- -

- - - - - - - -

Introduction

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 <- attr(meso_ds, "covariates")
-kable(pH, caption = "Covariate data")

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Covariate data
	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))
-}

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Dataset Richmond
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

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Dataset Richmond 2
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

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Dataset ERTC
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

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Dataset Toulouse
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

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Dataset Picket Piece
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

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Dataset 721
time	meso
0.00000	86.4
11.24366	61.4
22.48733	49.8
33.73099	41.0
44.97466	35.1

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Dataset 722
time	meso
0.00000	90.3
11.24366	52.1
22.48733	37.4
33.73099	21.2
44.97466	14.3

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Dataset 723
time	meso
0.00000	89.3
11.24366	70.8
22.48733	51.1
33.73099	42.7
44.97466	26.7

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Dataset 724
time	meso
0.000000	89.4
9.008208	65.2
18.016415	55.8
27.024623	46.0
36.032831	41.7

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Dataset 725
time	meso
0.00000	89.0
10.99058	35.4
21.98116	18.6
32.97174	11.6
43.96232	7.6

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Dataset 727
time	meso
0.00000	91.3
10.96104	63.2
21.92209	51.1
32.88313	42.0
43.84417	40.8

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Dataset 728
time	meso
0.00000	91.8
11.24366	43.6
22.48733	22.0
33.73099	15.9
44.97466	8.8

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Dataset 729
time	meso
0.00000	91.6
11.24366	60.5
22.48733	43.5
33.73099	28.4
44.97466	20.5

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Dataset 730
time	meso
0.00000	92.7
11.07446	58.9
22.14893	44.0
33.22339	46.0
44.29785	29.3

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Dataset 731
time	meso
0.00000	92.1
11.24366	64.4
22.48733	45.3
33.73099	33.6
44.97466	23.5

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Dataset 732
time	meso
0.00000	90.3
11.24366	58.2
22.48733	40.1
33.73099	33.1
44.97466	25.8

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Dataset 741
time	meso
0.00000	90.3
10.84712	68.7
21.69424	58.0
32.54136	52.2
43.38848	48.0

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Dataset 742
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)

status(f_sep_const[, 1:5]) |> kable()

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

	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

status(f_sep_const[, 6:18]) |> kable()

- ---------------- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

	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")

status(f_sep_tc[, 1:5]) |> kable()

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

	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

status(f_sep_tc[, 6:18]) |> kable()

	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.

f_saem_1 <- mhmkin(list(f_sep_const, f_sep_tc), cluster = cl)
-status(f_saem_1) |> kable()

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

	const	tc
SFO	OK	OK
FOMC	OK	OK
DFOP	OK	OK
SFORB	OK	OK
HS	OK	OK

All fits terminate without errors (status OK).

anova(f_saem_1) |> kable(digits = 1)

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

	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.

illparms(f_saem_1) |> kable()

- ----- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

	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.

f_saem_2 <- update(f_saem_1, no_random_effect = illparms(f_saem_1))
-status(f_saem_2) |> kable()

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

	const	tc
SFO	OK	OK
FOMC	OK	OK
DFOP	OK	OK
SFORB	OK	OK
HS	OK	OK

The updated fits terminate without errors.

illparms(f_saem_2) |> kable()

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

	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))

summary(sfo_pH)$confint_trans |> kable(digits = 2)

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

	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 ' ' 1

The 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

endpoints(sfo_pH, covariates = c(pH = 7.0))

$covariates
-     pH
-User  7
-
-$distimes
-        DT50    DT90
-meso 8.89035 29.5331

FOMC

fomc_pH <- saem(f_sep_const["FOMC", ], no_random_effect = "meso_0", covariates = pH,
-  covariate_models = list(log_alpha ~ pH))

summary(fomc_pH)$confint_trans |> kable(digits = 2)

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

	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.

fomc_pH_2 <- update(fomc_pH, no_random_effect = c("meso_0", "log_alpha"))
-illparms(fomc_pH_2)

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 ' ' 1

Model 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.

summary(fomc_pH_2)$confint_trans |> kable(digits = 2)

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

	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

endpoints(fomc_pH_2, covariates = c(pH = 7))

$covariates
-     pH
-User  7
-
-$distimes
-         DT50     DT90 DT50back
-meso 6.986239 27.02927 8.136621

DFOP

In the DFOP fits without covariate effects, random effects for two -degradation parameters (k2 and g) were -identifiable.

summary(f_saem_2[["DFOP", "const"]])$confint_trans |> kable(digits = 2)

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

	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.

summary(dfop_pH)$confint_trans |> kable(digits = 2)

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

	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 ' ' 1

When 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

endpoints(dfop_pH_2, covariates = c(pH = 7))

$covariates
-     pH
-User  7
-
-$distimes
-         DT50     DT90 DT50back  DT50_k1  DT50_k2
-meso 8.346428 28.34437 8.532507 4.191901 8.753618

SFORB

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))

summary(sforb_pH)$confint_trans |> kable(digits = 2)

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

	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 ' ' 1

The 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.

summary(sforb_pH_2)$confint_trans |> kable(digits = 2)

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

	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

endpoints(sforb_pH_2, covariates = c(pH = 7))

$covariates
-     pH
-User  7
-
-$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        18.26

HS

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))

summary(hs_pH)$confint_trans |> kable(digits = 2)

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

	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.

hs_pH_2 <- update(hs_pH, covariate_models = list(log_k1 ~ pH, log_k2 ~ pH))
-illparms(hs_pH_2)

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

summary(hs_pH_2)$confint_trans |> kable(digits = 2)

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

	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

endpoints(hs_pH_2, covariates = c(pH = 7))

$covariates
-     pH
-User  7
-
-$distimes
-         DT50     DT90 DT50back  DT50_k1  DT50_k2
-meso 8.298536 38.85371 11.69613 8.298536 15.71561

Comparison 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 -373.23

The 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

endpoints(dfop_pH_2, covariates = c(pH = 7))

$covariates
-     pH
-User  7
-
-$distimes
-         DT50     DT90 DT50back  DT50_k1  DT50_k2
-meso 8.346428 28.34437 8.532507 4.191901 8.753618

Conclusions

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

- -Hierarchical SFO fit with constant variance - -


-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:     Tue May 13 06:54:35 2025 
-Date of summary: Tue May 13 07:05:04 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 0.573 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
-
-

- -Hierarchical FOMC fit with constant variance - -


-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:     Tue May 13 06:54:36 2025 
-Date of summary: Tue May 13 07:05:04 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 0.832 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
-
-

- -Hierarchical DFOP fit with constant variance - -


-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:     Tue May 13 06:54:36 2025 
-Date of summary: Tue May 13 07:05:04 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 1.483 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
-
-

- -Hierarchical SFORB fit with constant variance - -


-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:     Tue May 13 06:54:37 2025 
-Date of summary: Tue May 13 07:05:04 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 1.563 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
-
-

- -Hierarchical HS fit with constant variance - -


-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:     Tue May 13 06:54:37 2025 
-Date of summary: Tue May 13 07:05:04 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 1.457 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

- -Hierarchichal SFO fit with pH influence - -


-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:     Tue May 13 06:54:52 2025 
-Date of summary: Tue May 13 07:05:04 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 1.802 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
-
-

- -Hierarchichal FOMC fit with pH influence - -


-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:     Tue May 13 06:54:54 2025 
-Date of summary: Tue May 13 07:05:04 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 1.512 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
-
-

- -Refined hierarchichal FOMC fit with pH influence - -


-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:     Tue May 13 06:54:57 2025 
-Date of summary: Tue May 13 07:05:04 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 2.52 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
-
-

- -Hierarchichal DFOP fit with pH influence - -


-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:     Tue May 13 06:55:00 2025 
-Date of summary: Tue May 13 07:05:04 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 2.218 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
-
-

- -Refined hierarchical DFOP fit with pH influence - -


-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:     Tue May 13 06:55:05 2025 
-Date of summary: Tue May 13 07:05:04 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 4.711 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
-
-

- -Further refined hierarchical DFOP fit with pH influence - -


-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:     Tue May 13 06:55:12 2025 
-Date of summary: Tue May 13 07:05:04 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 3.202 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
-
-

- -Hierarchichal SFORB fit with pH influence - -


-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:     Tue May 13 06:55:16 2025 
-Date of summary: Tue May 13 07:05:04 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 3.153 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
-
-

- -Refined hierarchichal SFORB fit with pH influence - -


-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:     Tue May 13 06:55:19 2025 
-Date of summary: Tue May 13 07:05:04 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 2.832 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
-
-

- -Hierarchichal HS fit with pH influence - -


-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:     Tue May 13 06:55:21 2025 
-Date of summary: Tue May 13 07:05:04 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 1.397 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
-
-

- -Refined hierarchichal HS fit with pH influence - -


-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:     Tue May 13 06:55:23 2025 
-Date of summary: Tue May 13 07:05:05 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 1.404 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] saemix_3.3      npde_3.5        knitr_1.49      mkin_1.2.10    
-[5] rmarkdown_2.29  nvimcom_0.9-167
-
-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  colorout_1.3-2    gridExtra_2.3     jquerylib_0.1.4  
- [9] scales_1.3.0      yaml_2.3.10       fastmap_1.2.0     lattice_0.22-6   
-[13] ggplot2_3.5.1     R6_2.6.1          generics_0.1.3    lmtest_0.9-40    
-[17] MASS_7.3-65       tibble_3.2.1      munsell_0.5.1     bslib_0.9.0      
-[21] pillar_1.10.1     rlang_1.1.5       cachem_1.1.0      xfun_0.51        
-[25] sass_0.4.9        cli_3.6.4         magrittr_2.0.3    digest_0.6.37    
-[29] grid_4.5.0        mclust_6.1.1      lifecycle_1.0.4   nlme_3.1-168     
-[33] vctrs_0.6.5       evaluate_1.0.3    glue_1.8.0        zoo_1.8-13       
-[37] colorspace_2.1-1  tools_4.5.0       pkgconfig_2.0.3   htmltools_0.5.8.1

Hardware info

CPU model: AMD Ryzen 9 7950X 16-Core Processor

MemTotal:       64927780 kB

- - - -

Testing covariate modelling in hierarchical -parent degradation kinetics with residue data on mesotrione

Johannes Ranke

Last change 13 May 2025 (rebuilt 2025-05-13)

Introduction

Test data

Separate evaluations

Hierarchical models without covariate

Hierarchical models with covariate

SFO

FOMC

DFOP

SFORB

HS

Comparison across parent models

Conclusions

Appendix

Hierarchical fit listings

Fits without covariate effects

Fits with covariate effects

Session info

Hardware info