[R-sig-ME] Residual Sum Squares Issues with Linear Mixed Models

[Howsun.Jow at newcastle.ac.uk]

I'm having problems understanding why the residual sum squares for a reduced
linear mixed model is sometimes smaller for a "reduced" model than for a
"full" model. Take the "Pastes" dataset for example:

fm3M <- lmer(strength ~ 1 + (1|batch) + (1|sample), Pastes), REML=F)
fm4M <- lmer(strength ~ 1 + (1|sample), Pastes), REML=F)

> sum(resid(fm3M)^2)
[1] 21.04984
> sum(resid(fm4M)^2)
[1] 21.03147

The reduced model seems to fit the data better than the full model. Is there
something fundamental I'm missing about linear mixed effects models?