Generalized Linear Models Save

Checked items are saved with the specified name; you can choose to overwrite existing variables with the same name as the new variables or avoid name conflicts by appendix suffixes to make the new variable names unique. See the topic Variable names for more information.

• Predicted value of mean of response. Saves model-predicted values for each case in the original response metric. When the response distribution is binomial and the dependent variable is binary, the procedure saves predicted probabilities. When the response distribution is multinomial, the item label becomes Cumulative predicted probability, and the procedure saves the cumulative predicted probability for each category of the response, except the last, up to the number of specified categories to save.
• Lower bound of confidence interval for mean of response. Saves the lower bound of the confidence interval for the mean of the response. When the response distribution is multinomial, the item label becomes Lower bound of confidence interval for cumulative predicted probability, and the procedure saves the lower bound for each category of the response, except the last, up to the number of specified categories to save.
• Upper bound of confidence interval for mean of response. Saves the upper bound of the confidence interval for the mean of the response. When the response distribution is multinomial, the item label becomes Upper bound of confidence interval for cumulative predicted probability, and the procedure saves the upper bound for each category of the response, except the last, up to the number of specified categories to save.
• Predicted category. For models with binomial distribution and binary dependent variable, or multinomial distribution, this saves the predicted response category for each case. This option is not available for other response distributions.
• Predicted value of linear predictor. Saves model-predicted values for each case in the metric of the linear predictor (transformed response via the specified link function). When the response distribution is multinomial, the procedure saves the predicted value for each category of the response, except the last, up to the number of specified categories to save.
• Estimated standard error of predicted value of linear predictor. When the response distribution is multinomial, the procedure saves the estimated standard error for each category of the response, except the last, up to the number of specified categories to save.

The following items are not available when the response distribution is multinomial.

• Cook's distance. A measure of how much the residuals of all cases would change if a particular case were excluded from the calculation of the regression coefficients. A large Cook's D indicates that excluding a case from computation of the regression statistics changes the coefficients substantially.
• Leverage value. Measures the influence of a point on the fit of the regression. The centered leverage ranges from 0 (no influence on the fit) to (N-1)/N.
• Raw residual. The difference between an observed value and the value predicted by the model.
• Pearson residual. The square root of the contribution of a case to the Pearson chi-square statistic, with the sign of the raw residual.
• Standardized Pearson residual. The Pearson residual multiplied by the square root of the inverse of the product of the scale parameter and 1−leverage for the case.
• Deviance residual. The square root of the contribution of a case to the Deviance statistic, with the sign of the raw residual.
• Standardized deviance residual. The Deviance residual multiplied by the square root of the inverse of the product of the scale parameter and 1−leverage for the case.
• Likelihood residual. The square root of a weighted average (based on the leverage of the case) of the squares of the standardized Pearson and standardized Deviance residuals, with the sign of the raw residual.

How To Save Variables to the Active Dataset for Generalized Linear Models

This feature requires the Advanced Statistics option.

1. From the menus choose:

   Analyze > Generalized Linear Models > Generalized Linear Models...

2. In the Generalized Linear Models dialog box, click Save.