runLogLikelihoodEstimation

[Monolix] Log-likelihood estimation

Run the log-likelihood estimation algorithm. Note that the population parameters must be already estimated (i.e. by calling runPopulationParameterEstimation). It is recommended to call runConditionalModeEstimation first if using the linearization method.
The following methods are available:

Method	Parameter	Log-Likelihood estimation by linearization
`linearization = TRUE`	Log-Likelihood estimation by Importance Sampling (default)	`linearization = FALSE`

The log-likelihood outputs(-2LL (OFV), AIC, BIC, BICc) are available using the getEstimatedLogLikelihood function.

Usage

R

runLogLikelihoodEstimation(linearization = FALSE)

Arguments

linearization (logical) [optional] TRUE to use linearization or FALSE to use stochastic approximation (the default)

See also

getEstimatedLogLikelihood to get the estimated log-likelihood
runPopulationParameterEstimation to estimate population parameters
runConditionalModeEstimation to estimate EBEs
runConditionalDistributionSampling to estimate the conditional distribution
runStandardErrorEstimation to estimate standard errors of the population parameters
runScenario to run multiple estimation tasks

Examples

R

initializeLixoftConnectors("monolix")
loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran"))
runPopulationParameterEstimation()
runLogLikelihoodEstimation()
logLike <- getEstimatedLogLikelihood()