This building block declares a two compartment distribution component for a pharmacokinetic model.
pk_distribution_2cmp( prm_vc = prm_log_normal("vc", median = 100, var_log = 0.1), prm_vp = prm_log_normal("vp", median = 5, var_log = 0.1), prm_q = prm_log_normal("q", median = 50, var_log = 0.1) )
| prm_vc | Parameter model for the central volume of distribution |
|---|---|
| prm_vp | Parameter model for the peripheral volume of distribution |
| prm_q | Parameter model for the inter-compartmental clearance |
A building block of type 'pk_component'
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) )
pk_model() for the creation of PK models
Other distribution components:
pk_distribution_1cmp(),
pk_distribution_3cmp()