test_data_from_UBA_2014.Rd
The datasets were used for the comparative validation of several kinetic evaluation software packages (Ranke, 2014).
test_data_from_UBA_2014
A list containing three datasets as an R6 class defined by mkinds
.
Each dataset has, among others, the following components
title
The name of the dataset, e.g. UBA_2014_WS_river
data
A data frame with the data in the form expected by mkinfit
Ranke (2014) Prüfung und Validierung von Modellierungssoftware als Alternative zu ModelMaker 4.0, Umweltbundesamt Projektnummer 27452
# \dontrun{ # This is a level P-II evaluation of the dataset according to the FOCUS kinetics # guidance. Due to the strong correlation of the parameter estimates, the # covariance matrix is not returned. Note that level P-II evaluations are # generally considered deprecated due to the frequent occurrence of such # large parameter correlations, among other reasons (e.g. the adequacy of the # model). m_ws <- mkinmod(parent_w = mkinsub("SFO", "parent_s"), parent_s = mkinsub("SFO", "parent_w"))#>#> Warning: Observations with value of zero were removed from the dataplot_sep(f_river)#> Warning: Could not calculate correlation; no covariance matrix#> Estimate se_notrans t value Pr(>t) Lower Upper #> parent_w_0 95.91998116 NA NA NA NA NA #> k_parent_w 0.41145375 NA NA NA NA NA #> k_parent_s 0.04663944 NA NA NA NA NA #> f_parent_w_to_parent_s 0.12467894 NA NA NA NA NA #> f_parent_s_to_parent_w 0.50000000 NA NA NA NA NA #> sigma 3.13612618 NA NA NA NA NAmkinerrmin(f_river)#> err.min n.optim df #> All data 0.1090929 5 6 #> parent_w 0.0817436 3 3 #> parent_s 0.1619965 2 3# This is the evaluation used for the validation of software packages # in the expertise from 2014 m_soil <- mkinmod(parent = mkinsub("SFO", c("M1", "M2")), M1 = mkinsub("SFO", "M3"), M2 = mkinsub("SFO", "M3"), M3 = mkinsub("SFO"), use_of_ff = "max")#>#> Warning: Observations with value of zero were removed from the data#> Estimate se_notrans t value Pr(>t) Lower #> parent_0 76.55425584 0.859186419 89.1008681 1.113862e-26 74.755958720 #> k_parent 0.12081956 0.004601919 26.2541703 1.077361e-16 0.111561576 #> k_M1 0.84258629 0.806165149 1.0451783 1.545282e-01 0.113778910 #> k_M2 0.04210878 0.017083049 2.4649452 1.170195e-02 0.018013823 #> k_M3 0.01122919 0.007245870 1.5497364 6.885076e-02 0.002909418 #> f_parent_to_M1 0.32240193 0.240785518 1.3389590 9.819221e-02 NA #> f_parent_to_M2 0.16099854 0.033691991 4.7785404 6.531224e-05 NA #> f_M1_to_M3 0.27921506 0.269425582 1.0363346 1.565282e-01 0.022977955 #> f_M2_to_M3 0.55641331 0.595121774 0.9349571 1.807710e-01 0.008002320 #> sigma 1.14005399 0.149696423 7.6157731 1.727024e-07 0.826735778 #> Upper #> parent_0 78.35255297 #> k_parent 0.13084582 #> k_M1 6.23974738 #> k_M2 0.09843271 #> k_M3 0.04334017 #> f_parent_to_M1 NA #> f_parent_to_M2 NA #> f_M1_to_M3 0.86450905 #> f_M2_to_M3 0.99489911 #> sigma 1.45337221mkinerrmin(f_soil)#> err.min n.optim df #> All data 0.09649963 9 20 #> parent 0.04721283 2 6 #> M1 0.26551208 2 5 #> M2 0.20327575 2 5 #> M3 0.05196550 3 4# }