runStandardErrorEstimation

[Monolix] Standard error estimation

Estimate the Fisher Information Matrix (FIM) and the standard errors of the population parameters. 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	Estimate the FIM by Stochastic Approximation
`linearization = FALSE` (default)	Estimate the FIM by Linearization	`linearization = TRUE`

The Fisher Information Matrix is available using getCorrelationOfEstimates function, while the standard errors are available using getEstimatedStandardErrors function.

Usage

R

runStandardErrorEstimation(linearization = FALSE)

Arguments

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

See also

getCorrelationOfEstimates to get the Fisher Information Matrix
getEstimatedStandardErrors to get the standard errors
runPopulationParameterEstimation to estimate population parameters
runConditionalModeEstimation to estimate EBEs
runConditionalDistributionSampling to estimate the conditional distribution
runLogLikelihoodEstimation to estimate the log-likelihood of the model
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()
runStandardErrorEstimation()
stdErrs = getEstimatedStandardErrors()