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#$Id$
#
# The CAKE R modules are based on mkin
# Modifications developed by Tessella Plc for Syngenta: Copyright (C) 2011 Syngenta
# Authors: Rob Nelson, Richard Smith, Tamar Christina
# Tessella Project Reference: 6245, 7247
# 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/>.”
#
CakeIrlsFit <- function (mkinmod, observed, parms.ini = rep(0.1, length(mkinmod$parms)),
state.ini = c(100, rep(0, length(mkinmod$diffs) - 1)), lower = 0,
upper = Inf, fixed_parms = NULL, fixed_initials = names(mkinmod$diffs)[-1],
plot = FALSE, quiet = FALSE, err = NULL, weight = "none",
scaleVar = FALSE, atol=1e-6, sannMaxIter = 10000, control=list(),
useSolnp = FALSE, method='L-BFGS-B',...)
{
### This is a modification based on the "mkinfit" function.
### version 0.1 July 20
###
# This version has been modified to expect SFO parameterised as k and flow fractions
# Based on code in IRLSkinfit
NAind <-which(is.na(observed$value))
mod_vars <- names(mkinmod$diffs)
observed <- subset(observed, name %in% names(mkinmod$map))
ERR <- rep(1,nrow(observed))
observed <- cbind(observed,err=ERR)
flag <- 0
obs_vars = unique(as.character(observed$name))
if (is.null(names(parms.ini)))
names(parms.ini) <- mkinmod$parms
mkindiff <- function(t, state, parms) {
time <- t
diffs <- vector()
for (box in mod_vars) {
diffname <- paste("d", box, sep = "_")
diffs[diffname] <- with(as.list(c(time, state, parms)),
eval(parse(text = mkinmod$diffs[[box]])))
}
return(list(c(diffs)))
}
if (is.null(names(state.ini)))
names(state.ini) <- mod_vars
parms.fixed <- parms.ini[fixed_parms]
optim_parms <- setdiff(names(parms.ini), fixed_parms)
parms.optim <- parms.ini[optim_parms]
state.ini.fixed <- state.ini[fixed_initials]
optim_initials <- setdiff(names(state.ini), fixed_initials)
state.ini.optim <- state.ini[optim_initials]
state.ini.optim.boxnames <- names(state.ini.optim)
if (length(state.ini.optim) > 0) {
names(state.ini.optim) <- paste(names(state.ini.optim),
"0", sep = "_")
}
costFunctions <- CakeInternalCostFunctions(mkinmod, state.ini.optim, state.ini.optim.boxnames,
state.ini.fixed, parms.fixed, observed, mkindiff, scaleVar, quiet, atol=atol)
############### Iteratively Reweighted Least Squares#############
## Start with no weighting
## a prefitting step since this is usually the most effective method
if(useSolnp)
{
pnames=names(c(state.ini.optim, parms.optim))
fn <- function(P){
names(P) <- pnames
FF<<-costFunctions$cost(P)
return(FF$model)}
a <- try(fit <- solnp(c(state.ini.optim, parms.optim),fun=fn,LB=lower,UB=upper,control=control),silent=TRUE)
#optimmethod <- method0
flag <- 1
if(class(a) == "try-error")
{
print('solnp fails, try PORT or other algorithm by users choice, might take longer time. Do something else!')
warning('solnp fails, switch to PORT or other algorithm by users choice')
## now using submethod already
a <- try(fit <- modFit(costFunctions$cost, c(state.ini.optim, parms.optim), lower = lower, upper = upper, method='Port',control=control))
flag <- 0
if(class(a) == "try-error")
{
fit <- modFit(costFunctions$cost, c(state.ini.optim, parms.optim), lower = lower, upper = upper, method=method,control=control)
}
}
}else{
fit <- modFit(costFunctions$cost, c(state.ini.optim, parms.optim), lower = lower,
upper = upper,control=control,...)
}
## print(fit$hessian)
if(length(control)==0)
{
irls.control <- list(maxIter=10,tol=1e-05)
control <- list(irls.control=irls.control)
}else{
if(is.null(control$irls.control))
{
irls.control <- list(maxIter=10,tol=1e-05)
control <- list(irls.control=irls.control)
}
}
irls.control <- control$irls.control
maxIter <- irls.control$maxIter
tol <- irls.control$tol
####
if(length(mkinmod$map)==1 || useSolnp){
## there is only one parent just do one iteration:
maxIter <- 0
if(flag==1)## fit from solnp
{
fit$ssr <- fit$values[length(fit$values)]
fit$residuals <-FF$residual$res
## mean square per varaible
if (class(FF) == "modCost") {
names(fit$residuals) <- FF$residuals$name
fit$var_ms <- FF$var$SSR/FF$var$N
fit$var_ms_unscaled <- FF$var$SSR.unscaled/FF$var$N
fit$var_ms_unweighted <- FF$var$SSR.unweighted/FF$var$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
}
err1 <- sqrt(fit$var_ms_unweighted)
ERR <- err1[as.character(observed$name)]
observed$err <-ERR
}
niter <- 1
## insure one IRLS iteration
diffsigma <- 100
olderr <- rep(1,length(mod_vars))
while(diffsigma>tol & niter<=maxIter)
{
if(flag==1 && useSolnp)## fit from solnp
{
fit$ssr <- fit$values[length(fit$values)]
fit$residuals <-FF$residual$res
## mean square per varaible
if (class(FF) == "modCost") {
names(fit$residuals) <- FF$residuals$name
fit$var_ms <- FF$var$SSR/FF$var$N
fit$var_ms_unscaled <- FF$var$SSR.unscaled/FF$var$N
fit$var_ms_unweighted <- FF$var$SSR.unweighted/FF$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$var_ms_unscaled <- NA
}
err <- sqrt(fit$var_ms_unweighted)
ERR <- err[as.character(observed$name)]
costFunctions$set.error(ERR)
diffsigma <- sum((err-olderr)^2)
cat("IRLS iteration at",niter, "; Diff in error variance ", diffsigma,"\n")
olderr <- err
if(useSolnp)
{
flag <- 1
a <- try(fit <- solnp(fit$par,fun=fn,LB=lower,UB=upper,control=control),silent=TRUE)
if(class(a) == "try-error")
{
flag <- 0
print('solnp fails during IRLS iteration, try PORT or other algorithm by users choice.This may takes a while. Do something else!') ## NOTE: because in kingui we switch off the warnings, we need to print out the message instead.
warning('solnp fails during IRLS iteration, switch to PORT or other algorithm by users choice')
fit <- modFit(costFunctions$cost, fit$par, lower = lower, upper = upper, method='Port',control=list())
}
}else{
fit <- modFit(costFunctions$cost, fit$par, lower = lower, upper = upper, control=control, ...)
}
## print(fit$hessian)
niter <- niter+1
### If not converged, reweight and fit
}
if(flag==1 && useSolnp){
## solnp used
optimmethod <- 'solnp'
fit$ssr <- fit$values[length(fit$values)]
fit$residuals <-FF$residual$res
## mean square per varaible
if (class(FF) == "modCost") {
names(fit$residuals) <- FF$residuals$name
fit$var_ms <- FF$var$SSR/FF$var$N
fit$var_ms_unscaled <- FF$var$SSR.unscaled/FF$var$N
fit$var_ms_unweighted <- FF$var$SSR.unweighted/FF$var$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
np <- length(c(state.ini.optim, parms.optim))
fit$rank <- np
fit$df.residual <- length(fit$residuals) - fit$rank
}
###########################################
fit$mkindiff <- mkindiff
fit$map <- mkinmod$map
fit$diffs <- mkinmod$diffs
out_predicted <- costFunctions$get.predicted()
# mkin_long_to_wide does not handle ragged data
fit$observed <- reshape(observed, direction="wide", timevar="name", idvar="time")
names(fit$observed) <- c("time", as.vector(unique(observed$name)))
predicted_long <- mkin_wide_to_long(out_predicted, time = "time")
fit$predicted <- out_predicted
fit$start <- data.frame(initial = c(state.ini.optim, parms.optim))
fit$start$type = c(rep("state", length(state.ini.optim)),
rep("deparm", length(parms.optim)))
fit$start$lower <- lower
fit$start$upper <- upper
fit$fixed <- data.frame(value = c(state.ini.fixed, parms.fixed))
fit$fixed$type = c(rep("state", length(state.ini.fixed)),
rep("deparm", length(parms.fixed)))
fit$errmin <- CakeChi2(mkinmod, observed, predicted_long, obs_vars, parms.optim, state.ini.optim, state.ini, parms.ini)
parms.all = c(fit$par, parms.fixed)
fit$penalties <- CakePenaltiesLong(parms.all, out_predicted, observed)
fit$distimes <- data.frame(DT50 = rep(NA, length(obs_vars)),
DT90 = rep(NA, length(obs_vars)), row.names = obs_vars)
fit$extraDT50<- data.frame(DT50 = rep(NA, 2), row.names = c("k1", "k2"))
for (obs_var in obs_vars) {
type = names(mkinmod$map[[obs_var]])[1]
fit$distimes[obs_var, ] = CakeDT(type,obs_var,parms.all,sannMaxIter)
}
fit$extraDT50[ ,c("DT50")] = CakeExtraDT(type,parms.all)
data <- merge(observed, predicted_long, by = c("time", "name"))
names(data) <- c("time", "variable", "observed","err-var", "predicted")
data$residual <- data$observed - data$predicted
data$variable <- ordered(data$variable, levels = obs_vars)
fit$data <- data[order(data$variable, data$time), ]
fit$costFunctions <- costFunctions
fit$upper <- upper
fit$lower <- lower
class(fit) <- c("CakeFit", "mkinfit", "modFit")
return(fit)
}
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