Output Variables Tab :: SAS/IML(R) Studio 13.1: User's Guide

Output Variables Tab

You can use the Output Variables tab to add analysis variables to the data table. (See Figure 24.24.) If you request a plot that uses one of the output variables, then that variable is automatically created even if you did not explicitly select the variable on the Output Variables tab.

For a multinomial response, residuals and influence diagnostics are not available.

The following list describes each output variable and indicates how the output variable is named. $Y$ represents the name of the response variable. If you use events/trials syntax, then $Y$ represents the name of the events variable.

Proportions for events/trials: adds a variable named Proportion_, where $E$ is the name of the events variable and $T$ is the name of the trials variable. The value of the variable is the ratio $E/T$ . This variable is added only when you use events/trials syntax.
Predicted values: adds predicted values. The variable is named GenP_.
Confidence limits for predicted values: adds 95% confidence limits for the predicted values. The variables are named GenLclm_ and GenUclm_.
Linear predictor: adds the linear predictor values. The variable is named GenXBeta_.
Raw residuals: adds residuals, which are calculated as observed values minus predicted values. The variable is named GenR_.
Pearson chi-square residuals: adds the Pearson chi-square residuals. The variable is named GenChiSqR_.
Deviance residuals: adds the deviance residuals. The variable is named GenDevR_.
Likelihood residuals: adds the likelihood residuals. The variable is named GenLikR_.
Cook’s D: adds Cook’s $D$ influence statistic. The variable is named GenCooksD_.
Leverage (H): adds the leverage statistic. The variable is named GenH_.
DFBETAS (influence on coefficients): adds $p$ variables, where $p$ is the number of parameters in the model. A classification variable with $k$ levels counts as $k$ parameters. The variables are scaled measures of the change in each parameter estimate and are calculated by deleting the $i$ th observation. Large values of DFBETAS indicate observations that are influential in estimating a given parameter. Belsley, Kuh, and Welsch (1980) recommend $2/\sqrt {n}$ as a size-adjusted cutoff. The variables are named DFBeta, for $j=1\ldots p$ .

Figure 24.24: The Output Variables Tab