Hi,   I have a problem with repeated measures using Proc Glimmix and wonder if anyone could help me look at it and find a solution. Here is my code:   Proc glimmix data=mortality plots=residualpanel; class insect treatment rep time; model mort=insect|treatment|time rep/ d=beta; random intercept / subject=rep; random time / residual subject=insect*treatment*rep type=AR(1); lsmeans insect*treatment*time/ slicediff=(insect treatment time) Adjust=Tukey lines plots=meanplot (cl join); run;    I am looking at the proportional mortality (mort) of two types of insects at various treatments (5 treatments) at repeated observations (3 time points).  Each treatment had 3 experimental units; the experiment was run in 2 repetitions (rep as block).  I would like to know the effect of insect, treatment, time, and interactions.  I use beta distribution.   The model works without Random statements.  But after adding Random for repeated measures, it first showed that "WARNING: Obtaining minimum variance quadratic unbiased estimates as starting values for the covariance parameters failed".   I realized PROC GLIMMIX can only have one observation per level of the repeated effect for each subject, and I have 3 experimental units for each treatment, a total of 2 insects * 5 treatments*2 repetitions*3 experimental units=60 units for each of the 3 observation times. Thus I added a column to label each unit (1-60 for each observation time) and changed the code as:   Proc glimmix data=mortality plots=residualpanel; class insect treatment rep unit time; model mort=insect|treatment|time rep/ d=beta; random intercept / subject=rep; random time / residual subject=unit type=AR(1); lsmeans insect*treatment*time/ slicediff=(insect treatment time) Adjust=Tukey lines plots=meanplot (cl join); run;   Then, there was an error "NOTE: Did not converge". I wonder what step was wrong and how I could fix it.   Many thanks! ... View more