为什么不同组之间的 p 值总是相同，有人知道我做错了什么吗？

Question

大家好我正在比较不同分布的权重。我的数据是这样格式化的：

Weight_G1   Gaussian_One
0.714484    Non_Treated 
0.6992871   Non_Treated 
0.7144726   Non_Treated 
0.7062965   Non_Treated 
0.7331027   Non_Treated 
0.6927765   Non_Treated 
0.5939587   Treated1
0.6230649   Treated1    
0.5935826   Treated1    
0.59120414  Treated1    
0.6069062   Treated1    
0.6024558   Treated1    
0.6285782   Treated2    
0.6328368   Treated2    
0.6339552   Treated2    
0.6193341   Treated2    
0.6412954   Treated2    
0.6160553   Treated2

我正在使用以下代码：

My_table <- structure(list(Weight_G1 = c(30.71604, 30.29721, 30.71587, 
30.91229, 30.77887, 30.54265, 28.38478, 28.84662, 28.32799, 
28.25268, 28.42514, 28.51167, 29.28263, 29.40063, 29.28390, 
29.27207, 29.34798, 29.08956), Gaussian_One = c("Non_Treated", 
"Non_Treated", "Non_Treated", "Non_Treated", "Non_Treated", "Non_Treated", 
"Treated1", "Treated1", "Treated1", "Treated1", "Treated1", "Treated1", 
"Treated2", "Treated2", "Treated2", "Treated2", "Treated2", "Treated2"
)), row.names = c(NA, -18L), class = c("tbl_df", "tbl", "data.frame"
))

library(ggpubr)

my_comparisons <- list( c("Non_Treated", "Treated1"), c("Non_Treated", "Treated2"), c("Treated1", "Treated2"))
    Mean1 <- ggboxplot(My_table, x = "Gaussian_One", y = "Weight_G1",lwd=1,
              color = "Gaussian_One", palette = "jco")+ 
      stat_compare_means(label = "p",comparisons = my_comparisons) # Add pairwise comparisons p-value

    Mean1<- Mean1+theme_pubr(base_size = 20)
    Mean1

p 值似乎总是相同的 0.22，我做错了什么？

Answer 1

我认为没有任何问题，所以这不一定是您问题的答案。我只想反驳另一个问题：你到底想做什么？

您创建了一个包含 p 值的漂亮绘图。虽然放入 paper/report 很好，但这不是我通常执行 'actual' 分析的方式。因此，我要问你的第一个问题是，你想做什么类型的 analysis/comparision？你不提那个，你只是说你认为你的结果很奇怪。但是您要分析的内容对于您使用 stat_compare_means.

所做的事情非常重要

我认为这个例子没有意义，但我可以很容易地重现它，所以不要将它用于你自己的分析（它只是一个例子）。所以说，你想做一个配对 t 检验。分析看起来像：

t1  <- df %>% filter(Gaussian_One == "Non_Treated") %>% pull(Weight_G1)
t2  <- df %>% filter(Gaussian_One == "Treated1") %>% pull(Weight_G1)
t3  <- df %>% filter(Gaussian_One == "Treated2") %>% pull(Weight_G1)

t.test(t1, t2, paired = TRUE)
t.test(t1, t3, paired = TRUE)
t.test(t2, t3, paired = TRUE)

分别导致

data:  t1 and t2
t = 13.166, df = 5, p-value = 4.515e-05
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.08708014 0.12933555
sample estimates:
mean of the differences 
              0.1082078

data:  t1 and t3
t = 22.133, df = 5, p-value = 3.497e-06
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 0.07194078 0.09084735
sample estimates:
mean of the differences 
             0.08139407

Paired t-test

data:  t2 and t3
t = -5.2963, df = 5, p-value = 0.003202
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.03982790 -0.01379965
sample estimates:
mean of the differences 
            -0.02681378

如果我们想在你的图表中绘制它，你会这样做：

ggboxplot(df, x = "Gaussian_One", y = "Weight_G1",lwd=1,
                   color = "Gaussian_One", palette = "jco")+ 
  stat_compare_means(method = "t.test", paired = TRUE, 
                     label = "p",comparisons = my_comparisons)

该图现在包含我也从配对 t 检验中获得的 p 值。

在您的示例中，您没有指定 comparing/calculating p 值的方法。我在文档中找不到默认使用的内容，但这可能解释了您的 'weird' 结果。因此，如果您返回绘图 table 并决定您实际想要查看的分析并将其添加到 stat_compare_means，您的结果将更有意义。

为什么不同组之间的 p 值总是相同，有人知道我做错了什么吗？

Why is p-value always the same between different group, Someone know what I am doing of wrong?

comparison

r

ggplot2

p-value