getContinuousObservationModel
[Monolix] Get continuous observation statistical model information
Get a summary of the information concerning the continuous observation statistical model(s) in the project. The following information is returned for each continuous observation:
prediction: (vector) name of the associated prediction (i.e. variable in the structural model).formula: (vector) formula applied to the observations, which depends on the mapping and error model chosen.distribution: (vector) distribution of the observations in the Gaussian space. The distribution type can be"normal","logNormal", or"logitNormal".limits: (vector< pair >) lower and upper limits imposed on the observation. Used only if the distribution is"logitNormal", otherwise this field is not included.errormodel: (vector) type of the associated error model. The error model type can be"constant","proportional","combined1", or"combined2".parameters: (vector) a vector of parameters for the residual error model.autocorrelation: (vector)"TRUE"to estimate autocorrelation, or"FALSE"otherwise (legacy only and not recommended to enable for new projects).
Usage
getContinuousObservationModel()Value
A list specifying the statistical model properties for each continuous observation model.
See also
getObservationInformation to get the continuous observations present in the current project
Set components of the continuous observation model(s): setObservationDistribution setObservationLimits setErrorModel
Examples
initializeLixoftConnectors("monolix")
# single observation
loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "theophylline_project.mlxtran"))
getContinuousObservationModel()
#> $prediction
#> CONC
#> "Cc"
#>
#> $formula
#> CONC
#> "CONC = Cc + (a + b*Cc) * e\n"
#>
#> $distribution
#> CONC
#> "normal"
#>
#> $errorModel
#> CONC
#> "combined1"
#>
#> $parameters
#> $parameters$CONC
#> [1] "a" "b" "c"
#>
#>
#> $autocorrelation
#> CONC
#> FALSE
#>
# multiple observations
loadProject(file.path(getDemoPath(), "1.creating_and_using_models", "1.1.libraries_of_models", "warfarinPKPD_project.mlxtran"))
getContinuousObservationModel()
#> $prediction
#> y1 y2
#> "Cc" "R"
#>
#> $formula
#> y1 y2
#> "y1 = Cc + (a1 + b1*Cc) * e\n" "y2 = R + a2 * e\n"
#>
#> $distribution
#> y1 y2
#> "normal" "normal"
#>
#> $errorModel
#> y1 y2
#> "combined1" "constant"
#>
#> $parameters
#> $parameters$y1
#> [1] "a1" "b1" "c1"
#>
#> $parameters$y2
#> [1] "a2"
#>
#>
#> $autocorrelation
#> y1 y2
#> FALSE FALSE
#>