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## -----------------------------------------------------------------------------
## Chi squared errmin function.
## -----------------------------------------------------------------------------
# Some of the CAKE R modules are based on mkin.
#
# 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 them and/or modify them 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/>
if(getRversion() >= '2.15.1') utils::globalVariables(c("name"))
CakeErrMin <- function(fit, alpha = 0.05)
{
parms.optim <- fit$par
kinerrmin <- function(errdata, n.parms) {
means.mean <- mean(errdata$value_mean, na.rm=TRUE)
df = length(errdata$value_mean) - n.parms
err.min <- sqrt((1 / qchisq(1 - alpha, df)) *
sum((errdata$value_mean - errdata$value_pred)^2)/(means.mean^2))
return(list(err.min = err.min, n.optim = n.parms, df = df))
}
means <- aggregate(value ~ time + name, data = fit$observed, mean, na.rm=TRUE)
errdata <- merge(means, fit$predicted, by = c("time", "name"),
suffixes = c("_mean", "_pred"))
errdata <- errdata[order(errdata$time, errdata$name), ]
# Remove values at time zero for variables whose value for state.ini is fixed,
# as these will not have any effect in the optimization and should therefore not
# be counted as degrees of freedom.
fixed_initials = gsub("_0$", "", rownames(subset(fit$fixed, type = "state")))
errdata <- subset(errdata, !(time == 0 & name %in% fixed_initials))
n.optim.overall <- length(parms.optim)
errmin.overall <- kinerrmin(errdata, n.optim.overall)
errmin <- data.frame(err.min = errmin.overall$err.min,
n.optim = errmin.overall$n.optim, df = errmin.overall$df)
rownames(errmin) <- "All data"
for (obs_var in fit$obs_vars)
{
errdata.var <- subset(errdata, name == obs_var)
# Check if initial value is optimised
n.initials.optim <- length(grep(paste(obs_var, ".*", "_0", sep=""), names(parms.optim)))
# Rate constants and DFOP parameters are attributed to the source variable
n.k.optim <- length(grep(paste("^k", obs_var, sep="_"), names(parms.optim)))
n.k1.dfop.optim <- length(grep(paste("^k1", obs_var, sep="_"), names(parms.optim)))
n.k2.dfop.optim <- length(grep(paste("^k2", obs_var, sep="_"), names(parms.optim)))
n.g.dfop.optim <- length(grep(paste("^g", obs_var, sep="_"), names(parms.optim)))
# Formation fractions are attributed to the target variable
n.ff.optim <- length(grep(paste("^f", ".*", obs_var, "$", sep=""), names(parms.optim)))
n.optim <- n.k.optim + n.k1.dfop.optim + n.k2.dfop.optim + n.g.dfop.optim + n.initials.optim + n.ff.optim
# FOMC, HS and IORE parameters are only counted if we are looking at the
# first variable in the model which is always the source variable
if (obs_var == fit$obs_vars[[1]]) {
if ("alpha" %in% names(parms.optim)) n.optim <- n.optim + 1
if ("beta" %in% names(parms.optim)) n.optim <- n.optim + 1
if ("k1" %in% names(parms.optim)) n.optim <- n.optim + 1
if ("k2" %in% names(parms.optim)) n.optim <- n.optim + 1
if ("tb" %in% names(parms.optim)) n.optim <- n.optim + 1
if ("N" %in% names(parms.optim)) n.optim <- n.optim + 1
}
# Calculate and add a line to the results
errmin.tmp <- kinerrmin(errdata.var, n.optim)
errmin[obs_var, c("err.min", "n.optim", "df")] <- errmin.tmp
}
return(errmin)
}
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