Since you are considering LOC a fixed effect (by putting it in the MODEL statement), you would not also include LOC as random effect (using the RANDOM statement).     If your replicates are actually block (which I am calling rep here), then you would want to use RANDOM rep(LOC);   ... View more

I think you are asking about testing Ho: Variance = 0 vs Ha: Variance >0. When you use the COVTEST statement in GLIMMIX, the procedure automatically uses a mixture of chi-square statistics when appropriate (and it tells you in the results).  The mixture is typically needed when the variance parameter is on the boundary (e.g., 0).  See  https://support.sas.com/kb/40/724.html to learn about COVTEST (this is different than the covtest option in MIXED).  The online documentation for GLIMMIX will explain COVTEST in more details, with simpler examples. This is a likelihood ratio test, where the ratio is compared with the chi-square statistic (mixture or otherwise, depending on the situation).  This is explained also in: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://support.sas.com/resources/papers/proceedings/proceedings/forum2007/177-2007.pdf   ... View more

It is not clear what you are trying to do here. It appears to me that you want to do a meta-analysis. That is, you have results from each site (means, SEs, ...), and now you want to combine to determine an overall mean and SE (effect size mean for the population of sites). If that is what you are doing, MIXED or GLIMMIX can certainly be used, where one overrides the residual with a weight term. There are tricks to the coding that are not obvious. But I don't know if that is what you are trying to do. It is possible that you simply want a hierarchical linear mixed model, but with separate residual for each site, all in one analysis (no meta-analysis). That also can be done, where you get a different residual for each site (sort of like using weights).    I can't give you code until you more fully describe what you are trying to do. Maybe with a toy example. ... View more

The specific details for PROC MIXED are here: https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.3/statug/statug_mixed_details58.htm#statug_mixed017845   Memory determination depends on a lot of things. For example, how one specifies the model can make a big difference. A statement such as random A A*B; can use more memory (and be slower to fit) than using: random int B / sub=A; because the latter is processed by subjects.  ... View more

I use MIXED and GLIMMIX much more than I do GENMOD. But.... I think you should make VID a class variable (add it to the CLASS statement). Also, if you have more than one observation for each combination of year and VID, you will have a problem. ... View more

As indicated elsewhere, the LSMEAN is not a simple arithmetic average of the observations. It is an estimate (prediction) based on the model. The arithmetic means will only agree with LSMEANS under very specific (limited) situations. Furthermore, the LSMEANS for a non-normal distribution will definitely not be equivalent to an arithmetic average of the raw data points. ... View more

Your choice of an unstructured covariance matrix (UN) could be problematic if you have many times. The number of parameters to estimate for variances and covariances grows geometrically with time. I recommend you check out the user's guide for examples of other choices for type=.  The second example in the MIXED user's guide is a good place to start, if you haven't already read it. Or read: https://www.jstor.org/stable/1400366?seq=1     ... View more

Best approach is to look at the studentized residuals in graphic form. Add PLOTS=studentpanel on the procedure statement. See the user guide about how to interpret. Normality is not overly important (within reason), but constant variability is important.  ... View more

If you have a recent version of SAS/STAT, you could directly use PROC BGLIMM for a full Bayesian analysis.  ... View more

The text indicated df calculated using the Satterthwaite method. But I have only scanned this. ... View more

And I just remembered this article that deals with prediction intervals for mixed models, also with extensive SAS code in the online supplement. I know that address Satterthwaite. https://onlinelibrary.wiley.com/doi/epdf/10.1002/sim.8386   ... View more

I see you are trying to calculate prediction intervals rather than confidence intervals. These are not straightforward for mixed models in SAS. I usually do this "brute force", but I have not dealt with the complexity of Satterthwaite or Kenward-Roger df adjustments (although I am a huge fan of KR adjustments, in general). These calculations can be quite challenging, but I think there may be some IML programs out there to do this (but maybe not for your problem).    Your question made me think of the following article that may be of help. I have not studied it -- it is sitting on my desk to study at some point. It deals with prediction intervals for mixed models, and the online supplement has extensive SAS code.    https://www.tandfonline.com/doi/full/10.1080/19466315.2020.1776762   ... View more

These are the initial estimates (guesses) of your variances, in the order in you random statements. You need to put something in, perhaps based on results from some other experiment. You can have sas try a range of guesses, and it will use the one that gives the minimum -2LL as the starting value for the optimization. Example parms (.2 to 2.5 by .2) (1 to 5 by 1) (.5 to 5 by .5); You would need ballpark estimates to start. ... View more