Hello, The following example [along with the actual data] provided in SAS shows how to test for differences in correlation matrices across independent samples using the CONTRAST statement: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_glimmix_sect021.htm The actual code to test for differences in correlation matrices between independent samples ("species") is as follows:    proc glimmix data=iris_univ;       class species var id;       model response = species*var;       random _residual_ / type=unr group=species subject=id;       covtest 'Equal Covariance Matrices'  homogeneity;       covtest 'Equal Correlation Matrices' general            0  0  0  0  1  0  0  0  0  0            0  0  0  0 -1  0  0  0  0  0,            0  0  0  0  1  0  0  0  0  0            0  0  0  0  0  0  0  0  0  0            0  0  0  0 -1  0  0  0  0  0,            0  0  0  0  0  1  0  0  0  0            0  0  0  0  0 -1  0  0  0  0,            0  0  0  0  0  1  0  0  0  0            0  0  0  0  0  0  0  0  0  0            0  0  0  0  0 -1  0  0  0  0,            0  0  0  0  0  0  1  0  0  0            0  0  0  0  0  0 -1  0  0  0,            0  0  0  0  0  0  1  0  0  0            0  0  0  0  0  0  0  0  0  0            0  0  0  0  0  0 -1  0  0  0,            0  0  0  0  0  0  0  1  0  0            0  0  0  0  0  0  0 -1  0  0,            0  0  0  0  0  0  0  1  0  0            0  0  0  0  0  0  0  0  0  0            0  0  0  0  0  0  0 -1  0  0,            0  0  0  0  0  0  0  0  1  0            0  0  0  0  0  0  0  0 -1  0,            0  0  0  0  0  0  0  0  1  0            0  0  0  0  0  0  0  0  0  0            0  0  0  0  0  0  0  0  -1 0,            0  0  0  0  0  0  0  0  0  1            0  0  0  0  0  0  0  0  0 -1,            0  0  0  0  0  0  0  0  0  1            0  0  0  0  0  0  0  0  0  0            0  0  0  0  0  0  0  0  0 -1 / estimates;    run; I would like to adjust this code such that the correlation matrices are being compared between DEPENDENT samples (e.g., same sample measured twice). Any help would be MUCH appreciated. Thanks, Ryan p.s. I've asked this same question on SAS-L. Apologies to those who read/respond to questions on SAS-L and here. ... View more

Hi, My glimmix code is: proc glimmix data=mydata method=quad; class group time subject; model y = group time group*time / s link=logit dist=binary; lsmeans group*time / ilink; random int / subject=subject; run; Suppose there are 5 groups and 10 time points (5X10 design). For one of the groups (i.e., group 2), there are no data for 4 of the 10 time points. Does this pose a problem for model convergence and/or validity of results? For example, is the estimate and standard error produced by the LSMEANS statement for group 1 at time 2 valid and interpreted in the same way as if one had a fully balanced design? While I have read that mixed models in general can handle unbalanced data, I have not seen any literature on missing cells. Any references or thoughts on the matter would be appreciated. Ryan ... View more