% 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 seven 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
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(1:7, 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[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]])
dmta_ds[["Elliot 1"]] <- NULL
dmta_ds[["Elliot 2"]] <- NULL
\dontrun{
# We don't use DFOP for the parent compound, as this gives numerical
# instabilities in the fits
sfo_sfo3p <- mkinmod(
DMTA = mkinsub("SFO", c("M23", "M27", "M31")),
M23 = mkinsub("SFO"),
M27 = mkinsub("SFO"),
M31 = mkinsub("SFO", "M27", sink = FALSE),
quiet = TRUE
)
dmta_sfo_sfo3p_tc <- mmkin(list("SFO-SFO3+" = sfo_sfo3p),
dmta_ds, error_model = "tc", quiet = TRUE)
print(dmta_sfo_sfo3p_tc)
# The default (test_log_parms = FALSE) gives an undue
# influence of ill-defined rate constants that have
# extremely small values:
plot(mixed(dmta_sfo_sfo3p_tc), test_log_parms = FALSE)
# If we disregards ill-defined rate constants, the results
# look more plausible, but the truth is likely to be in
# between these variants
plot(mixed(dmta_sfo_sfo3p_tc), test_log_parms = TRUE)
# We can also specify a default value for the failing
# log parameters, to mimic FOCUS guidance
plot(mixed(dmta_sfo_sfo3p_tc), test_log_parms = TRUE,
default_log_parms = log(2)/1000)
# As these attempts are not satisfying, we use nonlinear mixed-effects models
# f_dmta_nlme_tc <- nlme(dmta_sfo_sfo3p_tc)
# nlme reaches maxIter = 50 without convergence
f_dmta_saem_tc <- saem(dmta_sfo_sfo3p_tc)
# I am commenting out the convergence plot as rendering them
# with pkgdown fails (at least without further tweaks to the
# graphics device used)
#saemix::plot(f_dmta_saem_tc$so, plot.type = "convergence")
summary(f_dmta_saem_tc)
# As the confidence interval for the random effects of DMTA_0
# includes zero, we could try an alternative model without
# such random effects
# f_dmta_saem_tc_2 <- saem(dmta_sfo_sfo3p_tc,
# covariance.model = diag(c(0, rep(1, 7))))
# saemix::plot(f_dmta_saem_tc_2$so, plot.type = "convergence")
# This does not perform better judged by AIC and BIC
# saemix::compare.saemix(f_dmta_saem_tc$so, f_dmta_saem_tc_2$so)
}
}
\keyword{datasets}
