[R-sig-ME] Weighted-ML estimates

Murray Jorgensen maj at stats.waikato.ac.nz
Mon Feb 16 19:17:10 CET 2009

I wonder if in your situation you might be better off using a completely 
EM approach treating both cluster indicator variables and the random 
effects as the missing data?

Murray Jorgensen

H c wrote:
> Hi,
> I'll briefly describe the situation. An EM algorithm is being implemented in
> order to cluster observations into components of a mixture of mixed effects
> models.  It was our hope to use the lme() or lmer() function in the M-step
> to easily find weighted-ML parameter estimates.  Unfortunately, the mixed
> models are often quite complex including serial correlation structures etc.
> Weighted-ML estimates of certain parameters (such as the correlation
> parameters) does not seem likely in closed form or by using lme()/lmer().
>  Due to the weighted-likelihood function, it does not seem appropriate to
> simply use a weighted-least squares approach.  Are appropriate numeric
> approaches available? Has anyone had experience with mixtures of mixed
> models?
> any help would be greatly appreciated,
> Harlan
> 	[[alternative HTML version deleted]]
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Dr Murray Jorgensen      http://www.stats.waikato.ac.nz/Staff/maj.html
Department of Statistics, University of Waikato, Hamilton, New Zealand
Email: maj at waikato.ac.nz    majorgensen at ihug.co.nz      Fax 7 838 4155
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