Multinomial Logistic Regression Options
You can specify the following options for your Multinomial Logistic Regression:
Dispersion Scale. Allows you to specify the dispersion scaling value that will be used to correct the estimate of the parameter covariance matrix. Deviance estimates the scaling value using the deviance function (likelihood-ratio chi-square) statistic. Pearson estimates the scaling value using the Pearson chi-square statistic. You can also specify your own scaling value. It must be a positive numeric value.
Stepwise Options. These options give you control of the statistical criteria when stepwise methods are used to build a model. They are ignored unless a stepwise model is specified in the Model dialog box.
- Entry Probability. This is the probability of the likelihood-ratio statistic for variable entry. The larger the specified probability, the easier it is for a variable to enter the model. This criterion is ignored unless the forward entry, forward stepwise, or backward stepwise method is selected.
- Entry test. This is the method for entering terms in stepwise methods. Choose between the likelihood-ratio test and score test. This criterion is ignored unless the forward entry, forward stepwise, or backward stepwise method is selected.
- Removal Probability. This is the probability of the likelihood-ratio statistic for variable removal. The larger the specified probability, the easier it is for a variable to remain in the model. This criterion is ignored unless the backward elimination, forward stepwise, or backward stepwise method is selected.
- Removal Test. This is the method for removing terms in stepwise methods. Choose between the likelihood-ratio test and Wald test. This criterion is ignored unless the backward elimination, forward stepwise, or backward stepwise method is selected.
- Minimum Stepped Effects in Model. When using the backward elimination or backward stepwise methods, this specifies the minimum number of terms to include in the model. The intercept is not counted as a model term.
- Maximum Stepped Effects in Model. When using the forward entry or forward stepwise methods, this specifies the maximum number of terms to include in the model. The intercept is not counted as a model term.
- Hierarchically constrain entry and removal of terms. This option allows you to choose whether to place restrictions on the inclusion of model terms. Hierarchy requires that for any term to be included, all lower order terms that are a part of the term to be included must be in the model first. For example, if the hierarchy requirement is in effect, the factors Marital status and Gender must both be in the model before the Marital status*Gender interaction can be added. The three radio button options determine the role of covariates in determining hierarchy.
How to Define Options
This feature requires Custom Tables and Advanced Statistics.
- From the menus choose:
- In the Multinomial Logistic Regression dialog box, click Options.
- Specify the options you want.