Enables you to create the principal components based on Correlations, Covariances, or Unscaled.
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.
If you select the Bartlett Test option from the red triangle menu, hypothesis tests (Bartlett Test) are given for each eigenvalue (Jackson, 2003).
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.
Shows the results of the homogeneity test (appended to the Eigenvalues table) to determine if the eigenvalues have the same variance by calculating the Chi-square, degrees of freedom (DF), and the p-value (prob > ChiSq) for the test. See Bartlett (1937).
Shows columns corresponding to the loading for each component. These values are graphed in the Loading Plot.
Note: The degree of transparency for the table values indicate the distance of the absolute loading value from zero. Absolute loading values that are closer to zero are more transparent than absolute loading values that are farther from zero.
Shows or hides the summary information produced in the initial report. This information in shown in Principal Components on Correlations Report.
Shows a plot that overlays the Score Plot and the Loading Plot for the specified number of components.
Shows a graph of the eigenvalue for each component. This scree plot helps in visualizing the dimensionality of the data space.
Shows a matrix of scatterplots of the scores for pairs of principal components for the specified number of components. This plot in shown in Principal Components on Correlations Report (left-most plot).
Shows a matrix of two-dimensional representations of factor loadings for the specified number of components. 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. This information in shown in Principal Components on Correlations Report (right-most plot).
Imputes any missing values and creates a score plot. This option is available only if there are missing values.
Shows a 3D scatterplot of any principal component scores. When you first invoke the command, the first three principal components are presented.
The variables show as rays in the plot. These rays, called biplot rays, approximate the variables as a function of the principal components on the axes. If there are only two or three variables, the rays represent the variables exactly. The length of the ray corresponds to the eigenvalue or variance of the principal component.
Performs factor analysis-style rotations of the principal components, or factor analysis. See the Factor Analysis chapter in the Consumer Research book for details.
Performs a cluster analysis on the variables by dividing the variables into non-overlapping clusters (useful for grouping the columns). Variable clustering provides a method for grouping similar variables into representative groups. Each cluster can then be treated as a variable. Alternatively, variable clustering also identifies the most representative member in each cluster that may be used in the analysis instead of the cluster group variable.
Saves the principal component to the data table, with a formula for computing the components. The formula cannot evaluate rows with any missing values.
Saves the rotated components to the data table, with a formula for computing the components. This option appears after the Factor Analysis option is used. The formula cannot evaluate rows with missing values.
Imputes missing values, and saves the principal components to the data table. The column contains a formula for doing the imputation, and computing the principal components. This option is available only if there are missing values.
Imputes missing values, and saves the rotated components to the data table. The column contains a formula for doing the imputation, and computing the rotated components. This option appears after the Factor Analysis option is used, and if there are missing values.
Contains options that are available to all platforms. See the Using JMP book.