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: [EMAIL PROTECTED] http://www.ms.unimelb.edu.au ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
