Statistical test analysis, Friedman and Pairwise Wilcoxon

statistical.R

suppressWarnings(library(tidyverse))
suppressWarnings(library(ggpubr))
suppressWarnings(library(rstatix))

mydata <- read.csv("mydata-fixedhold.csv", sep=",", header = T)

myvars <- c("id", "cigarette", "bottle", "cup")
newdata <- mydata[myvars]


touch <- newdata %>%
  gather(key = "material", value = "fading", cigarette, bottle, cup) %>%
  convert_as_factor(id, material)
#head(touch, 20)

touch %>%
  group_by(material) %>%
  get_summary_stats(fading, type = "common")


ggboxplot(touch, x = "material", y = "fading", add = "jitter")

res.fried <- touch %>% friedman_test(fading ~ material |id)
res.fried
#X2(2) = 20.63, p = 0.000033106.


#Effect
#Kendall’s W uses the Cohen’s interpretation guidelines of 0.1 - < 0.3 (small effect), 0.3 - < 0.5 (moderate effect) and >= 0.5 (large effect).
touch %>% friedman_effsize(fading ~ material |id)
# W=0.54, magnitude = large effect



# pairwise comparisons
#A significant Friedman test can be followed up by pairwise Wilcoxon signed-rank tests for identifying which groups are different.

pwc <- touch %>%
  wilcox_test(fading ~ material, paired = TRUE, p.adjust.method = "bonferroni")
pwc

pwc <- pwc %>% add_xy_position(x = "material")
ggboxplot(touch, x = "material", y = "fading", add = "point") +
  stat_pvalue_manual(pwc, hide.ns = TRUE) +
  labs(
    subtitle = get_test_label(res.fried,  detailed = TRUE),
    caption = get_pwc_label(pwc)
  )