# $Id$
# Some of the CAKE R modules are based on mkin,
# Developed by Hybrid Intelligence (formerly Tessella), part of Capgemini Engineering,
# for Syngenta: Copyright (C) 2011-2022 Syngenta
# Tessella Project Reference: 6245, 7247, 8361, 7414, 10091
# The CAKE R modules are 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/>.
# Shifts parameters slightly away from boundaries specified in "lower" and
# "upper" (to avoid computational issues after parameter transforms in modFit).
ShiftAwayFromBoundaries <- function(parameters, lower, upper) {
parametersOnLowerBound = which(parameters == lower)
parameters[parametersOnLowerBound] <- parameters[parametersOnLowerBound] * (1 + .Machine$double.eps) + .Machine$double.xmin
parametersOnUpperBound = which(parameters == upper)
parameters[parametersOnUpperBound] <- parameters[parametersOnUpperBound] * (1 - .Machine$double.neg.eps) - .Machine$double.xmin
return(parameters)
}
# Adjusts stated initial values to put into the ODE solver.
#
# odeini: The initial values to adjust (in the form that would be fed into the ode function).
# cake.model: The expression of the model that we are solving.
# odeparms: The parameters for the ODE (in the form that would be fed into the ode function).
#
# Returns: Adjusted initial values.
AdjustOdeInitialValues <- function(odeini, cake.model, odeparms) {
odeini.names <- names(odeini)
for (ini.name in odeini.names) {
# For DFOP metabolites in two compartments, need to calculate some initial conditions for the ODEs.
if (!(ini.name %in% names(cake.model$diffs))) {
subcompartment1.name <- paste(ini.name, "1", sep = "_")
subcompartment2.name <- paste(ini.name, "2", sep = "_")
if (subcompartment1.name %in% names(cake.model$diffs) && subcompartment2.name %in% names(cake.model$diffs)) {
g.parameter.name = paste("g", ini.name, sep = "_")
odeini[[subcompartment1.name]] <- odeini[[ini.name]] * odeparms[[g.parameter.name]]
odeini[[subcompartment2.name]] <- odeini[[ini.name]] * (1 - odeparms[[g.parameter.name]])
}
}
}
# It is important that these parameters are stated in the same order as the differential equations.
return(odeini[names(cake.model$diffs)])
}
# Post-processes the output from the ODE solver (or analytical process), including recombination of sub-compartments.
#
# odeoutput: The output of the ODE solver.
# cake.model: The expression of the model that we are solving.
# atol: The tolerance to which the solution has been calculated.
#
# Returns: Post-processed/transformed ODE output.
PostProcessOdeOutput <- function(odeoutput, cake.model, atol) {
out_transformed <- data.frame(time = odeoutput[, "time"])
# Replace values that are incalculably small with 0.
for (col.name in colnames(odeoutput)) {
if (col.name == "time") {
next
}
# If we have non-NaN, positive outputs...
if (length(odeoutput[, col.name][!is.nan(odeoutput[, col.name]) & odeoutput[, col.name] > 0]) > 0) {
# ...then replace the NaN outputs.
odeoutput[, col.name][is.nan(odeoutput[, col.name])] <- 0
}
# Round outputs smaller than the used tolerance down to 0.
odeoutput[, col.name][odeoutput[, col.name] < atol] <- 0
}
# Re-combine sub-compartments (if required)
for (compartment.name in names(cake.model$map)) {
if (length(cake.model$map[[compartment.name]]) == 1) {
out_transformed[compartment.name] <- odeoutput[, compartment.name]
} else {
out_transformed[compartment.name] <- rowSums(odeoutput[, cake.model$map[[compartment.name]]])
}
}
return(out_transformed)
}
# Reorganises data in a wide format to a long format.
#
# wide_data: The data in wide format.
# time: The name of the time variable in wide_data (default "t").
#
# Returns: Reorganised data.
wide_to_long <- function(wide_data, time = "t") {
colnames <- names(wide_data)
if (!(time %in% colnames)) {
stop("The data in wide format have to contain a variable named ", time, ".")
}
vars <- subset(colnames, colnames != time)
n <- length(colnames) - 1
long_data <- data.frame(name = rep(vars, each = length(wide_data[[time]])),
time = as.numeric(rep(wide_data[[time]], n)), value = as.numeric(unlist(wide_data[vars])),
row.names = NULL)
return(long_data)
}
RunFitStep <- function(cost, costForExtraSolver, useExtraSolver, parameters, lower, upper, control) {
if (useExtraSolver) {
a <- try(fit <- solnp(parameters, fun = costForExtraSolver, LB = lower, UB = upper, control = control), silent = TRUE)
fitted_with_extra_solver <- TRUE
if (class(a) == "try-error") {
cat('Extra solver failed, trying PORT')
## now using submethod already
a <- try(fit <- modFit(cost, parameters, lower = lower, upper = upper, method = 'Port', control = control))
fitted_with_extra_solver <- FALSE
if (class(a) == "try-error") {
cat('PORT failed, trying L-BFGS-B')
fit <- modFit(cost, parameters, lower = lower, upper = upper, method = 'L-BFGS-B', control = control)
}
}
} else {
# modFit parameter transformations can explode if you put in parameters that are equal to a bound, so we move them away by a tiny amount.
all.optim <- ShiftAwayFromBoundaries(parameters, lower, upper)
fit <- modFit(cost, all.optim, lower = lower,
upper = upper, control = control)
fitted_with_extra_solver <- FALSE
}
return(list(fit = fit, fitted_with_extra_solver = fitted_with_extra_solver))
}
GetFitValuesAfterExtraSolver <- function(fit, cake_cost) {
fit$ssr <- fit$values[length(fit$values)]
fit$residuals <- cake_cost$residual$res
## mean square per varaible
if (class(cake_cost) == "modCost") {
names(fit$residuals) <- cake_cost$residuals$name
fit$var_ms <- cake_cost$var$SSR / cake_cost$var$N
fit$var_ms_unscaled <- cake_cost$var$SSR.unscaled / cake_cost$var$N
fit$var_ms_unweighted <- cake_cost$var$SSR.unweighted / cake_cost$var$N
names(fit$var_ms_unweighted) <- names(fit$var_ms_unscaled) <-
names(fit$var_ms) <- FF$var$name
} else fit$var_ms <- fit$var_ms_unweighted <- fit$var_ms_unscaled <- NA
return(fit)
}
GetOptimiserSpecificSetup <- function(optimiser) {
switch(optimiser,
OLS = GetOlsSpecificSetup(),
IRLS = GetIrlsSpecificSetup(),
MCMC = GetMcmcSpecificSetup())
}
GetOptimisationRoutine <- function(optimiser) {
switch(optimiser,
OLS = GetOlsOptimisationRoutine(),
IRLS = GetIrlsOptimisationRoutine(),
MCMC = GetMcmcOptimisationRoutine())
}
GetOptimiserSpecificWrapUp <- function(optimiser) {
switch(optimiser,
OLS = GetOlsSpecificWrapUp(),
IRLS = GetIrlsSpecificWrapUp(),
MCMC = GetMcmcSpecificWrapUp())
}
