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The Output Variables Tab

You can use the Output Variables tab (Figure 24.24) to add analysis variables to the data table. 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. represents the name of the response variable. If you use events/trials syntax, then represents the name of the events variable.

Proportions for events/trials

adds a variable named Proportion_, where is the name of the events variable and is the name of the trials variable. The value of the variable is the ratio . 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, calculated as observed 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 influence statistic. The variable is named GenCooksD_.

Leverage (H)

adds the leverage statistic. The variable is named GenH_.

DFBETAS (influence on coefficients)

adds variables, where is the number of parameters in the model. A classification variable with levels counts as parameters. The variables are scaled measures of the change in each parameter estimate and are calculated by deleting the th observation. Large values of DFBETAS indicate observations that are influential in estimating a given parameter. Belsley, Kuh, and Welsch (1980) recommend as a size-adjusted cutoff. The variables are named DFBeta, for .

Figure 24.24: The Output Variables Tab