The NESTED procedure performs random effects analysis
of variance for data from an experiment
with a nested (hierarchical) structure.*
A random effects model for data from a completely
nested design with two factors has the following form:
This model is appropriate for an experiment
with a multi-stage nested sampling design.
An example of this is given in Example 44.1,
where four turnip plants are randomly chosen (the first
factor), then three leaves are randomly chosen from each
plant (the second factor nested within the first), and
then two samples are taken from each leaf (the different
replications at fixed levels of the two factors).
- is the value of the dependent variable observed at
the rth replication with the first factor at its
ith level and the second factor at its jth level.
- is the overall (fixed) mean of the sampling population.
- are mutually uncorrelated random effects with zero means
and respective variances , ,and (the variance components).
Note that PROC NESTED is appropriate for models with only
classification effects; it does not handle models that contain
continuous covariates. For random effects models with covariates,
use either the GLM or MIXED procedure.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.