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#$Id$
# Generates fitted curves so the GUI can plot them
# Based on code in IRLSkinfit
# Author: Rob Nelson (Tessella)
# Modifications developed by Tessella Plc for Syngenta: Copyright (C) 2011 Syngenta
# Tessella Project Reference: 6245
#
# This program 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/>.”
CakePlotInit <- function(fit, xlim = range(fit$data$time), ...)
{
t.map.names <- names(fit$map)
metabolites <- grep("[A-Z]\\d", t.map.names, value = TRUE)
t.map.rest <- setdiff(t.map.names, metabolites)
# Generate the normal graphs.
final <- CakeOlsPlot(fit, xlim)
final_scaled <- final
if(length(metabolites) > 0){
for(i in 1:length(metabolites))
{
metabolite <- metabolites[i]
decay_var <- paste("k", metabolite, sep="_")
# calculate the new ffm and generate the two ffm scale charts
regex <- paste("f_(.+)_to", metabolite, sep="_")
decays = grep(regex, names(fit$par), value = TRUE)
ffm_fitted <- sum(fit$par[decays])
normal <- final
ffm_scale <- NULL
# Generate the DT50=1000d and ffm as fitted.
k_new <- fit$par[decay_var]*fit$distimes[metabolite,]["DT50"]/1000;
fit$par[decay_var]<- k_new[metabolite,]
dt50_1000_ffm_fitted <- CakeOlsPlot(fit, xlim)[metabolite]
naming <- c(names(final), paste(metabolite, "DT50_1000_FFM_FITTED", sep="_"))
normal <- c(final, dt50_1000_ffm_fitted)
names(normal) <- naming
final <- normal
# Generate the scaled FFM
if(ffm_fitted != 0)
{
ffm_scale <- 1 / ffm_fitted
final_scaled <- final[c("time", metabolite, paste(metabolite, "DT50_1000_FFM_FITTED", sep="_"))]
final_scaled[t.map.rest] <- NULL;
final_frame <- as.data.frame(final_scaled)
new_names <- c(paste(metabolite, "DT50_FITTED_FFM_1", sep="_"), paste(metabolite, "DT50_1000_FFM_1", sep="_"))
names(final_frame) <- c("time", new_names)
final_frame[new_names]<-final_frame[new_names]*ffm_scale;
cat("<PLOT MODEL START>\n")
write.table(final_frame, quote=FALSE)
cat("<PLOT MODEL END>\n")
}
}
}
cat("<PLOT MODEL START>\n")
write.table(final, quote=FALSE)
cat("<PLOT MODEL END>\n")
# View(final)
}
CakeOlsPlot <- function(fit, xlim = range(fit$data$time), scale_x = 1.0, ...)
{
solution = fit$solution
if ( is.null(solution) ) {
solution <- "deSolve"
}
atol = fit$atol
if ( is.null(atol) ) {
atol = 1.0e-6
}
fixed <- fit$fixed$value
names(fixed) <- rownames(fit$fixed)
parms.all <- c(fit$par, fixed)
ininames <- c(
rownames(subset(fit$start, type == "state")),
rownames(subset(fit$fixed, type == "state")))
odeini <- parms.all[ininames]
names(odeini) <- names(fit$diffs)
outtimes <- seq(0, xlim[2], length.out=101) * scale_x
odenames <- c(
rownames(subset(fit$start, type == "deparm")),
rownames(subset(fit$fixed, type == "deparm")))
odeparms <- parms.all[odenames]
# Solve the system
evalparse <- function(string)
{
eval(parse(text=string), as.list(c(odeparms, odeini)))
}
if (solution == "analytical") {
parent.type = names(fit$map[[1]])[1]
parent.name = names(fit$diffs)[[1]]
o <- switch(parent.type,
SFO = SFO.solution(outtimes,
evalparse(parent.name),
evalparse(paste("k", parent.name, sep="_"))),
FOMC = FOMC.solution(outtimes,
evalparse(parent.name),
evalparse("alpha"), evalparse("beta")),
DFOP = DFOP.solution(outtimes,
evalparse(parent.name),
evalparse("k1"), evalparse("k2"),
evalparse("g")),
HS = HS.solution(outtimes,
evalparse(parent.name),
evalparse("k1"), evalparse("k2"),
evalparse("tb")),
SFORB = SFORB.solution(outtimes,
evalparse(parent.name),
evalparse(paste("k", parent.name, "free_bound", sep="_")),
evalparse(paste("k", parent.name, "bound_free", sep="_")),
evalparse(paste("k", parent.name, "free_sink", sep="_")))
)
out <- cbind(outtimes, o)
dimnames(out) <- list(outtimes, c("time", parent.name))
}
if (solution == "eigen") {
coefmat.num <- matrix(sapply(as.vector(fit$coefmat), evalparse),
nrow = length(odeini))
e <- eigen(coefmat.num)
c <- solve(e$vectors, odeini)
f.out <- function(t) {
e$vectors %*% diag(exp(e$values * t), nrow=length(odeini)) %*% c
}
o <- matrix(mapply(f.out, outtimes),
nrow = length(odeini), ncol = length(outtimes))
dimnames(o) <- list(names(odeini), NULL)
out <- cbind(time = outtimes, t(o))
}
if (solution == "deSolve") {
out <- ode(
y = odeini,
times = outtimes,
func = fit$mkindiff,
parms = odeparms,
atol = atol
)
}
# Output transformation for models with unobserved compartments like SFORB
out_transformed <- data.frame(time = out[,"time"])
for (var in names(fit$map)) {
if(length(fit$map[[var]]) == 1) {
out_transformed[var] <- out[, var]
} else {
out_transformed[var] <- rowSums(out[, fit$map[[var]]])
}
}
return(out_transformed)
}
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