GLZ Results

Click the OK button in the GLZ Quick Specs dialog box to display the GLZ Results dialog box, which contains five tabs: Summary, Resid. 1, Resid. 2, Means, and Report. This dialog box will also be displayed when you click the OK (Run) button in either the GLZ Analysis Wizard Between Design dialog box, the GLZ Analysis Wizard Extended Options dialog box, or the GLZ Analysis Syntax Editor.

The Summary tab contains options to produce summaries of the main results, for example, tests of all effects, parameter estimates, overall goodness of fit tests, descriptive statistics, etc. The Resid. 1 tab contains options to produce predicted values as well as raw residuals, studentized residuals, Pearson residuals, etc. The Resid. 2 tab contains options to produce various advanced plots of predicted and residual statistics. The Means tab contains options to produce 1) observed unweighted means, 2) observed weighted means, and 3) predicted means (given the current model). Finally, the Report tab is used to send results to a report.

Modify. Click the Modify button to display the previous dialog box for the respective analysis (see Methods for Specifying Designs). You will then be able to modify the current analysis.

Close. Click the Close button to close the current results dialog and return to the Generalized Linear/Nonlinear Models Startup Panel.

Options. Click the Options button to display the Options menu.

By Group. Click the By Group button to display the By Group specification dialog box.

Note: Results for stepwise or best-subset regression. Unlike in the stepwise or best-subset results in General Regression Models (GRM), the results that can be reviewed from this results dialog always pertain to the full model, regardless of which effects were selected for inclusion during the model building procedure. The reason for this is that, unlike in GRM, the relationship between predictors, and their interactive effects (e.g., two predictors masking the effects of a third) are often much more complex. Also, unlike in GRM, because of the manner in which the p1, enter and p2, remove probabilities are determined (in forward stepwise selection, the score statistic is used to select new (significant) effects; while the Wald statistic is used during backward steps), the Stepwise (forward, backward) methods may result in the repetitive selection and removal of one or more predictors. Therefore, the stepwise results can be reviewed separately in this dialog, via the Model building button on the Summary tab. If, after comparing the overall model (with all effects) with the one suggested by the model building procedure, you decide to further evaluate the latter model, use the Make model button on the Summary tab to transfer that model to the Quick Specs dialog box or the GLZ Analysis Syntax Editor, and then fit that model to the data.

Note: Models that are not full-rank (e.g., overparameterized models). When redundant columns are detected during the evaluation of the design matrix, some difficulties arise when computing the Wald statistic for the overall model (see Summary of all effects option on the Summary tab), and when attempting to compute Type 3 LR (likelihood-ratio) tests of effects on the Summary tab (see also the GLM topic Six types of sums of squares). Specifically, because of the redundancy of some of the parameters in the model, independent tests of effects, controlling for all other parameters in the model (not belonging to the effect under consideration) cannot be computed. Therefore, the Summary of all effects and Type 3 LR test buttons will not be available (on the Summary tab) in that case.

Note: Reference level for categorical dependent (response) variables. The last category (level) that is specified for a categorical dependent (response) variable will be the reference category for the comparisons with the other categories. So, for example, if a multinomial dependent (response) variable with k = 3 levels is analyzed, the k-1 = 2 parameters for each predictor (effect column) pertain to the comparison of 1) the first level with the last level, and 2) the second level with the last level of the dependent (response) variable.

Note: Overdispersion parameter for models with discrete/categorical responses. Poisson, Binomial, Multinomial, and Ordinal multinomial distributions all have a default dispersion parameter of 1. The data, however, may exhibit greater variability than this allows. You can select the Overdispersion check box on the Summary tab and select either the Pearson Chi-square or Deviance option button. This enables you to specify a value for the dispersion parameter, φ, with the scale parameter σ= √φ.

When you specify Pearson Chi-square, STATISTICA uses the Pearson chi-square statistic divided by its degrees of freedom as an estimate of φ.

When you specify Deviance, STATISTICA uses the deviance divided by its degrees of freedom as an estimate of φ.

Changing the overdispersion parameter will affect the computation of the estimated parameter covariance matrix, the model likelihood, and all related statistics (e.g., standard errors, prediction errors, etc). For details, refer to McCullagh and Nelder, 1989.