Hello, @Ksharp
I spent a couple of weeks learning the general linear mixed model. The model is in the form of the formula in the screenshot.
We can use the maximum likelihood method or matrix of variance-covariance to solve the point and interval estimation of the regression coefficients. What I don't understand is: What is the difference between the G- and R-side models? Both belong to the general linear mixed model, so they have the same model formula.
Ou. That is a large topic. I think @lvm @SteveDenham could give you more details about it.
G-side random effect is for estimate coefficient.
For example : if you take SEX as fixed and random effect,
then the actually coefficient = coefficient (of fixed effect) +coefficient (of random effect) .
here coefficient (of fixed effect) is the same for Both Male and Female,
while coefficient (of random effect) is different for Male and Female.
The horizontal level/direction has random coefficient.
R-side random is design for RESIDUAL term.
For examle : if you take VISIT as random effect ,the residual from each VISIT is diffent/random.
The vertical level/direction has random coefficient.
Check resources for more info:
https://stats.oarc.ucla.edu/other/mult-pkg/introduction-to-generalized-linear-mixed-models/
https://support.sas.com/kb/37/109.html
https://support.sas.com/kb/37/110.html
https://blogs.sas.com/content/iml/2019/12/03/longitudinal-data-response-profile-model.html
https://blogs.sas.com/content/iml/2019/12/05/longitudinal-data-mixed-model.html
I realize this sort of jumping into the middle but I would suggest looking at the output file, especially the iteration history. If the history stops at 20 iterations with no other messages, then you have run up against the default maximum. If the history keeps rolling along and eventually has some other message, then there are some things to try. For a first attempt, I would suggest adding the following NLOPTIONS statement:
nloptions maxiter=1000 tech=nrridg;
For R-side models and a binary distribution, the ridged Newton-Raphson method often works better than the default quasi-Newton (QUANEW).
Good luck.
SteveDenham
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