R ggplot2 - ggpubr stat_compare_means does not work properly

Question

I'm trying to add a significant level of boxplots in the form of stars using ggplot2 and package ggpubr , but I have a lot of comparison, and I just want to show meaningful.

I am trying to use the hide.ns = TRUE option in stat_compare_means , but it obviously does not work , it may be a bug in the ggpubr package .

In addition, you can see that I rule group "PGMC4" of pairwise comparisons wilcox.test ; how can i leave this group also for kruskal.test ?

Last question: how does significance level work? How is * significantly lower than 0.05, ** lower than 0.025, *** lower than 0.01? What convention does ggpubr use? Does p-value or adjusted p-value show? If the latter, what is the configuration method? Bh?

Please check my MWE below and this link and this other one for reference

##############################
##MWE
set.seed(5)
#test df
mydf <- data.frame(ID=paste(sample(LETTERS, 163, replace=TRUE), sample(1:1000, 163, replace=FALSE), sep=''),
                   Group=c(rep('C',10),rep('FH',10),rep('I',19),rep('IF',42),rep('NA',14),rep('NF',42),rep('NI',15),rep('NS',10),rep('PGMC4',1)),
                   Value=rnorm(n=163))
#I don't want to compare PGMC4 cause I have only onw sample
groups <- as.character(unique(mydf$Group[which(mydf$Group!="PGMC4")]))
#function to make combinations of groups without repeating pairs, and avoiding self-combinations
expand.grid.unique <- function(x, y, include.equals=FALSE){
    x <- unique(x)
    y <- unique(y)
    g <- function(i){
        z <- setdiff(y, x[seq_len(i-include.equals)])
        if(length(z)) cbind(x[i], z, deparse.level=0)
    }
    do.call(rbind, lapply(seq_along(x), g))
}
#all pairs I want to compare
combs <- as.data.frame(expand.grid.unique(groups, groups), stringsAsFactors=FALSE)
head(combs)
my.comps <- as.data.frame(t(combs), stringsAsFactors=FALSE)
colnames(my.comps) <- NULL
rownames(my.comps) <- NULL
#pairs I want to compare in list format for stat_compare_means
my.comps <- as.list(my.comps)
head(my.comps)
pdf(file="test.pdf", height=20, width=25)
print(#or ggsave()
  ggplot(mydf, aes(x=Group, y=Value, fill=Group)) + geom_boxplot() +
    stat_summary(fun.y=mean, geom="point", shape=5, size=4) +
    scale_fill_manual(values=myPal) +
    ggtitle("TEST TITLE") +
    theme(plot.title = element_text(size=30),
      axis.text=element_text(size=12),
      axis.text.x = element_text(angle=45, hjust=1),
      axis.ticks = element_blank(),
      axis.title=element_text(size=20,face="bold"),
      legend.text=element_text(size=16)) +
  stat_compare_means(comparisons=my.comps, method="wilcox.test", label="p.signif", size=14) + #WHY DOES hide.ns=TRUE NOT WORK??? WHY DOES size=14 NOT WORK???
  stat_compare_means(method="kruskal.test", size=14) #GLOBAL COMPARISON ACROSS GROUPS (HOW TO LEAVE PGMC4 OUT OF THIS??)
)
dev.off()
##############################

MWE will create the following squares:

Questions:

1 How to make hide.ns = TRUE work?

2- How to increase size *?

3 How to exclude a group from kruskal.test comparison?

4- What is the * convention used by ggpubr and are the p values ​​indicated, edited or not?

Many thanks!

EDIT

In addition, when performing

stat_compare_means(comparisons=my.comps, method="wilcox.test", p.adjust.method="BH")

I do not get the same p values ​​as when doing

wilcox.test(Value ~ Group, data=mydf.sub)$p.value

where mydf.sub is a subset () of mydf for this comparison of the two groups.

ggpubr? p.values?

2

, , ggpubr ( ggplot2), NS , p-, wilcox.test() + p.adjust( "BH" ).

!

Answer 1

. , , pairwise.wilcox.test. ggsignif geom_signif p. y_position , .

library(tidyverse)
library(ggsignif)
library(broom)
# your list of combinations you want to compare
CN <- combn(levels(mydf$Group)[-9], 2, simplify = FALSE)
# the pvalues. I use broom and tidy to get a nice formatted dataframe. Note, I turned off the adjustment of the pvalues. 
pv <- tidy(with(mydf[ mydf$Group != "PGMC4", ], pairwise.wilcox.test(Value, Group, p.adjust.method = "none")))
#  data preparation 
CN2 <- do.call(rbind.data.frame, CN)
colnames(CN2) <- colnames(pv)[-3]
# subset the pvalues, by merging the CN list
pv_final <- merge(CN2, pv, by.x = c("group2", "group1"), by.y = c("group1", "group2"))
# fix ordering
pv_final <- pv_final[order(pv_final$group1), ] 
# set signif level
pv_final$map_signif <- ifelse(pv_final$p.value > 0.05, "", ifelse(pv_final$p.value > 0.01,"*", "**"))  

# the plot
ggplot(mydf, aes(x=Group, y=Value, fill=Group)) + geom_boxplot() +
  stat_compare_means(data=mydf[ mydf$Group != "PGMC4", ], aes(x=Group, y=Value, fill=Group), size=5) + 
  ylim(-4,30)+
  geom_signif(comparisons=CN,
              y_position = 3:30, annotation= pv_final$map_signif) + 
  theme_bw(base_size = 16)

vjust, textsize size . , ggsignif_0.3.0.

---

: , . vjust textsize , ggsignif_0.4.0 R version 3.4.1. y_position step_increase

# subset 
gr <- pv_final$p.value <= 0.05
CN[gr]

ggplot(mydf, aes(x=Group, y=Value, fill=Group)) + geom_boxplot() +
  stat_compare_means(data=mydf[ mydf$Group != "PGMC4", ], aes(x=Group, y=Value, fill=Group), size=5) + 
  geom_signif(comparisons=CN[gr], textsize = 12, vjust = 0.7, 
             step_increase=0.12, annotation= pv_final$map_signif[gr]) + 
  theme_bw(base_size = 16)

ggpubr. :

stat_compare_means(comparisons=CN[gr], method="wilcox.test", label="p.signif", color="red")