The NLMIXED Procedure
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Overview
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Getting Started
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Syntax
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DetailsModeling Assumptions and NotationIntegral ApproximationsBuilt-in Log-Likelihood FunctionsHierarchical Model SpecificationOptimization AlgorithmsFinite-Difference Approximations of DerivativesHessian ScalingActive Set MethodsLine-Search MethodsRestricting the Step LengthComputational ProblemsCovariance MatrixPredictionComputational ResourcesDisplayed OutputODS Table Names
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Examples
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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. PROC NLMIXED enables you to specify a conditional distribution for your data (given the random effects) having either a standard form (normal, binomial, Poisson) or a general distribution that you code using SAS programming statements.
PROC NLMIXED 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. A variety of alternative optimization techniques are available to carry out the maximization; the default is a dual quasi-Newton algorithm.
Successful convergence of the optimization problem results in parameter estimates along with their approximate standard errors based on the second derivative matrix of the likelihood function. PROC NLMIXED enables you to use the estimated model to construct predictions of arbitrary functions by using empirical Bayes estimates of the random effects. You can also estimate arbitrary functions of the nonrandom parameters, and PROC NLMIXED computes their approximate standard errors by using the delta method.