MonolixSuite in R
MonolixSuite in R
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getAssessmentResults

[Monolix] Get the results of the convergence assessment

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

R
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 phase

    • nbsmoothingiterations (integer) number of iterations during smoothing phase

    • convergence (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 estimation

    • method (character) fisher method used (importanceSampling or linearization)

    • AIC (double) Akaike Information Criterion

    • BIC (double) Bayesian Information Criterion

    • BICc (double) modified BIC

    • LL (double) log likelihood

    • chosenDegree (integer) [importanceSampling]

    • standardError (double) [importanceSampling]

    • convergence (data.frame) [importanceSampling]

See also

runAssessment to run the assessment

Examples

R
if (FALSE) {
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()
}