[R-sig-ME] Removing p.d. constraint for random effects in lme

[Ben Bolker]

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On 15-04-05 05:40 PM, R User wrote:
> 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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