Multiple Factor Analysis Platform Options

The Multiple Factor Analysis red triangle menu includes the following options.

Shows or hides the first eigenvalue for each block as well as the weight of that eigenvalue. The weight is the inverse of the square root of the first eigenvalue.

Eigenvalues

Shows or hides a table of eigenvalues that correspond to the consensus principal components, in order, from largest to smallest.

Eigenvectors

Shows or hides a table of the eigenvectors for each of the consensus principal components, in order, from left to right. Using these coefficients to form a linear combination of the original variables produces the consensus principal component variables. Following the standard convention, eigenvectors have norm 1.

Variable Loadings

Shows or hides the loadings for each column. The degree of transparency for the table values indicates 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.

Variable Partial Contributions

Shows or hides a table that contains the partial contributions of variables. The partial contributions enable you to see the percentage of variance that each variable contributes to each consensus principal component.

Variable Squared Cosines

Shows or hides a table that contains the squared cosines of variables. The sum of the squared cosine values across consensus principal components is equal to 1 for each variable. The squared cosines enable you to see how well the variables are represented by the consensus principal components.

Tip: For the variable loadings, variable partial contributions, and variable squared cosines, values near zero indicate that the variable has little effect on the consensus principal component. Values far from zero indicate a strong effect. The degree of transparency for the table values highlights these effects.

Summary Plots

Shows or hides the summary plots. The summary plots include the plot of the eigenvalues or score plot and the loading plot.

Consensus Map

Shows or hides the consensus map. See Consensus Map.

Biplot

Shows or hides a plot that is an overlay of the score and loadings plots. Use the controls to select any two dimensions for the plot.

Partial Axes Plot

Shows or hides a partial axes plot. This plot displays correlations between PCA scores from separate block analyses and the consensus principal component. Use the controls to select any two dimensions for the plot. Click on a block in the legend to highlight that block in the plot.

Display Options

Arrow Lines

Enables you to show or hide arrows on the loading plot, and the partial axes plot. Arrows are shown if the number of variables is 1000 or fewer. If there are more than 1000 variables, the arrows are off by default.

Show Labels

Shows or hides block name labels on all points in the consensus map and bi-plot. Shows or hides column name labels on all points in the partial axes plot.

Tip: Use row labels to identify centroids on the consensus map and data points on the loading plot.

RV Correlations

Shows or hides a matrix of squared correlation coefficients between blocks.

Lg Coefficients

Shows or hides a matrix of similarity measures between blocks.

Block Partial and Consensus Correlations

Shows or hides a matrix of correlation coefficients between block partial scores and consensus principal component scores. The matrix is rectangular because only correlations between concordant dimensions are displayed.

Block Partial Contributions

Shows or hides the sum of the variable contributions within the block.

Block Partial Inertias

Shows or hides the block contribution multiplied by the eigenvalue for the principal component and then divided by 100.

Block Squared Cosines

Shows or hides the block inertia squared and divided by the sum of squares used to calculate the eigenvalues. The values have a range between 0 and 1, and the sum of the Block Squared Cosines for a single block across all principal components is 1. The Block Squared Cosines can be considered as the percentage of the block variance explained by each principal component.

Save Individual Scores

Saves the item consensus principal components to new columns in the data table. If one or more categorical supplementary variables are used, this option also saves individual scores for each level of the supplementary variables to a new data table.

Save Individual Squared Cosines

Saves the item squared cosines to new columns in the data table. If one or more categorical supplementary variables are used, this option also saves categorical supplementary variable squared cosines to a new data table.

Save Individual Partial Contributions

Saves the item partial contributions to new columns in the data table. If one or more categorical supplementary variables are used, this option also saves categorical supplementary variable squared cosines to a new data table.

Save Block Partial Scores

Saves the block partial scores to a new data table.

Save Partial Axes Coordinates

Saves the partial axes coordinates to a new data table.

See Local Data Filter, Redo Menus, and Save Script Menus in the Using JMP book for more information about the following options:

Local Data Filter

Shows or hides the local data filter that enables you to filter the data used in a specific report.

Redo

Contains options that enable you to repeat or relaunch the analysis. In platforms that support the feature, the Automatic Recalc option immediately reflects the changes that you make to the data table in the corresponding report window.

Save Script

Contains options that enable you to save a script that reproduces the report to several destinations.