From 6476f5f49b373cd4cf05f2e73389df83e437d597 Mon Sep 17 00:00:00 2001
From: Johannes Ranke <johannes.ranke@jrwb.de>
Date: Thu, 13 Feb 2025 16:30:31 +0100
Subject: Axis legend formatting, update vignettes

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----
-title: "Hierarchical kinetic modelling of degradation data"
-author:
-date:
-output: mkin::hierarchical_kinetics
-geometry: margin=2cm
----
-
-\clearpage
-
-# Setup
-
-```{r packages, cache = FALSE, message = FALSE}
-library(mkin)
-library(knitr)
-library(saemix)
-library(parallel)
-library(readxl)
-```
-
-```{r n_cores, cache = FALSE}
-n_cores <- detectCores()
-
-if (Sys.info()["sysname"] == "Windows") {
-  cl <- makePSOCKcluster(n_cores)
-} else {
-  cl <- makeForkCluster(n_cores)
-}
-```
-
-\clearpage
-
-# Introduction
-
-This report shows hierarchical kinetic modelling for ...
-The data were obtained from ...
-
-```{r ds}
-data_path <- system.file(
-  "testdata", "lambda-cyhalothrin_soil_efsa_2014.xlsx",
-  package = "mkin")
-ds <- read_spreadsheet(data_path, valid_datasets = c(1:4, 7:13))
-covariates <- attr(ds, "covariates")
-```
-
-The covariate data are shown below.
-
-```{r results = "asis", dependson = "ds", echo = FALSE}
-kable(covariates, caption = "Covariate data for all datasets")
-```
-
-\clearpage
-
-The datasets with the residue time series are shown in the tables below. Please
-refer to the spreadsheet for details like data sources, treatment of values
-below reporting limits and time step normalisation factors.
-
-```{r results = "asis", dependson = "ds", echo = FALSE}
-for (ds_name in names(ds)) {
-  print(
-    kable(mkin_long_to_wide(ds[[ds_name]]),
-      caption = paste("Dataset", ds_name),
-      booktabs = TRUE, row.names = FALSE))
-  cat("\n\\clearpage\n")
-}
-```
-
-# Parent only evaluations
-
-The following code performs separate fits of the candidate degradation models
-to all datasets using constant variance and the two-component error model.
-
-```{r parent-sep, dependson = "ds"}
-parent_deg_mods <- c("SFO", "FOMC", "DFOP", "SFORB")
-errmods <- c(const = "constant variance", tc = "two-component error")
-parent_sep_const <- mmkin(
-  parent_deg_mods, ds,
-  error_model = "const",
-  cluster = cl, quiet = TRUE)
-parent_sep_tc <- update(parent_sep_const, error_model = "tc")
-```
-
-To select the parent model, the corresponding hierarchical fits are performed below.
-
-```{r parent-mhmkin, dependson = "parent-sep"}
-parent_mhmkin <- mhmkin(list(parent_sep_const, parent_sep_tc), cluster = cl)
-status(parent_mhmkin) |> kable()
-```
-
-All fits terminate without errors (status OK). The check for ill-defined
-parameters shows that not all random effect parameters can be robustly
-quantified.
-
-```{r dependson = "parent_mhmkin"}
-illparms(parent_mhmkin) |> kable()
-```
-
-Therefore, the fits are updated, excluding random effects that were
-ill-defined according to the `illparms` function. The status of the fits
-is checked.
-
-```{r parent-mhmkin-refined}
-parent_mhmkin_refined <- update(parent_mhmkin,
-  no_random_effect = illparms(parent_mhmkin))
-status(parent_mhmkin_refined) |> kable()
-```
-
-Also, it is checked if the AIC values of the refined fits are actually smaller
-than the AIC values of the original fits.
-
-```{r dependson = "parent-mhmkin-refined"}
-(AIC(parent_mhmkin_refined) < AIC(parent_mhmkin)) |> kable()
-```
-
-From the refined fits, the most suitable model is selected using the AIC.
-
-```{r parent-best, dependson = "parent-mhmkin"}
-aic_parent <- AIC(parent_mhmkin_refined)
-min_aic <- which(aic_parent == min(aic_parent), arr.ind = TRUE)
-best_degmod_parent <- rownames(aic_parent)[min_aic[1]]
-best_errmod_parent <- colnames(aic_parent)[min_aic[2]]
-anova(parent_mhmkin_refined) |> kable(digits = 1)
-parent_best <- parent_mhmkin_refined[[best_degmod_parent, best_errmod_parent]]
-```
-
-Based on the AIC, the combination of the `r best_degmod_parent` degradation
-model with the error model `r errmods[best_errmod_parent]` is identified to
-be most suitable for the degradation of the parent. The check below
-confirms that no ill-defined parameters remain for this combined model.
-
-```{r dependson = "parent-best"}
-illparms(parent_best)
-```
-
-The corresponding fit is plotted below.
-
-```{r dependson = "parent-best"}
-plot(parent_best)
-```
-The fitted parameters, together with approximate confidence
-intervals are listed below.
-
-```{r dependson = "parent-best"}
-parms(parent_best, ci = TRUE) |> kable(digits = 3)
-```
-
-To investigate a potential covariate influence on degradation parameters, a
-covariate model is added to the hierarchical model for each of the degradation
-parameters with well-defined random effects. Also, a version with covariate
-models for both of them is fitted.
-
-```{r parent-best-pH}
-parent_best_pH_1 <- update(parent_best, covariates = covariates,
-  covariate_models = list(log_k_lambda_free ~ pH))
-parent_best_pH_2 <- update(parent_best, covariates = covariates,
-  covariate_models = list(log_k_lambda_bound_free ~ pH))
-parent_best_pH_3 <- update(parent_best, covariates = covariates,
-  covariate_models = list(log_k_lambda_free ~ pH, log_k_lambda_bound_free ~ pH))
-```
-
-The resulting models are compared.
-
-```{r dependson = "parent-best-pH"}
-anova(parent_best, parent_best_pH_1, parent_best_pH_2, parent_best_pH_3) |>
-  kable(digits = 1)
-```
-
-The model fit with the lowest AIC is the one with a pH correlation of the
-desorption rate constant `k_lambda_bound_free`. Plot and parameter listing
-of this fit are shown below. Also, it is confirmed that no ill-defined
-variance parameters are found.
-
-```{r dependson = "parent-best-pH"}
-plot(parent_best_pH_2)
-```
-
-```{r dependson = "parent-best-pH"}
-illparms(parent_best_pH_2)
-parms(parent_best_pH_2, ci = TRUE) |> kable(digits = 3)
-```
-
-\clearpage
-
-# Pathway fits
-
-As an example of a pathway fit, a model with SFORB for the parent compound and
-parallel formation of two metabolites is set up.
-
-```{r path-1-degmod}
-if (!dir.exists("dlls")) dir.create("dlls")
-
-m_sforb_sfo2 = mkinmod(
-  lambda = mkinsub("SFORB", to = c("c_V", "c_XV")),
-  c_V = mkinsub("SFO"),
-  c_XV = mkinsub("SFO"),
-  name = "sforb_sfo2",
-  dll_dir = "dlls",
-  overwrite = TRUE, quiet = TRUE
-)
-```
-
-Separate evaluations of all datasets are performed with constant variance
-and using two-component error.
-
-```{r path-1-sep, dependson = c("path-1-degmod", "ds")}
-sforb_sep_const <- mmkin(list(sforb_path = m_sforb_sfo2), ds,
-  cluster = cl, quiet = TRUE)
-sforb_sep_tc <- update(sforb_sep_const, error_model = "tc")
-```
-
-The separate fits with constant variance are plotted.
-
-```{r dependson = "path-1-sep", fig.height = 9}
-plot(mixed(sforb_sep_const))
-```
-
-The two corresponding hierarchical fits, with the random effects for the parent
-degradation parameters excluded as discussed above, and including the covariate
-model that was identified for the parent degradation, are attempted below.
-
-```{r path-1, dependson = "path-1-sep"}
-path_1 <- mhmkin(list(sforb_sep_const, sforb_sep_tc),
-  no_random_effect = c("lambda_free_0", "log_k_lambda_free_bound"),
-  covariates = covariates, covariate_models = list(log_k_lambda_bound_free ~ pH),
-  cluster = cl)
-```
-
-```{r dependson = "path-1"}
-status(path_1) |> kable()
-```
-
-The status information shows that both fits were successfully completed.
-
-```{r dependson = "path-1"}
-anova(path_1) |> kable(digits = 1)
-```
-Model comparison shows that the two-component error model provides a much
-better fit.
-
-```{r dependson = "path-1"}
-illparms(path_1[["sforb_path", "tc"]])
-```
-
-Two ill-defined variance components are found. Therefore, the fit is
-repeated with the corresponding random effects removed.
-
-```{r path-1-refined, dependson = "path-1"}
-path_1_refined <- update(path_1[["sforb_path", "tc"]],
-  no_random_effect = c("lambda_free_0", "log_k_lambda_free_bound",
-    "log_k_c_XV", "f_lambda_ilr_2"))
-```
-
-The empty output of the illparms function indicates that there are no
-ill-defined parameters remaining in the refined fit.
-
-```{r dependson = "path-1-refined"}
-illparms(path_1_refined)
-```
-
-Below, the refined fit is plotted and the fitted parameters are shown together
-with their 95% confidence intervals.
-
-```{r dependson = "path-1-refined", fig.height = 9}
-plot(path_1_refined)
-```
-
-```{r dependson = "path-1-refined", fig.height = 9}
-parms(path_1_refined, ci = TRUE) |> kable(digits = 3)
-```
-
-\clearpage
-
-# Appendix
-
-## Listings of initial parent fits
-
-```{r listings-parent, results = "asis", echo = FALSE, dependson = "parent_mhmkin"}
-for (deg_mod in parent_deg_mods) {
-  for (err_mod in c("const", "tc")) {
-    caption <- paste("Hierarchical", deg_mod, "fit with", errmods[err_mod])
-    tex_listing(parent_mhmkin[[deg_mod, err_mod]], caption)
-  }
-}
-```
-
-## Listings of refined parent fits
-
-```{r listings-parent-refined, results = "asis", echo = FALSE, dependson = "parent_mhmkin_refined"}
-for (deg_mod in parent_deg_mods) {
-  for (err_mod in c("const", "tc")) {
-    caption <- paste("Refined hierarchical", deg_mod, "fit with", errmods[err_mod])
-    tex_listing(parent_mhmkin_refined[[deg_mod, err_mod]], caption)
-  }
-}
-```
-
-## Listings of pathway fits
-
-```{r listings-path-1, results = "asis", echo = FALSE, dependson = "path-1-refined"}
-tex_listing(path_1[["sforb_path", "const"]],
-  caption = "Hierarchical fit of SFORB-SFO2 with constant variance")
-tex_listing(path_1[["sforb_path", "tc"]],
-  caption = "Hierarchical fit of SFORB-SFO2 with two-component error")
-tex_listing(path_1_refined,
-  caption = "Refined hierarchical fit of SFORB-SFO2 with two-component error")
-```
-
-## Session info
-
-```{r echo = FALSE, cache = FALSE}
-parallel::stopCluster(cl)
-sessionInfo()
-```
-
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