SPSS® 12.0 Command Syntax Reference

Example
MODEL PROGRAM A=.6.
COMPUTE PRED=EXP(A*X).
NLR Y.
Overview
Options
NLR 1045
Nonlinear regression is used to estimate parameter values and regression statistics for models
that are not linear in their parameters. SPSS has two procedures for estimating nonlinear
equations. CNLR (constrained nonlinear regression), which uses a sequential quadratic
programming algorithm, is applicable for both constrained and unconstrained problems. NLR
(nonlinear regression), which uses a Levenberg-Marquardt algorithm, is applicable only for
unconstrained problems.
CNLR is more general. It allows linear and nonlinear constraints on any combination of
parameters. It will estimate parameters by minimizing any smooth loss function (objective
function), and can optionally compute bootstrap estimates of parameter standard errors and
correlations. The individual bootstrap parameter estimates can optionally be saved in a separate
SPSS data file.
Both programs estimate the values of the parameters for the model and, optionally,
compute and save predicted values, residuals, and derivatives. Final parameter estimates can
be saved in an SPSS data file and used in subsequent analyses.
CNLR and NLR use much of the same syntax. Some of the following sections discuss
features common to both procedures. In these sections, the notation [C]NLR means that either
the CNLR or NLR procedure can be specified. Sections that apply only to CNLR or only to NLR
are clearly identified.
The Model. You can use any number of transformation commands under MODEL PROGRAM
to define complex models.
Derivatives. You can use any number of transformation commands under DERIVATIVES to
supply derivatives.
Adding Variables to Working Data File. You can add predicted values, residuals, and derivatives
to the working data file with the SAVE subcommand.
Writing Parameter Estimates to a New Data File. You can save final parameter estimates as an
external SPSS data file using the OUTFILE subcommand; you can retrieve them in subsequent
analyses using the FILE subcommand.
Controlling Model-Building Criteria. You can control the iteration process used in the regression
with the CRITERIA subcommand.
Additional CNLR Controls. For CNLR, you can impose linear and nonlinear constraints on the
parameters with the BOUNDS subcommand. Using the LOSS subcommand, you can specify a
loss function for CNLR to minimize and, using the BOOTSTRAP subcommand, you can provide
bootstrap estimates of the parameter standard errors, confidence intervals, and correlations.