如何在混合效应模型中获得系数及其置信区间? [英] How to get coefficients and their confidence intervals in mixed effects models?

问题描述

在lm和glm模型中，我使用函数coef和confint达到目标:

In lm and glm models, I use functions coef and confint to achieve the goal:

m = lm(resp ~ 0 + var1 + var1:var2) # var1 categorical, var2 continuous
coef(m)
confint(m)

现在，我在模型中添加了随机效果-使用来自lme4包中的lmer函数的混合效果模型.但是然后，功能coef和confint对我来说不再起作用了！

Now I added random effect to the model - used mixed effects models using lmer function from lme4 package. But then, functions coef and confint do not work any more for me!

> mix1 = lmer(resp ~ 0 + var1 + var1:var2 + (1|var3)) 
                                      # var1, var3 categorical, var2 continuous
> coef(mix1)
Error in coef(mix1) : unable to align random and fixed effects
> confint(mix1)
Error: $ operator not defined for this S4 class

我试图用google搜索并使用文档，但没有结果.请指出正确的方向.

I tried to google and use docs but with no result. Please point me in the right direction.

我也在想这个问题是否更适合 https://stats.stackexchange.com/，但我认为技术比统计更重要，所以我得出结论，它最适合这里(SO)...您怎么看?

I was also thinking whether this question fits more to https://stats.stackexchange.com/ but I consider it more technical than statistical, so I concluded it fits best here (SO)... what do you think?

推荐答案

有两个新程序包， lmerTest 和 lsmeans ，可以为lmer和glmer输出计算95％的置信度限制.也许您可以调查一下?而且 coefplot2 ，我认为也可以做到(尽管Ben在下面指出，而不是lmerTest和lsmeans中使用的Kenward-Roger和/或Satterthwaite df近似值，而不是Wald统计量的标准误差).软件包lsmeans中的内置绘图功能(如软件包effects()中的内容一样)，btw还返回了lmer和glmer对象的95％置信度，但是通过在没有任何随机因素的情况下对模型进行了拟合来实现.显然是不正确的.

There are two new packages, lmerTest and lsmeans, that can calculate 95% confidence limits for lmer and glmer output. Maybe you can look into those? And coefplot2, I think can do it too (though as Ben points out below, in a not so sophisticated way, from the standard errors on the Wald statistics, as opposed to Kenward-Roger and/or Satterthwaite df approximations used in lmerTest and lsmeans)... Just a shame that there are still no inbuilt plotting facilities in package lsmeans (as there are in package effects(), which btw also returns 95% confidence limits on lmer and glmer objects but does so by refitting a model without any of the random factors, which is evidently not correct).

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