[R-sig-ME] degrees of freedom in mixed model

[S Ellison]

> library(lme4)
> model1 <- lmer(value~group + (1|animal), data=bip)
> summary(model1)
> 
.......
> so I'd then have:
> 
> qf(0.95,3,5) or qf(0.95,3,4)
> 
> for my critical F value?
> 
> Any advice (incuding whether the appraoch is right) would be useful.

It's the wrong approach.

You are using lmer, which uses maximum likelihood estimation, not classical sums of squares. The degrees of freedom don't mean the same thing, and the distribution of REML estimates of variance isn't necessarily chi-squared. So F is interpretable in the same way as it would be in classical anova.

If you want p-values from an lmer model, you could get hold of the lmerTest package. Other recommended approaches include variants on MCMC. There is a great deal of controversy on this point, though; try Googling "p-values from lmer" with particular attention to anything by Douglas Bates (the package author). You _should_ find enough to make you worry that the method used by lmerTest (which as I understand it implements a method used by SAS) comes with quite strong theoretical objections. I am quite sure the lmerTest authors know that perfectly well and offer lmerTest as a package for those who want to find out or for those whose management insist on a SAS-compatible answer. But if I read correctly, that doesn't make it the right thing to do

[Caveat - I'm a chemist. I could be wrong about this]

> 
> Best
> 
> iain
> 
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