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]
TRUE
to use linearization orFALSE
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 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()