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-rw-r--r--inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd9
1 files changed, 4 insertions, 5 deletions
diff --git a/inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd b/inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd
index e26213f5..38a6bd20 100644
--- a/inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd
+++ b/inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd
@@ -186,8 +186,7 @@ parms(parent_best_pH_2, ci = TRUE) |> kable(digits = 3)
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 m_sforb_sfo2}
+```{r path-1-degmod}
if (!dir.exists("dlls")) dir.create("dlls")
m_sforb_sfo2 = mkinmod(
@@ -203,7 +202,7 @@ m_sforb_sfo2 = mkinmod(
Separate evaluations of all datasets are performed with constant variance
and using two-component error.
-```{r path-sep, dependson = c("m_sforb_all", "ds")}
+```{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")
@@ -211,7 +210,7 @@ sforb_sep_tc <- update(sforb_sep_const, error_model = "tc")
The separate fits with constant variance are plotted.
-```{r dependson = "path-sep", fig.height = 9}
+```{r dependson = "path-1-sep", fig.height = 9}
plot(mixed(sforb_sep_const))
```
@@ -219,7 +218,7 @@ 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-sep"}
+```{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),

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