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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