lmer stand dev of coefficients

On Sun, Dec 21, 2008 at 9:40 AM, Daniel Ezra Johnson
<danielezrajohnson at gmail.com> wrote:

---------- Forwarded message ----------
From: Daniel Ezra Johnson <danielezrajohnson at gmail.com>
Date: Sun, Dec 21, 2008 at 3:39 PM
Subject: Re: [R-sig-ME] lmer stand dev of coefficients
To: Douglas Bates <bates at stat.wisc.edu>

Can you explain briefly what circumstances would lead these quantities
to be quite different?

First, I misspoke. (Note to self: Don't try to answer questions on
theory before the second cup of coffee.)  The standard deviation of
the BLUPs (or, as I prefer to call them, the conditional modes) of the
random effects are not an estimate of the conditional standard
deviation of the random effects given the data.  I can only make sense
of the conditional standard deviation of a particular random effect
and that would be much smaller than the observed standard deviation of
the conditional modes.

What I should have said is somewhat more subtle.  We know that the
conditional modes of the random effects have less variability than the
corresponding individual estimates of a parameter.  I enclose a script
and its output for a particularly simple example - a random-effects
model fit to the Dyestuff data from the lme4 package.  The design is a
balanced, one-way classification so the estimate of the mean yield is
simply the mean of the Yield variable.

We see that the conditional modes are always smaller in magnitude than
the deviations of the individual means from the overall mean.  The
fact that the ratio is constant is a consequence of the balanced
design.   We say that the conditional modes are shrunk towards zero
because the random effects have a finite variance.

The conditional modes are also shrunk relative to what would be
expected from the unconditional variance of the random effects, but I
find it more difficult to explain why.  It makes sense to me that the
mle of the unconditional standard deviation would be larger than the
standard deviation of the conditional modes but of the way the way the
likelihood criterion is formulated.

Perhaps someone else can explain why.

Suppose the random effect grouping factor is Subject.

On what basis would the software estimate the unconditional SD of (the
population of) Subjects to be something quite different (and as you
say, usually larger) than that of the particular group of Subjects in
the data?

Dan

On Sun, Dec 21, 2008 at 3:32 PM, Douglas Bates <bates at stat.wisc.edu> wrote:

On Sun, Dec 21, 2008 at 3:55 AM, Iasonas Lamprianou
<lamprianou at yahoo.com> wrote:

Dear friends
when I use sd(coef(mymodel)$myvariable) I get 0.21
However, the summary of the model gives
Error terms:
 Groups      Name        Std.Dev.
 myvariable (Intercept) 0.33
 Residual               0.76

Why dont I get the same value (0.21 instead of 0.33)?

Because they are estimates of different quantities:
sd(coef(mymodel)$myvariable) is an estimate (although it is not
entirely clear what the properties of such an estimate would be) of
the conditional standard deviation of the random effects given the
data, whereas 0.33 is the maximum likelihood estimate or REML estimate
of the unconditional standard deviation of the random effects.  We
would expect the conditional standard deviation to be smaller than the
unconditional standard deviation.

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