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The VARCOMP Procedure |

The VARCOMP procedure handles general linear models that have random effects. Random effects are classification effects with levels that are assumed to be randomly selected from an infinite population of possible levels. PROC VARCOMP estimates the contribution of each of the random effects to the variance of the dependent variable.

A single MODEL statement specifies the dependent variables and the effects: main effects, interactions, and nested effects. The effects must be composed of class variables; no continuous variables are allowed on the right side of the equal sign.

You can specify certain effects as fixed (nonrandom) by putting them first in the MODEL statement and indicating the number of fixed effects with the FIXED= option. An intercept is always fitted and assumed fixed. Except for the effects specified as fixed, all other effects are assumed to be random, and their contribution to the model can be thought of as an observation from a distribution that is normally and independently distributed.

The dependent variables are grouped based on the similarity of their
missing values. Each group of dependent variables is then analyzed
separately. The columns of the design matrix **X** are formed in the
same order in which the effects are specified in the MODEL statement. No
reparameterization is done. Thus, the columns of **X** contain only
0s and 1s.

You can specify four methods of estimation in the PROC VARCOMP statement using the METHOD= option. They are TYPE1 (based on computation of Type I sum of squares for each effect), MIVQUE0, Maximum Likelihood (METHOD=ML), and Restricted Maximum Likelihood (METHOD=REML).

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