From fc692554ecdaff66548cc3e6d666e44b7aaaa9af Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 3 Jan 2023 17:52:39 +0100 Subject: Neutral names for code chunks in the template --- .../templates/hierarchical_kinetics/skeleton/skeleton.Rmd | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) (limited to 'inst/rmarkdown/templates') 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), -- cgit v1.2.1