---
title: "Testing hierarchical parent degradation kinetics with residue data on dimethenamid and dimethenamid-P"
author: Johannes Ranke
date: Last change on 5 January 2023, last compiled on `r format(Sys.time(), "%e %B %Y")`
geometry: margin=2cm
toc: true
bibliography: references.bib
output:
pdf_document:
extra_dependencies: ["float", "listing"]
---
```{r setup, echo = FALSE, cache = FALSE}
options(width = 80) # For summary listings
knitr::opts_chunk$set(
comment = "", tidy = FALSE, cache = TRUE, fig.pos = "H", fig.align = "center"
)
```
\clearpage
# Introduction
The purpose of this document is to demonstrate how nonlinear hierarchical
models (NLHM) based on the parent degradation models SFO, FOMC, DFOP and HS
can be fitted with the mkin package.
It was assembled in the course of work package 1.1 of Project Number 173340
(Application of nonlinear hierarchical models to the kinetic evaluation of
chemical degradation data) of the German Environment Agency carried out in 2022
and 2023.
The mkin package is used in version `r packageVersion("mkin")`. It contains the
test data and the functions used in 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.
```{r packages, cache = FALSE, message = FALSE}
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)
}
```
\clearpage
# Data
The test data are available in the mkin package as an object of class
`mkindsg` (mkin dataset group) under the identifier `dimethenamid_2018`. The
following preprocessing steps are still necessary:
- The data available for the enantiomer dimethenamid-P (DMTAP) are renamed
to have the same substance name as the data for the racemic mixture
dimethenamid (DMTA). The reason for this is that no difference between their
degradation behaviour was identified in the EU risk assessment.
- The data for transformation products and unnecessary columns are discarded
- The observation times of each dataset are multiplied with the
corresponding normalisation factor also available in the dataset, in order to
make it possible to describe all datasets with a single set of parameters
that are independent of temperature
- Finally, datasets observed in the same soil (`Elliot 1` and `Elliot 2`) are
combined, resulting in dimethenamid (DMTA) data from six soils.
The following commented R code performs this preprocessing.
```{r data}
# Apply a function to each of the seven datasets in the mkindsg object to create a list
dmta_ds <- lapply(1:7, function(i) {
ds_i <- dimethenamid_2018$ds[[i]]$data # Get a dataset
ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA" # Rename DMTAP to DMTA
ds_i <- subset(ds_i, name == "DMTA", c("name", "time", "value")) # Select data
ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i] # Normalise time
ds_i # Return the dataset
})
# Use dataset titles as names for the list elements
names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title)
# Combine data for Elliot soil to obtain a named list with six elements
dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) #
dmta_ds[["Elliot 1"]] <- NULL
dmta_ds[["Elliot 2"]] <- NULL
```
\clearpage
The following tables show the `r length(dmta_ds)` datasets.
```{r results = "asis"}
for (ds_name in names(dmta_ds)) {
print(kable(mkin_long_to_wide(dmta_ds[[ds_name]]),
caption = paste("Dataset", ds_name),
label = paste0("tab:", ds_name), booktabs = TRUE))
cat("\n\\clearpage\n")
}
```
# Separate evaluations
In order to obtain suitable starting parameters for the NLHM fits, separate
fits of the four 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.
```{r f-sep-const, dependson = "data"}
deg_mods <- c("SFO", "FOMC", "DFOP", "HS")
f_sep_const <- mmkin(
deg_mods,
dmta_ds,
error_model = "const",
quiet = TRUE)
status(f_sep_const) |> kable()
```
In the table above, OK indicates convergence, and C indicates failure to
converge. All separate fits with constant variance converged, with the sole
exception of the HS fit to the BBA 2.2 data. To prepare for fitting NLHM using
the two-component error model, the separate fits are updated assuming
two-component error.
```{r f-sep-tc, dependson = "f-sep-const"}
f_sep_tc <- update(f_sep_const, error_model = "tc")
status(f_sep_tc) |> kable()
```
Using the two-component error model, the one fit that did not converge with
constant variance did converge, but other non-SFO fits failed to converge.
\clearpage
# Hierarchichal model fits
The following code fits eight versions of hierarchical models to the data,
using SFO, FOMC, DFOP and HS for the parent compound, and using either constant
variance or two-component error for the error model. The default parameter
distribution model in mkin allows for variation of all degradation parameters
across the assumed population of soils. In other words, each degradation
parameter is associated with a random effect as a first step. The `mhmkin`
function makes it possible to fit all eight versions in parallel (given a
sufficient number of computing cores being available) to save execution time.
Convergence plots and summaries for these fits are shown in the appendix.
```{r f-saem, dependson = c("f-sep-const", "f-sep-tc")}
f_saem <- mhmkin(list(f_sep_const, f_sep_tc), transformations = "saemix")
```
The output of the `status` function shows that all fits terminated
successfully.
```{r dependson = "f-saem"}
status(f_saem) |> kable()
```
The AIC and BIC values show that the biphasic models DFOP and HS give the best
fits.
```{r dependson = "f-saem"}
anova(f_saem) |> kable(digits = 1)
```
The DFOP model is preferred here, as it has a better mechanistic basis for
batch experiments with constant incubation conditions. Also, it shows the
lowest AIC and BIC values in the first set of fits when combined with the
two-component error model. Therefore, the DFOP model was selected for further
refinements of the fits with the aim to make the model fully identifiable.
## Parameter identifiability based on the Fisher Information Matrix
Using the `illparms` function, ill-defined statistical model parameters such
as standard deviations of the degradation parameters in the population and
error model parameters can be found.
```{r dependson = "f-saem"}
illparms(f_saem) |> kable()
```
According to the `illparms` function, the fitted standard deviation of the
second kinetic rate constant `k2` is ill-defined in both DFOP fits. This
suggests that different values would be obtained for this standard deviation
when using different starting values.
The thus identified overparameterisation is addressed by removing the random
effect for `k2` from the parameter model.
```{r f-saem-dfop-tc-no-ranef-k2, dependson = "f-saem"}
f_saem_dfop_tc_no_ranef_k2 <- update(f_saem[["DFOP", "tc"]],
no_random_effect = "k2")
```
For the resulting fit, it is checked whether there are still ill-defined
parameters,
```{r f-saem-dfop-tc-no-ranef-k2-illparms, dependson = "f-saem-dfop-tc-no-ranef-k2"}
illparms(f_saem_dfop_tc_no_ranef_k2)
```
which is not the case. Below, the refined model is compared with the previous
best model. The model without random effect for `k2` is a reduced version of
the previous model. Therefore, the models are nested and can be compared using
the likelihood ratio test. This is achieved with the argument `test = TRUE`
to the `anova` function.
```{r f-saem-dfop-tc-no-ranef-k2-comparison, dependson = "f-saem-dfop-tc-no-ranef-k2"}
anova(f_saem[["DFOP", "tc"]], f_saem_dfop_tc_no_ranef_k2, test = TRUE) |>
kable(format.args = list(digits = 4))
```
The AIC and BIC criteria are lower after removal of the ill-defined random
effect for `k2`. The p value of the likelihood ratio test is much greater
than 0.05, indicating that the model with the higher likelihood (here
the model with random effects for all degradation parameters
`f_saem[["DFOP", "tc"]]`) does not fit significantly better than the model
with the lower likelihood (the reduced model `f_saem_dfop_tc_no_ranef_k2`).
Therefore, AIC, BIC and likelihood ratio test suggest the use of the reduced model.
The convergence of the fit is checked visually.
```{r convergence-saem-dfop-tc-no-ranef-k2, fig.cap = "Convergence plot for the NLHM DFOP fit with two-component error and without a random effect on 'k2'", dependson = "f-saem-dfop-tc-no-ranef-k2", echo = FALSE, fig.width = 9, fig.height = 8}
plot(f_saem_dfop_tc_no_ranef_k2$so, plot.type = "convergence")
```
All parameters appear to have converged to a satisfactory degree. The final fit
is plotted using the plot method from the mkin package.
```{r plot-saem-dfop-tc-no-ranef-k2, fig.cap = "Plot of the final NLHM DFOP fit", dependson = "f-saem-dfop-tc-no-ranef-k2", fig.width = 9, fig.height = 5}
plot(f_saem_dfop_tc_no_ranef_k2)
```
Finally, a summary report of the fit is produced.
```{r summary-saem-dfop-tc-no-ranef-k2, dependson = "f-saem-dfop-tc-no-ranef-k2"}
summary(f_saem_dfop_tc_no_ranef_k2)
```
\clearpage
## Alternative check of parameter identifiability
The parameter check used in the `illparms` function is based on a quadratic
approximation of the likelihood surface near its optimum, which is calculated
using the Fisher Information Matrix (FIM). An alternative way to check
parameter identifiability [@duchesne_2021] based on a multistart approach
has recently been implemented in mkin.
The graph below shows boxplots of the parameters obtained in 50 runs of the
saem algorithm with different parameter combinations, sampled from the range of
the parameters obtained for the individual datasets fitted separately using
nonlinear regression.
```{r multistart-full, dependson = "f-saem"}
f_saem_dfop_tc_multi <- multistart(f_saem[["DFOP", "tc"]], n = 50, cores = 15)
```
```{r multistart-full-par, dependson = "multistart_full", fig.cap = "Scaled parameters from the multistart runs, full model", fig.width = 10, fig.height = 6}
par(mar = c(6.1, 4.1, 2.1, 2.1))
parplot(f_saem_dfop_tc_multi, lpos = "bottomright", ylim = c(0.3, 10), las = 2)
```
The graph clearly confirms the lack of identifiability of the variance of `k2` in
the full model. The overparameterisation of the model also indicates a lack of
identifiability of the variance of parameter `g`.
The parameter boxplots of the multistart runs with the reduced model shown
below indicate that all runs give similar results, regardless of the starting
parameters.
```{r multistart-reduced, dependson = "f-saem-dfop-tc-no-ranef-k2"}
f_saem_dfop_tc_no_ranef_k2_multi <- multistart(f_saem_dfop_tc_no_ranef_k2,
n = 50, cores = 15)
```
```{r multistart-reduced-par, dependson = "multistart_reduced", fig.cap = "Scaled parameters from the multistart runs, reduced model", fig.width = 10, fig.height = 5}
par(mar = c(6.1, 4.1, 2.1, 2.1))
parplot(f_saem_dfop_tc_no_ranef_k2_multi, ylim = c(0.5, 2), las = 2,
lpos = "bottomright")
```
When only the parameters of the top 25% of the fits are shown (based on a feature
introduced in mkin 1.2.2 currently under development), the scatter is even less
as shown below.
```{r multistart-reduced-par-llquant, dependson = "multistart_reduced", fig.cap = "Scaled parameters from the multistart runs, reduced model, fits with the top 25\\% likelihood values", fig.width = 10, fig.height = 5}
par(mar = c(6.1, 4.1, 2.1, 2.1))
parplot(f_saem_dfop_tc_no_ranef_k2_multi, ylim = c(0.5, 2), las = 2, llquant = 0.25,
lpos = "bottomright")
```
\clearpage
# Conclusions
Fitting the four parent degradation models SFO, FOMC, DFOP and HS as part of
hierarchical model fits with two different error models and normal
distributions of the transformed degradation parameters works without technical
problems. The biphasic models DFOP and HS gave the best fit to the data, but
the default parameter distribution model was not fully identifiable. Removing
the random effect for the second kinetic rate constant of the DFOP model
resulted in a reduced model that was fully identifiable and showed the lowest
values for the model selection criteria AIC and BIC. The reliability of the
identification of all model parameters was confirmed using multiple starting
values.
# Acknowledgements
The helpful comments by Janina Wöltjen of the German Environment Agency
are gratefully acknowledged.
# References
\vspace{1em}
::: {#refs}
:::
\clearpage
# Appendix
## Hierarchical model fit listings
```{r listings, results = "asis", echo = FALSE}
for (deg_mod in deg_mods) {
for (err_mod in c("const", "tc")) {
caption <- paste("Hierarchical mkin fit of the", deg_mod, "model with error model", err_mod)
summary_listing(f_saem[[deg_mod, err_mod]], caption)
}
}
```
## Hierarchical model convergence plots
```{r convergence-saem-sfo-const, fig.cap = "Convergence plot for the NLHM SFO fit with constant variance", dependson = "f-saem", echo = FALSE, fig.width = 9, fig.height = 5}
plot(f_saem[["SFO", "const"]]$so, plot.type = "convergence")
```
\clearpage
```{r convergence-saem-sfo-tc, fig.cap = "Convergence plot for the NLHM SFO fit with two-component error", dependson = "f-saem", echo = FALSE, fig.width = 9, fig.height = 5}
plot(f_saem[["SFO", "tc"]]$so, plot.type = "convergence")
```
\clearpage
```{r convergence-saem-fomc-const, fig.cap = "Convergence plot for the NLHM FOMC fit with constant variance", dependson = "f-saem", echo = FALSE, fig.width = 9, fig.height = 8}
plot(f_saem[["FOMC", "const"]]$so, plot.type = "convergence")
```
\clearpage
```{r convergence-saem-fomc-tc, fig.cap = "Convergence plot for the NLHM FOMC fit with two-component error", dependson = "f-saem", echo = FALSE, fig.width = 9, fig.height = 8}
plot(f_saem[["FOMC", "tc"]]$so, plot.type = "convergence")
```
\clearpage
```{r convergence-saem-dfop-const, fig.cap = "Convergence plot for the NLHM DFOP fit with constant variance", dependson = "f-saem", echo = FALSE, fig.width = 9, fig.height = 8}
plot(f_saem[["DFOP", "const"]]$so, plot.type = "convergence")
```
\clearpage
```{r convergence-saem-dfop-tc, fig.cap = "Convergence plot for the NLHM DFOP fit with two-component error", dependson = "f-saem", echo = FALSE, fig.width = 9, fig.height = 8}
plot(f_saem[["DFOP", "tc"]]$so, plot.type = "convergence")
```
\clearpage
```{r convergence-saem-hs-const, fig.cap = "Convergence plot for the NLHM HS fit with constant variance", dependson = "f-saem", echo = FALSE, fig.width = 9, fig.height = 8}
plot(f_saem[["HS", "const"]]$so, plot.type = "convergence")
```
\clearpage
```{r convergence-saem-hs-tc, fig.cap = "Convergence plot for the NLHM HS fit with two-component error", dependson = "f-saem", echo = FALSE, fig.width = 9, fig.height = 8}
plot(f_saem[["HS", "tc"]]$so, plot.type = "convergence")
```
\clearpage
## Session info
```{r, echo = FALSE}
parallel::stopCluster(cl)
sessionInfo()
```
## Hardware info
```{r, echo = FALSE}
if(!inherits(try(cpuinfo <- readLines("/proc/cpuinfo")), "try-error")) {
cat(gsub("model name\t: ", "CPU model: ", cpuinfo[grep("model name", cpuinfo)[1]]))
}
if(!inherits(try(meminfo <- readLines("/proc/meminfo")), "try-error")) {
cat(gsub("model name\t: ", "System memory: ", meminfo[grep("MemTotal", meminfo)[1]]))
}
```
