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Hi Is there any way to fit a bivaraite longitudinal mixed model using R. I have a data set with col names resp1 (Y_ij1), resp2 (Y_ij2), timepts (t_ij), unit(i) j=1,2,..,m and i=1,2,..n. I want to fit the following two models Model 1 Y_ij1, Y_ij2 | U_i = u_i ~ N(alpha + u_i + beta1*t_ij, Sigma) U_i ~ iid N(0, sigu^2) Sigma = bivariate AR structure alpha and beta are vectors of order 2. Model 2 Y_ij, Y_ij2 | U_i = u_i...

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...