# Random Effect Block (generalized linear mixed models)

Enter effects into the model by selecting one or more fields in the source list and adding them to the Effect builder list.

The type of effect created depends upon which Type you select. Categorical
(nominal, and ordinal) fields are used as factors in the model
and continuous fields are used as covariates.

- Interaction
- The combination of all fields appear as a single interaction at the bottom of the effects list.
- Main effects
- Dropped fields appear as separate main effects at the bottom of the effects list.
- All 2-way
- All possible pairs of the dropped fields appear as 2-way interactions at the bottom of the effects list.
- All 3-way
- All possible triplets of the dropped fields appear as 3-way interactions at the bottom of the effects list.
- All 4-way
- All possible triplets of the dropped fields appear as 4-way interactions at the bottom of the effects list.
- All 5-way
- All possible triplets of the dropped fields appear as 5-way interactions at the bottom of the effects list.

- Include Intercept
- The intercept is not included in the random effects model by default. If you can assume the data pass through the origin, you can exclude the intercept.
- Display parameter predictions for this block
- Specifies to display the random-effects parameter estimates.
- Subject Combination
- This allows you to specify random effect subjects from preset combinations of subjects from the
Variables dialog. For example, if
*School*,*Class*, and*Student*are defined as subjects on the Variables dialog, and in that order, then the Subject Combination dropdown list will have None, School, School * Class, and School * Class * Student as options. - Random effect covariance type
- This specifies the covariance structure for the residuals. The available structures are:
- First-order autoregressive (AR1)
- Autoregressive moving average (1,1) (ARMA11)
- Compound symmetry
- Diagonal
- Scaled identity
- Toeplitz
- Unstructured
- Variance components

- Define covariance groups by
- The categorical fields specified here define independent sets of random effects covariance parameters; one for each category defined by the cross-classification of the grouping fields. A different set of grouping fields can be specified for each random effect block. All subjects have the same covariance type; subjects within the same covariance grouping will have the same values for the parameters.