Modeling and Multivariate Methods - SAS

484 Analyzing Principal Components and Reducing Dimensionality Chapter 19
Report
Report
The initial principal components report (Figure 19.2) summarizes the variation of the specified Y variables
with principal components. The principal components are derived from an eigenvalue decomposition of the
correlation matrix, the covariance matrix, or on the unscaled and uncentered data.
The details in the report show how the principal components absorb the variation in the data. The principal
component points are derived from the eigenvector linear combination of the variables.
Figure 19.2 Principal Components/Factor Analysis Report
The report gives the eigenvalues and a bar chart of the percent of the variation accounted for by each
principal component. There is a Score plot and a Loadings plot as well.
Platform Options
The platform red-triangle menu has the following options:
Principal Components allows you to choose to create the principal components based on Correlations,
Covariances, or Unscaled.
Correlations
gives the correlations between the variables.
Covariance Matrix
gives the variances and covariances of the variables.
Eigenvalues lists the eigenvalue that corresponds to each principal component in order from largest to
smallest. The eigenvalues represent a partition of the total variation in the multivariate sample. They
sum to the number of variables when the principal components analysis is done on the correlation
matrix. Hypothesis tests are given for each eigenvalue (Jackson, 2003).