lixoftConnectors tar gz
# load and initialize the API
library(lixoftConnectors)
initializeLixoftConnectors(software="monolix")
# create a new project by setting a data set and a structural model
# replace <userFolder> by the path to your home directory
demoPath = 'C:/Users/username/lixoft/monolix/monolix2019R1/demos/1.creating_and_using_models/1.1.libraries_of_models/'
librariesPath = 'C:/ProgramData/Lixoft/MonolixSuite2019R1/factory/library/pk'
newProject(data = list(dataFile = paste0(demoPath,'data/warfarin_data.txt'),
headerTypes =c("id", "time", "amount", "observation", "obsid", "contcov", "catcov", "ignore"),
observationTypes = list(y1 = "continuous", y2 = "continuous" ),
mapping = list("1" = "y1")),
modelFile = paste0(librariesPath,'/oral1_1cpt_TlagkaVCl.txt'))
# set tasks in scenario
scenario <- getScenario()
scenario$tasks = c(populationParameterEstimation = T,
conditionalModeEstimation = T,
conditionalDistributionSampling = T,
standardErrorEstimation=T,
logLikelihoodEstimation=T)
scenario$linearization = TRUE
setScenario(scenario)
# ----------------------------------------------------------------------------
# convergence assessment: run 5 estimations with different initial estimates,
# store the results in tabestimates
# ----------------------------------------------------------------------------
popparams <- getPopulationParameterInformation()
tabestimates <- NULL; tabse <- NULL
for(i in 1:5){
# sample new initial estimates
popini <- sapply(1:nrow(popparams), function(j){runif(n=1, min=popparams$initialValue[j]/2, max=popparams$initialValue[j]*2)})
# set sampled values as new initial estimates
newpopparams <- popparams
newpopparams$initialValue <- popini
setPopulationParameterInformation(newpopparams)
# run the estimation
runScenario()
# store the estimates and s.e. in table
tabestimates <- cbind(tabestimates, getEstimatedPopulationParameters())
tabse <- cbind(tabse, getEstimatedStandardErrors()$stochasticApproximation)
}
