BookmarkSubscribeRSS Feed
🔒 This topic is solved and locked. Need further help from the community? Please sign in and ask a new question.

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.

1 ACCEPTED SOLUTION

Accepted Solutions
sld
Rhodochrosite | Level 12 sld
Rhodochrosite | Level 12

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

https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_mixed_sect0...

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.

 

View solution in original post

2 REPLIES 2
sld
Rhodochrosite | Level 12 sld
Rhodochrosite | Level 12

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

https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_mixed_sect0...

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.

 

Ksharp
Super User
If you could use PROC GLIMMIX , try :


random int time / type=chol 

sas-innovate-2024.png

Join us for SAS Innovate April 16-19 at the Aria in Las Vegas. Bring the team and save big with our group pricing for a limited time only.

Pre-conference courses and tutorials are filling up fast and are always a sellout. Register today to reserve your seat.

 

Register now!

What is ANOVA?

ANOVA, or Analysis Of Variance, is used to compare the averages or means of two or more populations to better understand how they differ. Watch this tutorial for more.

Find more tutorials on the SAS Users YouTube channel.

Discussion stats
  • 2 replies
  • 1851 views
  • 1 like
  • 3 in conversation