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NLIN Procedure


The NLIN procedure fits nonlinear regression models and estimates the parameters by nonlinear least squares or weighted nonlinear least squares. You specify the model with programming statements. This gives you great flexibility in modeling the relationship between the response variable and independent (regressor) variables. It does, however, require additional coding compared to model specifications in linear modeling procedures such as the REG, GLM, and MIXED procedures. The following are highlights of the NLIN procedure's features:

  • provides a high-quality automatic differentiator so that you do not need to specify first and second derivatives. You can, however, specify the derivatives if you wish.
  • solves the nonlinear least squares problem by one of the following four algorithms (methods):
    • steepest-descent or gradient method
    • Newton method
    • modified Gauss-Newton method
    • Marquardt method
  • enables you to confine the estimation procedure to a certain range of values of the parameters by imposing bounds on the estimates
  • computes Hougaard's measure of skewness
  • provides bootstrap estimates of confidence intervals for parameters and the covariance/correlation matrices of the parameter estimates
  • performs weighted estimation
  • creates an output data set that contains statistics that are calculated for each observation
  • creates a data set that contains the parameter estimates at each iteration
  • 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
  • automatically created graphs by using ODS Graphics

For further details see the SAS/STAT User's Guide: The NLIN Procedure
( PDF | HTML )

Examples