THESIS

Figure 1 Selected items from PROC UNIVARIATE output for germination period
with the analysis of the Solanum spp. dataset.
C. Selection of less correlated traits
outliers
Not all of the 25 ratio scale traits were considered in the cluster analysis of
89 accessions. Traits described to be highly correlated are regarded as surrogates of
each other and any representation of two highly correlated can be considered
redundant (Collantes, 2003; Laban, 2003). Hence, these traits are required to be
examined using the PROC CORR and PROC VARCLUS to determine the traits
considered to be highly correlated.
Using PROC CORR statement, the Pearson correlation coefficients were
calculated between 19 traits. Table 7 displays the upper triangular of the correlation
matrix for such traits. From the given correlation matrix, groups of highly correlated
traits were formed using VARCLUS procedure and setting the proportion of variation
explained by the cluster 95 %. This type of procedure utilizes oblique multiple-group
component analysis to generate both hierarchical and disjoint clustering of variables
(SAS, 1999). The PROC VARCLUS corresponding dendrogram (Figure 3). At 95 %
propor, the following traits were immediately considered in cluster analysis:
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