PROC NLMIXED Compared with Other SAS Procedures and Macros

The NLMIXED Procedure

PROC NLMIXED Compared with Other SAS Procedures and Macros

The models fit by PROC NLMIXED can be viewed as generalizations of the random coefficient models fit by the MIXED procedure. This generalization allows the random coefficients to enter the model nonlinearly, whereas in PROC MIXED they enter linearly. With PROC MIXED you can perform both maximum likelihood and restricted maximum likelihood (REML) estimation, whereas PROC NLMIXED only implements maximum likelihood. This is because the analog to the REML method in PROC NLMIXED would involve a high dimensional integral over all of the fixed-effects parameters, and this integral is typically not available in closed form. Finally, PROC MIXED assumes the data to be normally distributed, whereas PROC NLMIXED enables you to analyze data that are normal, binomial, or Poisson or that have any likelihood programmable with SAS statements.

PROC NLMIXED does not implement the same estimation techniques available with the NLINMIX and GLIMMIX macros. These macros are based on the estimation methods of Lindstrom and Bates (1990), Breslow and Clayton (1993), and Wolfinger and O'Connell (1993), and they iteratively fit a set of generalized estimating equations (refer to Chapters 11 and 12 of Littell et al. 1996 and to Wolfinger 1997). In contrast, PROC NLMIXED directly maximizes an approximate integrated likelihood.

This remark also applies to the SAS/IML macros MIXNLIN (Vonesh and Chinchilli 1997) and NLMEM (Galecki 1998).

PROC NLMIXED has close ties with the NLP procedure in SAS/OR software. PROC NLMIXED uses a subset of the optimization code underlying PROC NLP and has many of the same optimization-based options. Also, the programming statement functionality used by PROC NLMIXED is the same as that used by PROC NLP and the MODEL procedure in SAS/ETS software.

Chapter Contents
Previous
Next
Top