parfn: Parameter transformation function
In dMod: Dynamic Modeling and Parameter Estimation in ODE Models

Description Usage Arguments Value See Also Examples

Generate functions that transform one parameter vector into another by means of a transformation, pushing forward the jacobian matrix of the original parameter. Usually, this function is called internally, e.g. by P. However, you can use it to add your own specialized parameter transformations to the general framework.

1	parfn(p2p, parameters = NULL, condition = NULL)

`p2p`	a transformation function for one condition, i.e. a function `p2p(p, fixed, deriv)` which translates a parameter vector `p` and a vector of fixed parameter values `fixed` into a new parameter vector. If `deriv = TRUE`, the function should return an attribute `deriv` with the Jacobian matrix of the parameter transformation.
`parameters`	character vector, the parameters accepted by the function
`condition`	character, the condition for which the transformation is defined

object of class parfn, i.e. a function p(..., fixed, deriv, conditions, env). The argument pars should be passed via the ... argument.

Contains attributes "mappings", a list of p2p functions, "parameters", the union of parameters acceted by the mappings and "conditions", the total set of conditions.

sumfn, P

# 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)