SPSS® 12.0 Command Syntax Reference

GENLOG
Overview
GENLOG is available in the Advanced Models option.
GENLOG varlist[BY] varlist [WITH covariate varlist]
[/CSTRUCTURE=varname]
[/GRESID=varlist]
[/GLOR=varlist]
[/MODEL={POISSON** }]
{MULTINOMIAL}
[/CRITERIA=[CONVERGE({0.001**})][ITERATE({20**})][DELTA({0.5**})]
{n } {n } {n }
[CIN({95**})] [EPS({1E-8**})]
{n } {n }
[DEFAULT]
[/PRINT=[FREQ**][RESID**][ADJRESID**][DEV**]
[ZRESID][ITERATE][COV][DESIGN][ESTIM][COR]
[ALL] [NONE]
[DEFAULT]]
[/PLOT={DEFAULT** }]
{RESID([ADJRESID][DEV]) }
{NORMPROB([ADJRESID][DEV]) }
{NONE }
[/SAVE=tempvar (newvar)[tempvar (newvar)...]]
[/MISSING=[{EXCLUDE**}]]
{INCLUDE }
[/DESIGN=effect[(n)] effect[(n)]... effect {BY} effect...]
{* }
**Default if subcommand or keyword is omitted.
GENLOG is a general procedure for model fitting, hypothesis testing, and parameter estimation
for any model that has categorical variables as its major components. As such, GENLOG
subsumes a variety of related techniques, including general models of multiway contingency
tables, logit models, logistic regression on categorical variables, and quasi-independence
models.
GENLOG, following the regression approach, uses dummy coding to construct a design
matrix for estimation and produces maximum likelihood estimates of parameters by means
of the Newton-Raphson algorithm. Since the regression approach uses the original parameter
spaces, the parameter estimates correspond to the original levels of the categories and are
therefore easier to interpret.
HILOGLINEAR, which uses an iterative proportional-fitting algorithm, is more efficient
for hierarchical models and useful in model building, but it cannot produce parameter esti-
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