## Interpreting VARCLUS Procedure Output

Because PROC VARCLUS is a type of oblique component analysis, its
output is similar to the output from the FACTOR procedure for oblique
rotations. The scoring coefficients have the same meaning in
both PROC VARCLUS and PROC FACTOR; they are coefficients applied to the
standardized variables to compute component scores.
The cluster structure is analogous
to the factor structure containing the correlations between
each variable and each cluster component. A cluster pattern
is not displayed because it would be the same as the cluster
structure, except that zeros would appear in the same places
in which zeros appear in the scoring coefficients. The
intercluster correlations are analogous to interfactor
correlations; they are the correlations among cluster
components.
PROC VARCLUS also displays a cluster summary and a cluster listing.
The cluster summary gives the number of variables in each
cluster and the variation explained by the cluster
component. The latter is similar to the variation explained
by a factor but includes contributions from only the
variables in that cluster rather than from all variables, as
in PROC FACTOR. The proportion of variance explained
is obtained by dividing the variance
explained by the total variance of variables in the cluster.
If the cluster contains two or more variables and the
CENTROID option is not used, the second largest eigenvalue
of the cluster is also printed.

The cluster listing gives the variables in each cluster.
Two squared correlations are calculated for each cluster. The
column labeled "Own Cluster" gives the squared correlation
of the variable with its own cluster component. This value
should be higher than the squared correlation with any other
cluster unless an iteration limit has been exceeded or the
CENTROID option has been used. The larger the squared
correlation is, the better. The column labeled "Next Closest"
contains the next highest squared correlation of the
variable with a cluster component. This value is low if the
clusters are well separated. The column headed "1 -R**2 Ratio"
gives the ratio of one minus the "Own Cluster" *R*^{2} to
one minus the "Next Closest" *R*^{2}.
A small "1 -R**2 Ratio" indicates a good clustering.

Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.