# Continuous observation model

## Mlxtran observational model syntax

The DEFINITION: block in the [LONGITUDINAL] section is used to define the observational model:

```
DEFINITION:
observationName = {distribution = distributionType, prediction = predictionName, errorModel = errorModel(param)}
```

(notice that one can use type=continuous instead of distribution = distributionType)

For example, if the observation is a concentration with a combined error model (Concentration = Cc + (a+b*Cc)*e), the observational error model is written as

```
DEFINITION:
Concentration= {distribution = normal, prediction = Cc, errorModel=combined1(a, b)}
```

When the observational error is defined in the Mlxtran model file, the user must declare the observational model parameters (a and b in the presented example) as inputs.

## Rules and best practices

The eventual arguments of the error model can not be calculations, only input names.

In Monolix, the user sets the error model through the interface.

In Monolix, the name of the error models input parameters can not have any name.

The name of the input should correspond to the definition of the error model (ex. a for a constant error model, b for a proportional error model, (a,b) for a combined1 error model, …)

If there are several continuous outputs, the names of the error models input parameters should be linked to the order of the outputs (1 for the first error model, …)

For example, for a single output, a combined error model writes without any number as follows

CODE`DEFINITION: Concentration = {distribution = normal, prediction = Cc, errorModel=combined1(a, b)}`

For example, for two outputs, a combined error model and a constant error model write as follows

CODE`DEFINITION: Concentration = {distribution = normal, prediction = Cc, errorModel=combined1(a1, b1)} PCA = {distribution = normal, prediction = E, errorModel=constant(a2)}`

Notice that a parameter can not be shared by two error models. For example, in the previous Concentration/PCA example, we can not replace a2 by a1.