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predict.mkinmod <- function(mkinmod, odeparms, odeini, outtimes, solution_type = "deSolve", map_output = TRUE, atol = 1e-6) {
# Get the names of the state variables in the model
mod_vars <- names(mkinmod$diffs)
# Create function for evaluation of expressions with ode parameters and initial values
evalparse <- function(string)
{
eval(parse(text=string), as.list(c(odeparms, odeini)))
}
# Create a function calculating the differentials specified by the model
# if necessary
if (solution_type == "analytical") {
parent.type = names(mkinmod$map[[1]])[1]
parent.name = names(mkinmod$diffs)[[1]]
o <- switch(parent.type,
SFO = SFO.solution(outtimes,
evalparse(parent.name),
evalparse(paste("k", parent.name, sep="_"))),
FOMC = FOMC.solution(outtimes,
evalparse(parent.name),
evalparse("alpha"), evalparse("beta")),
DFOP = DFOP.solution(outtimes,
evalparse(parent.name),
evalparse("k1"), evalparse("k2"),
evalparse("g")),
HS = HS.solution(outtimes,
evalparse(parent.name),
evalparse("k1"), evalparse("k2"),
evalparse("tb")),
SFORB = SFORB.solution(outtimes,
evalparse(parent.name),
evalparse(paste("k", parent.name, "bound", sep="_")),
evalparse(paste("k", sub("free", "bound", parent.name), "free", sep="_")),
evalparse(paste("k", parent.name, sep="_")))
)
out <- cbind(outtimes, o)
dimnames(out) <- list(outtimes, c("time", sub("_free", "", parent.name)))
}
if (solution_type == "eigen") {
coefmat.num <- matrix(sapply(as.vector(mkinmod$coefmat), evalparse),
nrow = length(mod_vars))
e <- eigen(coefmat.num)
c <- solve(e$vectors, odeini)
f.out <- function(t) {
e$vectors %*% diag(exp(e$values * t), nrow=length(mod_vars)) %*% c
}
o <- matrix(mapply(f.out, outtimes),
nrow = length(mod_vars), ncol = length(outtimes))
dimnames(o) <- list(mod_vars, outtimes)
out <- cbind(time = outtimes, t(o))
}
if (solution_type == "deSolve") {
mkindiff <- function(t, state, parms) {
time <- t
diffs <- vector()
for (box in names(mkinmod$diffs))
{
diffname <- paste("d", box, sep="_")
diffs[diffname] <- with(as.list(c(time, state, parms)),
eval(parse(text=mkinmod$diffs[[box]])))
}
return(list(c(diffs)))
}
out <- ode(
y = odeini,
times = outtimes,
func = mkindiff,
parms = odeparms,
atol = atol
)
}
if (map_output) {
# Output transformation for models with unobserved compartments like SFORB
out_mapped <- data.frame(time = out[,"time"])
for (var in names(mkinmod$map)) {
if((length(mkinmod$map[[var]]) == 1) || solution == "analytical") {
out_mapped[var] <- out[, var]
} else {
out_mapped[var] <- rowSums(out[, mkinmod$map[[var]]])
}
}
return(out_mapped)
} else {
return(out)
}
}
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