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