obsfn: Observation function
In dMod: Dynamic Modeling and Parameter Estimation in ODE Models

Description Usage Arguments Details Value Examples

An observation function is a function is that is concatenated with a prediction function via prodfn to yield a new prediction function, see prdfn. Observation functions are generated by Y. Handling of the conditions is then organized by the obsfn object.

1	obsfn(X2Y, parameters = NULL, condition = NULL)

`X2Y`	the low-level observation function generated e.g. by Y.
`parameters`	character vector with parameter names
`condition`	character, the condition name

Observation functions can be "added" by the "+" operator, see sumfn. Thereby, observations for different conditions are merged or, overwritten. Observation functions can also be concatenated with other functions, e.g. observation functions (obsfn) or prediction functions (prdfn) by the "*" operator, see prodfn.

Object of class obsfn, i.e. a function x(..., fixed, deriv, conditions, env) which returns a prdlist. The arguments out (prediction) and pars (parameter values) should be passed via the ... argument.

# Define a time grid on which to make a prediction by peace-wise linear function.
# Then define a (generic) prediction function based on thid grid.
times <- 0:5
grid <- data.frame(name = "A", time = times, row.names = paste0("p", times))
x <- Xd(grid)

# Define an observable and an observation function
observables <- eqnvec(Aobs = "s*A")
g <- Y(g = observables, f = NULL, states = "A", parameters = "s")

# Collect parameters and define an overarching parameter transformation
# for two "experimental condtions".
dynpars <- attr(x, "parameters")
obspars <- attr(g, "parameters")
innerpars <- c(dynpars, obspars)

trafo <- structure(innerpars, names = innerpars)
trafo_C1 <- replaceSymbols(innerpars, paste(innerpars, "C1", sep = "_"), trafo)
trafo_C2 <- replaceSymbols(innerpars, paste(innerpars, "C2", sep = "_"), trafo)

p <- NULL
p <- p + P(trafo = trafo_C1, condition = "C1")
p <- p + P(trafo = trafo_C2, condition = "C2")

# Collect outer (overarching) parameters and 
# initialize with random values
outerpars <- attr(p, "parameters")
pars <- structure(runif(length(outerpars), 0, 1), names = outerpars)

# Predict internal/unobserved states
out1 <- (x*p)(times, pars)
plot(out1)

# Predict observed states in addition to unobserved
out2 <- (g*x*p)(times, pars)
plot(out2)