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. |