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simulates an ode model with extra work

gsl_odeiv2_fi.Rd

This function calls a C function which solves an initial value problem, calculates the sensitivity of the solution, log-likelihood value ll, gradient of ll amd Fisher-Information.

Usage

gsl_odeiv2_fi(
  odeModel,
  experiments,
  p,
  abs.tol = 1e-06,
  rel.tol = 1e-05,
  initial.step.size = 0.001,
  method = 0,
  omit = 0,
  time.out = 1,
  num.steps = 0
)

Arguments

odeModel

the name of the ODE model to simulate (a shared library of the same name will be dynamically loaded and needs to be created first). Alternatively this can be the ode object created by as_ode, with a shared library path attached to it.

experiments

a list of N simulation experiments (time, parameters, initial value, events)

p

a matrix of parameters with M columns

abs.tol

absolute tolerance, real scalar

rel.tol

relative tolerance, real scalar

initial.step.size

initial value for the step size; the step size will adapt to a value that observes the tolerances, real scalar

method

integration method (see method and name_method).

omit

an integer that indicates how many of these to omit in this order: fisher information, gradient of the log-likelihood, log-likelihood

time.out

in seconds (early rejection due to long simulation time). This can trigger at measurement times (outputTime).

num.steps

maximum number of steps the integration method is permitted to do; early rejection. This condition can trigger at any point during the integration.

Value

a list of the solution trajectories y(t;p) for all experiments (named like the experiments), as well as the output functions

Details

The model is always simulated using a shared library. The path to the shared library can be passed in three different ways:

  1. Character vector: odeModel <- c("AKAKR4","/tmp/path/AKAR4.so")

  2. A comment: comment(odeModel) <- "/tmp/path/AKAR4.so"

  3. As part of the ode object:

odeModel <- as_ode(m)`
so_path(odeModel) <- "/tmp/path/AKAR4.so"

The shared library needs to be created first. Either with R CMD SHLIB, shlib, or manually on the system's command line (bash, zsh, etc.).

Examples

# \donttest{
  requireNamespace("errors")
  f <- uqsa_example("AKAR4")
  m <- model_from_tsv(f)
  o <- as_ode(m)
  ex <- experiments(m,o)
  C <- generate_code(o)
  c_path(o) <- write_c_code(C)
  so_path(o) <- shlib(o)
  print(o)
#>                 Model name : AKAR4
#>                     C file : /tmp/Rtmp3WxBip/adf9204aaf2748b8/AKAR4.c [2026-05-15 14:20:51.290917]
#>             shared library : /tmp/Rtmp3WxBip/adf9204aaf2748b8/AKAR4.so [2026-05-15 14:20:51.290917]
#>  Number of state variables : 2
#>       Number of parameters : 5
#>          Number of outputs : 1
#>          Conservation laws : 2
#>            Transformations : no
  y <- gsl_odeiv2_fi(o,ex,values(m$Parameter))
  print(length(y))
#> [1] 3
  print(names(y[[1]]))
#> [1] "cpuSeconds"        "numSteps"          "status"           
#> [4] "state"             "func"              "logLikelihood"    
#> [7] "gradLogLikelihood" "FisherInformation"
  par(mfrow=c(length(ex),1))
  for (i in seq_along(y)){
      plot(
          errors::as.errors(ex[[i]]$outputTimes),
          ex[[i]]$data,
          xlab="time",
          ylab=rownames(y[[i]]$data)[1],
          main=names(ex)[i],
          ylim=c(100,200)
      )
      lines(ex[[i]]$outputTimes,drop(y[[i]]$func),col='red')
  }

# }