#' Produce predictions from a kinetic model using specific parameters
#'
#' This function produces a time series for all the observed variables in a
#' kinetic model as specified by \code{\link{mkinmod}}, using a specific set of
#' kinetic parameters and initial values for the state variables.
#'
#' @aliases mkinpredict mkinpredict.mkinmod mkinpredict.mkinfit
#' @param x A kinetic model as produced by \code{\link{mkinmod}}, or a kinetic
#' fit as fitted by \code{\link{mkinfit}}. In the latter case, the fitted
#' parameters are used for the prediction.
#' @param odeparms A numeric vector specifying the parameters used in the
#' kinetic model, which is generally defined as a set of ordinary
#' differential equations.
#' @param odeini A numeric vector containing the initial values of the state
#' variables of the model. Note that the state variables can differ from the
#' observed variables, for example in the case of the SFORB model.
#' @param outtimes A numeric vector specifying the time points for which model
#' predictions should be generated.
#' @param solution_type The method that should be used for producing the
#' predictions. This should generally be "analytical" if there is only one
#' observed variable, and usually "deSolve" in the case of several observed
#' variables. The third possibility "eigen" is faster but not applicable to
#' some models e.g. using FOMC for the parent compound.
#' @param method.ode The solution method passed via \code{\link{mkinpredict}}
#' to \code{\link{ode}} in case the solution type is "deSolve". The default
#' "lsoda" is performant, but sometimes fails to converge.
#' @param use_compiled If set to \code{FALSE}, no compiled version of the
#' \code{\link{mkinmod}} model is used, even if is present.
#' @param atol Absolute error tolerance, passed to \code{\link{ode}}. Default
#' is 1e-8, lower than in \code{\link{lsoda}}.
#' @param rtol Absolute error tolerance, passed to \code{\link{ode}}. Default
#' is 1e-10, much lower than in \code{\link{lsoda}}.
#' @param map_output Boolean to specify if the output should list values for
#' the observed variables (default) or for all state variables (if set to
#' FALSE). Setting this to FALSE has no effect for analytical solutions,
#' as these always return mapped output.
#' @param na_stop Should it be an error if deSolve::ode returns NaN values
#' @param \dots Further arguments passed to the ode solver in case such a
#' solver is used.
#' @import deSolve
#' @return A matrix with the numeric solution in wide format
#' @author Johannes Ranke
#' @examples
#'
#' SFO <- mkinmod(degradinol = mkinsub("SFO"))
#' # Compare solution types
#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
#' solution_type = "analytical")
#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
#' solution_type = "deSolve")
#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
#' solution_type = "deSolve", use_compiled = FALSE)
#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
#' solution_type = "eigen")
#'
#' # Compare integration methods to analytical solution
#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
#' solution_type = "analytical")[21,]
#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
#' method = "lsoda")[21,]
#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
#' method = "ode45")[21,]
#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
#' method = "rk4")[21,]
#' # rk4 is not as precise here
#'
#' # The number of output times used to make a lot of difference until the
#' # default for atol was adjusted
#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),
#' seq(0, 20, by = 0.1))[201,]
#' mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),
#' seq(0, 20, by = 0.01))[2001,]
#'
#' # Comparison of the performance of solution types
#' SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"),
#' m1 = list(type = "SFO"), use_of_ff = "max")
#' if(require(rbenchmark)) {
#' benchmark(replications = 10, order = "relative", columns = c("test", "relative", "elapsed"),
#' eigen = mkinpredict(SFO_SFO,
#' c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
#' c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
#' solution_type = "eigen")[201,],
#' deSolve_compiled = mkinpredict(SFO_SFO,
#' c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
#' c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
#' solution_type = "deSolve")[201,],
#' deSolve = mkinpredict(SFO_SFO,
#' c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
#' c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
#' solution_type = "deSolve", use_compiled = FALSE)[201,],
#' analytical = mkinpredict(SFO_SFO,
#' c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
#' c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
#' solution_type = "analytical", use_compiled = FALSE)[201,])
#' }
#'
#' \dontrun{
#' # Predict from a fitted model
#' f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE)
#' f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE, solution_type = "deSolve")
#' head(mkinpredict(f))
#' }
#'
#' @export
mkinpredict <- function(x, odeparms, odeini, outtimes, ...)
{
UseMethod("mkinpredict", x)
}
#' @rdname mkinpredict
#' @export
mkinpredict.mkinmod <- function(x,
odeparms = c(k_parent_sink = 0.1),
odeini = c(parent = 100),
outtimes = seq(0, 120, by = 0.1),
solution_type = "deSolve",
use_compiled = "auto",
method.ode = "lsoda", atol = 1e-8, rtol = 1e-10,
map_output = TRUE,
na_stop = TRUE,
...)
{
# Names of state variables and observed variables
mod_vars <- names(x$diffs)
obs_vars <- names(x$spec)
# Order the inital values for state variables if they are named
if (!is.null(names(odeini))) {
odeini <- odeini[mod_vars]
}
out_obs <- matrix(NA, nrow = length(outtimes), ncol = 1 + length(obs_vars),
dimnames = list(as.character(outtimes), c("time", obs_vars)))
out_obs[, "time"] <- outtimes
n_out_na <- 0 # to check if we get NA values with deSolve
if (solution_type == "analytical") {
# This is clumsy, as we wanted fast analytical predictions for mkinfit,
# which bypasses mkinpredict in the case of analytical solutions
pseudo_observed <-
data.frame(name = rep(obs_vars, each = length(outtimes)),
time = rep(outtimes, length(obs_vars)))
pseudo_observed$predicted <- x$deg_func(pseudo_observed, odeini, odeparms)
for (obs_var in obs_vars) {
out_obs[, obs_var] <- pseudo_observed[pseudo_observed$name == obs_var, "predicted"]
}
# We don't have solutions for unobserved state variables, the output of
# analytical solutions is always mapped to observed variables
return(out_obs)
}
if (solution_type == "eigen") {
evalparse <- function(string) {
eval(parse(text=string), as.list(c(odeparms, odeini)))
}
coefmat.num <- matrix(sapply(as.vector(x$coefmat), evalparse),
nrow = length(mod_vars))
e <- eigen(coefmat.num)
c <- solve(e$vectors, odeini)
f.out <- function(t) {
e$vectors %*% diag(exp(e$values * t), nrow=length(mod_vars)) %*% c
}
o <- matrix(mapply(f.out, outtimes),
nrow = length(mod_vars), ncol = length(outtimes))
out <- cbind(outtimes, t(o))
colnames(out) <- c("time", mod_vars)
}
if (solution_type == "deSolve") {
if (!is.null(x$cf) & use_compiled[1] != FALSE) {
out <- deSolve::ode(
y = odeini,
times = outtimes,
func = "diffs",
initfunc = "initpar",
dllname = inline::getDynLib(x$cf)[["name"]],
parms = odeparms[x$parms], # Order matters when using compiled models
method = method.ode,
atol = atol,
rtol = rtol,
...
)
} else {
mkindiff <- function(t, state, parms) {
time <- t
diffs <- vector()
for (box in names(x$diffs))
{
diffname <- paste("d", box, sep="_")
diffs[diffname] <- with(as.list(c(time, state, parms)),
eval(parse(text=x$diffs[[box]])))
}
return(list(c(diffs)))
}
out <- deSolve::ode(
y = odeini,
times = outtimes,
func = mkindiff,
parms = odeparms,
method = method.ode,
atol = atol,
rtol = rtol,
...
)
}
n_out_na <- sum(is.na(out))
if (n_out_na > 0 & na_stop) {
cat("odeini:\n")
print(odeini)
cat("odeparms:\n")
print(odeparms)
cat("out:\n")
print(out)
stop("Differential equations were not integrated for all output times because\n",
n_out_na, " NaN values occurred in output from ode()")
}
}
if (map_output) {
# Output transformation for models with unobserved compartments like SFORB
# if not already mapped in analytical solution
if (n_out_na > 0 & !na_stop) {
available <- c(TRUE, rep(FALSE, length(outtimes) - 1))
} else {
available <- rep(TRUE, length(outtimes))
}
for (var in names(x$map)) {
if((length(x$map[[var]]) == 1)) {
out_obs[available, var] <- out[available, var]
} else {
out_obs[available, var] <- out[available, x$map[[var]][1]] +
out[available, x$map[[var]][2]]
}
}
return(out_obs)
} else {
dimnames(out) <- list(time = as.character(outtimes), c("time", mod_vars))
return(out)
}
}
#' @rdname mkinpredict
#' @export
mkinpredict.mkinfit <- function(x,
odeparms = x$bparms.ode,
odeini = x$bparms.state,
outtimes = seq(0, 120, by = 0.1),
solution_type = "deSolve",
use_compiled = "auto",
method.ode = "lsoda", atol = 1e-8, rtol = 1e-10,
map_output = TRUE, ...)
{
mkinpredict(x$mkinmod, odeparms, odeini, outtimes, solution_type, use_compiled,
method.ode, atol, rtol, map_output, ...)
}
