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# API concerning the observation models

## Description of the functions of the API

 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.

 getObservationInformation  setObservationDistribution  setObservationLimits
 setErrorModel  setAutocorrelation

## 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)).

## 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}.

 getContinuousObservationModel

## 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.

 getContinuousObservationModel setPopulationParameterInformation

## 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"}.

 getContinuousObservationModel

## 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] }

 getContinuousObservationModel  getObservationInformation