Output Tab Descriptions | Predictive Modeling | Row Scores

Row Scores
The Row Scores tab contains the following elements:
These are Pearson correlations between the row scores. The correlations are typically zero between the second and higher components, depending on the centering of the predictors.
This plots the row scores against each other, colored by the dependent variables. There is one row score for each observation. Points that are misclassified have an "X" as a marker and those correctly predicted have an "O" marker. Models that predict the categories well exhibit separation of points into clusters.
You can use these plots to identify points that are not predicted well and to see which dimensions are associated with classification performance. They can also reveal hidden structure in the data similar to principal components, but these components accommodate the values of the dependent variable.
See Scatterplot Matrix for more information.
This plots the first three sets of row scores against each other in a three-dimensional scatterplot, colored by the dependent variables. There is one row score for each observation. Points that are misclassified have an "X" as a marker and those correctly predicted have an "O" marker. Models that predict the categories well exhibit separation of points into clusters. Click and drag the mouse to spin the plot.
You can use this plot to identify points that are not predicted well and to see which dimensions are associated with classification performance. They can also reveal hidden structure in the data similar to principal components, but these components accommodate the values of the dependent variable.
See Three-Dimensional Scatterplot for more information.