Observation models specify the observed variable, how an observation is expected to diverge from the model (i.e, the residual unexplained variability model), and parameter values. The observation model type is selected through the function name. The observed variable as well as the parameters are specified as function arguments.
The actual prediction from the model is the first argument of the function. It can be specified in a number of different ways:
obs_additive("effect")
obs_additive(~C["central"])
obs_additive(~base+slp*time)
If the definition contains a variable name on the left-hand side (as in conc~C["central"]
), the variable will appear in the generated model code.
This can be useful to make the model code more readable if the prediction is defined as a long equation.
The observation name can be specified via the name=
argument and is automatically derived if the argument is left empty. Adding an observation model
with an already existing name will replace the previous definition.
The variance of the error components are specified via the var_add=
and var_prop=
arguments of the function.
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