Multinomial logistic regression: Model
By default, the Multinomial logistic regression procedure produces a model with the factor and covariate main effects, but you can specify a custom model or request stepwise model selection in the Model dialog.
- Specify model
- A main-effects model contains the covariate and factor main effects but no interaction effects.
A full factorial model contains all main effects and all factor-by-factor interactions. It does not
contain covariate interactions. You can analyze a main effects model (default), a full factorial
model, or specify particular terms for a custom model.
- Apply full factorial model
- A full factorial model contains all factor main effects, all covariate main effects, and all factor-by-factor interactions. It does not contain covariate interactions. By default, a full factorial model is analyzed (when neither Generate terms nor Write terms are selected).
- Forced entry terms
- Terms added to the forced entry list are always included in the model.
- Generate terms
- The model depends on the nature of your data. Clicking Generate terms allows you to select the main effects and interactions that are of interest in your analysis. For more information, see Generate terms and Write terms.
- Write terms
- Select to include nested terms or when you want to explicitly build any term variable-by-variable. For more information, see Generate terms and Write terms.
- Stepwise terms
- Terms added to the stepwise list are included in the model according to one
of the following user-selected Stepwise method.
- Generate terms
- Clicking Generate terms allows you to select the main effects and interactions for the stepwise terms. For more information, see Generate terms and Write terms.
- Write terms
- Select to include nested stepwise terms or when you want to explicitly build any term variable-by-variable. For more information, see Generate terms and Write terms.
- Stepwise method
- The drop-down list provides for specifying how independent variables are entered into the analysis.
- Forward entry
- This method begins with no stepwise terms in the model. At each step, the most significant term is added to the model until none of the stepwise terms left out of the model would have a statistically significant contribution if added to the model.
- Backward elimination
- This method begins by entering all terms specified on the stepwise list into the model. At each step, the least significant stepwise term is removed from the model until all of the remaining stepwise terms have a statistically significant contribution to the model.
- Forward stepwise
- This method begins with the model that would be selected by the forward entry method. From there, the algorithm alternates between backward elimination on the stepwise terms in the model and forward entry on the terms left out of the model. This continues until no terms meet the entry or removal criteria.
- Backward stepwise
- This method begins with the model that would be selected by the backward elimination method. From there, the algorithm alternates between forward entry on the terms left out of the model and backward elimination on the stepwise terms in the model. This continues until no terms meet the entry or removal criteria.
- Intercept
- Estimate of the dependent variable when all the independent variables are 0.
- Include intercept in model
- Allows you to include or exclude an intercept term for the model. The setting is enabled by default.
Defining models for Multinomial logistic regression
This feature requires Custom Tables and Advanced Statistics.
- From the menus choose:
- Expand the Additional settings menu and click Model.
- Define the model settings.