This creates a closure that simulates the model, similar to simulator.c

This is a shorter alternative to simulator.c (C backend).

Usage

simc(experiments, modelName, parMap = identity)

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.
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, and approximate sensitivity matrices, for each state variable, function, time-point, and 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.

This version of the function does not use the parallel package at all and cannot add noise to the simulations.

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 <- sim(parABC)