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.

dimethenamid_2018

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

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)
#> <mkindsg> holding 7 mkinds objects
#> Title $title:  Aerobic soil degradation data on dimethenamid-P from the EU assessment in 2018 
#> Occurrence of observed compounds $observed_n:
#> DMTAP   M23   M27   M31  DMTA 
#>     3     7     7     7     4 
#> Time normalisation factors $f_time_norm:
#> [1] 1.0000000 0.9706477 1.2284784 1.2284784 0.6233856 0.7678922 0.6733938
#> Meta information $meta:
#>                      study  usda_soil_type study_moisture_ref_type rel_moisture
#> Calke        Unsworth 2014      Sandy loam                     pF2         1.00
#> Borstel  Staudenmaier 2009            Sand                     pF1         0.50
#> Elliot 1        Wendt 1997       Clay loam                   pF2.5         0.75
#> Elliot 2        Wendt 1997       Clay loam                   pF2.5         0.75
#> Flaach          König 1996 Sandy clay loam                     pF1         0.40
#> BBA 2.2         König 1995      Loamy sand                     pF1         0.40
#> BBA 2.3         König 1995      Sandy loam                     pF1         0.40
#>          study_ref_moisture temperature
#> Calke                    NA          20
#> Borstel               23.00          20
#> Elliot 1              33.37          23
#> Elliot 2              33.37          23
#> Flaach                   NA          20
#> BBA 2.2                  NA          20
#> BBA 2.3                  NA          20
dmta_ds <- lapply(1:7, function(i) {
  ds_i <- dimethenamid_2018$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)
#> <mmkin> object
#> Status of individual fits:
#> 
#>            dataset
#> model       Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot
#>   SFO-SFO3+ OK    OK      OK     OK      OK      OK    
#> 
#> OK: No warnings
# 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)
#> saemix version used for fitting:      3.2 
#> mkin version used for pre-fitting:  1.2.5 
#> R version used for fitting:         4.3.0 
#> Date of fit:     Fri May 19 17:28:53 2023 
#> Date of summary: Fri May 19 17:28:53 2023 
#> 
#> Equations:
#> d_DMTA/dt = - k_DMTA * DMTA
#> d_M23/dt = + f_DMTA_to_M23 * k_DMTA * DMTA - k_M23 * M23
#> d_M27/dt = + f_DMTA_to_M27 * k_DMTA * DMTA - k_M27 * M27 + k_M31 * M31
#> d_M31/dt = + f_DMTA_to_M31 * k_DMTA * DMTA - k_M31 * M31
#> 
#> Data:
#> 563 observations of 4 variable(s) grouped in 6 datasets
#> 
#> Model predictions using solution type deSolve 
#> 
#> Fitted in 299.056 s
#> Using 300, 100 iterations and 9 chains
#> 
#> Variance model: Two-component variance function 
#> 
#> Starting values for degradation parameters:
#>       DMTA_0   log_k_DMTA    log_k_M23    log_k_M27    log_k_M31 f_DMTA_ilr_1 
#>      95.5662      -2.9048      -3.8130      -4.1600      -4.1486       0.1341 
#> f_DMTA_ilr_2 f_DMTA_ilr_3 
#>       0.1385      -1.6700 
#> 
#> Fixed degradation parameter values:
#> None
#> 
#> Starting values for random effects (square root of initial entries in omega):
#>              DMTA_0 log_k_DMTA log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1
#> DMTA_0        4.802     0.0000    0.0000     0.000    0.0000       0.0000
#> log_k_DMTA    0.000     0.9834    0.0000     0.000    0.0000       0.0000
#> log_k_M23     0.000     0.0000    0.6983     0.000    0.0000       0.0000
#> log_k_M27     0.000     0.0000    0.0000     1.028    0.0000       0.0000
#> log_k_M31     0.000     0.0000    0.0000     0.000    0.9841       0.0000
#> f_DMTA_ilr_1  0.000     0.0000    0.0000     0.000    0.0000       0.7185
#> f_DMTA_ilr_2  0.000     0.0000    0.0000     0.000    0.0000       0.0000
#> f_DMTA_ilr_3  0.000     0.0000    0.0000     0.000    0.0000       0.0000
#>              f_DMTA_ilr_2 f_DMTA_ilr_3
#> DMTA_0             0.0000       0.0000
#> log_k_DMTA         0.0000       0.0000
#> log_k_M23          0.0000       0.0000
#> log_k_M27          0.0000       0.0000
#> log_k_M31          0.0000       0.0000
#> f_DMTA_ilr_1       0.0000       0.0000
#> f_DMTA_ilr_2       0.7378       0.0000
#> f_DMTA_ilr_3       0.0000       0.4451
#> 
#> Starting values for error model parameters:
#> a.1 b.1 
#>   1   1 
#> 
#> Results:
#> 
#> Likelihood computed by importance sampling
#>    AIC  BIC logLik
#>   2276 2273  -1120
#> 
#> Optimised parameters:
#>                    est.   lower   upper
#> DMTA_0          88.3192 83.8656 92.7729
#> log_k_DMTA      -3.0530 -3.5686 -2.5373
#> log_k_M23       -4.0620 -4.9202 -3.2038
#> log_k_M27       -3.8633 -4.2668 -3.4598
#> log_k_M31       -3.9731 -4.4763 -3.4699
#> f_DMTA_ilr_1     0.1346 -0.2150  0.4841
#> f_DMTA_ilr_2     0.1449 -0.2593  0.5491
#> f_DMTA_ilr_3    -1.3882 -1.7011 -1.0753
#> a.1              0.9156  0.8217  1.0095
#> b.1              0.1383  0.1216  0.1550
#> SD.DMTA_0        3.7280 -0.6949  8.1508
#> SD.log_k_DMTA    0.6431  0.2781  1.0080
#> SD.log_k_M23     1.0096  0.3782  1.6409
#> SD.log_k_M27     0.4583  0.1541  0.7625
#> SD.log_k_M31     0.5738  0.1942  0.9533
#> SD.f_DMTA_ilr_1  0.4119  0.1528  0.6709
#> SD.f_DMTA_ilr_2  0.4780  0.1806  0.7754
#> SD.f_DMTA_ilr_3  0.3657  0.1383  0.5931
#> 
#> Correlation: 
#>              DMTA_0  l__DMTA lg__M23 lg__M27 lg__M31 f_DMTA__1 f_DMTA__2
#> log_k_DMTA    0.0303                                                    
#> log_k_M23    -0.0229 -0.0032                                            
#> log_k_M27    -0.0372 -0.0049  0.0041                                    
#> log_k_M31    -0.0245 -0.0032  0.0022  0.0815                            
#> f_DMTA_ilr_1 -0.0046 -0.0006  0.0415 -0.0433  0.0324                    
#> f_DMTA_ilr_2 -0.0008 -0.0002  0.0214 -0.0267 -0.0893 -0.0361            
#> f_DMTA_ilr_3 -0.1755 -0.0135  0.0423  0.0775  0.0377 -0.0066    0.0060  
#> 
#> Random effects:
#>                   est.   lower  upper
#> SD.DMTA_0       3.7280 -0.6949 8.1508
#> SD.log_k_DMTA   0.6431  0.2781 1.0080
#> SD.log_k_M23    1.0096  0.3782 1.6409
#> SD.log_k_M27    0.4583  0.1541 0.7625
#> SD.log_k_M31    0.5738  0.1942 0.9533
#> SD.f_DMTA_ilr_1 0.4119  0.1528 0.6709
#> SD.f_DMTA_ilr_2 0.4780  0.1806 0.7754
#> SD.f_DMTA_ilr_3 0.3657  0.1383 0.5931
#> 
#> Variance model:
#>       est.  lower upper
#> a.1 0.9156 0.8217 1.009
#> b.1 0.1383 0.1216 0.155
#> 
#> Backtransformed parameters:
#>                   est.     lower    upper
#> DMTA_0        88.31924 83.865625 92.77286
#> k_DMTA         0.04722  0.028196  0.07908
#> k_M23          0.01721  0.007298  0.04061
#> k_M27          0.02100  0.014027  0.03144
#> k_M31          0.01882  0.011375  0.03112
#> f_DMTA_to_M23  0.14608        NA       NA
#> f_DMTA_to_M27  0.12077        NA       NA
#> f_DMTA_to_M31  0.11123        NA       NA
#> 
#> Resulting formation fractions:
#>               ff
#> DMTA_M23  0.1461
#> DMTA_M27  0.1208
#> DMTA_M31  0.1112
#> DMTA_sink 0.6219
#> 
#> Estimated disappearance times:
#>       DT50   DT90
#> DMTA 14.68  48.76
#> M23  40.27 133.76
#> M27  33.01 109.65
#> M31  36.84 122.38
# 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)
# }