required package : install.packages("ggpubr")
library(ggpubr)
data("ToothGrowth")
df_compare <- compare_means(len~supp, data=ToothGrowth, method = "wilcox.test", paired = TRUE,
group.by = "dose")
My question is how I can set the significant level based on 'p.adj' instead of 'p.format' because I want to add 'p.signif' on my plot but based on 'p.adj' ,and second question is how can I use report function from report package for this output? If someone has a suggestion or explanation, I would appreciate.
First I looked at the help page to see if I could find a simple parameter to set. No such luck. Then I looked at the code to see if there were an undocumented route to happiness. Again, no joy. So I found the section of code where the p.signif value was constructed and then worked backward to see whee the arguments were obtaine and finally applied base::p.adjust to those values. (It was an alteration of one line. Then I set the environment to the same as compare_means. Success.
compare_means # code appears on console
compare_means_adj <- # edit a copy of the code scraped from console
+ function (formula, data, method = "wilcox.test", paired = FALSE,
+ group.by = NULL, ref.group = NULL, symnum.args = list(),
+ p.adjust.method = "holm", ...)
+ {
+ . <- NULL
+ method.info <- .method_info(method)
+ method <- method.info$method
# --- leaving out a couple of pages of code
# --- one finds the relevant code at the very end
symnum.args$x <- p.adjust(res$p)
# --- that was the only line of code that was changed
environment(compare_means_adj) <- environment(compare_means)
(df_compare <- compare_means_adj(len~supp, data=ToothGrowth, method = "wilcox.test", paired = TRUE,
group.by = "dose"))
#------------------------------------
# A tibble: 3 × 9
dose .y. group1 group2 p p.adj p.format p.signif method
<dbl> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
1 0.5 len OJ VC 0.0330 0.066 0.033 ns Wilcoxon
2 1 len OJ VC 0.0137 0.041 0.014 * Wilcoxon
3 2 len OJ VC 1 1 1.000 ns Wilcoxon