search for: y_ijk

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 gl...

Hi there, I am looking to fit a two-way random effects model to an *unblalanced* layout, y_ijk = mu + a_i + b_j + eps_ijk, i=1,...,R, j=1,...,C, k=1,...,K_ij. I am interested first of all in estimates for the variance components, sigsq_a, sigsq_b and sigsq_error. In the balanced case, there are simple (MM, MLE) estimates for these; In the unbalanced setup, this is much more complicated...

I'm using the lme function in nmle to estimate the variance components of a fully nested two-level model: Y_ijk = mu + a_i + b_j(i) + e_k(j(i)) lme computes estimates of the variances for a, b, and e, call them v_a, v_b, and v_e, and I can use the intervals function to get confidence intervals. My understanding is that these intervals are probably not that robust plus I need intervals on the intraclass corr...

...produce the results in Montgomery D.C (2001, chap 13, example 13-1). Briefly, there are three suppliers, four batches nested within suppliers and three determinations of purity (response variable) on each batch. It is a two stage nested design, where suppliers are fixed and batches are random. y_ijk=mu+tau_i+beta_j(nested in tau_i)+epsilon_ijk Here are the data, purity<-c(1,-2,-2,1, -1,-3, 0,4, 0,-4, 1, 0, 1,0,-1,0, -2,4,0,3, -3,2,-2,2, 2,-2,1,3, 4,0,-1,2, 0,2,2,1) suppli<-factor(c(rep(1,12),rep(2,1...

...produce the results in Montgomery D.C (2001, chap 13, example 13-1). Briefly, there are three suppliers, four batches nested within suppliers and three determinations of purity (response variable) on each batch. It is a two stage nested design, where suppliers are fixed and batches are random. y_ijk=mu+tau_i+beta_j(nested in tau_i)+epsilon_ijk Here are the data, purity<-c(1,-2,-2,1, -1,-3, 0,4, 0,-4, 1, 0, 1,0,-1,0, -2,4,0,3, -3,2,-2,2, 2,-2,1,3, 4,0,-1,2, 0,2,2,1) suppli<-factor(c(rep(1,12),rep(2,1...

Dear R list, What is the proper way to specify a nested model so that the F values agree with the expected mean square errors? Specifically, suppose I have a design where "Heads" are nested within "Machines". I would like to model the following Y_ijk = Mu + Machine_i +Head_j(i) +Error_k(ij). Using the commands below, > summary(aov(Strain~Machine + Head%in%Machine )) Df Sum Sq Mean Sq F value Pr(>F) Machine 4 45.07 11.27 1.0532 0.38762 Machine:Head 15 282.87 18.86 1.7625 0.06252 . Residuals 60 642.00...

Hi al, I have a dataset (see attached), which basically involves 4 treatments for a chemotherapy drug. Samples were taken from 2 biopsy locations, and biopsy were taken at 2 time points. So each subject has 4 data points (from 2 biopsy locations and 2 time points). The objective is to study treatment difference.? I used lme to fit a mixed model that uses "biopsy.site nested within pid"