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The NLMIXED Procedure
Overview
Introduction
Literature on Nonlinear Mixed Models
PROC NLMIXED Compared with Other SAS Procedures and Macros
Getting Started
Nonlinear Growth Curves with Gaussian Data
Logistic-Normal Model with Binomial Data
Syntax
PROC NLMIXED Statement
ARRAY Statement
BOUNDS Statement
BY Statement
CONTRAST Statement
ESTIMATE Statement
ID Statement
MODEL Statement
PARMS Statement
PREDICT Statement
RANDOM Statement
REPLICATE Statement
Programming Statements
Details
Modeling Assumptions and Notation
Integral Approximations
Built-in Log-Likelihood Functions
Hierarchical Model Specification
Optimization Algorithms
Finite-Difference Approximations of Derivatives
Hessian Scaling
Active Set Methods
Line-Search Methods
Restricting the Step Length
Computational Problems
Covariance Matrix
Prediction
Computational Resources
Displayed Output
ODS Table Names
Examples
One-Compartment Model with Pharmacokinetic Data
Probit-Normal Model with Binomial Data
Probit-Normal Model with Ordinal Data
Poisson-Normal Model with Count Data
Failure Time and Frailty Model
Simulated Nested Linear Random-Effects Model
Overdispersion Hierarchical Nonlinear Mixed Model
References
Details: NLMIXED Procedure
Subsections:
Modeling Assumptions and Notation
Integral Approximations
Built-in Log-Likelihood Functions
Hierarchical Model Specification
Optimization Algorithms
Finite-Difference Approximations of Derivatives
Hessian Scaling
Active Set Methods
Line-Search Methods
Restricting the Step Length
Computational Problems
Covariance Matrix
Prediction
Computational Resources
Displayed Output
ODS Table Names
This section contains details about the underlying theory and computations of PROC NLMIXED.
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