Optimization Methods

PROC CALIS uses a variety of nonlinear optimization algorithms for computing parameter estimates. These algorithms are very complicated and do not always work for every data set. PROC CALIS generally informs you when the computations fail, usually by displaying an error message about the iteration limit being exceeded. When this happens, you might be able to correct the problem simply by increasing the iteration limit (MAXITER= and MAXFUNC=). However, it is often more effective to change the optimization method (OMETHOD=) or initial values. For more details, see the section Use of Optimization Techniques in Chapter 27: The CALIS Procedure, and see Bollen (1989, pp. 254–256).

PROC CALIS might sometimes converge to a local optimum rather than the global optimum. To gain some protection against local optima, you can run the analysis several times with different initial estimates. The RANDOM= option in the PROC CALIS statement is useful for generating a variety of initial estimates.