01-T检验和Wilcoxon 检验

思路流程：

代码：

rm(list = ls())

##ggpubr() 文章发表

library(ggpubr)
library(ggplot2)
library(tidyverse)


data("ToothGrowth")
#正态性
ggdensity(ToothGrowth$len, y="..density..")
ggdensity(ToothGrowth,x="len",color="supp",fill="supp",add = "median")

gghistogram(ToothGrowth,x="len",color="supp",fill="supp",bins=30,rug=T,add = "median")

ggqqplot(ToothGrowth,x="len",color = "supp")



shapiro.test(ToothGrowth$len)
shapiro.test(ToothGrowth$len[ToothGrowth$supp == "OJ"])
##IT：H0假设一般都是二者相同的，所以p>0.05，则没有显著性差异
with(ToothGrowth, shapiro.test(len[supp=="OJ"]))


with(ToothGrowth, expr = tapply(len, supp, shapiro.test))


#p_value<0.05（与正态分布有显著性差异）, Wilcoxon
wilcox.test(len~supp, data = ToothGrowth,exact=F,alternative="greater")
## two-sided, greater, less (greater和less是单边，需要<0.025才为显著性差异)
##paired 数据符合正态分布


##方差齐性检验（正态分布+方差检验 均不显著，才用T-test）
  var.test(len~supp,data = ToothGrowth)

#p>0.05 即差别不显著，方差齐性


##T检验
diff <- with(ToothGrowth, len[supp == 'OJ']-len[supp == "VC"])
shapiro.test(x = diff)

t.test(len~supp,data = ToothGrowth,alternative='t',var.equal=T, paired=T)


##作图
ggboxplot(ToothGrowth, x="supp",y="len",color = "supp") +
  stat_compare_means(method = 'wilcox.test', paired = T,
                     label.x = 2,label.y = 38)