Categorical Regression Output
The Output dialog box allows you to select the statistics to display in the output.
Tables. Produces tables for:
- Multiple R. Includes R ^{2}, adjusted R ^{2}, and adjusted R ^{2} taking the optimal scaling into account.
- ANOVA. This option includes regression and residual sums of squares, mean squares, and F. Two ANOVA tables are displayed: one with degrees of freedom for the regression equal to the number of predictor variables and one with degrees of freedom for the regression taking the optimal scaling into account.
- Coefficients. This option gives three tables: a Coefficients table that includes betas, standard error of the betas, t values, and significance; a Coefficients-Optimal Scaling table with the standard error of the betas taking the optimal scaling degrees of freedom into account; and a table with the zero-order, part, and partial correlation, Pratt's relative importance measure for the transformed predictors, and the tolerance before and after transformation.
- Iteration history. For each iteration, including the starting values for the algorithm, the multiple R and regression error are shown. The increase in multiple R is listed starting from the first iteration.
- Correlations of original variables. A matrix showing the correlations between the untransformed variables is displayed.
- Correlations of transformed variables. A matrix showing the correlations between the transformed variables is displayed.
- Regularized models and coefficients. Displays penalty values, R-square, and the regression coefficients for each regularized model. If a resampling method is specified or if supplementary objects (test cases) are specified, it also displays the prediction error or test MSE.
Resampling. Resampling methods give you an estimate of the prediction error of the model.
- Crossvalidation. Crossvalidation divides the sample into a number of subsamples, or folds. Categorical regression models are then generated, excluding the data from each subsample in turn. The first model is based on all of the cases except those in the first sample fold, the second model is based on all of the cases except those in the second sample fold, and so on. For each model, the prediction error is estimated by applying the model to the subsample excluded in generating it.
- .632 Bootstrap. With the bootstrap, observations are drawn randomly from the data with replacement, repeating this process a number of times to obtain a number bootstrap samples. A model is fit for each bootstrap sample. The prediction error for each model is estimated by applying the fitted model to the cases not in the bootstrap sample.
Category Quantifications. Tables showing the transformed values of the selected variables are displayed.
Descriptive Statistics. Tables showing the frequencies, missing values, and modes of the selected variables are displayed.
To Specify CATREG Output
This feature requires the Categories option.
- From the
menus choose:
- In the Categorical Regression dialog box, click Output.
- Select the output that you want.
- Click Continue.