PK components can be added to a [pk_model] and exist in three different types: absorption, distribution, and elimination. The absorption component is optional, distribution and elimination are not and need to be added for the PK model to be valid.
A PK model can only have one component of each type and adding a component with an already existing type will replace the previous definition. For example, the distribution component will be a two compartment model in the following snippet:
pkm <- pk_model() + pk_absorption_fo() + pk_distribution_1cmp() + pk_distribution_2cmp() + pk_elimination_linear() + obs_additive(conc~C["central"]) pkm
All PK component functions allow the specification of the parameter model via their arguments. Arguments that refer to a
parameter start with the prefix prm_
. The default parameter model can be deduced from the default arguments in the usage section of the help entry. The parameter name, specified via the name=
argument of the parameter model building block allows the renaming of the model parameters.
For example, the parameter prm_vc=
refers to the central volume of distribution parameter in the one compartment distribution PK component and the default parameter model is a log-normal distribution. The following code block specifies a normal distribution parameter model and names the parameter v
:
pk_distribution_1cmp( prm_vc = prm_normal("v", mean = 50, var = 25) )
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