Removing p.d. constraint for random effects in lme

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Hi,

I am trying to fit a mixed model using lme, with a multivariate
response. I would like to try and replicate a SAS proc mixed model
that has a type=un structure for random effects.  I am not very
experienced using lme, but it seems like one of the differences is
that lme constrains the random effect matrix to be positive
definite, whereas SAS does not impose this constraint (only
variances in SAS are constrained to be nonnegative).  Is there a
way to remove this positive definite constraint for random effects
from lme and how would this be specified in the model?  My current
model looks something like this:

lme(value ~ trait -1, data, random = ~ trait -1| line, correlation
=  corSymm( form = ~ 1|line/rep), weights = varIdent(form = ~ 1
|trait), control=control, method="REML")

Thanks, Jacqueline

This is likely to be difficult.

* Are you looking for positive *semi*definite variance-covariance
matrices (i.e. eigenvalues/variance >=0), or do you need to allow
(silly) negative definite var-cov matrices (eigenvalues/variances
strictly <0)?

* Can you give us more context?  Can you explain what a
non-positive-definite matrix would mean biologically in your example?
Can you show us a SAS example where you actually succeeded in fitting
a non-positive-definite (or negative-definite) variance-covariance matrix?

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