Output Variables Tab
Previous Page|Next Page

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_ $ET$ , 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_ $Y$ .

Confidence limits for predicted values

adds 95% confidence limits for the predicted values. The variables are named GenLclm_ $Y$ and GenUclm_ $Y$ .

Linear predictor

adds the linear predictor values. The variable is named GenXBeta_ $Y$ .

Raw residuals

adds residuals, which are calculated as observed values minus predicted values. The variable is named GenR_ $Y$ .

Pearson chi-square residuals

adds the Pearson chi-square residuals. The variable is named GenChiSqR_ $Y$ .

Deviance residuals

adds the deviance residuals. The variable is named GenDevR_ $Y$ .

Likelihood residuals

adds the likelihood residuals. The variable is named GenLikR_ $Y$ .

Cook’s D

adds Cook’s $D$ influence statistic. The variable is named GenCooksD_ $Y$ .

Leverage (H)

adds the leverage statistic. The variable is named GenH_ $Y$ .

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 $_ j$ , for $j=1\ldots p$.

Figure 24.24: The Output Variables Tab

The Output Variables Tab


Previous Page|Next Page|Top of Page
Copyright © SAS Institute Inc. All Rights Reserved.