I have unearthed a bug in the way lmer() deals with weights.
Adding weights causes an inflation of the variance estimates. The
phenomenon is easily demonstrated by comparing the following models,
all of which should be identical:
w<-rep(1,nrow(sleepstudy))
(fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) )
(fm2 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, weights = w) )
(fm3 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, weights = w/sum(w)) )
I have tried this with other datasets and models and find the same
general pattern. I find that the inflation factor is correlated with
sum(w) and is higher for cross-classified models than simple nested
ones.
The fixed effect estimates are also changed.
Best wishes, Nick
sessionInfo()
R version 2.6.2 (2008-02-08)
i386-apple-darwin8.10.1
locale:
en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lme4_0.999375-13 Matrix_0.999375-7 lattice_0.17-6
loaded via a namespace (and not attached):
[1] grid_2.6.2