R lsmeans调整多重比较 [英] R lsmeans adjust multiple comparison

问题描述

我使用lme4在R中运行混合效果logistig回归(通过调用glmer)，现在我试图进行事后比较.因为它们是成对的，所以Tukey应该没问题，但是我想手动调整应该进行的测试数量-现在可以进行12个测试，但是我只对6个比较感兴趣.

I used lme4 to run a mixed effects logistig regression (by calling glmer) in R and now I am trying to do post-hoc comparisons. As they are pairwise, Tukey should be OK,but I would like to manually adjust for how many tests the correction should be made - now it is made for 12 tests, but I am only intersted in 6 comparisons.

到目前为止，我的代码看起来像这样

My code looks like this so far

    for (i in seq_along(logmixed_ranks)) {
    print(lsmeans(logmixed_ranks[[i]], pairwise~rating_ranks*indicator_var, adjust="tukey"))
    }

我可能需要使用以下内容，但是我不确定如何使用.

Somehow I may need to use the following but I am not sure how.

      p.adjust(p, method = p.adjust.methods, n = length(p))

有人可以帮忙吗? 谢谢！ 劳拉

Can anybody help? Thanks! Laura

推荐答案

肯定有一个原因，您只想对6个比较进行调整，我想这是因为您想细分比较有条件地在因素之一上做.使用lsmeans很容易做到:

There must be a reason you want to adjust for only 6 comparisons, and I'm guessing is it is because you want to break down the comparisons you're doing conditionally on one of the factors. This is easy to do using lsmeans:

lsmeans(logmixed_ranks[[i]], 
    pairwise ~ rating_ranks | indicator_var, adjust = "tukey")

或

lsmeans(logmixed_ranks[[i]], 
    pairwise ~ indicator_var | rating_ranks, adjust = "tukey")

如果使用adjust = "mvt"，则将获得与glht的单步过程完全相同的调整.因此，我相信lsmeans不支持的唯一glht功能是多步骤测试.

By the way, if you use adjust = "mvt", you will obtain exactly the same adjustments that glht uses for its single-step procedure. So I believe the only glht features not supported by lsmeans are the multi-step tests.

为什么要列出一个glmer对象的列表使我感到困惑，但这似乎与我的答案没有关系.

I'm puzzled by why you have a list of glmer objects, but that does not seem germane to my answer.

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