Principal components analysis finds orthogonal linear combinations of a set of variates that maximize the variation
contained within them, thereby displaying most of the original variability in a smaller number of dimensions. It
operates on sums of squares and products, or a correlation matrix, or a matrix of variances and covariances, formed
from the data variates.
Data Values
Used to enter the names of the variates to be analysed. The
button allows multiple selections to be
copied from Available Data.
In command mode, the PCP directive has an extended syntax which allows you
to specify a matrix, pointer-of-variates, or SSPM as input data.
Analysis based on
Selects whether the analysis is based on the sums of squares and products, correlation, or variance-covariance matrix.
Rotate Loadings
Allows you to rotate loadings according to either the varimax or
quartimax criterion.
See Also