From 4327806d79feed15058466afa7ac8f3bcd43bfb5 Mon Sep 17 00:00:00 2001 From: Kris Sankaran Date: Fri, 6 Sep 2024 19:44:08 -0500 Subject: [PATCH] Revise the figure --- vignettes/IBD.Rmd | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/vignettes/IBD.Rmd b/vignettes/IBD.Rmd index df202b0..a95fc85 100644 --- a/vignettes/IBD.Rmd +++ b/vignettes/IBD.Rmd @@ -476,16 +476,21 @@ This situation is more ambiguous. It's possible that a confounder could be responsible for the indirect effect; however, it would have to be quite strong, perhaps stronger than is plausible. -```{r} +```{r, fig.width = 9, fig.height = 2.8} confound_ix |> left_join(sensitivity_path) |> ggplot(aes(rho, indirect_effect, group = contrast)) + geom_hline(yintercept = 0, linetype = 3, linewidth = 1) + + geom_vline(xintercept = 0, linetype = 3, linewidth = 1) + geom_line(linewidth = 2) + - facet_wrap(outcome ~ mediator, scales = "free_y") + labs(x = expression("Confounding parameter"~ rho), y = "Estimated Indirect Effect") + + theme( + axis.title = element_text(size = 16), + strip.text = element_text(size = 14) + ) + + facet_wrap(reorder(outcome, indirect_effect) ~ reorder(mediator, indirect_effect), scales = "free_y") ``` - # Hurdle-Lognormal Approach There are many zeros in the metabolites data, and sparse regression is not the