[R-sig-ME] Help understanding residual variance

[Ista Zahn]

Hi all,

I'm trying to understand what the residual variance in this model:

tmp <- subset(sleepstudy, Days == 1 | Days == 9)
m1 <- lmer(Reaction ~ 1 + Days + (1 + Days | Subject), data = tmp)
tmp$fitted1 <- fitted(m1)

represents. The way I read this specification, an intercept and a
slope is estimated for each subject. Since each subject only has two
measurements, I would expect the Reaction scores to be completely
accounted for by the slopes and intercepts. Yet they are not: the
Residual variance estimate is 440.278.

This is probably a stupid question, but I hope you will be kind enough
to humor me.

Best,
Ista