How to get coefficients and their confidence intervals in mixed effects models?

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)

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

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

EDIT: 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?

Solution 1:

Not sure when it was added, but now confint() is implemented in lme4. For example the following example works:

library(lme4)
m = lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
confint(m)

Solution 2:

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).

Solution 3:

I suggest that you use good old lme (in package nlme). It has confint, and if you need confint of contrasts, there is a series of choices (estimable in gmodels, contrast in contrasts, glht in multcomp).

Why p-values and confint are absent in lmer: see http://finzi.psych.upenn.edu/R/Rhelp02a/archive/76742.html .