I noticed there are something wrong with covariance estimates when fitting Generalized Linear Mixed Model, as it is shown below:
The model is a multinomial generalized mixed model, where the outcome is a 4-level categorical variable and all the predictors are continuous.
What is wrong with the results?
One estimate is 0, the other one is extremely small. And their standard error are both missing.
What makes a zero wrong? Those could be (and probably are) the correct calculations.
Hi Paige,
The model I am trying to fit is a categorical mixed model, where the outcome is a 4-level categorical variable. This means there should only be 3 rows of estimate in the covariance output, but there are 4 rows instead, which is not what I expected since one level should be used as reference.
I have attached the modified syntax if you are interested to run. The data is attached in the next post.
Best,
Bin
@binfang wrote:
This means there should only be 3 rows of estimate in the covariance output, but there are 4 rows instead, which is not what I expected since one level should be used as reference.
I don't think this is correct, I think there are covariance estimates for every level of your 4-level categorical variable. But honestly, it has been a very long time since I did PROC GLIMMIX with DIST=MULTINOMIAL. Maybe someone knows.
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