# Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018 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. data ## Format An 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 ```r dimethenamid_2018 ``` ## 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 ```r 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 ## Not run: # 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) ## End(Not run) ```

dimethenamid_2018 dataset