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This creates a closure that simulates the model

simulator.c.Rd

This function will use the parallel::mclapply to do the simulations simultaneously. Set options(mc.cores=detectCores()) or a similar sensible value: options(mc.cores=length(experiments))

Usage

simulator.c(
  experiments,
  modelName,
  parMap = identity,
  noise = FALSE,
  omit = 3,
  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.

omit

omit=0 returns all optional return values form the simulator, omit=1 will not calculate the fisher information (and thus not return it), omit=2 will omit the gradient of the log-likelihood, and omit=3 will omit the likelihood calculations alltogether. Omission is cumulative: omit=3 omits all the previously mentioned optional quantities.

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 the parameter vector (or matrix if more than one simulation per experiment is desired). The parameter vector this simnulator accepts is probably derived from the sampling space of a Bayesian method θ, so in the list of arguments, it is called parABC or (parMCMC would also have been a valid choice). These sampling parameters can be mapped to values the simulator can use via parMap. parModel <- parMap(parABC), where the ODE model is expected to work with parModel.

Some return values are optional and omiting them saves time.

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)