Obtaining minimum variance quadratic unbiased estimates as starting values for the covariance failed

[superbug]

I am using PROC GLIMMIX to do an analyses, My code is below

PROC GLIMMIX
DATA=mfirst METHOD=LAPLACE NOCLPRINT;
CLASS SCHOOLNUM;
MODEL PASS (EVENT="1")=peat timeaftergrad/CL DIST=BINARY LINK=LOGIT SOLUTION;
RANDOM INTERCEPT peat timeaftergrad/ SUBJECT=SCHOOLNUM S CL TYPE=VC;
/*parms (0.2) (1) / hold=2;*/
COVTEST /WALD;
ods output solutionR=mfirst_rest_mod2;
run;

I got the following error message

"Obtaining minimum variance quadratic unbiased estimates as starting values for the
covariance parameters failed"

Please help.

Thanks!

[superbug]

@Ksharp 

You helped a similar problem posted in 2017.

By your suggestion, I added "parms (0.2) (1) / hold=2" as commented out in the code, but got the following error message

"ERROR: 3 PARMS must be given instead of 2."

Do you have any idea how to fix the error so that to make my code work?

Thank you!

[Ksharp]

Sorry ,I can't help you . Maybe @SteveDenham  @lvm   @StatDave  could give you a hand .

[lvm]

This is because you have three variance components in your model. When you use the PARMS statement, you must give a starting value (or a held fixed value) for each variance (and/or covariance) component. Something like 

parms (2) (2) (.1);

Note: in the old post, the last parameter (the second in that case) was being held fixed (not estimated). This is because you used "/ hold=2" option, where the "2" here refers to the second variance component. If you want to estimate now all three variances, don't put in that option. 

With the error you got, you might have an overparameterized model. That is, possibly one of the variance components is actually 0 in your random-coefficients model. 

[superbug]

@lvm 

Thanks much for your reply!

Could you please explain the meaning of the numbers in the parenthesis?

parms (2) (2) (.1);

[lvm]

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.

[superbug]

@lvm 

Thanks much for your explanation!