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# Monolix API

## On the use of a R-usable API

We now propose to use Monolix with a full API.  It is now possible to have access to the project exactly by the same way as you would do with the interface. All the functions are described below.

### Installation and initialization

#### Installation of the connectors for R

To install the R-package, do the following

install.packages('mlxInstalFolder/mlxConnectors/R/MlxConnectors.tar.gz', repos = NULL, type="source")


Where mlxInstalFolder is the installation folder that is typically

• ‘C:/ProgramData/lixoft/MonolixSuite2018R1/’ for Windows OS
• ‘\$HOME/Lixoft/MonolixSuite2018R1/’ in Linux
• ‘ /Applications/MonolixSuite2018R1.app/’ for Mac OS

#### Initializing

When starting a new R session, you need to initialize the library and initialize the connectors as on the following

library(MlxConnectors)
initializeMlxConnectors(software = "monolix")


#### Making sure the installation is ok

To test if the installation is ok, you can load and run a project from the demos as on the following:

projectPath = 'mlxInstalFolder/resources/demos/monolix/1.creating_and_using_models/'
loadProject(paste0(projectPath ,'1.1.libraries_of_models/theophylline_project.mlxtran'))
runScenario()
getEstimatedPopulationParameters()


When doing that, you have the estimation of the population parameters (ka_pop, V_pop, Cl_pop, omega_ka, omega_V, omega_Cl, a, and b).

### Description of the functions concerning the observation model

• getContinuousObservationModel: Get a summary of the information concerning the continuous observation models in the project.
• getObservationInformation: Get the name, the type and the values of the observations present in the project.
• setAutocorrelation: Add or remove auto-correlation from the error model used on some of the observation models.
• setErrorModel: Set the error model type to be used with some of the observation models.
• setObservationDistribution: Set the distribution in the Gaussian space of some of the observation models.
• setObservationLimits: Set the minimum and the maximum values between which some of the observations can be found.

### Description of the functions concerning the population parameters

• getPopulationParameterInformation: Get the name, the initial value, the estimation method and, if relevant, MAP parameters value of the population parameters present in the project.
• setInitialEstimatesToLastEstimates: Set the initial value of all the population parameters present within the current project (fixed effects + individual variances + error model parameters) to the ones previously estimated.
• setPopulationParameterInformation: Set the initial value, the estimation method and, if relevant, the MAP parameters of one or several of the population parameters present within the current project (fixed effects + individual variances + error model parameters).

### Description of the functions concerning the project management

• getData: Get a description of the data used in the current project.
• getStructuralModel: Get the model file for the structural model used in the current project.
• loadProject: Load a project by parsing the mlxtran-formated file whose path has been given as an input.
• newProject: Create a new empty project providing model and data specification.
• saveProject: Save the current project as an Mlxtran-formated file.
• setData: Set project data giving a data file and specifying headers and observations types.
• setStructuralModel: Set the structural model.

### Description of the functions concerning the results

• getCorrelationOfEstimates: Get the inverse of the last estimated Fisher matrix computed either by all the Fisher methods used during the last scenario run or by the specific one passed in argument.
• getEstimatedIndividualParameters: Get the last estimated values for each subject of some of the individual parameters present within the current project.
• getEstimatedLogLikelihood: Get the values computed by using a log-likelihood algorithm during the last scenario run, with or without a method-based filter.
• getEstimatedPopulationParameters: Get the last estimated value of some of the population parameters present within the current project (fixed effects + individual variances + correlations + latent probabilities + error model parameters).
• getEstimatedRandomEffects: Get the random effects for each subject of some of the individual parameters present within the current project.
• getEstimatedStandardErrors: Get the last estimated standard errors of population parameters computed either by all the Fisher methods used during the last scenario run or by the specific one passed in argument.
• getLaunchedTasks: Get a list of the tasks which have results to provide.
• getSAEMiterations: Retrieve the successive values of some of the population parameters present within the current project (fixed effects + individual variances + correlations + latent probabilities + error model parameters) during the previous run of the SAEM algorithm.
• getSimulatedIndividualParameters: Get the simulated values for each replicate of each subject of some of the individual parameters present within the current project.
• getSimulatedRandomEffects: Get the simulated values for each replicate of each subject of some of the individual random effects present within the current project.