All - 

We are fitting an lme model with several fixed effects, a single random
effect, and an AR1 structure on the residuals.  To assess the model we
examine the residuals.  The predicted vs. residual plots look fine using
raw residuals or Pearson residuals (leaving aside serial correlations).
However, the normalized residuals - which should account for the AR1
structure - have a very strange feature.  For large predicted values,
they show HUGE residuals - residuals an order of magnitude larger than
the predicted values themselves.

Working to figure out what was going on, we constructed the
variance-covariance matrix of the residuals based on the parameter
estimates, and calculated the normalization matrix independently.  When
we multiplied that matrix by the vector of residuals, we ended up with
normalized residuals that looked fine - there were no extremely large
normalized residuals.

We would like to compare our hand-computed variance-covariance matrix to
the one used by lme, but we have not been able to figure out how to
extract that matrix.  In lme4, we would use VarCorr, but in lme4, as far
as we can tell, we can't have the AR1 correlation structure, which is
very important in our problem.

Is there some way to get at that matrix?  And has anyone else had the
normalized residuals blow up in this way?

Thanks,

Matt Wiener, Shubhankar Ray, Vladimir Svetnik
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