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
runLogLikelihoodEstimation(linearization = FALSE)
Arguments
- linearization
(logical) [optional]
TRUE
to use linearization orFALSE
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 distributionrunStandardErrorEstimation
to estimate standard errors of the population parametersrunScenario
to run multiple estimation tasks
Examples
initializeLixoftConnectors("monolix")
loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran"))
runPopulationParameterEstimation()
runLogLikelihoodEstimation()
logLike <- getEstimatedLogLikelihood()