Weighted-ML estimates
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 Phone +64 7 838 4773 wk Home +64 7 825 0441 Mobile 021 139 5862