The PCA_Reduce function outputs a table in which each row represents a principal component. The rows are in descending order based on the standard deviation, which is a measure of the variation in the data set that was captured by that principal component.
|Column Name||Data Type||Description|
|component_rank||INTEGER||Rank of the principal component. Components are ranked in descending order of standard deviation (and variance).|
|dimension_i||DOUBLE PRECISION||Values of the ith dimension of the data set. The table has one such column for each dimension.|
|sd||DOUBLE PRECISION||Standard deviation of the components in the eigenvector represented by the row.|
|var_proportion||DOUBLE PRECISION||Proportion of variance of the components in the eigenvector represented by the row.|
|cumulative_var||DOUBLE PRECISION||Cumulative variance of the components in the eigenvector represented by the row.|
|mean||VARCHAR||One row of this column contains a list of average values, one for each target_column_i in the input table. The list has this format:
[ average [, average ...] ]
The outer brackets appear in the table.
The other rows of this column contain NULL.