Interpreting VARCLUS Procedure Output

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Interpreting VARCLUS Procedure Output

Because the VARCLUS algorithm 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 that contains 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 omitted, the second largest eigenvalue of
the cluster is also displayed.

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 labeled “1–R**2 Ratio” gives the ratio of one minus the “Own Cluster” R square to one minus the “Next Closest” R square. A small “1–R**2 Ratio” indicates a good clustering.

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