Modeling and Multivariate Methods - SAS

120 Fitting Standard Least Squares Models Chapter 3
Restricted Maximum Likelihood (REML) Method
There are two layers to the model:
1. The top layer is the between-subject layer, in which the effect of being a fox or coyote (species effect) is
tested with respect to the variation from subject to subject. The bottom layer is the within-subject layer,
in which the repeated-measures factor for the four seasons (season effect) is tested with respect to the
variation from season to season within a subject. The within-subject variability is reflected in the residual
error.
2. The season effect can use the residual error for the denominator of its F-statistics. However, the
between-subject variability is not measured by residual error and must be captured with the subject
within species (subject[species]) effect in the model. The F-statistic for the between-subject effect
species uses this nested effect instead of residual error for its F-ratio denominator.
To create the split plot for this data, follow these steps:
1. Open the Animals.jmp sample data table.
2. Select Analyze > Fit Model.
3. Select miles and click Y.
4. Select species and subject and click Add.
5. In the Select Columns list, select species.
6. In the Construct Model Effects list, select subject.
7. Click Nest.
This adds the subject within species (subject[species]) effect to the model.
8. Select the nested effect subject[species].
9. Select Attributes > Random Effect.
This nested effect is now identified as an error term for the species effect and shows as
subject[species]&Random.
10. In the Select Columns list, select season and click Add.