Monolix API

Description of the functions concerning the CovariateModels

• addContinuousTransformedCovariate: Create a new continuous covariate by transforming an existing one.
• addDiscreteTransformedCovariate: Create a new discrete covariate by transforming an existing one.
• addMixture: Add mixture to the current project and set the associated number of modalities.
• getCovariateModels: Get, for each variability level, which are the covariates that can have influence on each one of the individual parameters of the project and which are the ones that are effectivly influing on it.
• getCovariateNames: Get all the covariate names of the project.
• removeTransformedCovariate: Remove some of the transformed covariates which have been previously created.
• setCovariateModels: Set which are the covariates influing on each one of the individual parameters of the project.

Description of the functions concerning the IndividualModel

• getAvailableIndividualParameterDistributions: Get the available distributions for the individual parameters.
• getCorrelationGroups: Get all the correlation groups of the current project associated to their level of variability.
• getIndividualParameterDistributions: Get the distribution in the gaussian space of some of the estimated individual parameters.
• getIndividualParameterFormula: Get the formula on the individual parameters of the project.
• getIndividualParameterNames: Get the individual parameters names.
• getIndividualParameterVariabilities: Check if the individual parameters of the project have inter-individual and/or intra-individual variability or not.
• getVariabilityLevels: Get a summary of the variability levels (inter-individual and/or intra-occasion variability) of the project.
• setCorrelationGroup: Set the correlation groups associated to some of the variability levels of the current project.
• setIndividualParameterDistribution: Set the distribution in the gaussian space of some of the estimated individual parameters.
• setIndividualParameterVariabilities: Add or remove inter-individual and/or intra-occasion variability from some of the individual parameters of the project.

Description of the functions concerning the ObservationModels

• getAvailableErrorModelTypes: Get a summary of the available error model types.
• getAvailableObservationDistributions: Get the available observation distributions.
• getErrorModels: Get a summary of the information concerning the error model of the observation models.
• getObservationModels: Get a summary of the information concerning all the observation models in the project.
• getOutputNames: Get the observation models name present in the current project.
• setAutoCorrelation: Add or remove autocorrelation from the error model used on on some of the observation models.
• setErrorModelbpositive: For each observation model passed in input, should the proportional coefficient of its error model be strictly positive or not.
• setErrorModelType: Set the error model type to be used with some of the observation models.
• setObservationDistribution: Set the distribution of the observations model.
• setObservationLogitlLimits: Set the minimum and the maximum values between which some of the observations can be found.
• setObservationModelName: Change the name one or several of the observations.

Description of the functions concerning the PopulationParameters

• getErrorModelParameterInitialValues: Get initial values of the all the error model parameters of population parameters of the project and check if this value is fixed or not.
• getFixedEffectInitialValues: Get the initial values of the all the fixed effects of population parameters of the project and check if this value is fixed or not.
• getIndividualVarianceInitialValues: Get the initial values of the all the individual variances of population parameters of the project and check if this value is fixed or not.
• getMixtures: Get the number of modalities of each one of the mixtures of the project.
• getPopulationParameterInitialValues: Get the intial value of some of the population parameter of the project (fixed effects + individual variances + correlations + latent probabilities + error model parameters).
• getPopulationParameterNames: Get a summary of the population parameters (fixed effects + individual variances + correlations + latent covariates probabilities + error model parameters) of the project.
• setPopulationParameterInitialValues: Set the intial value of one or several of the population parameters of the project (fixed effects + individual variances + error model parameters).
• setPopulationParameterInitialValuesToLastEstimates: Set the initial value of all the population parameters of the project (fixed effects + individual variances + error model parameters) to the ones previously estimated.
• setPopulationParametersAsFixed: Fix the value of one or several of the population parameters of the project (fixed effects + individual variances + error model parameters).

Description of the functions concerning the ProjectManagement

• loadFromFile: Fill the population parameters, the gaussian parameters and/or the individual values fields by reading them from files whose paths have been given as inputs.
• loadProject: Load a project by parsing the mlxtran-formated file whose path has been given as an input.
• newProject: Create a new empty project.
• saveProject: Save the current project as an Mlxtran-formated file.

Description of the functions concerning the Results

• getCorrelationsValue: Get last estimated values of the all the correlations of population parameters of the project.
• getErrorModelParameterValues: Get estimated values of the all the error model parameters of population parameters of the project.
• getFisherMatrix: Get the estimated fisher matrix computed either by all the fisher methods used during the last scenario run or by the specific one passed in argument.
• getFixedEffectValues: Get the estimated values of the fixed effects of population parameters of the project.
• getGaussianParameterValues: Get estimated values for each subject occasion of some of individual parameters of the project in the gaussian referential.
• getIndividualParameterValues: Get estimated values for each subject occasion of some of the individual parameters of the project.
• getIndividualRandomEffectValues: Get the random effects values for each subject occasion of some of the individual parameters of the project.
• getIndividualVarianceValues: Get estimated values of the all the individual variances of population parameters of the project.
• getLatentCovariateProbabilityValues: Get the estimated values of the all the latent covariates probabilities of population parameters of the project.
• getLogLikelihoodValues: Get the values computed by using a log-likelihood algorithm during the last scenario run, with or without a method-based filter.
• getPopulationParameterValues: Get the last estimated values of some of the population parameters of the project (fixed effects + individual variances + correlations + latent probabilities + error model parameters).
• getSAEMConvergenceReport: Retrieve the successive values of some of the population parameters of the project (fixed effects + individual variances + correlations + latent probabilities + error model parameters) during the previous run of the SAEM algorithm.
• getSAEMLogLikelihoodValue: Get the complete log-likelihood value computed during the last run of the SAEM algorithm.
• getStandardErrorValues: 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.

Description of the functions concerning the Scenario

• abort: Stop the current task run.
• estimateFisherInformationMatrix: Estimate the Fisher Information Matrix using a specific method.
• estimateIndividualParameters: Estimate individual parameters using a specific method.
• estimateLogLikelihood: Estimate the Log-Likelihood using a specific method.
• estimatePopulationParameters: Estimate population parameters using SAEM algorithm.
• getAvailableTasks: Get a summary of the tasks usable to build the scenario for the current project.
• getLastRunStatus: Return an execution report about the last run with a summary of the error which could have occured.
• getScenario: Get the list of tasks that will be run at the next call to runScenario.
• isRunning: Check if a scenario is currently running.
• runScenario: Run the scenario.
• sampleConditionalDistributions: Sample an element of the conditional distribution of the individual parameters.
• setScenario: Clear the current scenario and build a new one from a given list of tasks.

Description of the functions concerning the Settings

• getExportSettings: Get a summary of the export settings of the current project.
• getGeneralSettings: Get a summary of the project general settings of the project.
• getIndividualParametersCondMeanSettings: Get the value of one or several of the individual estimation conditional mean algorithm settings.
• getIndividualParametersCondModeSettings: Get the value of one or several of the individual estimation conditional mode algorithm settings.
• getLLImportanceSamplingSettings: Get the value of the Log-likelihood importance sampling algorithm settings.
• getMCMCSettings: Get the MCMC algorithm settings of the current project.
• getSAEMSettings: Get the SAEM algorithm settings of the current project.
• getSeed: Get the initial seed that is used to generate random numbers during the next scenario run.
• setExportSettings: Modify the value of one or several of the export settings of the current project.
• setGeneralSettings: Set the value of one or several general settings of the project.
• setIndividualParametersCondMeanSettings: Set the value of one or several of the individual estimation conditional mean algorithm settings.
• setIndividualParametersCondModeSettings: Set the value of one or several of the individual estimation conditional mode algorithm settings.
• setLLImportanceSamplingSettings: Set the value of the Log-likelihood importance sampling algorithm settings.
• setMCMCSettings: Set the value of one or several of the MCMC algorithm specific settings of the current project.
• setRandomSeed: Automatically generate a new random value to set the seed that will be used to generate random numbers during the next scenario run.
• setSAEMSettings: Set the value of one or several of the SAEM algorithm settings of the current project.
• setSeed: Set the value of the seed that will be used to generate random numbers during the next scenario run.