[R-sig-ME] Variance-covariance matrix for normalized residuals in lme

[Kingsford Jones]

On Thu, Nov 13, 2008 at 7:29 AM, Wiener, Matthew
<matthew_wiener at merck.com> wrote:
> 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?

Does nlme::getVarCov return what you're looking for?

> And has anyone else had the
> normalized residuals blow up in this way?
>

I don't recall ever seeing this, but a guess is high leverage points
(e.g. some outlying large values in the column space of the X matrix).

hth,

Kingsford Jones


> Thanks,
>
> Matt Wiener, Shubhankar Ray, Vladimir Svetnik
> Notice:  This e-mail message, together with any attachme...{{dropped:15}}
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>