The “Optimization Information” table displays important details about the optimization process.
The optimization technique that is displayed in the table is the technique that applies to any single optimization. For singly iterative methods that is the optimization method.
The number of parameters that are updated in the optimization equals the number of parameters in this table minus the number of equality constraints. The number of constraints is displayed if you fix covariance parameters with the HOLD= option in the PARMS statement. The GLIMMIX procedure also lists the number of upper and lower boundary constraints. Note that the procedure might impose boundary constraints for certain parameters, such as variance components and correlation parameters. Covariance parameters for which a HOLD= was issued have an upper and lower boundary equal to the parameter value.
If a residual scale parameter is profiled from the optimization, it is also shown in the “Optimization Information” table.
In a GLMM for which the parameters are estimated by one of the linearization methods, you need to initiate the process of computing the pseudo-response. This can be done based on existing estimates of the fixed effects, or by using the data themselves—possibly after some suitable adjustment—as an estimate of the initial mean. The default in PROC GLIMMIX is to use the data themselves to derive initial estimates of the mean function and to construct the pseudo-data. The “Optimization Information” table shows how the pseudo-data are determined initially. Note that this issue is separate from the determination of starting values for the covariance parameters. These are computed as minimum variance quadratic unbiased estimates (with 0 priors, MIVQUE0; Goodnight 1978a) or obtained from the value-list in the PARMS statement.
The ODS name of the table is OptInfo.