dMod (version 1.0.0)

odemodel: Generate the model objects for use in Xs (models with sensitivities)

Description

Generate the model objects for use in Xs (models with sensitivities)

Usage

odemodel(f, deriv = TRUE, forcings = NULL, events = NULL,
  outputs = NULL, fixed = NULL, estimate = NULL,
  modelname = "odemodel", solver = c("deSolve", "Sundials"),
  gridpoints = NULL, verbose = FALSE, ...)

Arguments

f

Something that can be converted to eqnvec, e.g. a named character vector with the ODE

deriv

logical, generate sensitivities or not

forcings

Character vector with the names of the forcings

events

data.frame of events with columns "var" (character, the name of the state to be affected), "time" (character or numeric, time point), "value" (character or numeric, value), "method" (character, either "replace" or "add"). See events. Events need to be defined here if they contain parameters, like the event time or value. If both, time and value are purely numeric, they can be specified in Xs(), too.

outputs

Named character vector for additional output variables.

fixed

Character vector with the names of parameters (initial values and dynamic) for which no sensitivities are required (will speed up the integration).

estimate

Character vector specifying parameters (initial values and dynamic) for which sensitivities are returned. If estimate is specified, it overwrites `fixed`.

modelname

Character, the name of the C file being generated.

solver

Solver for which the equations are prepared.

gridpoints

Integer, the minimum number of time points where the ODE is evaluated internally

verbose

Print compiler output to R command line.

...

Further arguments being passed to funC.

Value

list with func (ODE object) and extended (ODE+Sensitivities object)

Examples

# NOT RUN {
## Generate a compiled ODE model from an equation vector
## The model will not return sensitivities for "switch"
## Files will be generated in your working directory!

f <- eqnvec(A = "-k*A + switch*F")
model <- odemodel(f, forcings = "F", fixed = "switch")
print(model)

## Generate the same model from an equation list
f <- addReaction(NULL, from = "", to = "A", rate = "switch*F", description = "production")
f <- addReaction(f   , from = "A", to = "", rate = "k*A", description = "degradation")
print(f)

model <- odemodel(f, forcings = "F", fixed = "switch")
print(model)


# create forcings
forc1 <- data.frame(name = "F", time = seq(0,5, 0.1), value = sin(seq(0,5,0.1)))
forc2 <- data.frame(name = "F", time = seq(0,5, 0.1), value = exp(-seq(0,5,0.1)))
forc3 <- data.frame(name = "F", time= 0,              value = 0.1)


x <- Xs(model, forc1, condition = "forc1") + 
  Xs(model, forc2, condition = "forc2") + 
  Xs(model, forc3, condition = "forc3")

g <- Y(c(out1 = "F * A", out2 = "F"), x)

times <-  seq(0,5, 0.001)
pars <- setNames(runif(length(getParameters(x))), getParameters(x))

pred <- (g*x)(times, pars)  
plot(pred)


# }