Facilitate centering covariates

The motivation for this is to obtain confidence intervals for
covariate dependent parameters that are valid for a median/mean
value of the covariate.

Implementation by adding an argument to the 'saem' function, and
adapting the relevant functions working with 'saem' objects.

Vignettes, the template and tests were updated to use the new functionality.

1 files changed, 15 insertions, 10 deletions

diff --git a/vignettes/web_only/mesotrione_parent_2023.rmd b/vignettes/web_only/mesotrione_parent_2023.rmd
index 153c22ab..eb30b455 100644
--- a/vignettes/web_only/mesotrione_parent_2023.rmd
+++ b/vignettes/web_only/mesotrione_parent_2023.rmd
@@ -1,7 +1,7 @@
 ---
 title: "Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione"
 author: "Johannes Ranke"
-date: Last change 13 May 2025 (rebuilt `r Sys.Date()`)
+date: Last change 12 September 2025 (rebuilt `r Sys.Date()`)
 output:
   html_document:
     toc: true
@@ -155,15 +155,15 @@ illparms(f_saem_1) |> kable()
 ```
 
 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.
+the set of fits, excluding random effects for degradation parameters that were
+ill-defined according to the `illparms` function.
 
 ```{r f-saem-2, dependson = "f-saem-1"}
 f_saem_2 <- update(f_saem_1, no_random_effect = illparms(f_saem_1))
 status(f_saem_2) |> kable()
 ```
 
-The updated fits terminate without errors.
+The updated fits terminate without errors, and the only ill-defined
 
 ```{r dependson = "f-saem-2"}
 illparms(f_saem_2) |> kable()
@@ -187,7 +187,7 @@ has shown that only for such parameters a significant pH effect could be found.
 
 ```{r sfo-pH, dependson = "f-sep-const"}
 sfo_pH <- saem(f_sep_const["SFO", ], no_random_effect = "meso_0", covariates = pH,
-  covariate_models = list(log_k_meso ~ pH))
+  covariate_models = list(log_k_meso ~ pH), center_covariates = "median")
 ```
 
 ```{r dependson = "sfo-pH"}
@@ -230,7 +230,7 @@ endpoints(sfo_pH, covariates = c(pH = 7.0))
 
 ```{r fomc-pH, dependson = "f-sep-const"}
 fomc_pH <- saem(f_sep_const["FOMC", ], no_random_effect = "meso_0", covariates = pH,
-  covariate_models = list(log_alpha ~ pH))
+  covariate_models = list(log_alpha ~ pH), center_covariates = "median")
 ```
 
 ```{r dependson = "fomc-pH"}
@@ -298,7 +298,9 @@ and including covariate models for the two identifiable parameters `k2` and `g`.
 ```{r dfop-pH, dependson = "f-sep-const"}
 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))
+  covariate_models = list(log_k2 ~ pH, g_qlogis ~ pH),
+  center_covariates = "median"
+)
 ```
 
 The corresponding parameters for
@@ -326,7 +328,8 @@ Therefore, another attempt is made without the corresponding covariate model.
 ```{r dfop-pH-3, dependson = "f-sep-const"}
 dfop_pH_3 <- saem(f_sep_const["DFOP", ], no_random_effect = c("meso_0", "log_k1"),
   covariates = pH,
-  covariate_models = list(log_k2 ~ pH))
+  covariate_models = list(log_k2 ~ pH),
+  center_covariates = "median")
 illparms(dfop_pH_3)
 ```
 As the random effect for `g` is again ill-defined, the fit is repeated without it.
@@ -371,7 +374,8 @@ endpoints(dfop_pH_2, covariates = c(pH = 7))
 ```{r sforb-pH, dependson = "f-sep-const"}
 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))
+  covariate_models = list(log_k_meso_free ~ pH, log_k_meso_bound_free ~ pH),
+  center_covariates = "median")
 ```
 
 ```{r dependson = "sforb-pH"}
@@ -431,7 +435,8 @@ endpoints(sforb_pH_2, covariates = c(pH = 7))
 ```{r hs-pH, dependson = "f-sep-const"}
 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))
+  covariate_models = list(log_k1 ~ pH, log_k2 ~ pH, log_tb ~ pH),
+  center_covariates = "median")
 ```
 
 ```{r dependson = "hs-pH"}