From: Abderrahim Oulhaj <abderrahim.oulhaj_at_pharmacology.oxford.ac.uk>

Date: Tue 13 Sep 2005 - 02:59:39 EST

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 Received on Tue Sep 13 03:08:55 2005

Date: Tue 13 Sep 2005 - 02:59:39 EST

Dear All,

I wonder if there is an efficient way to fit the generalized linear mixed model for multivariate outcomes.

More specifically, Suppose that for a given subject i and at a given time j we observe a multivariate outcome Yij = (Y_ij1, Y_ij2, ..., Y_ijK). where Y_ijk is a binomial(n_ijk, p_ijk).

One way to jointly model the data is to use the following specification:

g(p_ijk) = beta_0k + b_0ik + (beta_1k + b_1ik)*x_ijk with k = 1,2 ...., K , g is a specified link function and (b_0ik,b_1ik) k=1,...K are random effects ...

I my case, the glmmPQL converges only and give good results when k is less than 3 (i.e. for a small number of random effects). I also used the gee (generalized estimating equations) to estimate the fixed effects and the same probleme ariseed with k.

Is there any help?

Thank you in advance,

Abderrahim Oulhaj

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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 Received on Tue Sep 13 03:08:55 2005

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