7.00.02 - PCAPlot - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Aster Analytics
Release Number
Release Date
September 2017
Content Type
Programming Reference
User Guide
Publication ID
English (United States)

The PCA function (Principal Component Analysis (PCA)) outputs a set of principal components, and each principal component is a linear combination of the set of original predictors.

In the PCA example output table pca_health_ev_scaled (see Output), the first-ranked principal component is:

-0.082 * age + 0.387 * bmi + (-0.0935) * bloodpressure + 0.042 * glucose …

The PCA_Plot function uses these coefficients from (the output table of the PCA function) to compute a principal component score for each observation. If the PCA function returned n principal components, the PCA_Plot function calculates n scores for each observation. That is, the n principal components replace the original, larger set of predictors for subsequent analyses.

The version of PCA_Reduce, a component of the PCA function, must be AA 6.21 or later.