dMod (version 0.3.1)

Y: Observation functions.

Description

Creates an object of type obsfn that evaluates an observation function and its derivatives based on the output of a model prediction function, see prdfn, as e.g. produced by Xs.

Usage

Y(g, f = NULL, states = NULL, parameters = NULL, condition = NULL, attach.input = TRUE, compile = FALSE, modelname = NULL, verbose = FALSE)

Arguments

g

Named character vector or equation vector defining the observation function

f

Named character or equation vector or object that can be converted to eqnvec. Represents the underlying ODE.

states

character vector, alternative definition of "states", usually the names of f

parameters

character vector, alternative definition of the "parameters", usually the symbols contained in "g" and "f" except for states and the code word time.

condition

either NULL (generic prediction for any condition) or a character, denoting the condition for which the function makes a prediction.

attach.input

logical, indiating whether the original input should be returned with the output.

compile

Logical, compile the function (see funC0)

modelname

Character, used if compile = TRUE, sets a fixed filename for the C file.

verbose

Print compiler output to R command line.

Value

Object of class obsfn, i.e. a function y(..., deriv = TRUE, conditions = NULL) representing the evaluation of the observation function. Arguments out (model prediction) and pars (parameter values) shoudl be passed by the ... argument. If out has the attribute "sensitivities", the result of y(out, pars), will have an attributed "deriv" which reflecs the sensitivities of the observation with respect to the parameters. If pars is the result of a parameter transformation p(pars) (see P), the Jacobian of the parameter transformation and the sensitivities of the observation function are multiplied according to the chain rule for differentiation.

Examples

# 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)