[R] mixed model question

[Dimitris Rizopoulos]

probably you could fit this model using the 'varIdent()' function for 
the 'weights' argument of the 'lme()' function in package "nlme".

I hope it helps.

Best,
Dimitris

----
Dimitris Rizopoulos
Ph.D. Student
Biostatistical Centre
School of Public Health
Catholic University of Leuven

Address: Kapucijnenvoer 35, Leuven, Belgium
Tel: +32/16/336899
Fax: +32/16/337015
Web: http://www.med.kuleuven.ac.be/biostat/
     http://www.student.kuleuven.ac.be/~m0390867/dimitris.htm


----- Original Message ----- 
From: "Benn Fine" <bennfine at yahoo.com>
To: <r-help at stat.math.ethz.ch>
Sent: Monday, March 28, 2005 10:06 PM
Subject: [R] mixed model question


>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/.
>
> Thanks,
>
> Benn
>
>
>
>
> ---------------------------------
>
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at 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
>