Description Usage Arguments Details Value See Also
This building block declares a two compartment distribution component for a pharmacokinetic model.
1 2 3 4 5 | 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 |
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:
1 2 3 4 5 6 7 | 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
:
1 2 3 | pk_distribution_1cmp(
prm_vc = prm_normal("v", mean = 50, var = 25)
)
|
A building block of type 'pk_component'
pk_model()
for the creation of PK models
Other distribution components:
pk_distribution_1cmp()
,
pk_distribution_3cmp()
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