dMod (version 0.3.1)

Xs: Model prediction function for ODE models.

Description

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

Usage

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

Arguments

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

Value

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.

Examples

## Not run: 
# 
# ################################################################  
# ## Walk through the most frequently used functions in
# ## connection with ODE models
# ################################################################  
#   
# library(deSolve)
# 
# ## Model definition (text-based, scripting part)
# r <- NULL
# r <- addReaction(r, "A", "B", "k1*A", "translation")
# r <- addReaction(r, "B",  "", "k2*B", "degradation")
# 
# f <- as.eqnvec(r)
# observables <- eqnvec(Bobs = "s1*B")
# 
# innerpars <- getSymbols(c(names(f), f, observables))
# trafo0 <- structure(innerpars, names = innerpars)
# trafo0 <- replaceSymbols(innerpars, 
#                          paste0("exp(", innerpars, ")"), 
#                          trafo0)
# 
# conditions <- c("a", "b")
# 
# trafo <- list()
# trafo$a <- replaceSymbols("s1", "s1_a", trafo0)
# trafo$b <- replaceSymbols("s1", "s1_b", trafo0)
# 
# events <- list()
# events$a <- data.frame(var = "A", time = 5, value = 1, method = "add")
# events$b <- data.frame(var = "A", time = 5, value = 2, method = "add")
# 
# 
# ## Model definition (generate compiled objects 
# ## and functions from above information) 
# 
# # ODE model
# <<<<<<< HEAD
# model <- odemodel(f, jacobian = "inz.lsodes")
# =======
# model <- odemodel(f)
# >>>>>>> development
# 
# # Observation function
# g <- Y(observables, f, compile = TRUE, modelname = "obsfn")
# 
# # Parameter transformation for steady states
# pSS <- P(f, method = "implicit", condition = NULL)
# 
# # Condition-specific transformation and prediction functions
# p0 <- x <- NULL
# for (C in conditions) {
#   p0 <- p0 + P(trafo[[C]], condition = C)
#   x <- x + Xs(model, events = events[[C]], condition = C)
# }
# 
# ## Process data
# 
# # Data
# data <- datalist(
#   a = data.frame(time = c(0, 2, 7),
#                  name = "Bobs",
#                  value = c(.3, .3, .3),
#                  sigma = c(.03, .03, .03)),
#   b = data.frame(time = c(0, 2, 7),
#                  name = "Bobs",
#                  value = c(.1, .1, .2),
#                  sigma = c(.01, .01, .02))
# )
# 
# ## Evaluate model/data
# 
# # Initialize times and parameters
# times <- seq(0, 10, .1)
# outerpars <- attr(p0, "parameters")
# pouter <- structure(rnorm(length(outerpars)), 
#                     names = outerpars)
# 
# # Plot prediction
# plot((g*x*p0)(times, pouter))
# plot((g*x*pSS*p0)(times, pouter))
# plotFluxes(pouter, g*x*p0, times, getFluxes(r)$B)
# 
# # Fit data with L2 constraint
# constr <- constraintL2(mu = 0*pouter, sigma = 5)
# myfit <- trust(normL2(data, g*x*p0) + constr, 
#                pouter, rinit = 1, rmax = 10)
# plot((g*x*p0)(times, myfit$argument), data)
# 
# # List of fits
# obj <- normL2(data, g*x*p0) + constr
# mylist <- mstrust(normL2(data, g*x*p0) + constr, 
#                   pouter, fits = 10, cores = 4, sd = 4)
# plot(mylist)
# pars <- as.parframe(mylist)
# plotValues(pars)
# 
# bestfit <- as.parvec(pars)
# 
# # Compute profiles of the fit
# profile <- profile(normL2(data, g*x*p0) + constr, 
#                    bestfit, "k1", limits = c(-5, 5))
# plotProfile(profile)
# 
# # Add a validation objective function
# vali <- datapointL2("A", 2, "mypoint", .1, condition = "a")
# # Get validation point parameters
# mypoint <- trust(normL2(data, g*x*p0) + constr + vali, 
#                  c(mypoint = 0), rinit = 1, rmax = 10, 
#                  fixed = bestfit)$argument
# # Compoute profile and plot
# profile <- profile(normL2(data, g*x*p0) + constr + vali, 
#                    c(bestfit, mypoint), "mypoint")
# plotProfile(profile)
# 
# 
# ## Using the controls() function to manipulate objects ------------------------
# 
# # List the available controls of an object
# controls(x)
# 
# # List a specific control
# controls(x, condition = "a", name = "optionsSens")
# 
# # Change specific control
# controls(x, condition = "a", name = "optionsSens") <- 
#   list(method = "lsoda", rtol = 1e-8, atol = 1e-8)
# 
# # Almost every function (objfn, parfn, prdfn, obsfn) saves the arguments 
# # by which it was invoked in a list named "controls", which can be manipulated
# # by the controls() function.
# 
# 
# ## New example: condition-specific observation parameters ---------------------
# 
# # Condition-specific observation parameters
# f <- eqnvec(A = "-k1*A", B = "k1*A - k2*B")
# observables <- eqnvec(Bobs = "s1*B")
# conditions <- c("scale1", "scale2")
# 
# dynpars <- getSymbols(c(names(f), f))
# obspars <- setdiff(getSymbols(observables), dynpars)
# trafo0 <- structure(c(dynpars, obspars), names = c(dynpars, obspars))
# 
# obspars_all <- outer(obspars, conditions, paste, sep = "_")
# trafo_all <- structure(c(dynpars, obspars_all), 
#                        names = c(dynpars, obspars_all))
# trafo_all <- replaceSymbols(dynpars, 
#                             paste0("exp(", dynpars, ")"), 
#                             trafo_all)
# trafo_all <- replaceSymbols(obspars_all, 
#                             paste0("exp(", obspars_all, ")"), 
#                             trafo_all)
# 
# pObs <- NULL
# for (C in conditions) {
#   pObs <- pObs + P(replaceSymbols(obspars, 
#                                   paste(obspars, C, sep = "_"), 
#                                   trafo0), 
#                    condition = C)
# }
# 
# 
# x <- Xs(model)
# p <- P(trafo_all)
# y <- g*pObs*x*p
# 
# times <- seq(0, 10, .1)
# outerpars <- attr(p, "parameters")
# pouter <- structure(rnorm(length(outerpars)), names = outerpars)
# 
# # Plot prediction
# plot((g*pObs*x*p)(times, pouter))
# 
# 
# ## End(Not run)