nlmixr2autoinit (version 1.0.0)

Fit_3cmpt_iv: Fit intravenous pharmacokinetic data to a three-compartment linear elimination model

Description

Fits intravenous (IV) pharmacokinetic data to a three-compartment model with linear (first-order) elimination using the naive pooled data approach. Supports multiple estimation methods provided by nlmixr2 and can optionally return only predicted concentrations to support efficient simulation workflows.

Usage

Fit_3cmpt_iv(
  data,
  est.method,
  input.cl,
  input.vc3cmpt,
  input.vp3cmpt,
  input.vp23cmpt,
  input.q3cmpt,
  input.q23cmpt,
  input.add,
  return.pred.only = FALSE,
  ...
)

Value

If return.pred.only = TRUE, returns a data.frame

with a single column cp (predicted concentrations). Otherwise, returns a fitted model object produced by nlmixr2.

Arguments

data

A data frame containing IV pharmacokinetic data formatted for nlmixr2.

est.method

Estimation method to use in nlmixr2. Must be one of: "rxSolve", "nls", "nlm", "nlminb", or "focei".

input.cl

Initial estimate of clearance (CL).

input.vc3cmpt

Initial estimate of central volume of distribution (V1).

input.vp3cmpt

Initial estimate of first peripheral volume of distribution (V2).

input.vp23cmpt

Initial estimate of second peripheral volume of distribution (V3).

input.q3cmpt

Initial estimate of first inter-compartmental clearance (Q1).

input.q23cmpt

Initial estimate of second inter-compartmental clearance (Q2).

input.add

Initial estimate of the additive residual error.

return.pred.only

Logical; if TRUE, returns a data frame with only predicted concentrations (cp) for all observations in the input data.

...

Additional arguments passed to nlmixr2(), such as a user-defined control = foceiControl(...) or other control settings.

Author

Zhonghui Huang

Examples

# \donttest{
dat <- Bolus_2CPT
# Fit using 'nls'
Fit_3cmpt_iv(
  data = dat,
  est.method = "nls",
  input.cl = 4,
  input.vc3cmpt = 70,
  input.vp3cmpt = 35,
  input.vp23cmpt = 5,
  input.q3cmpt = 4,
  input.q23cmpt = 4,
  input.add = 10
)
# Return only predicted concentrations
Fit_3cmpt_iv(
  data = dat,
  est.method = "rxSolve",
  input.cl = 4,
  input.vc3cmpt = 70,
  input.vp3cmpt = 35,
  input.vp23cmpt = 35,
  input.q3cmpt = 4,
  input.q23cmpt = 4,
  input.add = 10,
  return.pred.only = TRUE
)
# }