我想模拟不同种类的多重测试校正的效果，例如Bonferroni, Fisher's LSD, DUncan, Dunn-Sidak Newman-Keuls,Tukey等Anova...
我想我应该简单地运行一个常规的Anova. 然后接受p.value我通过使用计算的显着 s p.adjust。但我不明白这个p.adjust功能是如何工作的。能给我一些见解p.adjust()吗？
运行时：
> p.adjust(c(0.05,0.05,0.1),"bonferroni")
# [1] 0.15 0.15 0.30
有人可以解释这是什么意思吗？
谢谢您的回答。我对这一切都略知一二。但是我还是不明白 p.adjust 的输出。我希望...
P.adjust(0.08,'bonferroni',n=10)
... 将返回 0.008 而不是 0.8。n=10 并不意味着我要进行 10 次比较。并且不是 0.08 的“原始 alpha”（我的意思是如果我有一个简单的比较，我会用来拒绝 NULL 假设的阈值）
You'll have to read about each multiple testing correction technique, whether it be False Discovery Rate (FDR) or Family-Wise Error Rate (FWER). (Thanks to @thelatemail for pointing out to expand the abbreviations).
Bonferroni correction controls the FWER by setting the significance level alpha to alpha/n where n is the number of hypotheses tested in a typical multiple comparison (here n=3).
Let's say you are testing at 5% alpha. Meaning if your p-value is < 0.05, then you reject your NULL. For n=3, then, for Bonferroni correction, you could then divide alpha by 3 = 0.05/3 ~ 0.0167 and then check if your p-values are < 0.0167.
Equivalently (which is directly evident), instead of checking pval < alpha/n, you could take the n to the other side pval * n < alpha. So that the alpha remains the same value. So, your p-values get multiplied by 3 and then would be checked if they are < alpha = 0.05 for example.
Therefore, the output you obtain is the FWER controlled p-value and if this is < alpha (5% say), then you would reject the NULL, else you'd accept the NULL hypothesis.
For each tests, there are different procedures to control the false-positives due to multiple testing. Wikipedia might be a good start point to learn about other tests as to how they correct for controlling false-positives.
However, your output of p.adjust, gives in general multiple-testing corrected p-value. In case of Bonferroni, it is FWER controlled p-value. In case of BH method, it is FDR corrected p-value (or also otherwise called q-value).
Hope this helps a bit.