Proc glimmix not converging

[Season]

Two issues:

(1) I saw a PROC LOGISTIC code preceding the PROC GLIMMIX code. So your analytic process falls into the scenario I mentioned days ago. You can set the parameter estimates of the LOGISTIC procedure as the starting values of the GLIMMIX procedure. You do not necessarily need to follow the exact code of @jiltao, as that was used as an example. In addition, the four numbers in parentheses are flanked by "*/" or "/*", which essentially nullify them by setting them as appendices. Refer to SAS Help to see the exact method of  specifying starting values.
(2) How many categories are there in your dependent variable? How many observations fall into each category? You can show this in a one-way table easily get by PROC FREQ.

[Demographer]

1-Sorry I'm not sure what do you mean exactly by "You can set the parameter estimates of the LOGISTIC procedure as the starting values of the GLIMMIX procedure." Do you mean that if the intercepts of the model without covariate are for 0.5 0.2 0.3, I should use parms (0.5) (0.2) (0.3)?

2-4 categories

occ_reduced	Frequency	Percent	Cumulative Frequency	Cumulative Percent
HIGH	467666	36.55	467666	36.55
LOW	107108	8.37	574774	44.92
MED	601957	47.05	1176731	91.97
UNEM	102782	8.03	1279513	100.00

[Season]

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@Demographer wrote:

1-Sorry I'm not sure what do you mean exactly by "You can set the parameter estimates of the LOGISTIC procedure as the starting values of the GLIMMIX procedure." Do you mean that if the intercepts of the model without covariate are for 0.5 0.2 0.3, I should use parms (0.5) (0.2) (0.3)?

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Yes, that is what I mean. You can also additionally set the starting values of other parameters (e.g., regression coefficients of the variables in the model other than the intercept) as the estimates obtained from the previous LOGISTIC procedure call.

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@Demographer wrote:

2-4 categories

occ_reduced	Frequency	Percent	Cumulative Frequency	Cumulative Percent
HIGH	467666	36.55	467666	36.55
LOW	107108	8.37	574774	44.92
MED	601957	47.05	1176731	91.97
UNEM	102782	8.03	1279513	100.00

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In fixed effects logistic regression models, when one category of a categorical indepenent variable has too few observations or even zero observation, complete or quasi-complete separation can result, which means that the maximum likelihood estimator does not exist. When running SAS programs, this may be exemplified by failure of convergence. I could not remember if the same applies to the dependent variable as well and am also not that sure if such phenomena can be directly carried over to mixed effects model. But I saw that the second and fourth category of the dependent variable in your model both contain less than 10% of the entire observations in the dataset. Given that your log mentioned plenty of amount of removal of observations in model building process, it might be the case that too few observations whose dependent variable fall into the second and fourth category are eventually utilized. You can verify my conjecture on your own.

So, if probable, you may try to combine the second and fourth category and build the model again. If that still does not work or combining categories is not permitted, look through the independent variables to see if variables with categories having little or zero data points exist. If that still does not work, look through the independent variables to see if any continuous ones exist. If so, you might try categorizing them (e.g., transform the continuous variable of age into elderly vs. not elderly). If that still does not work... then I am sorry that I have no idea how to tackle this problem in this case.