This creates a closure that simulates the model

This is a shorter alternative to the runModel function (C backend).

Usage

simulator.c(
  experiments,
  modelName,
  parMap = identity,
  noise = FALSE,
  approximateSensitivity = FALSE,
  method = 0
)

Arguments

experiments: a list of experiments to simulate: inital values, inputs, time vectors, initial times
modelName: a string (with optional comment indicating an .so file) which points out the model to simulate
parMap: the model will be called with parMap(parABC); so any parameter transformation can happen there.
noise: boolean variable. If noise=TRUE, Gaussian noise is added to the output of the simulations. The standard deviation of the Gaussian noise is equal to the measurement error. If noise=FALSE the output is the deterministic solution of the ODE system. noise and sensitivity calculations are mutually exclusive.
parABC: the parameters for the model, subject to change by parMap.

Value

a closure that returns the model's output for a given parameter vector

Details

It returns a closure around: - experiments, - the model, and - parameter mapping

The returned function depends only on parABC (the sampling parameters). The simulation will be done suing the rgsl backend.

Examples

 #  model.sbtab <- SBtabVFGEN::sbtab_from_tsv(dir(pattern="[.]tsv$"))
 #  experiments <- SBtabVFGEN::sbtab.data(model.sbtab)
 #  parABC <- SBtabVFGEN::sbtab.quantity(model.sbtab$Parameter)

 #  modelName <- checkModel("<insert_model_name>_gvf.c")
 #  simulate <- simulator.c(experiments, modelName,  parABC)
 #  yf <- simulate(parABC)