The SURVEYPHREG Procedure

The MARGINS statement computes and compares predictive margins of fixed effects. The predictive margin for a specific level (group) of a fixed effect represents the average predicted response if all the observations in the data set were in that group (Lane and Nelder 1982; Chang, Gelman, and Pagano 1982). You can compute predictive margins for any effect in the MODEL statement that involves only classification variables.

Table 7 summarizes the options available in the MARGINS statement.

Table 7: MARGINS Statement Options

Option	Description
Construction and Computation of Predictive Margins
AT	Modifies the covariate value in computing predictive margins
DIFF	Computes differences of predictive margins
SLICEBY=	Partitions tests of interaction effects
SLICEDIFF	Computes differences of sliced predictive margins and determines the types of differences
Degrees of Freedom and p-Values
ADJUST=	Specifies the method of multiple comparison adjustment of predictive margin differences
ALPHA=	Specifies the confidence level ()
DF=	Assigns a specific value to degrees of freedom for t tests and confidence limits
STEPDOWN	Adjusts multiple comparison p-values further in a step-down fashion
Statistical Output
CL	Constructs confidence limits for predictive margins and/or predictive margin differences

For more information about the syntax of the MARGINS statement, see the section MARGINS Statement in Chapter 20, Shared Concepts and Topics.

Note: If your model has classification variables, then the MARGINS statement is allowed only if you also specify the PARAM=GLM option.