Hi Thomas,

 To make sure I well understood what you are asking, let's give an example
with a random regression model which has the following form:

Yij=U+sd(U)+(B+sd(B))*Xij + Eij  where U and B are the mean population
intercept and slope respectively and sd(U) and sd(B) the deviations of
those means.

If I well understood, you would like to obtain estimations error of  sd(U)
and sd(B)

If it is the case, they are 2 alternatives:

1) Fit the model with lme() in nlme and use the options intervals() to
obtain the CI for the different estimates.
2) Fit the model with lmer() in lme4 then use the profile-likelihood
function to obtain the CI for the random effects. You need to use the
development version of lme4 since the likelihood profiling is not
implemented yet in the normal version.

I would advise you to obtain the standardized versions of your estimates
first as it would much easier to interpret and to compare with other
studies. I don't know how to do that yet though as I am working on it...

 Hope it helps

 Tommy

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