| getContinuousObservationModel | Get a summary of the information concerning the continuous observation models in the project. |
| getObservationInformation | Get the name, the type and the values of the observations present in the project. |
| setAutocorrelation | Add or remove auto-correlation from the error model used on some of the observation models. |
| setErrorModel | Set the error model type to be used with some of the observation models. |
| setObservationDistribution | Set the distribution in the Gaussian space of some of the observation models. |
| setObservationLimits | Set the minimum and the maximum values between which some of the observations can be found. |
Get continuous observation models information
Description
Get a summary of the information concerning the continuous observation models in the project. The following information are provided.
- prediction: (vector<string>) name of the associated prediction
- formula: (vector<string>) formula applied on the observation
- distribution: (vector<string>) distribution of the observation in the Gaussian space. The distribution type can be “normal”, “logNormal”, or “logitNormal”.
- limits: (vector< pair<double,double> >) lower and upper limits imposed to the observation.
Used only if the distribution is logitNormal. If there is no logitNormal distribution, this field is empty. - errormodel: (vector<string>) type of the associated error model
- autocorrelation: (vector<bool>) defines if there is auto correlation
Call getObservationInformation to get a list of the continuous observations present in the current project.
Usage
getContinuousObservationModel()
Value
A list associating each continuous observation to its model properties.
See Also
getObservationInformation setObservationDistribution setObservationLimits
setErrorModel setAutocorrelation
Click here to see examples
## Not run:
obsModels = getContinuousObservationModel()
obsModels
-> $prediction
c(Conc = “Cc”)
$formula
c(Conc = “Conc = Cc + (a+b*Cc)*e”)
$distribution
c(Conc = “logitNormal”)
$limits
list(Conc = c(0,11.5))
$errormodel
c(Conc = “combined1”)
$autocorrelation
c(Conc = TRUE)
## End(Not run)
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Get observations information
Description
Get the name, the type and the values of the observations present in the project.
Usage
getObservationInformation()
Value
A list containing the name of the observations, their type and their values (id, time and observationName (and occasion if present in the data set)).
Click here to see examples
## Not run:
info = getObservationInformation()
info
-> $name
c(“concentration”)
-> $type
c(concentration = “continuous”)
-> $concentration
id time concentration
1 0.5 0.0
. . .
N 9.0 10.8
## End(Not run)
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Set auto-correlation
Description
Add or remove auto-correlation from the error model used on some of the observation models.
Call getObservationInformation to get a list of the observation models present in the current project.
Usage
setAutocorrelation(...)
Arguments
…
Sequence of comma-separated pairs {(string)”observationModel”,(boolean)hasAutoCorrelation}.
See Also
Click here to see examples
## Not run:
setAutocorrelation(Conc = TRUE)
setAutocorrelation(Conc = TRUE, Effect = FALSE)
## End(Not run)
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Set error model
Description
Set the error model type to be used with some of the observation models.
Call getObservationInformation to get a list of the observation models present in the current project.
Usage
setErrorModel(...)
Arguments
…
A list of comma-separated pairs {observationModel = (string)errorModelType}.
Details
Available error model types are :
| “constant” | obs = pred + a*err |
| “proportional” | obs = pred + (b*pred)*err |
| “combined1” | obs = pred + (b*pred^c + a)*err |
| “combined2” | obs = pred + sqrt(a^2 + (b^2)*pred^(2c))*err |
Error model parameters will be initialized to 1 by default.
Call setPopulationParameterInformation to modify their initial value.
The value of the exponent parameter is fixed by default when using the “combined1” and “combined2” models.
Use setPopulationParameterInformation to enable its estimation.
See Also
getContinuousObservationModel setPopulationParameterInformation
Click here to see examples
## Not run:
setErrorModel(Conc = “constant”, Effect = “combined1”)
## End(Not run)
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Set observation model distribution
Description
Set the distribution in the Gaussian space of some of the observation models.
Available distribution types are “normal”, “logNormal”, or “logitNormal”.
Call getObservationInformation to get a list of the available observation models within the current project.
Usage
setObservationDistribution(...)
Arguments
…
A list of comma-separated pairs {observationModel = (string)”distribution”}.
See Also
Click here to see examples
## Not run:
setObservationDistribution(Conc = “normal”)
setObservationDistribution(Conc = “normal”, Effect = “logNormal”)
## End(Not run)
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Set observation model distribution limits
Description
Set the minimum and the maximum values between which some of the observations can be found.
Used only if the distribution of the error model is “logitNormal”, else wise it will not be taken into account
Usage
setObservationLimits(...)
Arguments
…
A list of comma-separated pairs {observationModel = [(double)min,(double)max] }
See Also
getContinuousObservationModel getObservationInformation
Click here to see examples
## Not run:
setObservationLimits( Conc = c(-Inf,Inf), Effect = c(0,Inf) )
## End(Not run)
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