Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione
Johannes Ranke
Last change 13 May 2025 (rebuilt 2025-05-14)
Source:vignettes/web_only/mesotrione_parent_2023.rmd
mesotrione_parent_2023.rmdIntroduction
The purpose of this document is to test demonstrate how nonlinear hierarchical models (NLHM) based on the parent degradation models SFO, FOMC, DFOP and HS can be fitted with the mkin package, also considering the influence of covariates like soil pH on different degradation parameters. Because in some other case studies, the SFORB parameterisation of biexponential decline has shown some advantages over the DFOP parameterisation, SFORB was included in the list of tested models as well.
The mkin package is used in version 1.2.10, which is contains the
functions that were used for the evaluations. The saemix
package is used as a backend for fitting the NLHM, but is also loaded to
make the convergence plot function available.
This document is processed with the knitr package, which
also provides the kable function that is used to improve
the display of tabular data in R markdown documents. For parallel
processing, the parallel package is used.
library(mkin)
library(knitr)
library(saemix)
library(parallel)
n_cores <- detectCores()
if (Sys.info()["sysname"] == "Windows") {
cl <- makePSOCKcluster(n_cores)
} else {
cl <- makeForkCluster(n_cores)
}Test data
data_file <- system.file(
"testdata", "mesotrione_soil_efsa_2016.xlsx", package = "mkin")
meso_ds <- read_spreadsheet(data_file, parent_only = TRUE)The following tables show the covariate data and the 18 datasets that were read in from the spreadsheet file.
| pH | |
|---|---|
| Richmond | 6.2 |
| Richmond 2 | 6.2 |
| ERTC | 6.4 |
| Toulouse | 7.7 |
| Picket Piece | 7.1 |
| 721 | 5.6 |
| 722 | 5.7 |
| 723 | 5.4 |
| 724 | 4.8 |
| 725 | 5.8 |
| 727 | 5.1 |
| 728 | 5.9 |
| 729 | 5.6 |
| 730 | 5.3 |
| 731 | 6.1 |
| 732 | 5.0 |
| 741 | 5.7 |
| 742 | 7.2 |
for (ds_name in names(meso_ds)) {
print(
kable(mkin_long_to_wide(meso_ds[[ds_name]]),
caption = paste("Dataset", ds_name),
booktabs = TRUE, row.names = FALSE))
}| time | meso |
|---|---|
| 0.000000 | 91.00 |
| 1.179050 | 86.70 |
| 3.537149 | 73.60 |
| 7.074299 | 61.50 |
| 10.611448 | 55.70 |
| 15.327647 | 47.70 |
| 17.685747 | 39.50 |
| 24.760046 | 29.80 |
| 35.371494 | 19.60 |
| 68.384889 | 5.67 |
| 0.000000 | 97.90 |
| 1.179050 | 96.40 |
| 3.537149 | 89.10 |
| 7.074299 | 74.40 |
| 10.611448 | 57.40 |
| 15.327647 | 46.30 |
| 18.864797 | 35.50 |
| 27.118146 | 27.20 |
| 35.371494 | 19.10 |
| 74.280138 | 6.50 |
| 108.472582 | 3.40 |
| 142.665027 | 2.20 |
| time | meso |
|---|---|
| 0.000000 | 96.0 |
| 2.422004 | 82.4 |
| 5.651343 | 71.2 |
| 8.073348 | 53.1 |
| 11.302687 | 48.5 |
| 16.954030 | 33.4 |
| 22.605373 | 24.2 |
| 45.210746 | 11.9 |
| time | meso |
|---|---|
| 0.000000 | 99.9 |
| 2.755193 | 80.0 |
| 6.428782 | 42.1 |
| 9.183975 | 50.1 |
| 12.857565 | 28.4 |
| 19.286347 | 39.8 |
| 25.715130 | 29.9 |
| 51.430259 | 2.5 |
| time | meso |
|---|---|
| 0.000000 | 96.8 |
| 2.897983 | 63.3 |
| 6.761960 | 22.3 |
| 9.659942 | 16.6 |
| 13.523919 | 16.1 |
| 20.285879 | 17.2 |
| 27.047838 | 1.8 |
| time | meso |
|---|---|
| 0.000000 | 102.0 |
| 2.841195 | 73.7 |
| 6.629454 | 35.5 |
| 9.470649 | 31.8 |
| 13.258909 | 18.0 |
| 19.888364 | 3.7 |
| time | meso |
|---|---|
| 0.00000 | 86.4 |
| 11.24366 | 61.4 |
| 22.48733 | 49.8 |
| 33.73099 | 41.0 |
| 44.97466 | 35.1 |
| time | meso |
|---|---|
| 0.00000 | 90.3 |
| 11.24366 | 52.1 |
| 22.48733 | 37.4 |
| 33.73099 | 21.2 |
| 44.97466 | 14.3 |
| time | meso |
|---|---|
| 0.00000 | 89.3 |
| 11.24366 | 70.8 |
| 22.48733 | 51.1 |
| 33.73099 | 42.7 |
| 44.97466 | 26.7 |
| time | meso |
|---|---|
| 0.000000 | 89.4 |
| 9.008208 | 65.2 |
| 18.016415 | 55.8 |
| 27.024623 | 46.0 |
| 36.032831 | 41.7 |
| time | meso |
|---|---|
| 0.00000 | 89.0 |
| 10.99058 | 35.4 |
| 21.98116 | 18.6 |
| 32.97174 | 11.6 |
| 43.96232 | 7.6 |
| time | meso |
|---|---|
| 0.00000 | 91.3 |
| 10.96104 | 63.2 |
| 21.92209 | 51.1 |
| 32.88313 | 42.0 |
| 43.84417 | 40.8 |
| time | meso |
|---|---|
| 0.00000 | 91.8 |
| 11.24366 | 43.6 |
| 22.48733 | 22.0 |
| 33.73099 | 15.9 |
| 44.97466 | 8.8 |
| time | meso |
|---|---|
| 0.00000 | 91.6 |
| 11.24366 | 60.5 |
| 22.48733 | 43.5 |
| 33.73099 | 28.4 |
| 44.97466 | 20.5 |
| time | meso |
|---|---|
| 0.00000 | 92.7 |
| 11.07446 | 58.9 |
| 22.14893 | 44.0 |
| 33.22339 | 46.0 |
| 44.29785 | 29.3 |
| time | meso |
|---|---|
| 0.00000 | 92.1 |
| 11.24366 | 64.4 |
| 22.48733 | 45.3 |
| 33.73099 | 33.6 |
| 44.97466 | 23.5 |
| time | meso |
|---|---|
| 0.00000 | 90.3 |
| 11.24366 | 58.2 |
| 22.48733 | 40.1 |
| 33.73099 | 33.1 |
| 44.97466 | 25.8 |
| time | meso |
|---|---|
| 0.00000 | 90.3 |
| 10.84712 | 68.7 |
| 21.69424 | 58.0 |
| 32.54136 | 52.2 |
| 43.38848 | 48.0 |
| time | meso |
|---|---|
| 0.00000 | 92.0 |
| 11.24366 | 60.9 |
| 22.48733 | 36.2 |
| 33.73099 | 18.3 |
| 44.97466 | 8.7 |
Separate evaluations
In order to obtain suitable starting parameters for the NLHM fits,
separate fits of the five models to the data for each soil are generated
using the mmkin function from the mkin package. In a first
step, constant variance is assumed. Convergence is checked with the
status function.
deg_mods <- c("SFO", "FOMC", "DFOP", "SFORB", "HS")
f_sep_const <- mmkin(
deg_mods,
meso_ds,
error_model = "const",
cluster = cl,
quiet = TRUE)| Richmond | Richmond 2 | ERTC | Toulouse | Picket Piece | |
|---|---|---|---|---|---|
| SFO | OK | OK | OK | OK | OK |
| FOMC | OK | OK | OK | OK | C |
| DFOP | OK | OK | OK | OK | OK |
| SFORB | OK | OK | OK | OK | OK |
| HS | OK | OK | C | OK | OK |
| 721 | 722 | 723 | 724 | 725 | 727 | 728 | 729 | 730 | 731 | 732 | 741 | 742 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SFO | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK |
| FOMC | OK | OK | C | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK |
| DFOP | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK |
| SFORB | OK | OK | OK | OK | OK | OK | OK | C | OK | OK | OK | OK | OK |
| HS | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK |
In the tables above, OK indicates convergence and C indicates failure to converge. Most separate fits with constant variance converged, with the exception of two FOMC fits, one SFORB fit and one HS fit.
f_sep_tc <- update(f_sep_const, error_model = "tc")| Richmond | Richmond 2 | ERTC | Toulouse | Picket Piece | |
|---|---|---|---|---|---|
| SFO | OK | OK | OK | OK | OK |
| FOMC | OK | OK | OK | OK | OK |
| DFOP | C | OK | OK | OK | OK |
| SFORB | OK | OK | OK | OK | OK |
| HS | OK | OK | C | OK | OK |
| 721 | 722 | 723 | 724 | 725 | 727 | 728 | 729 | 730 | 731 | 732 | 741 | 742 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| SFO | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK | OK |
| FOMC | OK | OK | C | OK | C | C | OK | C | OK | C | OK | C | OK |
| DFOP | C | OK | OK | OK | C | OK | OK | OK | OK | C | OK | C | OK |
| SFORB | C | OK | OK | OK | C | OK | OK | C | OK | OK | OK | C | OK |
| HS | OK | OK | OK | OK | OK | OK | OK | OK | OK | C | OK | OK | OK |
With the two-component error model, the set of fits that did not converge is larger, with convergence problems appearing for a number of non-SFO fits.
Hierarchical models without covariate
The following code fits hierarchical kinetic models for the ten combinations of the five different degradation models with the two different error models in parallel.
| const | tc | |
|---|---|---|
| SFO | OK | OK |
| FOMC | OK | OK |
| DFOP | OK | OK |
| SFORB | OK | OK |
| HS | OK | OK |
All fits terminate without errors (status OK).
| npar | AIC | BIC | Lik | |
|---|---|---|---|---|
| SFO const | 5 | 800.0 | 804.5 | -395.0 |
| SFO tc | 6 | 801.9 | 807.2 | -394.9 |
| FOMC const | 7 | 787.4 | 793.6 | -386.7 |
| FOMC tc | 8 | 788.9 | 796.1 | -386.5 |
| DFOP const | 9 | 787.6 | 795.6 | -384.8 |
| SFORB const | 9 | 787.4 | 795.4 | -384.7 |
| HS const | 9 | 781.9 | 789.9 | -382.0 |
| DFOP tc | 10 | 787.4 | 796.3 | -383.7 |
| SFORB tc | 10 | 795.8 | 804.7 | -387.9 |
| HS tc | 10 | 783.7 | 792.7 | -381.9 |
The model comparisons show that the fits with constant variance are
consistently preferable to the corresponding fits with two-component
error for these data. This is confirmed by the fact that the parameter
b.1 (the relative standard deviation in the fits obtained
with the saemix package), is ill-defined in all fits.
| const | tc | |
|---|---|---|
| SFO | sd(meso_0) | sd(meso_0), b.1 |
| FOMC | sd(meso_0), sd(log_beta) | sd(meso_0), sd(log_beta), b.1 |
| DFOP | sd(meso_0), sd(log_k1) | sd(meso_0), sd(g_qlogis), b.1 |
| SFORB | sd(meso_free_0), sd(log_k_meso_free_bound) | sd(meso_free_0), sd(log_k_meso_free_bound), b.1 |
| HS | sd(meso_0) | sd(meso_0), b.1 |
For obtaining fits with only well-defined random effects, we update
the set of fits, excluding random effects that were ill-defined
according to the illparms function.
| const | tc | |
|---|---|---|
| SFO | OK | OK |
| FOMC | OK | OK |
| DFOP | OK | OK |
| SFORB | OK | OK |
| HS | OK | OK |
The updated fits terminate without errors.
| const | tc | |
|---|---|---|
| SFO | b.1 | |
| FOMC | b.1 | |
| DFOP | b.1 | |
| SFORB | b.1 | |
| HS | b.1 |
No ill-defined errors remain in the fits with constant variance.
Hierarchical models with covariate
In the following sections, hierarchical fits including a model for the influence of pH on selected degradation parameters are shown for all parent models. Constant variance is selected as the error model based on the fits without covariate effects. Random effects that were ill-defined in the fits without pH influence are excluded. A potential influence of the soil pH is only included for parameters with a well-defined random effect, because experience has shown that only for such parameters a significant pH effect could be found.
SFO
sfo_pH <- saem(f_sep_const["SFO", ], no_random_effect = "meso_0", covariates = pH,
covariate_models = list(log_k_meso ~ pH))| est. | lower | upper | |
|---|---|---|---|
| meso_0 | 91.35 | 89.27 | 93.43 |
| log_k_meso | -6.66 | -7.97 | -5.35 |
| beta_pH(log_k_meso) | 0.59 | 0.37 | 0.81 |
| a.1 | 5.48 | 4.71 | 6.24 |
| SD.log_k_meso | 0.35 | 0.23 | 0.47 |
The parameter showing the pH influence in the above table is
beta_pH(log_k_meso). Its confidence interval does not
include zero, indicating that the influence of soil pH on the log of the
degradation rate constant is significantly greater than zero.
anova(f_saem_2[["SFO", "const"]], sfo_pH, test = TRUE)Data: 116 observations of 1 variable(s) grouped in 18 datasets
npar AIC BIC Lik Chisq Df Pr(>Chisq)
f_saem_2[["SFO", "const"]] 4 797.56 801.12 -394.78
sfo_pH 5 783.09 787.54 -386.54 16.473 1 4.934e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1The comparison with the SFO fit without covariate effect confirms that considering the soil pH improves the model, both by comparison of AIC and BIC and by the likelihood ratio test.
plot(sfo_pH)
Endpoints for a model with covariates are by default calculated for the median of the covariate values. This quantile can be adapted, or a specific covariate value can be given as shown below.
endpoints(sfo_pH)$covariates
pH
50% 5.75
$distimes
DT50 DT90
meso 18.52069 61.52441
endpoints(sfo_pH, covariate_quantile = 0.9)$covariates
pH
90% 7.13
$distimes
DT50 DT90
meso 8.237019 27.36278$covariates
pH
User 7
$distimes
DT50 DT90
meso 8.89035 29.5331FOMC
fomc_pH <- saem(f_sep_const["FOMC", ], no_random_effect = "meso_0", covariates = pH,
covariate_models = list(log_alpha ~ pH))| est. | lower | upper | |
|---|---|---|---|
| meso_0 | 92.84 | 90.75 | 94.93 |
| log_alpha | -2.21 | -3.49 | -0.92 |
| beta_pH(log_alpha) | 0.58 | 0.37 | 0.79 |
| log_beta | 4.21 | 3.44 | 4.99 |
| a.1 | 5.03 | 4.32 | 5.73 |
| SD.log_alpha | 0.00 | -23.77 | 23.78 |
| SD.log_beta | 0.37 | 0.01 | 0.74 |
As in the case of SFO, the confidence interval of the slope parameter
(here beta_pH(log_alpha)) quantifying the influence of soil
pH does not include zero, and the model comparison clearly indicates
that the model with covariate influence is preferable. However, the
random effect for alpha is not well-defined any more after
inclusion of the covariate effect (the confidence interval of
SD.log_alpha includes zero).
illparms(fomc_pH)[1] "sd(log_alpha)"Therefore, the model is updated without this random effect, and no ill-defined parameters remain.
anova(f_saem_2[["FOMC", "const"]], fomc_pH, fomc_pH_2, test = TRUE)Data: 116 observations of 1 variable(s) grouped in 18 datasets
npar AIC BIC Lik Chisq Df Pr(>Chisq)
f_saem_2[["FOMC", "const"]] 5 783.25 787.71 -386.63
fomc_pH_2 6 767.49 772.83 -377.75 17.762 1 2.503e-05 ***
fomc_pH 7 770.07 776.30 -378.04 0.000 1 1
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Model comparison indicates that including pH dependence significantly improves the fit, and that the reduced model with covariate influence results in the most preferable FOMC fit.
| est. | lower | upper | |
|---|---|---|---|
| meso_0 | 93.05 | 90.98 | 95.13 |
| log_alpha | -2.91 | -4.18 | -1.63 |
| beta_pH(log_alpha) | 0.66 | 0.44 | 0.87 |
| log_beta | 3.95 | 3.29 | 4.62 |
| a.1 | 4.98 | 4.28 | 5.68 |
| SD.log_beta | 0.40 | 0.26 | 0.54 |
plot(fomc_pH_2)
endpoints(fomc_pH_2)$covariates
pH
50% 5.75
$distimes
DT50 DT90 DT50back
meso 17.30248 82.91343 24.95943$covariates
pH
User 7
$distimes
DT50 DT90 DT50back
meso 6.986239 27.02927 8.136621DFOP
In the DFOP fits without covariate effects, random effects for two
degradation parameters (k2 and g) were
identifiable.
| est. | lower | upper | |
|---|---|---|---|
| meso_0 | 93.61 | 91.58 | 95.63 |
| log_k1 | -1.53 | -2.27 | -0.79 |
| log_k2 | -3.42 | -3.73 | -3.11 |
| g_qlogis | -1.67 | -2.57 | -0.77 |
| a.1 | 4.74 | 4.02 | 5.45 |
| SD.log_k2 | 0.60 | 0.38 | 0.81 |
| SD.g_qlogis | 0.94 | 0.33 | 1.54 |
A fit with pH dependent degradation parameters was obtained by
excluding the same random effects as in the refined DFOP fit without
covariate influence, and including covariate models for the two
identifiable parameters k2 and g.
dfop_pH <- saem(f_sep_const["DFOP", ], no_random_effect = c("meso_0", "log_k1"),
covariates = pH,
covariate_models = list(log_k2 ~ pH, g_qlogis ~ pH))The corresponding parameters for the influence of soil pH are
beta_pH(log_k2) for the influence of soil pH on
k2, and beta_pH(g_qlogis) for its influence on
g.
| est. | lower | upper | |
|---|---|---|---|
| meso_0 | 92.84 | 90.85 | 94.84 |
| log_k1 | -2.82 | -3.09 | -2.54 |
| log_k2 | -11.48 | -15.32 | -7.64 |
| beta_pH(log_k2) | 1.31 | 0.69 | 1.92 |
| g_qlogis | 3.13 | 0.47 | 5.80 |
| beta_pH(g_qlogis) | -0.57 | -1.04 | -0.09 |
| a.1 | 4.96 | 4.26 | 5.65 |
| SD.log_k2 | 0.76 | 0.47 | 1.05 |
| SD.g_qlogis | 0.01 | -9.96 | 9.97 |
illparms(dfop_pH)[1] "sd(g_qlogis)"Confidence intervals for neither of them include zero, indicating a
significant difference from zero. However, the random effect for
g is now ill-defined. The fit is updated without this
ill-defined random effect.
dfop_pH_2 <- update(dfop_pH,
no_random_effect = c("meso_0", "log_k1", "g_qlogis"))
illparms(dfop_pH_2)[1] "beta_pH(g_qlogis)"Now, the slope parameter for the pH effect on g is
ill-defined. Therefore, another attempt is made without the
corresponding covariate model.
dfop_pH_3 <- saem(f_sep_const["DFOP", ], no_random_effect = c("meso_0", "log_k1"),
covariates = pH,
covariate_models = list(log_k2 ~ pH))
illparms(dfop_pH_3)[1] "sd(g_qlogis)"As the random effect for g is again ill-defined, the fit
is repeated without it.
dfop_pH_4 <- update(dfop_pH_3, no_random_effect = c("meso_0", "log_k1", "g_qlogis"))
illparms(dfop_pH_4)While no ill-defined parameters remain, model comparison suggests
that the previous model dfop_pH_2 with two pH dependent
parameters is preferable, based on information criteria as well as based
on the likelihood ratio test.
anova(f_saem_2[["DFOP", "const"]], dfop_pH, dfop_pH_2, dfop_pH_3, dfop_pH_4)Data: 116 observations of 1 variable(s) grouped in 18 datasets
npar AIC BIC Lik
f_saem_2[["DFOP", "const"]] 7 782.94 789.18 -384.47
dfop_pH_4 7 767.35 773.58 -376.68
dfop_pH_2 8 765.14 772.26 -374.57
dfop_pH_3 8 769.00 776.12 -376.50
dfop_pH 9 769.10 777.11 -375.55
anova(dfop_pH_2, dfop_pH_4, test = TRUE)Data: 116 observations of 1 variable(s) grouped in 18 datasets
npar AIC BIC Lik Chisq Df Pr(>Chisq)
dfop_pH_4 7 767.35 773.58 -376.68
dfop_pH_2 8 765.14 772.26 -374.57 4.2153 1 0.04006 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1When focussing on parameter identifiability using the test if the
confidence interval includes zero, dfop_pH_4 would still be
the preferred model. However, it should be kept in mind that parameter
confidence intervals are constructed using a simple linearisation of the
likelihood. As the confidence interval of the random effect for
g only marginally includes zero, it is suggested that this
is acceptable, and that dfop_pH_2 can be considered the
most preferable model.
plot(dfop_pH_2)
endpoints(dfop_pH_2)$covariates
pH
50% 5.75
$distimes
DT50 DT90 DT50back DT50_k1 DT50_k2
meso 18.36876 73.51841 22.13125 4.191901 23.98672$covariates
pH
User 7
$distimes
DT50 DT90 DT50back DT50_k1 DT50_k2
meso 8.346428 28.34437 8.532507 4.191901 8.753618SFORB
sforb_pH <- saem(f_sep_const["SFORB", ], no_random_effect = c("meso_free_0", "log_k_meso_free_bound"),
covariates = pH,
covariate_models = list(log_k_meso_free ~ pH, log_k_meso_bound_free ~ pH))| est. | lower | upper | |
|---|---|---|---|
| meso_free_0 | 93.42 | 91.32 | 95.52 |
| log_k_meso_free | -5.37 | -6.94 | -3.81 |
| beta_pH(log_k_meso_free) | 0.42 | 0.18 | 0.67 |
| log_k_meso_free_bound | -3.49 | -4.92 | -2.05 |
| log_k_meso_bound_free | -9.98 | -19.22 | -0.74 |
| beta_pH(log_k_meso_bound_free) | 1.23 | -0.21 | 2.67 |
| a.1 | 4.90 | 4.18 | 5.63 |
| SD.log_k_meso_free | 0.35 | 0.23 | 0.47 |
| SD.log_k_meso_bound_free | 0.13 | -1.95 | 2.20 |
The confidence interval of
beta_pH(log_k_meso_bound_free) includes zero, indicating
that the influence of soil pH on k_meso_bound_free cannot
reliably be quantified. Also, the confidence interval for the random
effect on this parameter (SD.log_k_meso_bound_free)
includes zero.
Using the illparms function, these ill-defined
parameters can be found more conveniently.
illparms(sforb_pH)[1] "sd(log_k_meso_bound_free)" "beta_pH(log_k_meso_bound_free)"To remove the ill-defined parameters, a second variant of the SFORB model with pH influence is fitted. No ill-defined parameters remain.
sforb_pH_2 <- update(sforb_pH,
no_random_effect = c("meso_free_0", "log_k_meso_free_bound", "log_k_meso_bound_free"),
covariate_models = list(log_k_meso_free ~ pH))
illparms(sforb_pH_2)The model comparison of the SFORB fits includes the refined model without covariate effect, and both versions of the SFORB fit with covariate effect.
anova(f_saem_2[["SFORB", "const"]], sforb_pH, sforb_pH_2, test = TRUE)Data: 116 observations of 1 variable(s) grouped in 18 datasets
npar AIC BIC Lik Chisq Df Pr(>Chisq)
f_saem_2[["SFORB", "const"]] 7 783.40 789.63 -384.70
sforb_pH_2 7 770.94 777.17 -378.47 12.4616 0
sforb_pH 9 768.81 776.83 -375.41 6.1258 2 0.04675 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1The first model including pH influence is preferable based on information criteria and the likelihood ratio test. However, as it is not fully identifiable, the second model is selected.
| est. | lower | upper | |
|---|---|---|---|
| meso_free_0 | 93.32 | 91.16 | 95.48 |
| log_k_meso_free | -6.15 | -7.43 | -4.86 |
| beta_pH(log_k_meso_free) | 0.54 | 0.33 | 0.75 |
| log_k_meso_free_bound | -3.80 | -5.20 | -2.40 |
| log_k_meso_bound_free | -2.95 | -4.26 | -1.64 |
| a.1 | 5.08 | 4.38 | 5.79 |
| SD.log_k_meso_free | 0.33 | 0.22 | 0.45 |
plot(sforb_pH_2)
endpoints(sforb_pH_2)$covariates
pH
50% 5.75
$ff
meso_free
1
$SFORB
meso_b1 meso_b2 meso_g
0.09735824 0.02631699 0.31602120
$distimes
DT50 DT90 DT50back DT50_meso_b1 DT50_meso_b2
meso 16.86549 73.15824 22.02282 7.119554 26.33839$covariates
pH
User 7
$ff
meso_free
1
$SFORB
meso_b1 meso_b2 meso_g
0.13315233 0.03795988 0.61186191
$distimes
DT50 DT90 DT50back DT50_meso_b1 DT50_meso_b2
meso 7.932495 36.93311 11.11797 5.205671 18.26HS
hs_pH <- saem(f_sep_const["HS", ], no_random_effect = c("meso_0"),
covariates = pH,
covariate_models = list(log_k1 ~ pH, log_k2 ~ pH, log_tb ~ pH))| est. | lower | upper | |
|---|---|---|---|
| meso_0 | 93.33 | 91.47 | 95.19 |
| log_k1 | -5.81 | -7.27 | -4.36 |
| beta_pH(log_k1) | 0.47 | 0.23 | 0.72 |
| log_k2 | -6.80 | -8.76 | -4.83 |
| beta_pH(log_k2) | 0.54 | 0.21 | 0.87 |
| log_tb | 3.25 | 1.25 | 5.25 |
| beta_pH(log_tb) | -0.10 | -0.43 | 0.23 |
| a.1 | 4.49 | 3.78 | 5.21 |
| SD.log_k1 | 0.37 | 0.24 | 0.51 |
| SD.log_k2 | 0.29 | 0.10 | 0.48 |
| SD.log_tb | 0.25 | -0.07 | 0.57 |
illparms(hs_pH)[1] "sd(log_tb)" "beta_pH(log_tb)"According to the output of the illparms function, the
random effect on the break time tb cannot reliably be
quantified, neither can the influence of soil pH on tb. The
fit is repeated without the corresponding covariate model, and no
ill-defined parameters remain.
Model comparison confirms that this model is preferable to the fit without covariate influence, and also to the first version with covariate influence.
anova(f_saem_2[["HS", "const"]], hs_pH, hs_pH_2, test = TRUE)Data: 116 observations of 1 variable(s) grouped in 18 datasets
npar AIC BIC Lik Chisq Df Pr(>Chisq)
f_saem_2[["HS", "const"]] 8 780.08 787.20 -382.04
hs_pH_2 10 766.47 775.37 -373.23 17.606 2 0.0001503 ***
hs_pH 11 769.80 779.59 -373.90 0.000 1 1.0000000
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1| est. | lower | upper | |
|---|---|---|---|
| meso_0 | 93.33 | 91.50 | 95.15 |
| log_k1 | -5.68 | -7.09 | -4.27 |
| beta_pH(log_k1) | 0.46 | 0.22 | 0.69 |
| log_k2 | -6.61 | -8.34 | -4.88 |
| beta_pH(log_k2) | 0.50 | 0.21 | 0.79 |
| log_tb | 2.70 | 2.33 | 3.08 |
| a.1 | 4.45 | 3.74 | 5.16 |
| SD.log_k1 | 0.36 | 0.22 | 0.49 |
| SD.log_k2 | 0.23 | 0.02 | 0.43 |
| SD.log_tb | 0.55 | 0.25 | 0.85 |
plot(hs_pH_2)
endpoints(hs_pH_2)$covariates
pH
50% 5.75
$distimes
DT50 DT90 DT50back DT50_k1 DT50_k2
meso 14.68725 82.45287 24.82079 14.68725 29.29299$covariates
pH
User 7
$distimes
DT50 DT90 DT50back DT50_k1 DT50_k2
meso 8.298536 38.85371 11.69613 8.298536 15.71561Comparison across parent models
After model reduction for all models with pH influence, they are compared with each other.
anova(sfo_pH, fomc_pH_2, dfop_pH_2, dfop_pH_4, sforb_pH_2, hs_pH_2)Data: 116 observations of 1 variable(s) grouped in 18 datasets
npar AIC BIC Lik
sfo_pH 5 783.09 787.54 -386.54
fomc_pH_2 6 767.49 772.83 -377.75
dfop_pH_4 7 767.35 773.58 -376.68
sforb_pH_2 7 770.94 777.17 -378.47
dfop_pH_2 8 765.14 772.26 -374.57
hs_pH_2 10 766.47 775.37 -373.23The DFOP model with pH influence on k2 and
g and a random effect only on k2 is finally
selected as the best fit.
The endpoints resulting from this model are listed below. Please refer to the Appendix for a detailed listing.
endpoints(dfop_pH_2)$covariates
pH
50% 5.75
$distimes
DT50 DT90 DT50back DT50_k1 DT50_k2
meso 18.36876 73.51841 22.13125 4.191901 23.98672$covariates
pH
User 7
$distimes
DT50 DT90 DT50back DT50_k1 DT50_k2
meso 8.346428 28.34437 8.532507 4.191901 8.753618Conclusions
These evaluations demonstrate that covariate effects can be included for all types of parent degradation models. These models can then be further refined to make them fully identifiable.
Appendix
Hierarchical fit listings
Fits without covariate effects
saemix version used for fitting: 3.3
mkin version used for pre-fitting: 1.2.10
R version used for fitting: 4.5.0
Date of fit: Wed May 14 05:12:46 2025
Date of summary: Wed May 14 05:13:35 2025
Equations:
d_meso/dt = - k_meso * meso
Data:
116 observations of 1 variable(s) grouped in 18 datasets
Model predictions using solution type analytical
Fitted in 0.71 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
Starting values for degradation parameters:
meso_0 log_k_meso
90.832 -3.192
Fixed degradation parameter values:
None
Starting values for random effects (square root of initial entries in omega):
meso_0 log_k_meso
meso_0 6.752 0.0000
log_k_meso 0.000 0.9155
Starting values for error model parameters:
a.1
1
Results:
Likelihood computed by importance sampling
AIC BIC logLik
800 804.5 -395
Optimised parameters:
est. lower upper
meso_0 92.0705 89.9917 94.1493
log_k_meso -3.1641 -3.4286 -2.8996
a.1 5.4628 4.6421 6.2835
SD.meso_0 0.0611 -98.3545 98.4767
SD.log_k_meso 0.5616 0.3734 0.7499
Correlation:
meso_0
log_k_meso 0.1132
Random effects:
est. lower upper
SD.meso_0 0.0611 -98.3545 98.4767
SD.log_k_meso 0.5616 0.3734 0.7499
Variance model:
est. lower upper
a.1 5.463 4.642 6.284
Backtransformed parameters:
est. lower upper
meso_0 92.07053 89.99172 94.14933
k_meso 0.04225 0.03243 0.05505
Estimated disappearance times:
DT50 DT90
meso 16.41 54.5
saemix version used for fitting: 3.3
mkin version used for pre-fitting: 1.2.10
R version used for fitting: 4.5.0
Date of fit: Wed May 14 05:12:46 2025
Date of summary: Wed May 14 05:13:35 2025
Equations:
d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso
Data:
116 observations of 1 variable(s) grouped in 18 datasets
Model predictions using solution type analytical
Fitted in 0.842 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
Starting values for degradation parameters:
meso_0 log_alpha log_beta
93.0520 0.6008 3.4176
Fixed degradation parameter values:
None
Starting values for random effects (square root of initial entries in omega):
meso_0 log_alpha log_beta
meso_0 6.287 0.00 0.000
log_alpha 0.000 1.53 0.000
log_beta 0.000 0.00 1.724
Starting values for error model parameters:
a.1
1
Results:
Likelihood computed by importance sampling
AIC BIC logLik
787.4 793.6 -386.7
Optimised parameters:
est. lower upper
meso_0 93.5648 91.42864 95.7009
log_alpha 0.7645 0.28068 1.2484
log_beta 3.6597 3.05999 4.2594
a.1 5.0708 4.29823 5.8435
SD.meso_0 0.1691 -34.01517 34.3535
SD.log_alpha 0.3764 0.05834 0.6945
SD.log_beta 0.3903 -0.06074 0.8414
Correlation:
meso_0 log_lph
log_alpha -0.2839
log_beta -0.3443 0.8855
Random effects:
est. lower upper
SD.meso_0 0.1691 -34.01517 34.3535
SD.log_alpha 0.3764 0.05834 0.6945
SD.log_beta 0.3903 -0.06074 0.8414
Variance model:
est. lower upper
a.1 5.071 4.298 5.843
Backtransformed parameters:
est. lower upper
meso_0 93.565 91.429 95.701
alpha 2.148 1.324 3.485
beta 38.850 21.327 70.770
Estimated disappearance times:
DT50 DT90 DT50back
meso 14.8 74.64 22.47
saemix version used for fitting: 3.3
mkin version used for pre-fitting: 1.2.10
R version used for fitting: 4.5.0
Date of fit: Wed May 14 05:12:47 2025
Date of summary: Wed May 14 05:13:35 2025
Equations:
d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
* meso
Data:
116 observations of 1 variable(s) grouped in 18 datasets
Model predictions using solution type analytical
Fitted in 1.168 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
Starting values for degradation parameters:
meso_0 log_k1 log_k2 g_qlogis
93.14689 -2.05241 -3.53079 -0.09522
Fixed degradation parameter values:
None
Starting values for random effects (square root of initial entries in omega):
meso_0 log_k1 log_k2 g_qlogis
meso_0 6.418 0.000 0.000 0.00
log_k1 0.000 1.018 0.000 0.00
log_k2 0.000 0.000 1.694 0.00
g_qlogis 0.000 0.000 0.000 2.37
Starting values for error model parameters:
a.1
1
Results:
Likelihood computed by importance sampling
AIC BIC logLik
787.6 795.6 -384.8
Optimised parameters:
est. lower upper
meso_0 93.6684 91.63599 95.7008
log_k1 -1.7354 -2.61433 -0.8565
log_k2 -3.4015 -3.73323 -3.0697
g_qlogis -1.6341 -2.66133 -0.6069
a.1 4.7803 4.01269 5.5479
SD.meso_0 0.1661 -30.97086 31.3031
SD.log_k1 0.1127 -2.59680 2.8223
SD.log_k2 0.6394 0.41499 0.8638
SD.g_qlogis 0.8166 0.09785 1.5353
Correlation:
meso_0 log_k1 log_k2
log_k1 0.1757
log_k2 0.0199 0.2990
g_qlogis 0.0813 -0.7431 -0.3826
Random effects:
est. lower upper
SD.meso_0 0.1661 -30.97086 31.3031
SD.log_k1 0.1127 -2.59680 2.8223
SD.log_k2 0.6394 0.41499 0.8638
SD.g_qlogis 0.8166 0.09785 1.5353
Variance model:
est. lower upper
a.1 4.78 4.013 5.548
Backtransformed parameters:
est. lower upper
meso_0 93.66841 91.63599 95.70082
k1 0.17633 0.07322 0.42466
k2 0.03332 0.02392 0.04643
g 0.16327 0.06529 0.35277
Estimated disappearance times:
DT50 DT90 DT50back DT50_k1 DT50_k2
meso 16.04 63.75 19.19 3.931 20.8
saemix version used for fitting: 3.3
mkin version used for pre-fitting: 1.2.10
R version used for fitting: 4.5.0
Date of fit: Wed May 14 05:12:47 2025
Date of summary: Wed May 14 05:13:35 2025
Equations:
d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *
meso_free + k_meso_bound_free * meso_bound
d_meso_bound/dt = + k_meso_free_bound * meso_free - k_meso_bound_free *
meso_bound
Data:
116 observations of 1 variable(s) grouped in 18 datasets
Model predictions using solution type analytical
Fitted in 1.256 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
Starting values for degradation parameters:
meso_free_0 log_k_meso_free log_k_meso_free_bound
93.147 -2.305 -4.230
log_k_meso_bound_free
-3.761
Fixed degradation parameter values:
None
Starting values for random effects (square root of initial entries in omega):
meso_free_0 log_k_meso_free log_k_meso_free_bound
meso_free_0 6.418 0.0000 0.000
log_k_meso_free 0.000 0.9276 0.000
log_k_meso_free_bound 0.000 0.0000 2.272
log_k_meso_bound_free 0.000 0.0000 0.000
log_k_meso_bound_free
meso_free_0 0.000
log_k_meso_free 0.000
log_k_meso_free_bound 0.000
log_k_meso_bound_free 1.447
Starting values for error model parameters:
a.1
1
Results:
Likelihood computed by importance sampling
AIC BIC logLik
787.4 795.4 -384.7
Optimised parameters:
est. lower upper
meso_free_0 93.6285 91.6262 95.631
log_k_meso_free -2.8314 -3.1375 -2.525
log_k_meso_free_bound -3.2213 -4.4695 -1.973
log_k_meso_bound_free -2.4246 -3.5668 -1.282
a.1 4.7372 3.9542 5.520
SD.meso_free_0 0.1634 -32.7769 33.104
SD.log_k_meso_free 0.4885 0.3080 0.669
SD.log_k_meso_free_bound 0.2876 -1.7955 2.371
SD.log_k_meso_bound_free 0.9942 0.2181 1.770
Correlation:
ms_fr_0 lg_k_m_ lg_k_ms_f_
log_k_meso_free 0.2332
log_k_meso_free_bound 0.1100 0.5964
log_k_meso_bound_free -0.0413 0.3697 0.8025
Random effects:
est. lower upper
SD.meso_free_0 0.1634 -32.7769 33.104
SD.log_k_meso_free 0.4885 0.3080 0.669
SD.log_k_meso_free_bound 0.2876 -1.7955 2.371
SD.log_k_meso_bound_free 0.9942 0.2181 1.770
Variance model:
est. lower upper
a.1 4.737 3.954 5.52
Backtransformed parameters:
est. lower upper
meso_free_0 93.62849 91.62622 95.63075
k_meso_free 0.05893 0.04339 0.08004
k_meso_free_bound 0.03990 0.01145 0.13903
k_meso_bound_free 0.08851 0.02825 0.27736
Estimated Eigenvalues of SFORB model(s):
meso_b1 meso_b2 meso_g
0.15333 0.03402 0.20881
Resulting formation fractions:
ff
meso_free 1
Estimated disappearance times:
DT50 DT90 DT50back DT50_meso_b1 DT50_meso_b2
meso 14.79 60.81 18.3 4.521 20.37
saemix version used for fitting: 3.3
mkin version used for pre-fitting: 1.2.10
R version used for fitting: 4.5.0
Date of fit: Wed May 14 05:12:48 2025
Date of summary: Wed May 14 05:13:35 2025
Equations:
d_meso/dt = - ifelse(time <= tb, k1, k2) * meso
Data:
116 observations of 1 variable(s) grouped in 18 datasets
Model predictions using solution type analytical
Fitted in 1.653 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
Starting values for degradation parameters:
meso_0 log_k1 log_k2 log_tb
92.920 -2.409 -3.295 2.471
Fixed degradation parameter values:
None
Starting values for random effects (square root of initial entries in omega):
meso_0 log_k1 log_k2 log_tb
meso_0 6.477 0.0000 0.0000 0.00
log_k1 0.000 0.8675 0.0000 0.00
log_k2 0.000 0.0000 0.4035 0.00
log_tb 0.000 0.0000 0.0000 1.16
Starting values for error model parameters:
a.1
1
Results:
Likelihood computed by importance sampling
AIC BIC logLik
781.9 789.9 -382
Optimised parameters:
est. lower upper
meso_0 93.34242 91.4730 95.2118
log_k1 -2.77312 -3.0826 -2.4637
log_k2 -3.61854 -3.8430 -3.3941
log_tb 2.00266 1.3357 2.6696
a.1 4.47693 3.7059 5.2479
SD.meso_0 0.07963 -63.1661 63.3253
SD.log_k1 0.47817 0.2467 0.7097
SD.log_k2 0.39216 0.2137 0.5706
SD.log_tb 0.94683 0.4208 1.4728
Correlation:
meso_0 log_k1 log_k2
log_k1 0.1627
log_k2 0.0063 -0.0301
log_tb 0.0083 -0.3931 -0.1225
Random effects:
est. lower upper
SD.meso_0 0.07963 -63.1661 63.3253
SD.log_k1 0.47817 0.2467 0.7097
SD.log_k2 0.39216 0.2137 0.5706
SD.log_tb 0.94683 0.4208 1.4728
Variance model:
est. lower upper
a.1 4.477 3.706 5.248
Backtransformed parameters:
est. lower upper
meso_0 93.34242 91.47303 95.21181
k1 0.06247 0.04584 0.08512
k2 0.02682 0.02143 0.03357
tb 7.40872 3.80282 14.43376
Estimated disappearance times:
DT50 DT90 DT50back DT50_k1 DT50_k2
meso 16 76 22.88 11.1 25.84
Fits with covariate effects
saemix version used for fitting: 3.3
mkin version used for pre-fitting: 1.2.10
R version used for fitting: 4.5.0
Date of fit: Wed May 14 05:13:00 2025
Date of summary: Wed May 14 05:13:35 2025
Equations:
d_meso/dt = - k_meso * meso
Data:
116 observations of 1 variable(s) grouped in 18 datasets
Model predictions using solution type analytical
Fitted in 1.343 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
Starting values for degradation parameters:
meso_0 log_k_meso
90.832 -3.192
Fixed degradation parameter values:
None
Starting values for random effects (square root of initial entries in omega):
meso_0 log_k_meso
meso_0 6.752 0.0000
log_k_meso 0.000 0.9155
Starting values for error model parameters:
a.1
1
Results:
Likelihood computed by importance sampling
AIC BIC logLik
783.1 787.5 -386.5
Optimised parameters:
est. lower upper
meso_0 91.3481 89.2688 93.4275
log_k_meso -6.6614 -7.9715 -5.3514
beta_pH(log_k_meso) 0.5871 0.3684 0.8059
a.1 5.4750 4.7085 6.2415
SD.log_k_meso 0.3471 0.2258 0.4684
Correlation:
meso_0 lg_k_ms
log_k_meso 0.0414
beta_pH(log_k_meso) -0.0183 -0.9917
Random effects:
est. lower upper
SD.log_k_meso 0.3471 0.2258 0.4684
Variance model:
est. lower upper
a.1 5.475 4.709 6.242
Backtransformed parameters:
est. lower upper
meso_0 91.348139 8.927e+01 93.427476
k_meso 0.001279 3.452e-04 0.004741
Covariates used for endpoints below:
pH
50% 5.75
Estimated disappearance times:
DT50 DT90
meso 18.52 61.52
saemix version used for fitting: 3.3
mkin version used for pre-fitting: 1.2.10
R version used for fitting: 4.5.0
Date of fit: Wed May 14 05:13:03 2025
Date of summary: Wed May 14 05:13:35 2025
Equations:
d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso
Data:
116 observations of 1 variable(s) grouped in 18 datasets
Model predictions using solution type analytical
Fitted in 1.897 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
Starting values for degradation parameters:
meso_0 log_alpha log_beta
93.0520 0.6008 3.4176
Fixed degradation parameter values:
None
Starting values for random effects (square root of initial entries in omega):
meso_0 log_alpha log_beta
meso_0 6.287 0.00 0.000
log_alpha 0.000 1.53 0.000
log_beta 0.000 0.00 1.724
Starting values for error model parameters:
a.1
1
Results:
Likelihood computed by importance sampling
AIC BIC logLik
770.1 776.3 -378
Optimised parameters:
est. lower upper
meso_0 92.840646 90.750461 94.9308
log_alpha -2.206602 -3.494546 -0.9187
beta_pH(log_alpha) 0.577505 0.369805 0.7852
log_beta 4.214099 3.438851 4.9893
a.1 5.027768 4.322028 5.7335
SD.log_alpha 0.004034 -23.766993 23.7751
SD.log_beta 0.374640 0.009252 0.7400
Correlation:
meso_0 log_lph bt_H(_)
log_alpha -0.0865
beta_pH(log_alpha) -0.0789 -0.8704
log_beta -0.3544 0.3302 0.1628
Random effects:
est. lower upper
SD.log_alpha 0.004034 -23.766993 23.78
SD.log_beta 0.374640 0.009252 0.74
Variance model:
est. lower upper
a.1 5.028 4.322 5.734
Backtransformed parameters:
est. lower upper
meso_0 92.8406 90.75046 94.9308
alpha 0.1101 0.03036 0.3991
beta 67.6332 31.15113 146.8404
Covariates used for endpoints below:
pH
50% 5.75
Estimated disappearance times:
DT50 DT90 DT50back
meso 17.28 76.37 22.99
saemix version used for fitting: 3.3
mkin version used for pre-fitting: 1.2.10
R version used for fitting: 4.5.0
Date of fit: Wed May 14 05:13:08 2025
Date of summary: Wed May 14 05:13:35 2025
Equations:
d_meso/dt = - (alpha/beta) * 1/((time/beta) + 1) * meso
Data:
116 observations of 1 variable(s) grouped in 18 datasets
Model predictions using solution type analytical
Fitted in 4.184 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
Starting values for degradation parameters:
meso_0 log_alpha log_beta
93.0520 0.6008 3.4176
Fixed degradation parameter values:
None
Starting values for random effects (square root of initial entries in omega):
meso_0 log_alpha log_beta
meso_0 6.287 0.00 0.000
log_alpha 0.000 1.53 0.000
log_beta 0.000 0.00 1.724
Starting values for error model parameters:
a.1
1
Results:
Likelihood computed by importance sampling
AIC BIC logLik
767.5 772.8 -377.7
Optimised parameters:
est. lower upper
meso_0 93.0536 90.9771 95.1300
log_alpha -2.9054 -4.1803 -1.6304
beta_pH(log_alpha) 0.6590 0.4437 0.8744
log_beta 3.9549 3.2860 4.6239
a.1 4.9784 4.2815 5.6754
SD.log_beta 0.4019 0.2632 0.5406
Correlation:
meso_0 log_lph bt_H(_)
log_alpha -0.0397
beta_pH(log_alpha) -0.0899 -0.9146
log_beta -0.3473 0.2038 0.1919
Random effects:
est. lower upper
SD.log_beta 0.4019 0.2632 0.5406
Variance model:
est. lower upper
a.1 4.978 4.281 5.675
Backtransformed parameters:
est. lower upper
meso_0 93.05359 90.97713 95.1300
alpha 0.05473 0.01529 0.1958
beta 52.19251 26.73597 101.8874
Covariates used for endpoints below:
pH
50% 5.75
Estimated disappearance times:
DT50 DT90 DT50back
meso 17.3 82.91 24.96
saemix version used for fitting: 3.3
mkin version used for pre-fitting: 1.2.10
R version used for fitting: 4.5.0
Date of fit: Wed May 14 05:13:11 2025
Date of summary: Wed May 14 05:13:35 2025
Equations:
d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
* meso
Data:
116 observations of 1 variable(s) grouped in 18 datasets
Model predictions using solution type analytical
Fitted in 2.18 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
Starting values for degradation parameters:
meso_0 log_k1 log_k2 g_qlogis
93.14689 -2.05241 -3.53079 -0.09522
Fixed degradation parameter values:
None
Starting values for random effects (square root of initial entries in omega):
meso_0 log_k1 log_k2 g_qlogis
meso_0 6.418 0.000 0.000 0.00
log_k1 0.000 1.018 0.000 0.00
log_k2 0.000 0.000 1.694 0.00
g_qlogis 0.000 0.000 0.000 2.37
Starting values for error model parameters:
a.1
1
Results:
Likelihood computed by importance sampling
AIC BIC logLik
769.1 777.1 -375.5
Optimised parameters:
est. lower upper
meso_0 92.843344 90.8464 94.84028
log_k1 -2.815685 -3.0888 -2.54261
log_k2 -11.479779 -15.3203 -7.63923
beta_pH(log_k2) 1.308417 0.6948 1.92203
g_qlogis 3.133036 0.4657 5.80035
beta_pH(g_qlogis) -0.565988 -1.0394 -0.09262
a.1 4.955518 4.2597 5.65135
SD.log_k2 0.758963 0.4685 1.04943
SD.g_qlogis 0.005215 -9.9561 9.96656
Correlation:
meso_0 log_k1 log_k2 b_H(_2) g_qlogs
log_k1 0.2706
log_k2 -0.0571 0.1096
beta_pH(log_k2) 0.0554 -0.1291 -0.9937
g_qlogis -0.1125 -0.5062 -0.1305 0.1294
beta_pH(g_qlogis) 0.1267 0.4226 0.0419 -0.0438 -0.9864
Random effects:
est. lower upper
SD.log_k2 0.758963 0.4685 1.049
SD.g_qlogis 0.005215 -9.9561 9.967
Variance model:
est. lower upper
a.1 4.956 4.26 5.651
Backtransformed parameters:
est. lower upper
meso_0 9.284e+01 9.085e+01 9.484e+01
k1 5.986e-02 4.556e-02 7.866e-02
k2 1.034e-05 2.221e-07 4.812e-04
g 9.582e-01 6.144e-01 9.970e-01
Covariates used for endpoints below:
pH
50% 5.75
Estimated disappearance times:
DT50 DT90 DT50back DT50_k1 DT50_k2
meso 20.23 88.45 26.62 11.58 36.23
saemix version used for fitting: 3.3
mkin version used for pre-fitting: 1.2.10
R version used for fitting: 4.5.0
Date of fit: Wed May 14 05:13:14 2025
Date of summary: Wed May 14 05:13:35 2025
Equations:
d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
* meso
Data:
116 observations of 1 variable(s) grouped in 18 datasets
Model predictions using solution type analytical
Fitted in 2.424 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
Starting values for degradation parameters:
meso_0 log_k1 log_k2 g_qlogis
93.14689 -2.05241 -3.53079 -0.09522
Fixed degradation parameter values:
None
Starting values for random effects (square root of initial entries in omega):
meso_0 log_k1 log_k2 g_qlogis
meso_0 6.418 0.000 0.000 0.00
log_k1 0.000 1.018 0.000 0.00
log_k2 0.000 0.000 1.694 0.00
g_qlogis 0.000 0.000 0.000 2.37
Starting values for error model parameters:
a.1
1
Results:
Likelihood computed by importance sampling
AIC BIC logLik
765.1 772.3 -374.6
Optimised parameters:
est. lower upper
meso_0 93.3333 91.2427 95.42394
log_k1 -1.7997 -2.9124 -0.68698
log_k2 -8.1810 -10.1819 -6.18008
beta_pH(log_k2) 0.8064 0.4903 1.12257
g_qlogis 3.3513 -1.1792 7.88182
beta_pH(g_qlogis) -0.8672 -1.7661 0.03177
a.1 4.9158 4.2277 5.60390
SD.log_k2 0.3946 0.2565 0.53281
Correlation:
meso_0 log_k1 log_k2 b_H(_2) g_qlogs
log_k1 0.1730
log_k2 0.0442 0.5370
beta_pH(log_k2) -0.0392 -0.4880 -0.9923
g_qlogis -0.1536 0.1431 -0.1129 0.1432
beta_pH(g_qlogis) 0.1504 -0.3151 -0.0196 -0.0212 -0.9798
Random effects:
est. lower upper
SD.log_k2 0.3946 0.2565 0.5328
Variance model:
est. lower upper
a.1 4.916 4.228 5.604
Backtransformed parameters:
est. lower upper
meso_0 9.333e+01 9.124e+01 95.42394
k1 1.654e-01 5.435e-02 0.50309
k2 2.799e-04 3.785e-05 0.00207
g 9.661e-01 2.352e-01 0.99962
Covariates used for endpoints below:
pH
50% 5.75
Estimated disappearance times:
DT50 DT90 DT50back DT50_k1 DT50_k2
meso 18.37 73.52 22.13 4.192 23.99
saemix version used for fitting: 3.3
mkin version used for pre-fitting: 1.2.10
R version used for fitting: 4.5.0
Date of fit: Wed May 14 05:13:23 2025
Date of summary: Wed May 14 05:13:35 2025
Equations:
d_meso/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
* meso
Data:
116 observations of 1 variable(s) grouped in 18 datasets
Model predictions using solution type analytical
Fitted in 3.211 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
Starting values for degradation parameters:
meso_0 log_k1 log_k2 g_qlogis
93.14689 -2.05241 -3.53079 -0.09522
Fixed degradation parameter values:
None
Starting values for random effects (square root of initial entries in omega):
meso_0 log_k1 log_k2 g_qlogis
meso_0 6.418 0.000 0.000 0.00
log_k1 0.000 1.018 0.000 0.00
log_k2 0.000 0.000 1.694 0.00
g_qlogis 0.000 0.000 0.000 2.37
Starting values for error model parameters:
a.1
1
Results:
Likelihood computed by importance sampling
AIC BIC logLik
767.4 773.6 -376.7
Optimised parameters:
est. lower upper
meso_0 93.3011 91.1905 95.4118
log_k1 -2.1487 -2.7607 -1.5367
log_k2 -8.1039 -10.4225 -5.7853
beta_pH(log_k2) 0.7821 0.4126 1.1517
g_qlogis -1.0373 -1.9337 -0.1409
a.1 5.0095 4.3082 5.7108
SD.log_k2 0.4622 0.3009 0.6235
Correlation:
meso_0 log_k1 log_k2 b_H(_2)
log_k1 0.2179
log_k2 0.0337 0.5791
beta_pH(log_k2) -0.0326 -0.5546 -0.9932
g_qlogis 0.0237 -0.8479 -0.6571 0.6123
Random effects:
est. lower upper
SD.log_k2 0.4622 0.3009 0.6235
Variance model:
est. lower upper
a.1 5.009 4.308 5.711
Backtransformed parameters:
est. lower upper
meso_0 9.330e+01 9.119e+01 95.411751
k1 1.166e-01 6.325e-02 0.215084
k2 3.024e-04 2.975e-05 0.003072
g 2.617e-01 1.263e-01 0.464832
Covariates used for endpoints below:
pH
50% 5.75
Estimated disappearance times:
DT50 DT90 DT50back DT50_k1 DT50_k2
meso 17.09 73.67 22.18 5.943 25.54
saemix version used for fitting: 3.3
mkin version used for pre-fitting: 1.2.10
R version used for fitting: 4.5.0
Date of fit: Wed May 14 05:13:26 2025
Date of summary: Wed May 14 05:13:35 2025
Equations:
d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *
meso_free + k_meso_bound_free * meso_bound
d_meso_bound/dt = + k_meso_free_bound * meso_free - k_meso_bound_free *
meso_bound
Data:
116 observations of 1 variable(s) grouped in 18 datasets
Model predictions using solution type analytical
Fitted in 2.649 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
Starting values for degradation parameters:
meso_free_0 log_k_meso_free log_k_meso_free_bound
93.147 -2.305 -4.230
log_k_meso_bound_free
-3.761
Fixed degradation parameter values:
None
Starting values for random effects (square root of initial entries in omega):
meso_free_0 log_k_meso_free log_k_meso_free_bound
meso_free_0 6.418 0.0000 0.000
log_k_meso_free 0.000 0.9276 0.000
log_k_meso_free_bound 0.000 0.0000 2.272
log_k_meso_bound_free 0.000 0.0000 0.000
log_k_meso_bound_free
meso_free_0 0.000
log_k_meso_free 0.000
log_k_meso_free_bound 0.000
log_k_meso_bound_free 1.447
Starting values for error model parameters:
a.1
1
Results:
Likelihood computed by importance sampling
AIC BIC logLik
768.8 776.8 -375.4
Optimised parameters:
est. lower upper
meso_free_0 93.4204 91.3213 95.5195
log_k_meso_free -5.3742 -6.9366 -3.8117
beta_pH(log_k_meso_free) 0.4232 0.1769 0.6695
log_k_meso_free_bound -3.4889 -4.9243 -2.0535
log_k_meso_bound_free -9.9797 -19.2232 -0.7362
beta_pH(log_k_meso_bound_free) 1.2290 -0.2107 2.6687
a.1 4.9031 4.1795 5.6268
SD.log_k_meso_free 0.3454 0.2252 0.4656
SD.log_k_meso_bound_free 0.1277 -1.9459 2.2012
Correlation:
ms_fr_0 lg_k_m_ b_H(___) lg_k_ms_f_ lg_k_ms_b_
log_k_meso_free 0.1493
beta_pH(log_k_meso_free) -0.0930 -0.9854
log_k_meso_free_bound 0.2439 0.4621 -0.3492
log_k_meso_bound_free 0.2188 0.1292 -0.0339 0.7287
beta_pH(log_k_meso_bound_free) -0.2216 -0.0797 -0.0111 -0.6566 -0.9934
Random effects:
est. lower upper
SD.log_k_meso_free 0.3454 0.2252 0.4656
SD.log_k_meso_bound_free 0.1277 -1.9459 2.2012
Variance model:
est. lower upper
a.1 4.903 4.18 5.627
Backtransformed parameters:
est. lower upper
meso_free_0 9.342e+01 9.132e+01 95.51946
k_meso_free 4.635e-03 9.716e-04 0.02211
k_meso_free_bound 3.054e-02 7.268e-03 0.12829
k_meso_bound_free 4.633e-05 4.482e-09 0.47894
Covariates used for endpoints below:
pH
50% 5.75
Estimated Eigenvalues of SFORB model(s):
meso_b1 meso_b2 meso_g
0.1121 0.0256 0.3148
Resulting formation fractions:
ff
meso_free 1
Estimated disappearance times:
DT50 DT90 DT50back DT50_meso_b1 DT50_meso_b2
meso 16.42 75.2 22.64 6.185 27.08
saemix version used for fitting: 3.3
mkin version used for pre-fitting: 1.2.10
R version used for fitting: 4.5.0
Date of fit: Wed May 14 05:13:30 2025
Date of summary: Wed May 14 05:13:35 2025
Equations:
d_meso_free/dt = - k_meso_free * meso_free - k_meso_free_bound *
meso_free + k_meso_bound_free * meso_bound
d_meso_bound/dt = + k_meso_free_bound * meso_free - k_meso_bound_free *
meso_bound
Data:
116 observations of 1 variable(s) grouped in 18 datasets
Model predictions using solution type analytical
Fitted in 3.186 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
Starting values for degradation parameters:
meso_free_0 log_k_meso_free log_k_meso_free_bound
93.147 -2.305 -4.230
log_k_meso_bound_free
-3.761
Fixed degradation parameter values:
None
Starting values for random effects (square root of initial entries in omega):
meso_free_0 log_k_meso_free log_k_meso_free_bound
meso_free_0 6.418 0.0000 0.000
log_k_meso_free 0.000 0.9276 0.000
log_k_meso_free_bound 0.000 0.0000 2.272
log_k_meso_bound_free 0.000 0.0000 0.000
log_k_meso_bound_free
meso_free_0 0.000
log_k_meso_free 0.000
log_k_meso_free_bound 0.000
log_k_meso_bound_free 1.447
Starting values for error model parameters:
a.1
1
Results:
Likelihood computed by importance sampling
AIC BIC logLik
770.9 777.2 -378.5
Optimised parameters:
est. lower upper
meso_free_0 93.3196 91.1633 95.4760
log_k_meso_free -6.1460 -7.4306 -4.8614
beta_pH(log_k_meso_free) 0.5435 0.3329 0.7542
log_k_meso_free_bound -3.8001 -5.2027 -2.3975
log_k_meso_bound_free -2.9462 -4.2565 -1.6359
a.1 5.0825 4.3793 5.7856
SD.log_k_meso_free 0.3338 0.2175 0.4502
Correlation:
ms_fr_0 lg_k_m_ b_H(___ lg_k_ms_f_
log_k_meso_free 0.1086
beta_pH(log_k_meso_free) -0.0426 -0.9821
log_k_meso_free_bound 0.2513 0.1717 -0.0409
log_k_meso_bound_free 0.1297 0.1171 -0.0139 0.9224
Random effects:
est. lower upper
SD.log_k_meso_free 0.3338 0.2175 0.4502
Variance model:
est. lower upper
a.1 5.082 4.379 5.786
Backtransformed parameters:
est. lower upper
meso_free_0 93.319649 9.116e+01 95.47601
k_meso_free 0.002142 5.928e-04 0.00774
k_meso_free_bound 0.022369 5.502e-03 0.09095
k_meso_bound_free 0.052539 1.417e-02 0.19478
Covariates used for endpoints below:
pH
50% 5.75
Estimated Eigenvalues of SFORB model(s):
meso_b1 meso_b2 meso_g
0.09736 0.02632 0.31602
Resulting formation fractions:
ff
meso_free 1
Estimated disappearance times:
DT50 DT90 DT50back DT50_meso_b1 DT50_meso_b2
meso 16.87 73.16 22.02 7.12 26.34
saemix version used for fitting: 3.3
mkin version used for pre-fitting: 1.2.10
R version used for fitting: 4.5.0
Date of fit: Wed May 14 05:13:32 2025
Date of summary: Wed May 14 05:13:35 2025
Equations:
d_meso/dt = - ifelse(time <= tb, k1, k2) * meso
Data:
116 observations of 1 variable(s) grouped in 18 datasets
Model predictions using solution type analytical
Fitted in 1.833 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
Starting values for degradation parameters:
meso_0 log_k1 log_k2 log_tb
92.920 -2.409 -3.295 2.471
Fixed degradation parameter values:
None
Starting values for random effects (square root of initial entries in omega):
meso_0 log_k1 log_k2 log_tb
meso_0 6.477 0.0000 0.0000 0.00
log_k1 0.000 0.8675 0.0000 0.00
log_k2 0.000 0.0000 0.4035 0.00
log_tb 0.000 0.0000 0.0000 1.16
Starting values for error model parameters:
a.1
1
Results:
Likelihood computed by importance sampling
AIC BIC logLik
769.8 779.6 -373.9
Optimised parameters:
est. lower upper
meso_0 93.32599 91.4658 95.1862
log_k1 -5.81463 -7.2710 -4.3583
beta_pH(log_k1) 0.47472 0.2334 0.7160
log_k2 -6.79633 -8.7605 -4.8322
beta_pH(log_k2) 0.54151 0.2124 0.8706
log_tb 3.24674 1.2470 5.2465
beta_pH(log_tb) -0.09889 -0.4258 0.2280
a.1 4.49487 3.7766 5.2132
SD.log_k1 0.37191 0.2370 0.5068
SD.log_k2 0.29210 0.0994 0.4848
SD.log_tb 0.25353 -0.0664 0.5735
Correlation:
meso_0 log_k1 b_H(_1) log_k2 b_H(_2) log_tb
log_k1 0.0744
beta_pH(log_k1) -0.0452 -0.9915
log_k2 0.0066 -0.0363 0.0376
beta_pH(log_k2) -0.0071 0.0372 -0.0391 -0.9939
log_tb -0.0238 -0.1483 0.1362 -0.3836 0.3696
beta_pH(log_tb) 0.0097 0.1359 -0.1265 0.3736 -0.3653 -0.9905
Random effects:
est. lower upper
SD.log_k1 0.3719 0.2370 0.5068
SD.log_k2 0.2921 0.0994 0.4848
SD.log_tb 0.2535 -0.0664 0.5735
Variance model:
est. lower upper
a.1 4.495 3.777 5.213
Backtransformed parameters:
est. lower upper
meso_0 93.325994 9.147e+01 9.519e+01
k1 0.002984 6.954e-04 1.280e-02
k2 0.001118 1.568e-04 7.969e-03
tb 25.706437 3.480e+00 1.899e+02
Covariates used for endpoints below:
pH
50% 5.75
Estimated disappearance times:
DT50 DT90 DT50back DT50_k1 DT50_k2
meso 15.65 79.63 23.97 15.16 27.55
saemix version used for fitting: 3.3
mkin version used for pre-fitting: 1.2.10
R version used for fitting: 4.5.0
Date of fit: Wed May 14 05:13:35 2025
Date of summary: Wed May 14 05:13:35 2025
Equations:
d_meso/dt = - ifelse(time <= tb, k1, k2) * meso
Data:
116 observations of 1 variable(s) grouped in 18 datasets
Model predictions using solution type analytical
Fitted in 1.852 s
Using 300, 100 iterations and 3 chains
Variance model: Constant variance
Starting values for degradation parameters:
meso_0 log_k1 log_k2 log_tb
92.920 -2.409 -3.295 2.471
Fixed degradation parameter values:
None
Starting values for random effects (square root of initial entries in omega):
meso_0 log_k1 log_k2 log_tb
meso_0 6.477 0.0000 0.0000 0.00
log_k1 0.000 0.8675 0.0000 0.00
log_k2 0.000 0.0000 0.4035 0.00
log_tb 0.000 0.0000 0.0000 1.16
Starting values for error model parameters:
a.1
1
Results:
Likelihood computed by importance sampling
AIC BIC logLik
766.5 775.4 -373.2
Optimised parameters:
est. lower upper
meso_0 93.3251 91.49823 95.1520
log_k1 -5.6796 -7.08789 -4.2714
beta_pH(log_k1) 0.4567 0.22400 0.6894
log_k2 -6.6083 -8.33839 -4.8781
beta_pH(log_k2) 0.4982 0.20644 0.7899
log_tb 2.7040 2.33033 3.0777
a.1 4.4452 3.73537 5.1551
SD.log_k1 0.3570 0.22104 0.4930
SD.log_k2 0.2252 0.01864 0.4318
SD.log_tb 0.5488 0.24560 0.8521
Correlation:
meso_0 log_k1 b_H(_1) log_k2 b_H(_2)
log_k1 0.0740
beta_pH(log_k1) -0.0453 -0.9912
log_k2 0.0115 -0.0650 0.0661
beta_pH(log_k2) -0.0116 0.0649 -0.0667 -0.9936
log_tb -0.0658 -0.1135 0.0913 -0.1500 0.1210
Random effects:
est. lower upper
SD.log_k1 0.3570 0.22104 0.4930
SD.log_k2 0.2252 0.01864 0.4318
SD.log_tb 0.5488 0.24560 0.8521
Variance model:
est. lower upper
a.1 4.445 3.735 5.155
Backtransformed parameters:
est. lower upper
meso_0 93.325134 9.150e+01 95.152036
k1 0.003415 8.352e-04 0.013962
k2 0.001349 2.392e-04 0.007611
tb 14.939247 1.028e+01 21.707445
Covariates used for endpoints below:
pH
50% 5.75
Estimated disappearance times:
DT50 DT90 DT50back DT50_k1 DT50_k2
meso 14.69 82.45 24.82 14.69 29.29
Session info
R version 4.5.0 (2025-04-11)
Platform: x86_64-pc-linux-gnu
Running under: Debian GNU/Linux 12 (bookworm)
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0 LAPACK version 3.11.0
locale:
[1] LC_CTYPE=de_DE.UTF-8 LC_NUMERIC=C
[3] LC_TIME=de_DE.UTF-8 LC_COLLATE=de_DE.UTF-8
[5] LC_MONETARY=de_DE.UTF-8 LC_MESSAGES=de_DE.UTF-8
[7] LC_PAPER=de_DE.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C
time zone: Europe/Berlin
tzcode source: system (glibc)
attached base packages:
[1] parallel stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] rmarkdown_2.29 nvimcom_0.9-167 saemix_3.3 npde_3.5
[5] knitr_1.49 mkin_1.2.10
loaded via a namespace (and not attached):
[1] gtable_0.3.6 jsonlite_1.9.0 dplyr_1.1.4 compiler_4.5.0
[5] tidyselect_1.2.1 gridExtra_2.3 jquerylib_0.1.4 systemfonts_1.2.1
[9] scales_1.3.0 textshaping_1.0.0 readxl_1.4.4 yaml_2.3.10
[13] fastmap_1.2.0 lattice_0.22-6 ggplot2_3.5.1 R6_2.6.1
[17] generics_0.1.3 lmtest_0.9-40 MASS_7.3-65 htmlwidgets_1.6.4
[21] tibble_3.2.1 desc_1.4.3 munsell_0.5.1 bslib_0.9.0
[25] pillar_1.10.1 rlang_1.1.5 cachem_1.1.0 xfun_0.51
[29] fs_1.6.5 sass_0.4.9 cli_3.6.4 pkgdown_2.1.1
[33] magrittr_2.0.3 digest_0.6.37 grid_4.5.0 mclust_6.1.1
[37] lifecycle_1.0.4 nlme_3.1-168 vctrs_0.6.5 evaluate_1.0.3
[41] glue_1.8.0 cellranger_1.1.0 codetools_0.2-20 ragg_1.3.3
[45] zoo_1.8-13 colorspace_2.1-1 tools_4.5.0 pkgconfig_2.0.3
[49] htmltools_0.5.8.1