SPSS® 12.0 Command Syntax Reference

1216 PLUM
CELLINFO Cell Information. Observed and expected frequencies by category and
cumulative, Pearson residual for cumulative and category frequencies, and
observed and expected probabilities of each response category separately
and cumulatively by covariate pattern combination.
CORB Asymptotic Correlation Matrix of the Parameter Estimates.
COVB Asymptotic Covariance Matrix of the Parameter Estimates.
FIT Goodness-of-Fit Statistics. The Pearson’s Chi-square and the Likelihood
Ratio Chi-square statistics. The statistics are computed based on the
classification specified on the variable list.
HISTORY Iteration History. The table contains log-likelihood function value and
parameter estimates every n iterations. The default value is n = 1. The first
and the last iterations are always printed if HISTORY is specified and
regardless of the value of n.
KERNEL Use the kernel of the log-likelihood function for display instead of the
complete log-likelihood function.
TPARALLEL Test of Parallel Lines Assumption. Produce a chi-squared score test of the
parallel lines assumption.
PARAMETER Parameter Statistics. The parameter estimates, the standard errors, the
significances, and the confidence interval.
SUMMARY Model Summary. The Cox & Snell’s R 2 , the Nagelkerke’s R 2 , and the
McFadden’s R 2 statistics.
SAVE Subcommand
The SAVE subcommand puts casewise post-estimation statistics back into the active file.
• The new variables must have valid SPSS variable names that are not in use in the working
file.
• The rootname must be a valid SPSS variable name.
• The new variables are saved to the working file in the order the keywords are specified
on the subcommand.
ESTPROB Estimated probabilities of classifying a factor/covariate pattern into the
response categories. The predicted probabilities of the first n categories are
saved. The default number of categories is 25. To specify a number of
categories without a rootname, put a colon before the number.
PREDCAT The response category that has the maximum expected probability for a
factor/covariate pattern.
PCPROB Estimated probability of classifying a factor/covariate pattern into the
predicted category. This probability is the maximum of the estimated
probabilities of the factor/covariate pattern.