Workspace Node: GLZ Custom Design - Results - Residuals 2 Tab

In the GLZ Custom Design node dialog box, under the Results heading, select the Residuals 2 tab to access options to produce spreadsheets and plots of various predicted and residual statistics. The results are placed in the Reporting Documents after running (updating) the project. For details regarding the computation and interpretation of these residual statistics, refer to McCullagh and Nelder, 1989.

Element Name Description
Sample
Analysis / Cross-validation / Both Select the respective option button to specify which type of sample to base the predicted and residual statistics. You can display spreadsheets for all observations that were used to compute the current results (select Analysis), all observations that were not used to compute the current results, but have valid data for all predictor and dependent variables (select Cross-validation), or all observations in both the Analysis sample and the Cross-validation sample (select Both). If these options are not available, no cross-validation sample was specified on the Advanced tab.
Pred. & P. resid. Produces a scatterplot of the predicted values vs. Pearson residuals.
Pred. & D. resid. Produces a scatterplot of the predicted values vs. deviance residuals.
Obs. & P. resid. Produces a scatterplot of the observed values vs. Pearson residuals.
Obs. & D. resid. Produces a scatterplot of the observed values vs. deviance residuals.
Res. & P. resid. Produces a scatterplot of the raw residuals vs. Pearson residuals.
Res. & D. resid. Produces a scatterplot of the raw residuals vs. deviance residuals.
Pred. & leverage. Produces a scatterplot of the predicted values vs. leverage values.
Pred. & uwgt. lev Produces a scatterplot of the predicted values vs. unweighted leverage values.
Pred. & Diff. X2 Produces a scatterplot of the predicted values vs. differential Pearson Chi-square statistics.
Lev. & Diff. X2 Produces a scatterplot of the leverage values vs. differential Pearson Chi-square statistics.
Pred. & Diff. Dev. Produces a scatterplot of the predicted values vs. differential deviance statistics.
Lev. & Diff. Dev Produces a scatterplot of the leverage values vs. differential deviance statistics.
Pred. & Cook D Produces a scatterplot of the predicted values vs. generalized Cook's distances.
Lev. & Cook D Produces a scatterplot of the leverage values vs. generalized Cook's distances.
Aggregation Select the Aggregation check box to compute the predicted values (and related statistics, e.g., residuals) in terms of predicted frequencies. In models with categorical response variables, predicted values (and related statistics, e.g., residuals) can be computed in terms of the raw data or for aggregated frequency counts. For example, in the Binomial case (see Distribution and link function), and for raw data, you can think of the response variable as having two possible values: 0 (zero) or 1. Accordingly, predicted values should be computed that fall in the range from 0 (zero) to 1 (e.g., classification probabilities). If the Aggregation check box is selected, Statistica considers the aggregated (tabulated) data set. In that case, you can think of the response variable as a frequency count, reflecting the number of observations that fall into the respective categories. This is easiest imagined in the case where the predictors are also categorical in nature: The resulting aggregated data file would be a multi-way frequency table.

Options / C / W. See Common Options.

OK Click the OK button to accept all the specifications made in the dialog box and to close it. The analysis results will be placed in the Reporting Documents node after running (updating) the project.