The NESTED Procedure

Overview

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:

$y_{ijr} = \mu + \alpha_i + \beta_{ij} + \epsilon_{ijr}$

where

y_ijr: 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.
$\mu$: is the overall (fixed) mean of the sampling population.
$\alpha_i,\beta_{ij},\epsilon_{ijr}$: are mutually uncorrelated random effects with zero means and respective variances $\sigma_{\alpha}^2$ , $\sigma_{\beta}^2$ ,and $\sigma_{\epsilon}^2$ (the variance components).

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).

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.

Contrasted with Other SAS Procedures

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