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

In dMod: Dynamic Modeling and Parameter Estimation in ODE Models

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)



## End(Not run)

dMod documentation built on Jan. 27, 2021, 1:07 a.m.