In this page, we present the installation procedure of the R-package that allow to run Monolix from R.
The R package lixoftConnectors is located in the installation directory as tar.gz ball. It can be installed directly using Rstudio (Tools > Install packages > from package archive file) or with the following R command:
install.packages(packagePath, repos = NULL, type="source", INSTALL_opts ="--no-multiarch")
with the packagePath = ‘<installDirectory>/connectors/lixoftConnectors.tar.gz’ where <installDirectory> is the MonolixSuite installation directory.
With the default installation directory, the command is:
# for Windows OS install.packages("C:/ProgramData/Lixoft/MonolixSuite2019R2/connectors/lixoftConnectors.tar.gz", repos = NULL, type="source", INSTALL_opts ="--no-multiarch")
# for Mac OS install.packages("/Applications/MonolixSuite2019R2.app/Contents/Resources/\ monolixSuite/connectors/lixoftConnectors.tar.gz", repos = NULL, type="source", INSTALL_opts ="--no-multiarch")
The lixoftConnectors package depends on the RJSONIO package that may need to be installed from CRAN first using:
Notice that for MonolixSuite2018 version, the R package is called MlxConnectors .
When starting a new R session, you need to load the library and initialize the connectors with the following commands
library(lixoftConnectors) initializeLixoftConnectors(software = "monolix")
In some cases, it may be necessary to specify the path to the installation directory of the Lixoft suite. If no path is given, the one written in the <user home>/lixoft/lixoft.ini file is used (usually “C:/ProgramData/Lixoft/MonolixSuiteXXXX” for Windows). where XXXX corresponds to the version of MonolixSuite
library(lixoftConnectors) initializeLixoftConnectors(software = "monolix", path = "/path/to/MonolixSuite/")
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:
demoPath = '<userFolder>/lixoft/monolix/monolix2019R1/demos/1.creating_and_using_models/' loadProject(paste0(demoPath ,'1.1.libraries_of_models/theophylline_project.mlxtran')) runScenario() getEstimatedPopulationParameters()
where <userFolder> is the user’s home folder (on windows C:/Users/toto if toto is your username). These three commands should output the estimated population parameters (ka_pop, V_pop, Cl_pop, omega_ka, omega_V, omega_Cl, a, and b).