Description
Interface to combine an ODE and its sensitivity equations
into one model function x(times, pars, forcings, events, deriv = TRUE) returning ODE output and sensitivities.Usage
Xs(func, extended, forcings = NULL, events = NULL,
optionsOde = list(method = "lsoda"), optionsSens = list(method =
"lsodes"))
Arguments
func
return value from funC(f) where f defines the ODE.
extended
return value from funC(c(f, sensitivitiesSymb(f))).
forcings
data.frame with columns name (factor), time (numeric) and value (numeric).
The ODE forcings.
events
data.frame of events with columns "var" (character, the name of the state to be
affected), "time" (numeric, time point), "value" (numeric, value), "method" (character, either
"replace", "add" or "multiply"). See event optionsOde
list with arguments to be passed to odeC() for the ODE integration.
optionsSens
list with arguments to be passed to odeC() for integration of the extended system
Value
- A model prediction function
x(times, pars, forcings, events, deriv = TRUE) representing
the model evaluation. The result of
x(times, pars, forcings, events, deriv = TRUE) contains
attributes "sensitivities" and "deriv" with the sensitivities if deriv=TRUE.
If deriv=FALSE, sensitivities are not computed (saving time).
If pars is
the result of p(pouter) (see P), the Jacobian of the parameter transformation
and the sensitivities of the ODE are multiplied according to the chain rule for
differentiation. The result is saved in the attributed "deriv",
i.e. in this case the attibutes "deriv" and "sensitivities" do not coincide.