Modeling and Multivariate Methods - SAS

Chapter 19 Analyzing Principal Components and Reducing Dimensionality 485
Platform Options
Eigenvectors shows columns of values that correspond to the eigenvectors for each of the principal
components, in order, from left to right. Using these coefficients to form a linear combination of the
original variables produces the principal component variables.
Bartlett Test shows the hypothesis test for each eigenvalue (Jackson, 2003).
Loading Matrix
shows columns corresponding to the factor loading for each component.
Summary Plots shows or hides the summary information produced in the initial report. This
information in shown in Figure 19.2.
Biplot
shows a plot which overlays the Score Plot and the Loading Plot.
Scree Plot shows a scree plot of the eigenvalues vs. the number of components. This plot is useful for
visualizing the dimensionality of the data space.
Score Plot
shows a matrix of scatterplots of the scores for pairs of principal components.
Loading Plot shows a matrix of two-dimensional representations of factor loadings. The loading plot
labels variables if the number of variables is 30 or fewer. If there are more than 30 variables, the labels are
off by default.
Score Plot with Imputation imputes any missing values and creates a score plot. This option is available
only if there are missing values.
3D Score Plot shows a 3D scatterplot of any principal component scores. When you first invoke the
command, the first three principal components are presented.