# The GLIMMIX Procedure

### NLOPTIONS Statement

• NLOPTIONS <options>;

Most models fit with the GLIMMIX procedure typically have one or more nonlinear parameters. Estimation requires nonlinear optimization methods. You can control the optimization through options in the NLOPTIONS statement.

Several estimation methods of the GLIMMIX procedure (METHOD= RSPL, MSPL, RMPL, MMPL) are doubly iterative in the following sense. The generalized linear mixed model is approximated by a linear mixed model based on current values of the covariance parameter estimates. The resulting linear mixed model is then fit, which is itself an iterative process (with some exceptions). On convergence, new covariance parameters and fixed-effects estimates are obtained and the approximated linear mixed model is updated. Its parameters are again estimated iteratively. It is thus reasonable to refer to outer and inner iterations. The outer iterations involve the repeated updates of the linear mixed models, and the inner iterations are the iterative steps that lead to parameter estimates in any given linear mixed model. The NLOPTIONS statement controls the inner iterations. The outer iteration behavior can be controlled with options in the PROC GLIMMIX statement, such as the MAXLMMUPDATE= , PCONV= , and ABSPCONV= options. If the estimation method involves a singly iterative approach, then there is no need for the outer cycling and the model is fit in a single optimization controlled by the NLOPTIONS statement (see the section Singly or Doubly Iterative Fitting).

The syntax and options of the NLOPTIONS statement are described in the section NLOPTIONS Statement in Chapter 19: Shared Concepts and Topics.

Note that in a GLMM with pseudo-likelihood estimation, specifying TECHNIQUE=NONE has the same effect as specifying the NOITER option in the PARMS statement. If you estimate the parameters by METHOD= LAPLACE or METHOD= QUAD , TECHNIQUE=NONE applies to the optimization after starting values have been determined.

The GLIMMIX procedure applies the default optimization technique shown in Table 44.14, depending on your model.

Table 44.14: Default Techniques

Model Family

Setting

TECHNIQUE=

GLM

DIST=NORMAL

NONE

GLM

otherwise

NEWRAP

GLMM

PARMS  NOITER, PL

NONE

GLMM

binary data, PL

NRRIDG

GLMM

otherwise

QUANEW