Does SAS have the native ability to implement a second order autoregressive covariance structure within either PROC MIXED or PROC GLIMMIX?  I am aware that I can specify a first order autoregressive structure with TYPE = AR(1) on either the RANDOM or REPEATED statement line.  However, using AR(2) returns and error of "Invalid order specification". I am surprised that AR(2) does not appear to be a valid function. Thanks, Matt ... View more

Hello, I typically compute intraclass correlations using the Gelman & Hill (2006) method (ratio of the between-group variance to the total data variance) using proc mixed or glimmix with the unstructured variance/covariance structure. I noticed in the SAS documentation on Glimmix that the Cholesky root parameterization is generally recommended over a completely unstructured matrix (http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_glimmix_sect009.htm) and (http://blogs.sas.com/content/iml/2012/02/08/use-the-cholesky-transformation-to-correlate-and-uncorrelate-variables/) I was performing initial data cleaning/model building/specification and noticed that a multiplicative transformation (in this case, taking the dependent variable times 100) resulted in a drastically different ICC from the original model (both using the Cholesky parameterization).  Moreover, the completely unstructured matrix does not suffer this difference with a multiplicative transform (it also results in a notably different ICC than the Cholesky-root ICC's). I suspect that the differences between type=UN and type=CHOL are enough that only UN should be used for ICC calculations, but is anyone able to explain why a simple transform results in such a great discrepancy with the CHOL parameterization? I thought maybe others with more knowledge in Cholesky transformations may be able to enlighten me. Best, Matt ... View more