ODDSRATIO
<'label'> variable </ options> ;
The ODDSRATIO statement produces odds ratios for variable
even when the variable is involved in interactions with other covariates, and for classification variables that use any parameterization.
You can also specify variables on which constructed effects are based, in addition to the names of COLLECTION or MULTIMEMBER effects. You can specify several ODDSRATIO statements.
If variable
is continuous, then the odds ratios honor any values specified in the UNITS statement. If variable
is a classification variable, then odds ratios comparing each pairwise difference between the levels of variable
are produced. If variable
interacts with a continuous variable, then the odds ratios are produced at the mean of the interacting covariate by default.
If variable
interacts with a classification variable, then the odds ratios are produced at each level of the interacting covariate by
default. The computed odds ratios are independent of the parameterization of any classification variable.
The odds ratios are uniquely labeled by concatenating the following terms to variable
:

If this is a polytomous response model, then prefix the response variable and the level describing the logit followed by a
colon; for example, “Y 0:”.

If variable
is continuous and the UNITS statement provides a value that is not equal to 1, then append “Units=value”; otherwise, if variable
is a classification variable, then append the levels being contrasted; for example, “cat vs dog”.

Append all interacting covariates preceded by “At”; for example, “At X=1.2 A=cat”.
If you are also creating odds ratio plots, then this label is displayed on the plots (see the PLOTS option for more information). If you specify a 'label' in the ODDSRATIO statement, then the odds ratios produced by this
statement are also labeled: 'label', 'label 2', 'label 3',…, and these are the labels used in the plots. If there are any
duplicated labels across all ODDSRATIO statements, then the corresponding odds ratios are not displayed on the plots.
The following options are available:

AT(covariate=valuelist  REF  ALL<...covariate=valuelist  REF  ALL>)

specifies fixed levels of the interacting covariates. If a specified covariate does not interact with the variable, then its AT list is ignored.
For continuous interacting covariates, you can specify one or more numbers in the valuelist. For classification covariates, you can specify one or more formatted levels of the covariate enclosed in single quotes (for
example, A=’cat’ ’dog’
), you can specify the keyword REF to select the referencelevel, or you can specify the keyword ALL to select all levels
of the classification variable. By default, continuous covariates are set to their means, while CLASS covariates are set to
ALL. For a model that includes a classification variable A
={cat,dog} and a continuous covariate X
, specifying AT(A=’cat’ X=7 9)
will set A
to ’cat’, and X
to 7 and then 9.

CL=WALD  PL  BOTH

specifies whether to
create Wald or profilelikelihood confidence limits, or both. By default, Wald confidence limits are produced.

DIFF=REF  ALL

specifies whether the odds ratios for a classification
variable
are computed against the reference level, or all pairs of variable
are compared. By default, DIFF=ALL. The DIFF= option is ignored when variable
is continuous.

PLCONV=value

controls the convergence criterion for
confidence intervals based on the profilelikelihood function. The quantity value must be a positive number, with a default value of 1E–4. The PLCONV= option has no effect if profilelikelihood confidence
intervals (CL=PL) are not requested.

PLMAXITER=n

specifies the maximum number of iterations to
perform. By default, PLMAXITER=25. If convergence is not attained in n iterations, the odds ratio or the confidence limits are set to missing. The PLMAXITER= option has no effect if profilelikelihood
confidence intervals (CL=PL) are not requested.

PLSINGULAR=value

specifies the tolerance for testing the
singularity of the Hessian matrix (NewtonRaphson algorithm) or the expected value of the Hessian matrix (Fisher scoring algorithm).
The test requires that a pivot for sweeping this matrix be at least this number times a norm of the matrix. Values of the
PLSINGULAR= option must be numeric. By default, value is the machine epsilon times 1E7, which is approximately 1E–9. The PLSINGULAR= option has no effect if profilelikelihood
confidence intervals (CL=PL) are not requested.