path: root/R/mkinpredict.R

# Copyright (C) 2010-2016,2018 Johannes Ranke
# Some lines in this code are copyright (C) 2013 Eurofins Regulatory AG
# Contact: jranke@uni-bremen.de

# This file is part of the R package mkin

# mkin is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any later
# version.

# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more
# details.

# You should have received a copy of the GNU General Public License along with
# this program. If not, see <http://www.gnu.org/licenses/>

mkinpredict <- function(x, odeparms, odeini,
  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, ...)
{
  UseMethod("mkinpredict", x)
}

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, ...)
{

  # Get the names of the state variables in the model
  mod_vars <- names(x$diffs)

  # Order the inital values for state variables if they are named
  if (!is.null(names(odeini))) {
    odeini <- odeini[mod_vars]
  }

  # 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(x$map[[1]])[1]
    parent.name = names(x$diffs)[[1]]
    o <- switch(parent.type,
      SFO = SFO.solution(outtimes,
          evalparse(parent.name),
          ifelse(x$use_of_ff == "min",
      evalparse(paste("k", parent.name, "sink", sep="_")),
      evalparse(paste("k", parent.name, sep="_")))),
      FOMC = FOMC.solution(outtimes,
          evalparse(parent.name),
          evalparse("alpha"), evalparse("beta")),
      IORE = IORE.solution(outtimes,
          evalparse(parent.name),
          ifelse(x$use_of_ff == "min",
      evalparse(paste("k__iore", parent.name, "sink", sep="_")),
      evalparse(paste("k__iore", parent.name, sep="_"))),
            evalparse("N_parent")),
      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, "sink", sep="_")))
    )
    out <- data.frame(outtimes, o)
    names(out) <- c("time", sub("_free", "", parent.name))
  }
  if (solution_type == "eigen") {
    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 <- data.frame(outtimes, t(o))
    names(out) <- c("time", mod_vars)
  }
  if (solution_type == "deSolve") {
    if (!is.null(x$cf) & use_compiled[1] != FALSE) {
      out <- ode(
        y = odeini,
        times = outtimes,
        func = "func",
        initfunc = "initpar",
        dllname = getDynLib(x$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 <- ode(
        y = odeini,
        times = outtimes,
        func = mkindiff,
        parms = odeparms,
        method = method.ode,
        atol = atol,
        rtol = rtol,
        ...
      )
    }
    if (sum(is.na(out)) > 0) {
      stop("Differential equations were not integrated for all output times because\n",
     "NaN values occurred in output from ode()")
      }
  }
  if (map_output) {
    # Output transformation for models with unobserved compartments like SFORB
    out_mapped <- data.frame(time = out[,"time"])
    for (var in names(x$map)) {
      if((length(x$map[[var]]) == 1) || solution_type == "analytical") {
        out_mapped[var] <- out[, var]
      } else {
        out_mapped[var] <- rowSums(out[, x$map[[var]]])
      }
    }
    return(out_mapped)
  } else {
    return(out)
  }
}

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, ...)
}