Xs_steady: Steady state prediction function for ODE models.
In dlill/MRAr: Functions to do MRA with

Description Usage Arguments Value

Interface to combine an ODE and its sensitivity equations into one model function x(times, pars, deriv = TRUE) returning ODE output and sensitivities.

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Xs_steady(odemodel, forcings = NULL, events = NULL, names = NULL,
  condition = NULL, optionsOde = list(method = "lsoda"),
  optionsSens = list(method = "lsodes"))

`odemodel`	object of class odemodel
`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 events. Within `Xs()` a `data.frame` of additional events is generated to reset the sensitivities appropriately, depending on the event method. ATTENTION: The addional events are not dynamically recalculated. If you call the prediction function with alternative events, the prediction is fine but the sensitivities can be wrong.
`names`	character vector with the states to be returned. If NULL, all states are returned.
`condition`	either NULL (generic prediction for any condition) or a character, denoting the condition for which the function makes a prediction.
`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

Object of class prdfn. If the function is called with parameters that result from a parameter transformation (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.

dlill/MRAr documentation built on May 16, 2019, 7:24 a.m.