has been run with linearization and/or stochastic approximation method and has results, FALSE otherwise. To run this task, use runStandardErrorEstimation.
- logLikelihoodEstimation: character vector containing “linearization” and/or “importanceSampling” if the “Likelihood” task has been run with linearization and/or importance sampling method and has results, FALSE otherwise. To run this task, use runLogLikelihoodEstimation.
- plots: TRUE (because there are always at least Observed Data plots).
Click here to see examples
# getLaunchedTasks() ## End(Not run) # run "Population Parameters", "EBEs" and "Likelihood" with linearization and output tasks with results: initializeLixoftConnectors("monolix") loadProject(paste0(getDemoPath(),"/1.creating_and_using_models/1.1.libraries_of_models/theophylline_project.mlxtran")) runPopulationParameterEstimation() runConditionalModeEstimation() runLogLikelihoodEstimation(linearization = T) getLaunchedTasks() ## Not run: $populationParameterEstimation [1] TRUE $conditionalDistributionSampling [1] FALSE $conditionalModeEstimation [1] TRUE $standardErrorEstimation [1] FALSE $logLikelihoodEstimation [1] "linearization" $plots [1] TRUE ## 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] (character) 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
# 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 are not available until the “Conditional Distribution” task (individual estimation with conditional mean) has been run.
Usage
getSimulatedIndividualParameters(...)
Arguments
... |
(character) 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 data.frame giving the last simulated values of the individual parameters of interest for each replicate of each subject.
See Also
Click here to see examples
# 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 . . ... ... ... simParams = getSimulatedIndividualParameters("Cl", "V") # retrieve the values of the simulated individual parameters "Cl" and "V" ## 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 are not available until the “Conditional Distribution” task (individual estimation with conditional mean) has been run.
Usage
getSimulatedRandomEffects(...)
Arguments
... |
(character) 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 data.frame giving the last simulated values of the individual random effects of interest for each replicate of each subject.
See Also
getIndividualParameterModel, getSimulatedIndividualParameters
Click here to see examples
# 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 . . ... ... ... simEtas = getSimulatedRandomEffects("Cl", "V") # retrieve the values of the simulated random effects for "Cl" and "V" ## 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
# 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 |
(logical) [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
# 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 logical which equals TRUE if the last run has successfully completed,
- a summary of the errors which could have occurred.
Click here to see examples
# lastRunInfo = getLastRunStatus() lastRunInfo$status -> TRUE lastRunInfo$report -> "" ## End(Not run)
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[Monolix – PKanalix – 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 logical
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 logical
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 logical
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
runEstimationrunSimulation in Simulx,
runCAEstimation. in Monolix. The CA task in PKanalix cannot be part of a scenario, it must be run with
Value
The list of tasks that corresponds to the current scenario, indexed by task names.
See Also
Click here to see examples
# [MONOLIX] scenario = getScenario() scenario -> $tasks populationParameterEstimation conditionalDistributionSampling conditionalModeEstimation standardErrorEstimation logLikelihoodEstimation plots TRUE TRUE TRUE FALSE FALSE FALSE $linearization = T $plotList = "outputplot", "vpc" [PKANALIX] scenario = getScenario() scenario nca be TRUE FALSE [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.
If exportchartsdata preference is set to TRUE with setPreferences, runscenario generates the charts data in the result folder.
Every task can also be run separately with a specific function, such as
runEstimationrunSimulation in Simulx,
runCAEstimation. in Monolix. The CA task in PKanalix cannot be part of a scenario, it must be run with
See Also
Click here to see examples
# 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
MonolixrunScenario. Setting the scenario is equivalent to selecting tasks in
PKanalix,
Simulx GUI that will be performed when clicking on RUN. or
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 logical
NOTE: by default the logical 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 logical
NOTE: By default the logical 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 logical
NOTE: By default the logical 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
runEstimationrunSimulation in Simulx,
runCAEstimation. in Monolix. The CA task in PKanalix cannot be part of a scenario, it must be run with
See Also
Click here to see examples
# [MONOLIX] scenario = getScenario() scenario$tasks = c(populationParameterEstimation = T, conditionalModeEstimation = T, conditionalDistributionSampling = T) setScenario(scenario) [PKANALIX] scenario = getScenario() scenario = c(nca = T, be = F) setScenario(scenario) [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 conditional distribution which can be sampled from (i.e. for diagnostic plots) or used to estimate
individual parameters (i.e. the conditional mean). Note that the population
parameters must be already estimated (i.e. by calling runPopulationParameterEstimation).
Usage
runConditionalDistributionSampling()
Details
The associated method keyword is “conditionalMean” when calling getEstimatedIndividualParameters, getEtaShrinkage,
or getEstimatedRandomEffects.
See Also
getEstimatedIndividualParameters to get the mean of the conditional distribution
runPopulationParameterEstimation to estimate population parameters
runConditionalModeEstimation to estimate EBEs
runStandardErrorEstimation to estimate standard errors of the population parameters
runLogLikelihoodEstimation to estimate the log-likelihood of the model
runScenario to run multiple estimation tasks
Click here to see examples
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) runPopulationParameterEstimation() runConditionalDistributionSampling() indivParams_condMean = getEstimatedIndividualParameters(method = "conditionalMean")
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[Monolix] Estimation of the conditional modes (EBEs)
Description
Estimate the individual parameters using the conditional mode estimation algorithm (EBEs). Note that the population
parameters must be already estimated (i.e. by calling runPopulationParameterEstimation).
Usage
runConditionalModeEstimation()
Details
The associated method keyword is “conditionalMode” when calling getEstimatedIndividualParameters, getEtaShrinkage,
or getEstimatedRandomEffects.
See Also
getEstimatedIndividualParameters to get the EBEs
runPopulationParameterEstimation to estimate population parameters
runConditionalDistributionSampling to estimate the conditional distribution
runStandardErrorEstimation to estimate standard errors of the population parameters
runLogLikelihoodEstimation to estimate the log-likelihood of the model
runScenario to run multiple estimation tasks
Click here to see examples
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) runPopulationParameterEstimation() runConditionalModeEstimation() indivParams_EBEs = getEstimatedIndividualParameters(method = "conditionalMode")
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[Monolix] Log-likelihood estimation
Description
Run the log-likelihood estimation algorithm. Note that the population
parameters must be already estimated (i.e. by calling runPopulationParameterEstimation). It is recommended
to call runConditionalModeEstimation first if using the linearization method.
The following methods are available:
Method | Parameter |
Log-Likelihood estimation by linearization | linearization = TRUE |
Log-Likelihood estimation by Importance Sampling (default) | linearization = FALSE |
The log-likelihood outputs(-2LL (OFV), AIC, BIC, BICc) are available using the getEstimatedLogLikelihood function.
Usage
runLogLikelihoodEstimation(linearization = FALSE)
Arguments
linearization |
(logical) [optional] TRUE to use linearization orFALSE to use stochastic approximation (the default) |
See Also
getEstimatedLogLikelihood to get the estimated log-likelihood
runPopulationParameterEstimation to estimate population parameters
runConditionalModeEstimation to estimate EBEs
runConditionalDistributionSampling to estimate the conditional distribution
runStandardErrorEstimation to estimate standard errors of the population parameters
runScenario to run multiple estimation tasks
Click here to see examples
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) runPopulationParameterEstimation() runLogLikelihoodEstimation() logLike <- getEstimatedLogLikelihood()
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[Monolix] Population parameter estimation
Description
Estimate the population parameters with the SAEM algorithm.
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()
Arguments
parameters |
[optional] (data.frame) specify initial values per individual. A data.frame with column id and columns for each parameter, similar to that returned by getEstimatedIndividualParameters. |
Details
The associated method keyword is “saem” when calling getEstimatedIndividualParameters, getEtaShrinkage,
or getEstimatedRandomEffects.
See Also
getEstimatedPopulationParameters to get the estimated population parameters
getPopulationParameterInformation to get the initial values
setPopulationParameterInformation to set the initial values
runConditionalModeEstimation to estimate EBEs
runConditionalDistributionSampling to estimate the conditional distribution
runStandardErrorEstimation to estimate standard errors of the population parameters
runLogLikelihoodEstimation to estimate the log-likelihood of the model
runScenario to run multiple estimation tasks
Click here to see examples
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) runPopulationParameterEstimation() popParams <- getEstimatedPopulationParameters()
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[Monolix] Standard error estimation
Description
Estimate the Fisher Information Matrix (FIM) and the standard errors of the population parameters. Note that the population
parameters must be already estimated (i.e. by calling runPopulationParameterEstimation). It is recommended
to call runConditionalModeEstimation first if using the linearization method.
The following methods are available:
Method | Parameter |
Estimate the FIM by Stochastic Approximation | linearization = FALSE (default) |
Estimate the FIM by Linearization | linearization = TRUE |
The Fisher Information Matrix is available using getCorrelationOfEstimates function,
while the standard errors are available using getEstimatedStandardErrors function.
Usage
runStandardErrorEstimation(linearization = FALSE)
Arguments
linearization |
(logical) [optional] TRUE to use linearization orFALSE to use stochastic approximation (the default) |
See Also
getCorrelationOfEstimates to get the Fisher Information Matrix
getEstimatedStandardErrors to get the standard errors
runPopulationParameterEstimation to estimate population parameters
runConditionalModeEstimation to estimate EBEs
runConditionalDistributionSampling to estimate the conditional distribution
runLogLikelihoodEstimation to estimate the log-likelihood of the model
runScenario to run multiple estimation tasks
Click here to see examples
# loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran")) runPopulationParameterEstimation() runStandardErrorEstimation() stdErrs = getEstimatedStandardErrors()
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