Can anyone provide me with some insight on why the random statement has an estimate of 0?
I have multiple data sets with the same issue (although the code runs just fine on others). The number of replicates ranges from 4 to 6 between the sets (most have 5) with 5 or more levels of treatment.
I am running a proc mixed model and using the random statement to control for replicate (and also to see if there is an effect).
This is the output I get for the random section:
Covariance Parameter Estimates Cov ParmEstimate StandardError Z Value Pr > Z
rep 0 . . .
Residual 0.000250 0.000072 3.460 .0003
Are there any tips or tricks that would allow me to get an estimate in this section?
Any advice would be appreciated.
Is the MIXED procedure also telling you that the estimated G matrix is not positive definite?
Most likely, this result implies that there is little variation among reps for this response variable. The procedure has set the estimate to zero (hence, the SE is missing) and continued on its merry way. See Section III in this paper
http://support.sas.com/resources/papers/proceedings12/332-2012.pdf
In the documentation
under Parameter Constraints, you'll find:
"For some data sets the final estimate of a parameter might equal one of its boundary constraints. This is usually not a cause for concern, but it might lead you to consider a different model. For instance, a variance component estimate can equal zero; in this case, you might want to drop the corresponding random effect from the model. However, be aware that changing the model in this fashion can affect degrees-of-freedom calculations.
If that doesn't seem to be the reason, then you'll want to post your code and an example dataset so that people here have more to work with.
Is the MIXED procedure also telling you that the estimated G matrix is not positive definite?
Most likely, this result implies that there is little variation among reps for this response variable. The procedure has set the estimate to zero (hence, the SE is missing) and continued on its merry way. See Section III in this paper
http://support.sas.com/resources/papers/proceedings12/332-2012.pdf
In the documentation
under Parameter Constraints, you'll find:
"For some data sets the final estimate of a parameter might equal one of its boundary constraints. This is usually not a cause for concern, but it might lead you to consider a different model. For instance, a variance component estimate can equal zero; in this case, you might want to drop the corresponding random effect from the model. However, be aware that changing the model in this fashion can affect degrees-of-freedom calculations.
If that doesn't seem to be the reason, then you'll want to post your code and an example dataset so that people here have more to work with.
If you could use PROC GLIMMIX , try : random int time / type=chol
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