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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dimethenamid_2018.R
\docType{data}
\name{dimethenamid_2018}
\alias{dimethenamid_2018}
\title{Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018}
\format{
An \link{mkindsg} object grouping eight datasets with some meta information
}
\source{
Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria (2018)
Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour
Rev. 2 - November 2017
\url{https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716}
}
\usage{
dimethenamid_2018
}
\description{
The datasets were extracted from the active substance evaluation dossier
published by EFSA. Kinetic evaluations shown for these datasets are intended
to illustrate and advance kinetic modelling. The fact that these data and
some results are shown here does not imply a license to use them in the
context of pesticide registrations, as the use of the data may be
constrained by data protection regulations.
}
\details{
The R code used to create this data object is installed with this package
in the 'dataset_generation' directory. In the code, page numbers are given for
specific pieces of information in the comments.
}
\examples{
print(dimethenamid_2018)
dmta_ds <- lapply(1:8, function(i) {
ds_i <- dimethenamid_2018$ds[[i]]$data
ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA"
ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]
ds_i
})
names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title)
dmta_ds[["Borstel"]] <- rbind(dmta_ds[["Borstel 1"]], dmta_ds[["Borstel 2"]])
dmta_ds[["Borstel 1"]] <- NULL
dmta_ds[["Borstel 2"]] <- NULL
dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]])
dmta_ds[["Elliot 1"]] <- NULL
dmta_ds[["Elliot 2"]] <- NULL
\dontrun{
dfop_sfo3_plus <- mkinmod(
DMTA = mkinsub("DFOP", c("M23", "M27", "M31")),
M23 = mkinsub("SFO"),
M27 = mkinsub("SFO"),
M31 = mkinsub("SFO", "M27", sink = FALSE),
quiet = TRUE
)
f_dmta_mkin_tc <- mmkin(
list("DFOP-SFO3+" = dfop_sfo3_plus),
dmta_ds, quiet = TRUE, error_model = "tc")
nlmixr_model(f_dmta_mkin_tc)
# The focei fit takes about four minutes on my system
system.time(
f_dmta_nlmixr_focei <- nlmixr(f_dmta_mkin_tc, est = "focei",
control = nlmixr::foceiControl(print = 500))
)
summary(f_dmta_nlmixr_focei)
plot(f_dmta_nlmixr_focei)
# Using saemix takes about 18 minutes
system.time(
f_dmta_saemix <- saem(f_dmta_mkin_tc, test_log_parms = TRUE)
)
# nlmixr with est = "saem" is pretty fast with default iteration numbers, most
# of the time (about 2.5 minutes) is spent for calculating the log likelihood at the end
# The likelihood calculated for the nlmixr fit is much lower than that found by saemix
# Also, the trace plot and the plot of the individual predictions is not
# convincing for the parent. It seems we are fitting an overparameterised
# model, so the result we get strongly depends on starting parameters and control settings.
system.time(
f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem",
control = nlmixr::saemControl(print = 500, logLik = TRUE, nmc = 9))
)
traceplot(f_dmta_nlmixr_saem$nm)
summary(f_dmta_nlmixr_saem)
plot(f_dmta_nlmixr_saem)
}
}
\keyword{datasets}
|
