VarCorr vs ranef

Thanks so much for the reply.  This still seems very strange.  Even if the
differences between population and subject effects is the issue, wouldn't
one expect a bit more similarity between the actual effects for the subjects
and the population effects inferred from those effects?  Dr. Bates or anyone
else, can you resolve this mystery?  Alternatively, is there a way to get
population estimates of the random effects for subjects (contradiction in
terms?), like fitted.lme with the level = 0 argument?

All this is in service of an attempt to gain a simple scatterplot between
two random effects that closely reflect the estimates from VarCorr or
summary.  I'm sure someone must have a method for this already worked out.
pairs.lme plots the raw data from ranef, so the discrepancy is still a
problem there.

Thanks so much for your help.

- DC

On Sun, Aug 31, 2008 at 6:13 AM, Daniel Ezra Johnson <
danielezrajohnson at gmail.com> wrote:

On Sun, Aug 31, 2008 at 6:53 AM, D Chaws <cat.dev.urandom at gmail.com>
wrote:

Can someone tell me why correlations between raw random effects are
different from that provided in VarCorr for lme models?
For example:

fm1 = lme(distance ~ I(age-8), random = ~ 1 + I(age-8) | Subject, data =
Orthodont)
R# VarCorr(fm1)
Subject = pdLogChol(1 + I(age - 8))
           Variance StdDev Corr
(Intercept) 3.55937  1.8866 (Intr)
I(age - 8)  0.05127  0.2264 0.209
Residual    1.71620  1.3100

and

R# cor(ranef(fm1))
           (Intercept) I(age - 8)
(Intercept)      1.0000     0.5764
I(age - 8)       0.5764     1.0000

This isn't a complete answer, but the figures in VarCorr and the model
summary are the population estimates for the random effects (the
parameters) while everything derived from ranef() refers to the actual
Subjects in the data (the BLUPs).

Look at:

sd(ranef(fm1))

(Intercept)  I(age - 8)
 1.7359554   0.1557322

Those figures don't match the VarCorr standard deviations either,
especially the second.

I don't know why the BLUPs pattern differently, exactly, but I did
look at plot(coefs(fm1)) which suggested Sex should be added as a
fixed effect. Once I did that, the correlation between the random
effects changed quite a lot (but was still different between VarCorr
and ranef; the population correlation was actually negative...)

D

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