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
runStandardErrorEstimation(linearization = FALSE)Arguments
- linearization
(logical) [optional]
TRUEto use linearization orFALSEto 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 distributionrunLogLikelihoodEstimation to estimate the log-likelihood of the model runScenario 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()
runStandardErrorEstimation()
stdErrs = getEstimatedStandardErrors()