# Some of the CAKE R modules are based on mkin,
# Based on mcmckinfit as modified by Bayer
# Modifications 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/>.
# Performs an Markov chain Monte Carlo least squares fit on a given CAKE model.
#
# cake.model: The model to perform the fit on (as generated by CakeModel.R).
# observed: Observation data to fit to.
# parms.ini: Initial values for the parameters being fitted.
# state.ini: Initial state (i.e. initial values for concentration, the dependent variable being modelled).
# lower: Lower bounds to apply to parameters.
# upper: Upper bound to apply to parameters.
# fixed_parms: A vector of names of parameters that are fixed to their initial values.
# fixed_initials: A vector of compartments with fixed initial concentrations.
# quiet: Whether the internal cost functions should execute more quietly than normal (less output).
# niter: The number of MCMC iterations to apply.
# atol: The tolerance to apply to the ODE solver.
# dfopDtMaxIter: The maximum number of iterations to apply to DFOP DT calculation.
# control: ...
# useExtraSolver: Whether to use the extra solver for this fit (only used for the initial first fit).
CakeMcmcFit <- function(cake.model,
observed,
parms.ini,
state.ini,
lower,
upper,
fixed_parms = NULL,
fixed_initials,
quiet = FALSE,
niter = 1000,
verbose = TRUE,
seed = NULL,
atol = 1e-6,
dfopDtMaxIter = 10000,
control = list(),
useExtraSolver = FALSE) {
fit <- CakeFit("MCMC",
cake.model,
observed,
parms.ini,
state.ini,
lower,
upper,
fixed_parms,
fixed_initials,
quiet,
niter = niter,
verbose = verbose,
seed = seed,
atol = atol,
dfopDtMaxIter = dfopDtMaxIter,
control = control,
useExtraSolver = useExtraSolver)
return(fit)
}
GetMcmcSpecificSetup <- function() {
return(function(
cake.model,
state.ini.optim,
state.ini.optim.boxnames,
state.ini.fixed,
parms.fixed,
observed,
mkindiff,
quiet,
atol,
solution,
...) {
seed <- list(...)$seed
costFunctions <- CakeInternalCostFunctions(cake.model, state.ini.optim, state.ini.optim.boxnames, state.ini.fixed,
parms.fixed, observed, mkindiff = mkindiff, quiet, atol = atol, solution = solution)
bestIteration <<- -1;
costWithStatus <- function(P, ...) {
r <- costFunctions$cost(P)
if (r$cost == costFunctions$get.best.cost()) {
bestIteration <<- costFunctions$get.calls();
cat(' MCMC best so far: c', r$cost, 'it:', bestIteration, '\n')
}
arguments <- list(...)
if (costFunctions$get.calls() <= arguments$maxCallNo) {
cat("MCMC Call no.", costFunctions$get.calls(), '\n')
}
return(r)
}
# Set the seed
if (is.null(seed)) {
# No seed so create a random one so there is something to report
seed <- runif(1, 0, 2 ^ 31 - 1)
}
seed <- as.integer(seed)
set.seed(seed)
return(list(costFunctions = costFunctions, costWithStatus = costWithStatus, maxIter = NULL, tol = NULL, seed = seed))
})
}
GetMcmcOptimisationRoutine <- function() {
return(function(costFunctions, costForExtraSolver, useExtraSolver, parms, lower, upper, control, ...) {
mcmcArgs <- list(...)
cake.model <- mcmcArgs$cake.model
costWithStatus <- mcmcArgs$costWithStatus
observed <- mcmcArgs$observed
niter <- mcmcArgs$niter
verbose <- mcmcArgs$verbose
# Runs a pre-fit with no weights first, followed by a weighted step (extra solver with first, not with second)
# Run optimiser, no weighting
fitStepResult <- RunFitStep(costFunctions$cost, costForExtraSolver, useExtraSolver, parms, lower, upper, control)
fit <- fitStepResult$fit
fitted_with_extra_solver <- fitStepResult$fitted_with_extra_solver
# Process extra solver output if it was used
if (fitted_with_extra_solver) {
fit <- GetFitValuesAfterExtraSolver(fit, FF)
}
# One reweighted estimation
# Estimate the error variance(sd)
tmpres <- fit$residuals
oldERR <- observed$err
err <- rep(NA, length(cake.model$map))
for (i in 1:length(cake.model$map)) {
box <- names(cake.model$map)[i]
ind <- which(names(tmpres) == box)
tmp <- tmpres[ind]
err[i] <- sd(tmp)
}
names(err) <- names(cake.model$map)
ERR <- err[as.character(observed$name)]
observed$err <- ERR
costFunctions$set.error(ERR)
olderr <- rep(1, length(cake.model$map))
diffsigma <- sum((err - olderr) ^ 2)
## At least do one iteration step to get a weighted LS
fit <- modFit(f = costFunctions$cost, p = fit$par, lower = lower, upper = upper)
# Run MCMC optimiser with output from weighted fit
# Apply iterative re-weighting here (iterations fixed to 1 for now):
# Do modMCMC as below, we should also pass in the final priors for subsequent iterations
# Use modMCMC average as input to next modMCMC run (as done in block 3 to get final parameters)
# use errors from previous step as inputs to modMCMC cov0 and var0 at each iteration
fs <- summary(fit)
cov0 <- if (all(is.na(fs$cov.scaled))) NULL else fs$cov.scaled * 2.4 ^ 2 / length(fit$par)
var0 <- fit$var_ms_unweighted
costFunctions$set.calls(0);
costFunctions$reset.best.cost()
res <- modMCMC(f = costWithStatus, p = fit$par, maxCallNo = niter, jump = cov0, lower = lower, upper = upper, prior = NULL, var0 = var0, wvar0 = 0.1, niter = niter, outputlength = niter, burninlength = 0, updatecov = niter, ntrydr = 1, drscale = NULL, verbose = verbose)
return(list(fit = fitStepResult$fit, fitted_with_extra_solver = fitStepResult$fitted_with_extra_solver, res = res))
})
}
GetMcmcSpecificWrapUp <- function() {
return(function(fit, ...) {
args <- list(...)
res <- args$res
seed <- args$seed
costWithStatus <- args$costWithStatus
observed <- args$observed
parms.fixed <- args$parms.fixed
# Replace mean from modFit with mean from modMCMC
fnm <- function(x) mean(res$pars[, x])
fit$par <- sapply(dimnames(res$pars)[[2]], fnm)
fit$bestpar <- res$bestpar
fit$costfn <- costWithStatus
parms.all <- c(fit$par, parms.fixed)
data <- observed
data$err <- rep(NA, length(data$time))
fit$seed <- seed
fit$res <- res
np <- length(parms.all)
fit$rank <- np
fit$df.residual <- length(fit$residuals) - fit$rank
class(fit) <- c("CakeMcmcFit", "mkinfit", "modFit") # Note different class to other optimisers
return(list(fit = fit, parms.all = parms.all, data = data))
})
}
# Summarise a fit
# The MCMC summary is separate from the others due to the difference in the outputs of the modMCMC and modFit.
summary.CakeMcmcFit <- function(object, data = TRUE, distimes = TRUE, halflives = TRUE, ff = TRUE, cov = FALSE, ...) {
param <- object$par
pnames <- names(param)
p <- length(param)
#covar <- try(solve(0.5*object$hessian), silent = TRUE) # unscaled covariance
mcmc <- object$res
covar <- cov(mcmc$pars)
rdf <- object$df.residual
message <- "ok"
rownames(covar) <- colnames(covar) <- pnames
#se <- sqrt(diag(covar) * resvar)
fnse <- function(x) sd(mcmc$pars[, x]) #/sqrt(length(mcmc$pars[,x]))
se <- sapply(dimnames(mcmc$pars)[[2]], fnse)
tval <- param / se
if (!all(object$start$lower >= 0)) {
message <- "Note that the one-sided t-test may not be appropriate if
parameter values below zero are possible."
warning(message)
} else message <- "ok"
# Filter the values for t-test, only apply t-test to k-values
t.names <- grep("k(\\d+)|k_(.*)", names(tval), value = TRUE)
t.rest <- setdiff(names(tval), t.names)
t.values <- c(tval)
t.values[t.rest] <- NA
t.result <- pt(t.values, rdf, lower.tail = FALSE)
# Now set the values we're not interested in for the lower
# and upper bound we're not interested in to NA
t.param <- c(param)
t.param[t.names] <- NA
# calculate the 90% confidence interval
alpha <- 0.10
lci90 <- t.param + qt(alpha / 2, rdf) * se
uci90 <- t.param + qt(1 - alpha / 2, rdf) * se
# calculate the 95% confidence interval
alpha <- 0.05
lci95 <- t.param + qt(alpha / 2, rdf) * se
uci95 <- t.param + qt(1 - alpha / 2, rdf) * se
param <- cbind(param, se, tval, t.result, lci90, uci90, lci95, uci95)
dimnames(param) <- list(pnames, c("Estimate", "Std. Error",
"t value", "Pr(>t)", "Lower CI (90%)", "Upper CI (90%)", "Lower CI (95%)", "Upper CI (95%)"))
# Residuals from mean of MCMC fit
resvar <- object$ssr / rdf
modVariance <- object$ssr / length(object$data$residual)
ans <- list(ssr = object$ssr,
residuals = object$data$residuals,
residualVariance = resvar,
sigma = sqrt(resvar),
modVariance = modVariance,
df = c(p, rdf), cov.unscaled = covar,
cov.scaled = covar * resvar,
info = object$info, niter = object$iterations,
stopmess = message,
par = param)
ans$diffs <- object$diffs
ans$data <- object$data
ans$additionalstats <- CakeAdditionalStats(object$data)
ans$seed <- object$seed
ans$start <- object$start
ans$fixed <- object$fixed
ans$errmin <- object$errmin
ans$penalties <- object$penalties
if (distimes) {
ans$distimes <- object$distimes
ans$extraDT50 <- object$extraDT50
ans$ioreRepDT <- object$ioreRepDT
ans$fomcRepDT <- object$fomcRepDT
}
if (halflives) ans$halflives <- object$halflives
if (ff) ans$ff <- object$ff
class(ans) <- c("summary.CakeFit", "summary.mkinfit", "summary.modFit")
return(ans)
}
