R help - Mar 2013 - dispersion indicator for clustered data

Hi,

I have a dataset with clustered data (observations within groups) and would like
to make some descriptive plots.

Now, I am a little bit lost on how to present the dispersion of the data (what
kind of residuals to plot).
I could compute the standard error of the mean (SEM) ignoring the clustering
(very low values and misleading) or I could first aggregate the data by
calculating th mean for each group and calculate the SEM for this means.
But I am not so sure what implication these two approaches have. In the end, I
take the clustering into account by fitting a random-intercept regression model
? however for plotting I would like to have a descriptive dispersion indicator
of the data.

Now, I heard a lot about 'clustered' or 'robust' standard
errors.
Is there some kind of correction I can apply to the simple SEM formula
(sd(x)/sqrt(m)) to take care of correlated observations within clusters?
Or are there bootstrapping or jackknife approaches implemented in R (or cran
package) which give me unbiased variance estimation for clustered data?

thanks for any suggestions!