SPSS® 12.0 Command Syntax Reference

PROBIT 1265
Natural Response Rates. You can instruct PROBIT to estimate the natural response rate
(threshold) of the model or supply a known natural response rate to be used in the solution
with the NATRES subcommand.
Algorithm Control Parameters. You can specify values of algorithm control parameters, such as
the limit on iterations, using the CRITERIA subcommand.
Statistics. By default, PROBIT calculates frequencies, fiducial confidence intervals, and the
relative median potency. It also produces a plot of the observed probits or logits against the
values of a single independent variable. Optionally, you can use the PRINT subcommand to
request a test of the parallelism of regression lines for different levels of the grouping variable
or to suppress any or all of these statistics.
Basic Specification
• The basic specification is the response-count variable, keyword OF, the observationcount
variable, keyword WITH, and at least one independent variable.
• PROBIT calculates maximum-likelihood estimates for the parameters of the default probit
response model and automatically displays estimates of the regression coefficient and
intercept terms, their standard errors, a covariance matrix of parameter estimates, and a
Pearson chi-square goodness-of-fit test of the model.
Subcommand Order
• The variable specification must be first.
• Subcommands can be named in any order.
Syntax Rules
• The variables must include a response count, an observation count, and at least one
predictor. A categorical grouping variable is optional.
• All subcommands are optional and each can appear only once.
• Generally, data should not be entered for individual observations. PROBIT expects
predictor values, response counts, and the total number of observations as the input case.
• If the data are available only in a case-by-case form, use AGGREGATE first to compute the
required response and observation counts.
Operations
• The transformed response variable is predicted as a linear function of other variables
using the nonlinear-optimization method. Note that the previous releases used the iteratively
weighted least-squares method, which has a different way of transforming the
response variables. See the MODEL subcommand on p. 1268.