I'm running a repeated measures analysis in proc glimmix, but the analysis keeps is incorrectly assigning the number of observations per subject.    The experiment measured survival of individual blocks from four different geographic locations that were all grown in one location. I measured whether an individual plant was still alive (survival 1, 0 dead) every week over the summer until all plants died naturally.  Factors:   Home site - geographic locations a plant is from (fixed effect)  Block - one of four locations at the site an individual was grown at (random effect)  Genotype - family that a seed came from (family structure) - (random effect)  Visit - week 1 through 11 that I took a census of each plant (random repeated measure effect)  Tag - unique id for each individual plant   This is a sample of the data: homsite genotype ind tag block visit survival HF 1 3 563 4 1 1 HF 1 4 381 2 1 1 HF 1 5 6 1 1 1 HF 1 7 195 3 1 1 HF 104 2 231 3 1 1 HF 104 3 578 4 1 1 HF 104 7 61 1 1 1 HF 104 8 444 2 1 1 HF 105 3 584 4 1 1    PROC GLIMMIX  data = survival; CLASS homesite genotype block visit tag; MODEL survival(event='1') = homesite /dist=binary ddfm = kr; RANDOM genotype(homesite) block homesite*block; RANDOM visit/subject=tag type=vc residual; RUN; QUIT;   When I run the above code and include a random statement for the repeated measures factor of visit with tag as the subject, the output shows that proc glimmix is only registering one subject which has 6512 observations. There should be 11 observations per 592 subjects. Furthermore the random effects (covariance parameters) do not have any significance level only estimates/se.    Dimensions G-side Cov. Parameters 3 R-side Cov. Parameters 1 Columns in X 15 Columns in Z 168 Subjects (Blocks in V) 1 Max Obs per Subject 6512     thank you for any help! ... View more