R: Solve PK Model Through 'deSolve' Package or Analytical...

solve_fun {pksensi}

R Documentation

Solve PK Model Through deSolve Package or Analytical Function

Description

Solve time-dependent quantities/concentrations of different variables in PK model through the imported ode function in deSolve package. It can also be used to solve the function with analytical solution.

Usage

solve_fun(
  x,
  time = NULL,
  initParmsfun = "initParms",
  initState,
  dllname = NULL,
  func = "derivs",
  initfunc = "initmod",
  outnames,
  method = "lsode",
  rtol = 1e-08,
  atol = 1e-12,
  model = NULL,
  lnparam = F,
  vars = NULL,
  tell = T,
  ...
)

Arguments

`x`	a list of storing information in the defined sensitivity function.
`time`	a vector to define the given time sequence.
`initParmsfun`	a character for the given specific initial parameter function.
`initState`	a vector that define the initial values of state variables for the ODE system.
`dllname`	a string giving the name of the shared library (without extension) that contains the compiled function.
`func`	the name of the function in the dynamically loaded shared library.
`initfunc`	the name of the initialization function (which initialises values of parameters), as provided in dllname.
`outnames`	the names of output variables calculated in the compiled function `func`.
`method`	method used by integrator (deSolve).
`rtol`	argument passed to integrator (deSolve).
`atol`	argument passed to integrator (deSolve).
`model`	the defined analytical equation with functional output.
`lnparam`	a logical value that make the statement of the log-transformed parameter (default FALSE).
`vars`	a character for the selected output.
`tell`	a logical value to automatically combine the result y to decoupling simulation x.
`...`	additional arguments for `deSolve::ode` method.

References

Soetaert, K. E., Petzoldt, T., & Setzer, R. W. (2010). Solving differential equations in R: package deSolve. Journal of Statistical Software, 33(9), 1–25.

Examples

  q <- "qunif"
  q.arg <- list(list(min = 0.6, max = 1.0),
   list(min = 0.5, max = 1.5),
   list(min = 0.02, max = 0.3),
   list(min = 20, max = 60))

  params <- c("F","KA","KE","V")

  set.seed(1234)
  x <- rfast99(params = params, n = 200, q = q, q.arg = q.arg, rep = 20)

  time <- seq(from = 0.25, to = 12.25, by = 0.5)
  y <- solve_fun(x, model = FFPK, time = time, vars = "output")

  pksim(y) # Visualize uncertainty of model output