[R] mixed model question

From: Benn Fine <bennfine_at_yahoo.com>
Date: Tue 29 Mar 2005 - 06:06:33 EST

I am trying to fit a linear mixed model of the form

y_ij = X_ij \beta + delta_i + e_ij

where e_ij ~N(0,s^2_ij) with s_ij known
and delta_i~N(0,tau^2)

I looked at the ecme routine in package:pan, but this routine does not allow for different Vi (variance covariance matrix of the e_i vector) matrices for each cluster.  

Is there an easy way to fit this model in R or should I bite the bullet and code the likelihood functions ?  

Also, is there an easy way to fit a Bayesian version of this ? Again there is mgibbs.lmm but it also does not allow easily for a different Vi matrix for each cluster/.



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