On Tue, 7 Aug 2007, Doran, Harold wrote:
> Lucy:
>
> Why are you interested in robust standard errors from lme? Typically,
> robust standard errors are sought when there is model misspecification
> due to ignoring some covariance among units with a group.
>
> But, a mixed model is designed to directly account for covariances among
> units within a group such that the standard errors more adequately
> represent the true sampling variance of the parameters.
I think it's a perfectly reasonable thing to want, but it is not easy to
provide because of the generality of lme(). For example, models with crossed
effects need special handling, and possibly so do time-series or spatial
covariance models with large numbers of observations per group.
I imagine that misspecification of the variance, rather than the correlation,
would be the main concern, just as it is with independent observations. Of
course the model-robust variances would only be useful if the sample size of
top-level units was large enough, and if the variance components were not of
any direct interest.
> So, the lme standard errors are robust in a sense that they are
> presumably correct if you have your model correctly specified.
To paraphrase the Hitchikers' Guide: This must be some definition of the word
'robust' that I was not previously aware of. :)
-thomas
Thomas Lumley Assoc. Professor, Biostatistics
[EMAIL PROTECTED] University of Washington, Seattle
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