[R] How to efficiently extract or construct the residual covariance matrix from lme()?
Andrew Robinson
A.Robinson at ms.unimelb.edu.au
Wed Apr 19 02:40:31 CEST 2006
Dear R-community,
I'm trying to get the estimated residual covariance matrices from an
lme object. If we write the model as:
Y = X \beta + Z b + \epsilon
and assume that b ~ N(0, P) and \epsilon ~ N(0, \Sigma), where P is
non-diagonal and \Sigma might have correlation and weights components,
then I'm looking for efficient estimates of
\Sigma
and
ZPZ' + \Sigma
I can find P easily enough, but I'm wondering if there's an easy way
to get at Z and \Sigma. Also I can move to lmer() if that simplifies
the problem.
Cheers
Andrew
--
Andrew Robinson
Department of Mathematics and Statistics Tel: +61-3-8344-9763
University of Melbourne, VIC 3010 Australia Fax: +61-3-8344-4599
Email: a.robinson at ms.unimelb.edu.au http://www.ms.unimelb.edu.au
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