Estimated Means (generalized linear mixed models)
This tab allows you to display the estimated marginal means for levels of factors and factor interactions. Estimated marginal means are not available for multinomial models.
- Terms
- The model terms in the Fixed Effects that are entirely comprised of categorical fields are listed here. Check each term for which you want the model to produce estimated marginal means.
- Contrast Type
- This specifies the type of contrast to use for the levels of the contrast field.
- None
- No contrasts are produced.
- Pairwise
- Produces pairwise comparisons for all level combinations of the specified factors. This is the only available contrast for factor interactions.
- Deviation
- Contrasts compare each level of the factor to the grand mean.
- Simple
- Contrasts compare each level of the factor, except the last, to the last level. The "last" level is determined by the sort order for factors specified on the Build Options. Note that all of these contrast types are not orthogonal.
- Contrast Field
- This specifies a factor, the levels of which are compared using the selected contrast type. If
None is selected as the contrast type, no contrast field can (or need) be selected.
- Continuous Fields
- The listed continuous fields are extracted from the terms in the Fixed Effects that use continuous fields. When computing estimated marginal means, covariates are fixed at the specified values. Select the mean or specify a custom value.
- Adjust for multiple comparisons using
- When performing hypothesis tests with multiple contrasts, the overall significance level can be
adjusted from the significance levels for the included contrasts. This allows you to choose the
adjustment method.
- Least significant difference
- This method does not control the overall probability of rejecting the hypotheses that some linear contrasts are different from the null hypothesis values.
- Sequential Bonferroni
- This is a sequentially step-down rejective Bonferroni procedure that is much less conservative in terms of rejecting individual hypotheses but maintains the same overall significance level.
- Sequential Sidak
- This is a sequentially step-down rejective Sidak procedure that is much less conservative in terms of rejecting individual hypotheses but maintains the same overall significance level.
The least significant difference method is less conservative than the sequential Sidak method, which in turn is less conservative than the sequential Bonferroni; that is, least significant difference will reject at least as many individual hypotheses as sequential Sidak, which in turn will reject at least as many individual hypotheses as sequential Bonferroni.
- Display estimated means in terms of
- This specifies whether to compute estimated marginal means based on the original scale of the
target or based on the link function transformation.
- Original target scale
- Computes estimated marginal means for the target. Note that when the target is specified using the events/trials option, this gives the estimated marginal means for the events/trials proportion rather than for the number of events.
- Link function transformation
- Computes estimated marginal means for the linear predictor.