diff options
Diffstat (limited to 'inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd')
| -rw-r--r-- | inst/rmarkdown/templates/hierarchical_kinetics/skeleton/skeleton.Rmd | 9 |
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), |