The OUTPUT statement creates a new SAS data set that contains coefficients, marginal means, and information about the original
and transformed variables. The information about original and transformed variables composes the score partition of the data
set; observations have _TYPE_
=’SCORE’. The coefficients and marginal means compose the coefficient partition of the data set; observations have _TYPE_
=’M COEFFI’ or _TYPE_
=’MEAN’. Other values of _TYPE_ are possible; for details, see "_TYPE_
and _NAME_
Variables" later in this chapter. For details about data set structure, see the section Output Data Set. To specify the name of the output data set, use the OUT=
option.
To control the contents of the data set and variable names, use one or more of the o-options. You can also specify these options in the PROC TRANSREG statement.
Table 104.5 summarizes the options available in the OUTPUT statement. These options include the OUT= option and all of the o-options. Many of the statistics created in the OUTPUT statement are exactly the same as statistics created by PROC REG. More details are given in the sections Predicted and Residual Values, Model Fit and Diagnostic Statistics in Chapter 85: The REG Procedure, and Chapter 4: Introduction to Regression Procedures.
Table 104.5: Options Available in the OUTPUT Statement
Option |
Description |
---|---|
Identify output data set |
|
Outputs data set |
|
Predicted Values, Residuals, Scores |
|
Outputs canonical scores |
|
Outputs individual confidence limits |
|
Outputs mean confidence limits |
|
Specifies design matrix coding |
|
Replaces dependent variables |
|
Replaces independent variables |
|
Outputs leverage |
|
Does not restore missing values |
|
Suppresses output of scores |
|
Outputs predicted values |
|
Outputs redundancy variables |
|
Replaces all variables |
|
Outputs residuals |
|
Output Data Set Coefficients |
|
Outputs coefficients |
|
Outputs ideal point coordinates |
|
Outputs marginal means |
|
Outputs redundancy analysis coefficients |
|
Output Data Set Variable Name Prefixes |
|
Specifies dependent variable approximations |
|
Specifies independent variable approximations |
|
Specifies canonical dependent variables |
|
Specifies conservative individual lower CL |
|
Specifies canonical independent variables |
|
Specifies conservative-individual-upper CL |
|
Specifies conservative-mean-lower CL |
|
Specifies conservative-mean-upper CL |
|
Specifies METHOD=MORALS untransformed dependent |
|
Specifies liberal-individual-lower CL |
|
Specifies liberal-individual-upper CL |
|
Specifies liberal-mean-lower CL |
|
Specifies liberal-mean-upper CL |
|
Specifies residuals |
|
Specifies predicted values |
|
Specifies redundancy variables |
|
Specifies transformed dependents |
|
Specifies transformed independents |
|
Macros Variables |
|
Creates macro variables |
|
Other Options |
|
Outputs dependent and independent approximations |
|
Outputs canonical correlation coefficients |
|
Outputs canonical elliptical point coordinates |
|
Outputs canonical point coordinates |
|
Outputs canonical quadratic point coordinates |
|
Outputs approximations to transformed dependents |
|
Outputs approximations to transformed independents |
|
Outputs elliptical point coordinates |
|
Outputs point coordinates |
|
Outputs quadratic point coordinates |
|
Outputs multiple regression coefficients |
For the coefficients partition, the COEFFICIENTS , COORDINATES , and MEANS o-options provide the coefficients that are appropriate for your model. For more explicit control of the coefficient partition, use the options that control details and prefixes. The following list provides details about these options.