SAS/STAT Software

NLMIXED Procedure

The NLMIXED procedure fits nonlinear mixed models—that is, models in which both fixed and random effects enter nonlinearly. These models have a wide variety of applications, two of the most common being pharmacokinetics and overdispersed binomial data. The following are highlights of the NLMIXED procedure's features:

  • enables you to specify a conditional distribution for your data (given the random effects) having either a standard form or a general distribution that you code using SAS programming statements. The standard forms include the following:
    • normal
    • binary
    • binomial
    • gamma
    • negative binomial
    • Poisson
  • fits nonlinear mixed models by maximizing an approximation to the likelihood integrated over the random effects. Different integral approximations are available, the principal ones being adaptive Gaussian quadrature and a first-order Taylor series approximation.
  • enables you to use the estimated model to construct predictions of arbitrary functions by using empirical Bayes estimates of the random effects
  • enables you to specify more than one RANDOM statement in order to fit hierarchical nonlinear mixed models
  • enables you to estimate arbitrary functions of the nonrandom parameters and compute their approximate standard errors by using the delta method
  • constructs predictions of an expression across all of the observations in the input data set
  • accommodates models in which different subjects have identical data
  • performs BY group processing, which enables you to obtain separate analyses on grouped observations
  • creates a SAS data set that corresponds to any output table

For further details see the NLMIXED Procedure